Multiscale Micro-CT Imaging on Sediment Cores: Unravelling the Paleoflow Directions in a Megaturbidite (Lake Lucerne, Switzerland)

FACULTY OF SCIENCES Master of Science in geology

Multiscale micro-CT imaging on sediment cores: Unravelling the paleoflow directions in a megaturbidite (Lake Lucerne, Switzerland)

Maxim Deprez

Academic year 2015–2016

Master’s dissertation submitted in partial fulfillment of the requirements for the degree of Master in Science in Geology

Promotor: Dr. M. Van Daele Co-promotor: Dr. M. Boone Jury: Prof. Dr. V. Cnudde, Prof. Dr. D. Van Rooij

Acknowledgments

Besides time, a lot of people are necessary to brew a master’s thesis. All of them deserve a lot of my gratitude.

First of all, I would like to thank both of my promoters, Maarten Van Daele and Marijn Boone, for merging both of their scientific worlds into my thesis. Maarten, without your insights and expertise in sedimentological research, your delivered data and suggestions and especially your extreme skills in taking U-channels, I certainly would have been lost in the Great Turbidite Forest. Thank you for guiding me through it safely. Marijn, your excessive knowledge of X-ray computed tomography, your problem-solving way of thinking and your drive to deliver every CT scan as a piece of perfection was the foundation of the thesis. To both, thank you very much!

Furthermore, I would like to thank Prof. Veerle Cnudde and Jeroen Van Stappen, to help to get the subject and workflow on track and make sure it was all well-advised. Many thanks go also to the staff of UGCT, for using their facility and equipment, and the people of RCMG, who were all open to offer help at any time, with anything.

Dear classmates, you guys delivered the five best years of my scholar history. Your drive and enthusiasm in class, as well as outside on the field and other ‘student-related’ activities were truly inspiring. Thank you for the necessary coffee breaks during writing or letting me join you when I was wandering through the corridors of the S8, desperately looking for a free computer to work on. The end of our career at the university does not mean goodbye to me. Friends, many thanks to you all!

To my non-geology friends, thank you to snap my dissertation balloon once in while over the past months. Your will to still wanting to take me out, aware that I could annoy you with fun facts about geology, is astonishing and admirable. You fellows are awesome, thank you!

The largest amount of gratitude goes to my family, who not only dragged me through the past months, but supported me unconditionally throughout my life and lifted me up to the person I am today. Your capability to stand the alteration of overly simplistic humour, an overload of energy and ‘just-leave-me-alone’ moments during exam periods and the last weeks before the deadline, is sincerely remarkable! Dank je mama, papa en Manon om er voor me te zijn.

Without all of you, writing this dissertation would have been a hideous task and most likely never completed. I am grateful to you all.

Table of Content

1. INTRODUCTION ...... 1

2. GEOGRAPHICAL AND GEOLOGICAL SETTING ...... 3

3. THE INFLUENCE OF EARTHQUAKES ON LAKE SEDIMENTS ...... 5

3.1. TERMINOLOGY REGARDING DENSITY FLOWS ...... 5 3.2. EARTHQUAKES AND DENSITY FLOWS ...... 8 3.3. ‘SEICHE-WAVE’ WATER MOVEMENT ...... 10 3.4. FLOW DIRECTIONS ...... 11 4. THE 1601 A.D. EARTHQUAKE AND THE RELATED SEDIMENTS ...... 15

4.1. THE EARTHQUAKE EVENT ...... 15 4.2. PREVIOUS RESEARCH ON 1601 AD EARTHQUAKE RELATED SEDIMENTS ...... 15 5. X-RAY COMPUTED TOMOGRAPHY ...... 23

5.1. INTRODUCTION ...... 23 5.1.1. X-rays and the development of Computed Tomography (CT) ...... 23 5.1.2. Use in geological applications ...... 24 5.2. BASIC PRINCIPLES OF X-RAY TRANSMISSION CT ...... 24 5.2.1. Interaction X-rays with matter ...... 24 5.2.2. X-ray transmission CT ...... 26 5.3. X-RAY SYSTEMS ...... 27 5.3.1. Basic components ...... 27 5.3.2. Medical CT scanner ...... 28 5.3.3. Micro-CT scanner ...... 28 5.4. IMAGE QUALITY AND ARTEFACTS ...... 29 5.4.1. Resolution ...... 29 5.4.2. Discretization effects and Partial Volume effects ...... 30 5.4.3. Ring artefacts ...... 31 5.5. DATA VISUALISATION ...... 31 5.6. 3D DATA ANALYSIS ...... 31 6. METHODS ...... 33

6.1. PREVIOUS WORK ...... 33 6.1.1. Coring ...... 33 6.1.2. Measurements on closed core ...... 34 6.1.3. Orientation ...... 35 6.2. SUBSAMPLING ...... 35 6.2.1. Lucerne and Eklutna Straws ...... 37 6.2.2. U-channel ...... 37 6.2.3. Straw subsamples ...... 38 6.3. X-RAY CT ...... 38 6.3.1. Scanning...... 38 6.3.2. Processing and analysis ...... 39

6.3.3. Orienting CT-derived data towards the magnetic north ...... 40 6.4. LASER-DIFFRACTION GRAIN-SIZE ANALYSIS ...... 41 7. RESULTS ...... 43

7.1. TEST STRAWS ...... 43 7.1.1. µCT images and processing ...... 43 7.1.2. Grain size ...... 47 7.1.3. Orientation ...... 50 7.2. U-CHANNEL ...... 52 7.2.1. µCT images and processing ...... 52 7.2.2. Grain size ...... 54 7.2.3. Orientation ...... 56 7.2.4. Sedimentary structures ...... 66 8. DISCUSSION ...... 69

8.1. EVALUATION OF µCT IN SEDIMENTOLOGICAL RESEARCH ...... 69 8.1.1. Proposed workflow ...... 69 8.1.2. µCT and grain size ...... 70 8.1.3. µCT and grain orientation ...... 71 8.2. FLOW HISTORY DETERMINATION ...... 73 8.2.1. Flow model...... 73 8.2.2. µCT: sedimentary structures and fabric ...... 73 8.2.3. µCT: grain orientation analysis ...... 78 8.2.4. Combination of visual and computational analysis ...... 81 9. CONCLUSION AND FUTURE OUTLOOK ...... 83

10. REFERENCES ...... 85

1. Introduction

Earthquakes, together with other natural hazards, are amongst the only affairs the human race cannot predict within a reasonable time-scale. However, in tectonically active regions, like the Alpine mountain chain, large earthquake events appear in a more or less periodic manner. Since it is human nature to control the environment surrounding us, a lot of research is put into temporally and spatially mapping these large earthquakes in order to derive recurrence patterns. Studies of lake sediments are highly efficient for this cause. For Lake Lucerne, combining the appearance of earthquake induced deposits in seismic profiles with historical records of events (Schnellmann et al., 2002; Siegenthaler et al., 1987) and carbon dating on sediment cores (Schnellmann et al., 2006), leads to an assessment of the recurrence pattern.

For rather highly inhabited areas within tectonically active zones, a hazard assessment is necessarily made in order to reduce the damage caused by an event as much as possible. Near lakes, not only the direct damage to the infrastructure caused by the earthquake is important, but also indirect damage due earthquake-induced lake water movements. An oscillating water body or a ‘seiche’ movement can originate by both the earthquake waves themselves (Bondevik et al., 2013) and/or earthquake-induced slope failures in the lake (Chapron et al., 1999). To be able to perform a risk assessment of the latter, the processes from slope failure towards a possible inundation of settlements has to be known in great detail, which also includes the flow regime at the bottom of the lake and the different sediments that are deposited during an event.

Amongst earthquake events, also floods, excessive sediment loads, etc. tend to cause slope failure. To differentiate between the causes of the failure, one has to check the spatial occurrence of mass- flow deposits. Earthquakes induce multiple failures at the same time and, hence, mass-flow deposits should occur on the same horizon in the lake sediments on seismic profiles. Moreover, as mass flows tend to disintegrate into turbidity currents, earthquake-induced sediments in the centre of the basin are characterised by the appearance of a sequence of turbidites. Since those different turbidite pulses all have another source, the direction of the current has to be different. This can be reflected in sedimentary structures and fabrics within the stacked turbidite (Van Daele et al., 2014).

A theoretical flow model was made by Vermassen (2015) for the density flows produced in Lake Lucerne during the 1601 A.D. historical earthquake in Central Switzerland. It is based on a combination of seismic profiling on Lake Lucerne, X-ray computed tomography (CT) 3D imaging, grain-size analysis and, in smaller extent, X-ray micro computed tomography (µCT) 3D imaging on cores retrieved from the same lake. In his model, he points out that several pulses of density flows contributed to the sediments in a megaturbidite. Moreover, the author was even able link the pulses to several sources by using proxies for flow direction derived from his data. Furthermore, he contributed a part of the sediments within the megaturbidite to a seiche water movement. 3D X-ray micro computed tomography imaging, however, only had a small contribution in the development of the flow model, mostly due to a lack of knowledge on how to apply the technique on sediments. Nevertheless, the author concluded that further research on this topic is necessary in order to explore the limits of the applicability of µCT in sedimentological research and to improve the theoretical flow model.

The goals of this dissertation are based on those conclusions of Vermassen (2015). The first objective is to develop and optimize a workflow to image sediments with the highest resolution possible, but without destroying the internal structure. Hence, the first aim is purely methodological, which includes trial and error to discover the limits of the technique. A second goal is to use this newly

1. Introduction established workflow to determine orientations in sediment fabrics and single particles, such as mineral grains, organic material and mud clasts. This in order to to validate and, if possible, improve the theoretical model presented by Vermassen (2015). For these analyses, one core coming from Lake Lucerne, LU14-12-G, with distinct sandy pulses at the base of the megaturbidite, is highlighted.

Sediment particles range from metre size to nanometre size or even smaller. To assess the applicability of X-ray µCT on different grain-size ranges, two samples, one from Lucerne and one from Eklutna Lake (Alaska, USA), with a different grain-size distribution were retrieved, scanned and analysed for grain size and orientation. Since a higher resolution can be obtained by µCT when the object of interest is smaller, straws were used to subsample straight from the Lucerne and Eklutna core. Hereafter, a gradual reduction of the sample size was done for sediments of core LU14-12. First a U-channel subsample (2 cm x 2 cm x 11 cm) was taken at the region of interest, which is the base of the megaturbidite, and scanned with X-ray µCT at the highest possible resolution. Then straws were inserted in the top of the U-channel and pushed down to take subsamples of 4mm diameter. The U- channel was scanned with the straws in it to check for possible induced deformation while sampling.

Afterwards, the results of the µCT determined grain-size distributions are discussed, as well as the different orientations obtained for the grains, organic matter and mud clasts. An assessment of the applicability of the method can be made hereafter. To link those preferential orientations to actual paleoflow directions, the behaviour of the different sediment phases in different flow regimes is important to interpret the results and is therefore also discussed.

2. Geographical and geological setting

Due to the collision of the Eurasian and the African plate, the Alpine formation started in the Cretaceous and the mountain chain is presently still growing. The collision has resulted in deep faults running along the Alps that occasionally cause earthquakes. Two major NE-SW running faults intersect Lake Lucerne, dividing the local lithology in three units (Schnellmann et al., 2006). The North Alpine Front (AF) separates the Alpine Helvetic Wildhorn nappe from the Subalpine Molasse. The former consists of Cretaceous marl and limestone while the latter lithology contains imbricated conglomerates deposited in the Cenozoic Molasse basin. These conglomerates are locally addressed with the term ‘Nagelfluh’. The AF runs through the Vitznau basin, the basin on which this dissertation is focussed, with roughly the same strike as the basin itself, resulting in a different lithology of the northern (Alpine Helvetic Wildhorn nappe) and southern (Subalpine Molasse) border of the basin. The second major fault, the Subalpine front (SAF), is found more to the North and separates the Subalpine Molasse from the Plateau Molasse, which mainly exists out of sandstone (Vermassen, 2015). Figure 2.1 gives an overview of the geographical setting, including these lithological units and faults.

Figure 2.1: A combination of a Google Earth satellite image of Lake Lucerne and surroundings and a bathymetry map of the lake. The contour lines of the latter are spaced 30 m from each other. Mountains (yellow crosses) and settlements (white squares) that are further mentioned in this dissertation are indicated. The inset shows the location of Lake Lucerne in Europe. AF = Alpine Front, SAF = Subalpine Front. C = Chrüztrichter basin, G = Gersau Basin, K = Küssnacht basin, T = Treib Basin, U = Uri Basin, V = Vitznau Basin. The reconstructed epicentre of the 1601 A.D. earthquake is marked with a red star (Schnellmann et al, 2006). The dashed frames point out the location of different figures further used in this dissertation. Figure adapted from Vermassen (2015).

Lake Lucerne is a Swiss peri-alpine lake at the northern margin of the Swiss Alps with a total surface area of 113.6 km2. Presently, the lake has an average surface elevation of 433.6 m above sea-level (BAFU, 2009). An important property of the lake is the seven sub-basins out of which it consists, each separated by a high topographical feature on the lake bottom, such as sills and moraines. Combined with the elongated, fjord-like shapes of the sub-basins, these features accentuate the glacial erosive

2. Geographical and geological setting

origin of the lake (Hilbe et al., in press.). The deepest point of the lake (215 m) is found in the Gersau Basin. The Küssnacht Basin is the shallowest one, with a maximal depth of 75 m. The lake has four primary inflow rivers (Reuss, Muota, Engelberger Aa & Sarner Aa) that provide 80 % of the total water input, but the water only flows out via the Reuss river, which leaves the lake through the city of Lucerne.

The Vitznau basin is one of the largest sub-basins of Lake Lucerne and is located between the township of Vitznau on the eastern side, the town of Weggis at the northern margin and the abandoned quarry of Obermatt on the southern side of the basin. To describe the sedimentation, the entire lake can be divided in a proximal part including the south-eastern sub-basins and a more distal part. The sedimentation in the proximal sub-basins is mostly influenced by the presence of delta- forming tributaries. Since none of the four primary input rivers end up in the distal Vitznau basin, it is characterized by a relatively low sedimentation rate and the particles that settle are largely hemipelagic of origin (Hilbe and Anselmetti, 2015).

These sediments are regularly interrupted by mass movement deposits and these can be linked to important earthquakes in Central Switzerland (Schnellmann et al., 2006). Research of the stratigraphy of the last 15 000 years in Lake Lucerne showed that the temporal and spatial distribution of the strong earthquakes. The distribution of strong earthquakes over time in Central Switzerland throughout the last 15000 years has been irregular with a peak in Late Glacial/Early Holocene times (Schnellmann et al., 2006). Gisler et al. (2004) states that the seismicity in Central Switzerland is rather low since the start of digital seismological measurements in 1975. Historical records do however point out some dates on which important earthquakes have occurred. The largest was on September 18th, 1601 with a magnitude of about 5.9 (Mw), followed by several other damaging earthquakes in 1774 (Mw 5.3), 1775 (Mw 3.9), 1777 (Mw 5.1) and 1964 (Mw 5.3) (Fäh et al., 2011).

3. The influence of earthquakes on lake sediments

Several sedimentary processes have influenced the deposits of interest in Lake Lucerne. To fully understand what caused the presence of certain sedimentological features, an overview of the most important processes, their causes and results is compiled in this section. 3.1. Terminology regarding density flows In the title of this dissertation appears the word ‘megaturbidite’. Although it seems logical that this means the subject is a relatively large deposit resulting from a turbidity current, the dubious and not well defined terminology concerning this matter urges to clarify this term and how it differs from other frequently used words, e.g. homogenite. However, before addressing megaturbidites, a general overview of the terminology around ‘turbidites’ is necessary.

Turbidite The first paradigm already starts with the term ‘turbidite’. Bouma (1962) was the first one to propose a general model (figure 3.1) that explains the sedimentary structures and grain-size variation in turbidites with the depositional processes in a turbidity current. This kind of current is defined as a gravity-driven density flow wherein the particles are kept in motion only by the fluid turbulence (Middleton and Hampton, 1973). Hence, these currents can only maintain a very low concentration of particles and they have a very low competence to transport coarse sand and gravel over a several kilometres distance, and certainly not on slopes not exceeding a couple degrees. Most of the described coarse grained turbidites are subsequently not deposited by turbidity currents sensu strictu and a problem in terminology arises (Mulder and Alexander, 2001).

For this cause, the Bouma (1962) model was later customized for turbidity currents with a high density by Lowe (1982) (figure 3.1). This means that the density flows that are capable of transporting coarse sand grains and gravel were from then on classified by ‘high-density turbidity currents’ and that the grains were not only kept in motion by upward force of the turbidity, but also by grain interaction forces. This counteracts the original definition of a turbidity current. However, while proceeding, it is possible for a high-density turbidity current to transform into a low-density turbidity current and the Bouma sequence can be fulfilled still. The adaptation of the model concerned the dividing of Bouma’s Ta division into six units (R1, R2, R3, S1, S2 and S3).

Not only for coarse-grained turbidites problems were encountered, also to classify and interpret fine- grained turbidites, the Bouma sequence was not satisfactory. The finer parts of the turbidites studied by Bouma, do not contain any sedimentary structures and was therefore seen as one unit (Te). As Stow and Shanmugam (1980) did see a variation of sedimentary structures within finer-grained turbidites, a new sequence for fine-grained turbidites was therefore proposed. This sequence, consisting of nine divisions (T0-T8) can be considered as an equivalent for the Tc, Td and Te divisions of the Bouma sequence. All the modifications to Bouma’s original sequence can be summarized into figure 3.1 (Shanmugam, 2000).

3. The influence of earthquakes on lake sediments

Figure 3.1: The coarse-grained turbidite sequence by Lowe (1982), the classic sequence (Bouma, 1962) and the sequence for fine-grained turbidites (Stow and Shanmugam (1980) from left to right respectively. Figure from Shanmugam (2000).

This overview rises the expectation that an ideal turbidite consists of a sequence wherein all of the 16 divisions proposed are present. A model to show this was even made by Shanmugam (2000). Still, such an ideal turbidite sequence has never been found and, therefore, Shanmugam (2000) rejects his model immediately. Flume tank experiments indicate that most of the interpreted turbidites are eventually sandy debris flows. Hence, Shanmugam (2000) argues that the term ‘turbidite’ should be utilised only in case the deposit results from a real turbidity current. Moreover, the latter term asks for a redefinition in order to exclude sandy debris flows from the definition.

To clarify the complicated terminology regarding density flows, Mulder and Alexander (2001) summarized important literature concerning this topic, focussed on some matter that was particularly not well understood and eventually proposed a fairly simple classification of sediment- bearing density flows (figure 3.2). Cohesiveness of particles, flow duration, sediment concentration and particle support mechanism are the four pillars for this new classification. A first separation is made between cohesive and non-cohesive flows. Mud and debris flows are considered as cohesive flows while non-cohesive flows are further divided into hyperconcentrated density flows, concentrated density flows and turbidity flows. The latter two are also classified as ‘surge-like’ or ‘quasi-steady’ currents. As a matter of fact, flows are unstable over time and therefore, multiple components that would be classified differently can occur. For this reason, the classification of Mulder and Alexander (2001) does not have the purpose to classify all outcrops, but it has to be seen as a tool to help education and to focus thought.

3. The influence of earthquakes on lake sediments

Figure 3.2: The classification of density flows proposed by Mulder and Alexander (2001).

Homogenite and megaturbidite A second paradigm concerns the use of the terms ‘homogenite’ and ‘megaturbidite’. The former was used first by Kastens and Cita (1981) for sediments in the Mediterranean Sea, particularly one stratigraphic unit that filled up several sub-basins and that appeared to be acoustically transparent. The authors attributed some important characteristics to their homogenite: (i) the thickness is large relative to the rest of the stratigraphic column; (ii) a fining upward trend, consistent with a gravitational settling of the particles in a single event; (iii) a total lack of sedimentary structures in both the split cores and radiographs. The latter is considered to be the most notable characteristic of a homogenite and leads to the name of the sedimentary unit. Another reason for the choice of name was the lack of knowledge about this sediment type. The homogenite was eventually interpreted as

3. The influence of earthquakes on lake sediments

the result of intrabasinal turbidity currents triggered by tsunami waves, which would be in turn created by a volcano caldera collapse.

Later the term homogenite was used for similar deposits in basins around the world (Chapron et al., 1999; Tripsanas et al., 2004; Beck, 2009; Petersen et al., 2014), but also in Lake Lucerne for earthquake triggered sediments (Siegenthaler et al., 1987). Nevertheless, the term ‘homogenite’ has difficulties to find general acceptation. Especially because the sediments contributing to the homogenite are sometimes not entirely homogeneous. However, the term is still in use to be consistent to previous work of many authors and therefore, the definition is slightly modified to ‘the sedimentary expression of a unique event, with a definite stratigraphic position’. This definition is similar to that of ‘megaturbidite’ (Cita et al., 1996).

In order to reduce the use of different terms for large-scale turbidites, Mutti et al. (1984) introduced the term ‘megaturbidite’. However, no generally accepted definition of this term exists (Reeder et al., 2000). The most popular definition is given by (Bouma, 1987) and refers to megaturbidites as thick, extensive, relatively homogeneous deposits from a turbidity current. Moreover, additional criteria were proposed by Bouma for the purpose of classifying a certain deposit as a megaturbidite: (i) the layer should be thick relative to the host rock; (ii) the deposit has to be laterally extensive; (iii) the composition should be different from the host rock; (iv) it should lack submarine fan geometries. In earlier definitions (Ricci Lucchi and Valmori, 1980), megaturbidites had to be thick in absolute numbers, now the deposit has to be ‘thick’ relative to the host sedimentary record. Therefore, the term megaturbidite can be used in lacustrine research.

The sediments of Lake Lucerne, studied in this dissertation, have been referred to as both homogenites (Siegenthaler et al., 1987) and megaturbidites (Schnellmann et al., 2002; Hilbe and Anselmetti, 2014; Vermassen, 2015). Since the goal of the dissertation is to derive flow directions via sedimentological features in the base of the homogenite/megaturbidite and the most recent research consistently uses megaturbidite, the latter was used in the dissertation. 3.2. Earthquakes and density flows Several mechanisms can result in density flows. Next to earthquake events are the most common processes: (i) storms, internal, or tidal waves; (ii) sediment loading; (iii) tsunamis; (iv) flood discharges; (v) volcanic eruptions; and (vi) bolide impacts (Goldfinger et al., 2012; Talling, 2014)). The first correlation between an earthquake event and turbidity current was made by Heezen and Ewing (1952). The Grand Banks earthquake in 1929 caused slumps resulting in turbidity currents breaking several trans-Atlantic telephone cables sequentially. To make a distinction in the cause of the density flow, these earthquake-triggered turbidites are referred to as ‘seismoturbidites’. The latter was defined by Mutti et al. (1984) to refer to repetitive megaturbidites deposited in highly tectonically mobile basins that compris a volumetrically significant proportion within otherwise normal turbidite sequences. However, a criterion to distinguish seismoturbidites from aseismic turbidites was not presented. Nevertheless, the authors supposed that the large volume and extent of certain turbidites reflects their seismic origin (Mutti et al., 1984).

In paleoseismological research, seismoturbidites are frequently used to derive recurrence patterns in fault activities. A challenge is then to determine whether or not a turbidite has its origin in an earthquake event. Several methods have been developed to distinguish seismoturbidites from aseismic density-flow deposits and are summarised below.

3. The influence of earthquakes on lake sediments

The confluence test An earthquake will cause multiple slopes to fail synchronously and hence, the deposition of multiple turbidites is a good indicator for a seismic origin (Van Daele et al., 2014). However, proving that those turbidites were deposited in a simultaneous manner is not that simple. For that reason, the confluence test was developed (Goldfinger et al., 2007). The test involves counting of the amount of turbidites found in a sedimentary column of a channel, fed by several tributaries, and compare them with the amount of turbidites found in the tributaries, above the confluence.

Vertical grain-size evolution Beck (2009) performed a study on a series of turbidites in a French Alpine lake. A relatively large delta that collects a considerable amount of run-off during storms is the source of those turbidites. However, to find criteria that differentiate partial delta failure due to storms from seismically triggered failures is no sinecure. Several vertical evolutions of different grain size parameters were plotted on graphs. This resulted in distinguishable evolution pathways for the two kinds of turbidites. Five seismoturbidites could be separated from the sedimentary column and validated the technique by matching with dates of known earthquake events.

Coeval generation of turbidites in different basins An earthquake is a regional event and hence, it seems logical that several basins or lakes, located in a perimeter relatively close to the epicentre, have to be influenced by the trigger. Multiple lakes have been studied for this phenomenon near rupture zones in Chile and New-Zealand (Moernaut et al., 2014; Howarth et al., 2014 respectively). For each of the lakes, a turbidite stratigraphy was developed and combined with precise dating, in order to correlate between the different lakes. Turbidite horizons that match over several lakes are interpreted to have a seismic origin. Moreover, correlating relatively recently deposited turbidites with known events, confirms this method.

Composition Also the composition can indicate the trigger of density flows. Turbidites with a similar composition as the background normal sediments, are most likely remobilised during earthquake events. Turbidites, with a composition that is significantly different than the background, are more probable to originate in a source outside the basin and must have entered the lake during flood events for instance (Simonneau et al., 2013; Moernaut et al., 2014)

Stacked turbidites Nakajima and Kanai (2000) proposed the principle of amalgamated or stacked turbidites after noticing odd sedimentological and mineralogical features within what was expected to be a single turbidites. Those contained (i) irregular sequences of Bouma divisions, (ii) grain size breaks or fluctuations that lead to a separation of different turbidite pulses and (iii) abrupt changes in mineralogical content at the transition between pulses. The latter indicates that the turbidite has a different source area for each single pulse. The concept was further investigated on cores from the Cascadian and Californian margins (Goldfinger et al., 2007; Nelson et al., 2012). By combining X-ray CT and correlation of physical properties, stacked turbidites can now be easily identified. Also the lack of hemipelagic sediment between the pulses demonstrates the synchronous origin of the different pulses.

3. The influence of earthquakes on lake sediments

3.3. ‘Seiche-wave’ water movement According to the NOAA (United States National Oceanic and Atmospheric Administration, 2016) a seiche is a standing wave oscillation in a body of water. A seiche can be confused with a tidal wave, since the period of the former can go up to 8 hours. The phenomenon can be caused in any enclosed or semi-enclosed basin by both seismic and aseismic agents. A cause that is not related to seismicity, for example, is the influence of strong winds combined with a quick change in atmospheric pressure. These conditions have caused seiche movements in the Great Lakes (USA) (NOAA, 2016). Seiches related to earthquakes can be induced either directly or indirectly. The propagation of the seismic waves can result in a seiche and is therefore a direct result of the earthquake (Bondevik et al., 2013). Indirectly, the event can create slope failures, which can lead to seiche waves on its turn (figure 3.3) (Chapron et al., 1999).

Different authors argue that a seiche movement can have a large influence on the sedimentation in basins during or after earthquakes. To explain why, one first has to look to the physics of the standing wave in a waterbody. The maximum velocity (umax) of the bottom current induced by the standing wave can be derived from the linear wave theory and is given below (Chapron et al., 1999):

𝑔 푢 = 푎√ 푚푎푥 ℎ

With a = amplitude, h = lake depth and g = gravity acceleration. Imagine a seiche movement with an amplitude of 1,5 m in for instance Lake Lucerne, which has an average depth of 100 m. The resulting maximal velocity of the bottom water can then be calculated as 0.5 m/s. This resulting velocity certainly prevents the deposition of mud particles and, furthermore, these currents are even sufficient to erode and transport the underlying sediments in case it consists of grain sizes below coarse sand (Hjulström diagram). From this can be deduced that a seiche movement keeps all the fine material produced by slumping in an oscillating suspension plume and at that the bottom currents are capable to rework older sediments. As the amplitude of the wave decreases, the coarsest particles will settle first. The finest particles in the suspension cloud will settle when the seiche movement has ceased. (Vermassen, 2015).

The final result of this process in the sedimentary record has been described before and is frequently linked to homogenites and megaturbidites. Campos et al. (2013a, 2013b) describe homogenite- turbidite associations in the Marmara Sea Basin and the Gulf of Corinth. The base of these associations was defined as coarse sandy, with fining upward sequences and seen as the turbidite part. A sharp transition separates a thick, fine, homogeneous part (homogenite) from the turbidite. However, by looking at X-ray radiographs and grain size profiles (Campos et al., 2013b), the lower part of the homogenite showed a ‘mixed term’, which was considered to be a result of the oscillating water movement. The three different units were interpreted as a sequence of (i) the sediments deposited by traction load in a density flow, (ii) unstable suspension fallout affected by a seiche wave induced bottom current and (iii) a stable fall-out from a thick suspension plume (figure 3.3). More or less the same succession of layers was also noticed by Vermassen (2015) in Lake Lucerne, but also in Lac du Bourget a similar layer was studied (Chapron et al., 1999; Beck, 2009).

3. The influence of earthquakes on lake sediments

Figure 3.3: The 1882 earthquake caused the presence of a homogenite in Lac Le Bourget. A schematic representation of the proposed depositional process is given here. (Beck, 2009). A) Slope failure caused by earthquake shaking results in slump- evolved turbidites. B) Simultaneously the seismically induced oscillatory movement of the water (seiche) increases the ‘segregation’ of fine and coarse sediments (clay and silt/sand, respectively) by keeping the fine fraction in suspension longer. On the slopes, the seiche wave induced bottom currents are stronger leading to the deposition of a thicker coarse fraction. Figure from Beck (2009). 3.4. Flow directions A current tends to organise the fabric of the sediment whether or not the latter got deposited out of a flow or are just influenced as bedload. Sedimentary structures, such as ripples, cross lamination, imbrication of grains and mud clasts, etc., is a result of these flow related processes. Moreover, sedimentary structures, or fabrics, can lead to a lot of information regarding the direction of the flow. Nonetheless, only a couple of methods are currently used to derive the latter.

Elongated paramagnetic and ferromagnetic grains tend to have their longest shape axis aligned with their maximum magnetic susceptibility axis and the same counts for the shortest axis and weakest susceptibility (Rees, 1983). Therefore, directions of the flow that have resulted in a certain deposit can be determined by analysing the anisotropy of magnetic susceptibility (AMS) of that sediment. However, the orientation depends the flow regime and the grain size of the contributing sediments (figure 3.4). Generally, a moderate flow velocity (<1.2 m/s) will generate alignments parallel to the flow, while faster flows result in a perpendicular orientation of the longest axis (Flood et al., 1985). The method is also most successfully applied on sediments ranging from clay to fine sand and has booked good results in paleoflow research on tsunami deposits (Wassmer et al., 2010; Schneider et al., 2014) and on turbidites (Felletti et al., 2016; Tamaki et al., 2015). Moreover, as the standard sample size necessary for this kind of research is rather large (10 cm3), AMS was not applied in this dissertation.

3. The influence of earthquakes on lake sediments

Figure 3.4: Main types of anisotropic grain shape fabrics. The different orientation of the shape axes of the grains cause different patterns in upper hemisphere stereogram projections of the magnetic susceptibility. Imbricated and non- imbricated fabrics are depicted. The a-axis, b-axis and c-axis are the long, intermediate and short grain axis respectively. The direction of the strongest anisotropy of magnetic susceptibility is kmax, consequently kint and kmin are respectively the intermediate and weakest directions. Figure from Felleti et al. (2016).

3. The influence of earthquakes on lake sediments

Also flow directions in sediments that are considered to be seiche deposits have been studied before. Mulder et al. (2009) made thin slices of rocks coming from a Cretaceous megaturbidite, outcropping in the Pyrenees. Afterwards, the orientations of the grains within the slices were examined. The outcrop exists of two distinct units: a lower coarse-grained unit with fining upward and sedimentary structures and a fine-grained upper unit lacking any sedimentary structures. The lower unit was interpreted as a deposit resulting out of traction load of a turbidite that was reflected multiple times in the basin, while the body of water was oscillating. The second unit was then explained by a seiche wave controlled deposition of the fine silt and clay out of an oscillating suspension cloud.

As shown by Van Daele et al. (2014), different flow directions can be recognised in single pulses of stacked turbidites within sediment cores. As multiple slope failures have occurred in the studied Chilean fjord during the 2007 earthquake, one can expect that the directions of the different pulses in a stacked turbidite will differ from pulse to pulse. In this study, X-ray CT scanning revealed sedimentary structures, such as folds, imbrication of mud clasts, ripples and convolution, at the base of pulses within the stacked turbidites (figure 3.5). Furthermore, it seems logical that these directions point out the source areas of the different pulses. Eventually, the authors were able to derive flow pathways for every pulse from the obtained orientations.

Figure 3.5: Flow directions can be derived from the sedimentary structures sketched here, as they can be recognised in 3D volumes produced by X-ray CT. Figure from Van Daele et al. (2014)

Especially last paragraph is of great importance to this dissertation, since the objective is to image possible sedimentary structures with a higher resolution and hence, on an even smaller scale than Van Daele et al. (2014).

4. The 1601 A.D. earthquake and the related sediments

4.1. The earthquake event On September 18th, 1601, an earthquake struck Central Switzerland with the epicentre located in Unterwalden, 15 km south of Lucerne. The moment magnitude was estimated at approximately Mw 5.9, the macroseismic intensity (MSK) at the epicentre reached VIII on the Medvedev–Sponheuer– Karnik scale. A complete report of the earthquake event was written by eye-witness and city clerk of Lucerne, Renward Cysat (1545 – 1614) (Schnellmann, 2002). His observations of the time, duration and intensity of the earthquake led to an assessment of the earthquake intensity near Lake Lucerne, being MSK VII (Schwarz-Zanetti et al., 2003). This implicates that the city of Lucerne had to suffer major damage to its infrastructures, e.g. chimneys and other masonry collapsed, walls cracked.

Not only facts concerning the earthquake itself were remarked by Cysat, also information about rockfalls, damage and lake water behaviour were collected by interviewing inhabitants of other villages around the lake. The most peculiar and frightening fact noticed by the citizens of Lucerne that day, was the periodical advance and retreat of the water in the Reuss river. In other words, a drying up of the river was alternated with a forceful return of the water and this happened six times within the first hour after the earthquake. Lake shore inhabitants told Cysat that immediately after the event, the water in the Gersau basin rose immensely high and that an oscillating movement of the lake water could be noticed in the Uri basin up to eight days after the earthquake. Schnellmann et al. (2002) interpreted these observations and linked it to a tsunami of approximately four meter high and subsequently a seiche movement. The latter had a period of about ten minutes and an amplitude of one to two meter, derived from the observations of the Reuss. The observations were compared to models based on initial slope failures (Schnellmann et al., 2002, Hilbe & Anselmetti, 2014) and were proven consistent.

Furthermore, several rockfalls were noticed, but a remarkably large one occurred at the Bürgenstock mountain and caused a tsunami on top of the oscillating lake water. Also shore collapses were described, as well as the movement of loose floating parts up to four metres higher than water level. Within a perimeter of one kilometre around the lake shores, parts of the land got inundated. 4.2. Previous research on 1601 AD earthquake related sediments Siegenthaler et al. (1987) were the first to correlate seismic and stratigraphic data to known earthquake events in Lake Lucerne. These authors concluded that the volume of slumped material could be roughly matched with the magnitude and distance from the epicentre. Afterwards, the sediment infill of the lake was investigated by Schnellmann et al. (2002; 2006) with the purpose of establishing a mass-movement chronology and determining paleoseismic events over the last 15 000 years. Together with research on the geotechnical properties of the failed sediments in the source areas (Strasser et al., 2006) and on the morphologies of the failed areas and deposits (Hilbe et al., 2011; Hilbe & Anselmetti, 2014), a detailed picture of the youngest mass movements within the entire lake was developed.

Also the infill of the Vitznau basin did not escape from a large amount of research. The basin is characterised by a glaciolacustrine infill of more than 100 m thick ending with 5 to 10 m of Holocene sediments. These are grey to brown laminated mud, intercalated by coarser grained turbidites. Biochemical varves are present in the top meter. Three types of mass movement related deposits were recognised by Schnellmann et al. (2002): rockfall cones, mass flow deposits and (mega-) turbidites (figure 4.1).

4. The 1601 A.D. earthquake and related sediments

- Rockfall cones

These features consist of piled up debris found at the foot of steep cliffs at the edge of the basin. Blocks of rock cannot be penetrated by seismic waves. However, due to their irregular morphology and defined position within a basin, these features can be detected rather easily.

- Mass flow deposits

Mass flow deposits arise from either onshore rockfalls or subaquatic slope failures. The exact trigger mechanism can be derived from seismic profiles. If a rockfall is responsible for the deposit, high amplitude internal reflections will be noticed. If the mass movement is induced by a subaquatic slope failure, the deposit will appear rather transparent. These bodies are characterised by a particular geometry of basinward thinning and an abrupt ending.

- (Mega)turbidites

Mass flows can disintegrate and create density currents or turbidity currents and therefore, turbidites are found at the most distal part of these deposits in Lake Lucerne. Lacustrine turbidites will fill the deepest part of a basin with a convex shape, with the thickest part of the turbidite in the deepest part of the basin. The term ‘megaturbidite’ is used to indicate a turbidite deposit that is of considerable thickness relative to most of the other turbidites found in the basin of interest. The turbidite emphasised in this dissertation is approximately 2 m thick and consists of homogenous mud with a graded silty-sandy base. Owing to its size, this deposit can be defined as a megaturbidite.

Concerning the A.D. 1601 (further referred to as 1601) earthquake event, all three types of mass movement derived deposits described above, were found in the Vitznau basin. The first large mass flow deposit was discovered by Hsü and Kelts (1985). The authors owed the presence of these deposits in the northern part of the basin to a large landslide that propagated into the lake in 1795. Two years later, Siegenthaler et al. (1987) stated that the mass flow deposit is a result from a failure caused by the 1601 earthquake. This theory was later confirmed by radiocarbon dating (Schnellmann et al., 2006). Besides the earliest discovered slope failure of the northern part of the Vitznau basin, other mass flows could reach the centre of the basin, such as mass flows coming from the eastern side. Also some rockfall cones were noticed in seismic profiles. The most important one is the Bürgenstock rockfall, which had a significant impact on lake water behaviour and mass flow generation. All of the 1601 earthquake related sediments were grouped and mapped by Schnellmann et al. (2002, 2006) (figure 4.2.).

4. The 1601 A.D. earthquake and related sediments

Figure 4.1.: A general overview of the bathymetry of the Vitznau basin with the different seismic lines is given in A (adapted from Hilbe et al., 2011). B, C and D are seismic profiles picturing the different types of mass-movement deposits in Lake Lucerne related to the A.D. 1601 earthquake (adapted from Schnellmann et al., 2006). B represents a giant subaqueous rockfall cone and stacked rockfall-evolved mass-flow deposits at the Bürgenstock Mountain. In C, a large mass-flow deposit coming from the northern part of the basin is visible, together with a megaturbidite at its distal end. The rockfall in the south is due to a rockfall the Obermatt quarry in A.D. 1964. D shows mass-flow deposits coming from both the eastern and the northern part of the basin. The megaturbidite is directly overlaying the two mass-flow deposits. The hard reflectors dipping underneath the mass-flow deposits, seen in profile C and D, are interpreted by Vermassen (2015) as sand layers from the base of the megaturbidite.

4. The 1601 A.D. earthquake and related sediments

Figure 4.2.: Distributions and thickness of slump deposits corresponding to the 1601 earthquake in the Vitznau basin. The hachured area in the centre of the basin represents the megaturbidite, the red star indicates the location of the Burgenstock rockfall (adapted from Schnellmann et al., 2002).

This dissertation has two main goals. The first one is to evaluate the potential of µCT as a tool to study sediment cores and turbidites. The second one is to possibly validate and improve the flow model (figure 4.4) proposed by Vermassen (2015). In order to do so, one core, LU14-12-G-2-2-A (figure 4.3), taken by the latter is highlighted. The sediments within this core are a good representation of the influence of the 1601 earthquake on sediments in the centre the basin.

Figure 4.3: Line scan image of core LU14-12-G-2-2-A. Left is the top, right the bottom. The lineated right unit is background sediment. The base of the megaturbidite is located at the start of the rough interface in the middle. Going to the left, one enters in seiche-wave deposits that gradually changes into finer grained deposits towards the top of the core.

The bottom of the core is characterised by background sedimentation in the form of alternating black, brown and beige laminae. An abrupt transition to a coarser sandy layer is visible at 32 cm core depth followed by a 5 cm thick organic rich layer. These were interpreted as the base of a megaturbidite, with a couple of sandy layers in between the organic rich layers, indicating the stacked origin of the base. At 27 cm the sediments undergo a transition to a homogenous brown mud. The transition is characterised by layers of alternating organic matter content and are interpreted as sediments deposited during the seiche-wave movement. The particles can originate straight from the suspension cloud or can be eroded from the turbidite below due to strong bottom

4. The 1601 A.D. earthquake and related sediments

currents and redeposited afterwards. The upper part, the homogenous mud, is fine material deposited by gravity in the hours/days/weeks after the earthquake event.

In order to gain information about the applicability of µCT for determining the average grain orientation, it is necessary to have some knowledge of the expected directions of the paleoflow. Vermassen (2015) attempted to derive the depositional conditions of the sediments discussed in this dissertation and has developed a theoretical model for this (figure 4.4). The proposed model for the flow behaviour within the Vitznau basin after the 1601 earthquake, is based on seismic profiles, grain-size data, CT and, for the first time, µCT-data on retrieved cores from within the megaturbidite. The author explained the deposition of the megaturbidite in three steps.

The first step was the deposition of the base of the megaturbidite, which consists of stacked turbidites. An attempt to identify the source areas of those pulses was done by doing CT, grain-size analysis, mineralogical analysis and, for the first time, µCT. The base of the theoretical model consists of 5 different sources of sliding, identified by Schnellmann et al. (2002), further completed with a sixth source in the southeast of the Vitznau basin (indicated with a yellow arrow). In this dissertation, the latter source is referred to as ‘Vitznau 2’, for the reason that Vermassen (2015) did not distinguish the name of this source from the main Vitznau source. The other sources are named to present-day settlements near the source area and are referred to as Weggis, Rietsort, Obermatt and Bürgenstock.

Looking at the scars the slides made in the floor and edges of the basin and the mineralogy of those areas, one could more or less assess the impact of the sources on the sedimentation in the centre of the basin. The Weggis slide seems the largest but the sediment that slid down is probably all lacustrine, and has therefore a very fine-grained origin. Hence, the production of a coarse-grained turbidite base out of the mass-flow has most likely not occurred. The Rietsort source has an entirely different morphology, with scars that are located more on top of the locally steep slope. For this reason, coarse-grained sediment will eventually occur in the Rietsort deposits. The same counts for the both of the Vitnau sources, the and Obermatt source. Eye-witnesses of the earthquake, mentioned even a large rockfall at the the Bürgenstock source and hence, this probably had a significant impact on water behaviour and sedimentation on the lake floor.

Three individual mass-flow deposits related to the 1601 A.D. earthquake were recognised in the seismic data (Schnellmann et al., 2002; Vermassen, 2015) and since these mass flows tend to form density flows, these three directions should be considered for the pulses in core LU14-12. Herein, the base of the turbidite exists of pulses of sand interbedded with organic matter. Due to the resemblance with other cores retrieved north-east from LU14-12, the pulses were considered to come from the north-eastern source areas, Weggis and Rietsort. However, as the Rietsort source area is less deep on the lake slopes, the organic matter is most likely to originate from this area. Moreover, a seismic profile through the line were core LU14-12 was retrieved from, indicates a sand layer underneath a mass-flow deposits. Also, because of the steeper slope at Rietsort, it seems natural that a density flow with a relatively high velocity was generated. This leads to the conclusion that a density flow of the Rietsort area reached the centre of the basin first. The orientation of the branches present in the core was analysed and an alignment towards the NE-SW direction popped up, which argues in favour of this conclusion.

On the east-west seismic profile (figure 4.2 D), another feature, important for the flow direction, was detected. The position of the Vitznau mass-flow deposit (from the east) on top of a coarse sand layer, and a small sand layer, superimposed on that mass-flow, appear on the seismic line. This supports

4. The 1601 A.D. earthquake and related sediments

the theory that a first density flow, most likely from the Rietsort area, got overridden by a density flow originating from the Vitznau source in the eastern part of the basin. Now, as all the base and upper sand layers of the cores taken in the eastern side of the basin were analysed for grain size, a different grain-size distribution came up for both layers indeed (Vermassen, 2015). Hence, given the location of core LU14-12, there is a good chance that one of the pulses has originated in the east.

The third major mass flow depicted in the seismic data, happened at Obermatt. However, contribution to the sediments in the eastern part is most unlikely, due to the small extent of the deposits. The Burgenstock rock-fall also possibly caused a density flow, nevertheless, these deposits were not found on the seismic data by Vermassen (2015).

A next step, Vermassen (2015) describes, is dominated by the oscillating ‘seiche-wave’ movement of the lake water. With the water, also the cloud of fine-grained suspended sediment is affected and the finest particles were kept in suspension. As the oscillating movement decreases in amplitude and frequency, the currents within the cloud and on the bottom of the basin will weaken and finer particles will be able to settle. Furthermore, a seiche-wave movement is characterised by a fluctuating flow regime at the bottom of the basin. Stronger and weaker currents alternate as the wave movement continues. The sediments deposited from this process can therefore show grain size variations, since smaller particles can be deposited when the current is less strong. When the current gets stronger, only coarser grains are able to fall out of suspension. Locally, a strong seiche movement could possibly even be capable to erode the upper part of the stacked turbidite and could focus the reworked sediments in a deposit of inversely graded laminae on top of the remains of the stacked turbidite. The sediment focusing becomes less important as the seiche movement fades. However, to reconstruct directions of paleoflows, reworking of the stacked turbidite is certainly disadvantageous. Again, reworking is however not necessarily happening while the seiche movement continues. In core LU14-12, Vermassen (2015) argues that the laminations between the turbidite part and the homogeneous part are a result of that movement.

The last step in the deposition history is the settling of fine-grained sediments under stable water conditions, wherein gravity is the only factor in the settling process.

4. The 1601 A.D. earthquake and related sediments

Figure 4.4: The proposed theoretical model from Vermassen (2015) that indicates which turbidity current would have arrived at which core location and in what order. Turbidity currents tend to evolve out of mass-flows and therefore the colours of the boxes (turbidite) are matched with the colours of the mass-flow arrows. When the boxes are filled in with two colours, it means that the turbidity currents arrived at approximately the same time at that core location. The transparent areas correspond with the extent of the mass-flow deposits. The names of the mass-flows and the evolved turbidity currents are derived from villages or places close to the source area of that pulse. The red dots are core locations where a seiche lamination was found in the retrieved cores, the green dots are the locations of cores wherein an alteration of sand and organic matter was found at the megaturbidite base.

5. X-ray computed tomography

5.1. Introduction 5.1.1. X-rays and the development of Computed Tomography (CT) X-rays are electromagnetic radiation with a wavelength ranging from 0.01 and 10 nm, which is an order of magnitude smaller than visible light (figure 5.1). Unlike the latter however, it has the unique ability to penetrate matter and enables to image the internal structure of that matter. Therefore, X- rays are widely used in healthcare to image internal parts of patients, starting with Röntgen in 1895. Back then, the nature of these rays was unknown and the term ‘X-rays was chosen for this reason.

Figure 5.1: The wavelength spectrum of electromagnetic radiation (Seibert, 2004).

As previously mentioned the imaging technique was further developed in the first decades of the 20th century especially for medicinal purposes. As the rays interact differently with different phases within a non-uniformly composed material, 2D images can be created. Within a human body, that exists of hard bone and soft organic tissue, these features can be distinguished by their difference in density. However, since and X-ray image is a projection of a 3D object on a 2D detector screen, a lot of information is lost and misinterpretations can occur (Cnudde et al., 2006).

To overcome this problem, the computed tomography (CT) technique was developed. The mathematical principle of CT was already known in the beginning of the 20th century. However, it was only until the development of the X-ray image intensifier in the 1950s that experiments with the CT principle could start. The X-ray image intensifier allowed recording and displaying of the X-ray movie using a TV camera and a monitor. Therefore, it was only until the late 1960s to early 1970s that commercial applications of CT saw the light. The first CT scanner was built by Cormack and Hounsfield and is also known as computed axial tomography (CAT) scanner (Losano et al., 1999). At first, the CAT was only used for medical examination. However, the industry quickly saw advantages in CT and efforts were done to apply the technique in the industrial environment by the end of the 1970s. Also in this period, when analogue to digital converters and computers were adapted to conventional fluoroscopic image intensifier/TV systems, the evolution of the CT technique was boosted (Cnudde et al, 2006).

5. X-ray computed tomography

5.1.2. Use in geological applications First, X-ray CT scanning was mainly used in medicine to image a patient’s bone structures, but its potential to image other non-uniformly composed materials became clear fast. Therefore, it was also introduced in different scientific research fields within earth sciences like palaeontology, sedimentology, petrology, soil science and fluid-flow and natural building stone preservation research (Cnudde et al., 2006).

Palaeontology was the first field of research within geology to use X-ray CT scanning for determining evolution of mammal skulls (Fourie, 1974). Since the technique in non-destructive, irreplaceable fossils were also scanned to investigate, i.e. early human skulls (Conroy and Vannier, 1984) and an Archaeopteryx fossil (Haubitz et al., 1988). Precious pieces of materials, such as meteorites, also exploited the non-invasive character of X-ray CT scanning (Arnold et al., 1982). Furthermore, soil science utilized the technique to calculate soil densities (Hainsworth and Aylmore, 1983; Petrovic et al., 1982) and petroleum engineers as well for experimenting with fluid-flow in porous media (Wellington and Vinegar, 1987). The latter experiments were the first to explore the high temporal resolution of the technique. In the early 1990s, medical CT with a spatial resolution of 250 µm conquered the fields within geology entirely. Porfyroblast of garnet were examined in various rock types (Carlson and Denison, 1992), macroporosity of soils was determined (Peyton et al., 1992). Furthermore, deeper in the 1990s, fractures (Keller, 1997), porosity and density of natural building stones (Jacobs et al., 1997, 1995; Klobes et al., 1997) were investigated. However most relevant to this dissertation is the first application of CT on sedimentary structures (Kenter, 1989) and further development to derive flow directions on those structures (e.g. Van Daele et al., 2014).

With an improving technology, the technique was also pushed to new limits. Micron and submicron resolutions could be obtained and therefore also the name of the technique was reformed to high- resolution CT or micro-CT. The latter started to be widely used in the beginning of the twenty-first century to visualise 3D pore structures and create 3D petrography (Ketcham and Carlson, 2001). More studies proved that micro-CT was a promising technique in very diverse fields of research within earth sciences. For example, to image the heterogeneity in the matrix and to determine the grain size and sample porosity, rock and soil samples were scanned in biological quarantine with micro-CT (Allen et al., 2002) and in the field of glaciology, micro-CT images of snow and ice with 10 µm spatial resolution were obtained (Coléou and Barnola, 2001).

In the last decade, the images derived from micro-CT gained a lot in quality. This has led to the ability of scanning samples with a spatial resolution up to 400 nm. Hence the name micro-CT was changed into nano-CT or sub-micro-CT for scans with a resolution approaching this value. In the last couple of years, even 4D imaging, and therefore the depicting the evolution of processes within materials, was made possible (Cnudde and Boone, 2013). It is clear that the technique is constantly under development and improving and that a lot of applications within geosciences still are still explored. 5.2. Basic principles of X-ray transmission CT 5.2.1. Interaction X-rays with matter Four physical processes are responsible for attenuation of X-rays when passing through matter: photoelectric absorption, Compton scattering, Rayleigh scattering and pair production. When an incoming X-ray photon knocks out an electron of the inner electron shell of an atom, photoelectric absorption has occurred. The total energy of the incoming X-ray is then transferred to that inner electron. Compton scattering happens if the incoming X-ray photon interacts with an electron and the photon loses part of its energy and is deflected in a different direction. This process is also known

5. X-ray computed tomography

as inelastic scattering. The opposite is Rayleigh or elastic scattering, where none of the energy of the incoming photon is lost. If the photon hits a nucleus, a particle and an anti-particle are created by this interaction. This is called pair production (Seibert and Boone, 2005). Since these processes take place at an atomic level, the amount of attenuation is determined by the density and the atomic number of the atoms present in the material.

The main idea behind X-ray computed tomography is to visualise and characterise a volume of a certain material in an non-destructive manner, using the variability in attenuation of the X-rays with the different phases within that matter. Attenuation takes place when X-rays are removed from the main X-ray beam by interaction with matter. To quantify attenuation, the intensity of the X-ray beam is measured before (I0) and after (I) passing through an object. These two parameters are related with the linear attenuation coefficient (µ) and the thickness of the object (s) by the Lambert-Beer’s law:

(−µ푠) 퐼/퐼0 = 푒 From this equation, one can derive that I decreases in function of distance (s), since the exponential argument is negative. This indicates that the attenuation of the incoming X-ray beam gets larger with increasing distance or thickness of the material. The linear attenuation coefficient depends on the density and effective atomic number of the material. When the µ-value of a material is high, X-rays will penetrate only over a relative short distance into the material. When different materials, with different absorption properties, are present within the scanned object, the Lambert-Beer’s equation is given by the summation of the µ of every single object multiplied by the lateral extent of that object. This principle is illustrated in figure 5.2 and given by the next equation:

퐼 = 퐼0 푒푥푝 [∑(−µi si)] 푖

Figure 5.2: This figure is a schematic representation of the Lambert-Beer’s law. Three different types of material are depicted here. The upper object is uniformly consisting of material with attenuation coefficient µ1. The two other objects will have an absorption dependant on both the attenuation coefficients and the thicknesses of the multiple materials present within them. Therefore, the sum of the three µ-values, each multiplied by the respective thicknesses, should be taken into account for the Lambert-Beer’s law.

This law, however, is only valid for monochromatic X-rays, as the linear attenuation coefficient is dependable on the energy of X-rays. This property makes it possible to assess the variability of µ in function of the X-ray energy. Curves for different geological materials, of which figure 5.3 is an example, can be found online databases (NIST XCOM). The curves can be used to gain insight into

5. X-ray computed tomography

what the expectations are when a geological sample is scanned. As shown in figure 5.3, the µ-values of different minerals tend to converge as the energy of the incoming X-rays is higher. The plotted curves relate to the different phases in the dataset highlighted in this dissertation. Identification problems for the individual mineral phases and the organic matter within the geological sample arise when it is scanned at higher energies.

Attenuation (μ) vs. Source Energy 1000

quartz 100 calcite

water

10 μ C

0 10 60 110 160 210 Energy (keV) Figure 5.3: Linear attenuation coefficient variability over X-ray energy for quartz, calcite, water and organic matter (C). The difference in µ between the two minerals and organic matter present at lower X-ray energies, tends to fall away when the energy rises, resulting in identification problems. The same happens for water and organic matter when scanned at a low energy.

As mentioned before, in a radiograph, the attenuation coefficients are added up along one X-ray path. Consequently, information is lost concerning different phases in the scanned object. To overcome this and to create a 3D distribution of the attenuation coefficient in the object, CT has to come into play. 5.2.2. X-ray transmission CT A CT-device consist of three basic parts, including an X-ray source, an X-ray detector and a device to create a rotational motion between the sample and the source-detector system. Various configurations of the CT-system are possible, whereby sample size and the appropriate resolution determine the required configuration (Mees et al., 2003; Cnudde et al., 2006). To reconstruct a 3D volume, hundreds of radiographs have to be taken in different angles varying from 0° to 360°. The incoming X-rays are collected using an X-ray detector, which is usually a 2D-pixel array like a CCD camera, a flat panel detector or an image intensifier and then converted to digital radiographs. Cross- sections through the scanned object are then reconstructed by a computer algorithm, developed by Feldkamp et al. (1984)

The principle used in all current CT scanners is called filtered back-projection and is explained by figure 5.4. To start the explanation, the principle of simple back-projection has to be clarified first. In (a) a single attenuation profile is shown that lacks any depth information and so the information taken up by the X-ray beam is smeared out, or back-projected along its path. When this is done over several angles, a star-burst pattern develops for every single point in the object (b), causing blurriness of edges of features and degradation of contrast. Therefore, a filter-function has to be

5. X-ray computed tomography

applied on the measured data before back-projecting (c) and the term filtered back-projection comes to existence. In (d) the star-burst pattern is cleared out and the image produced will be an accurate representation of the truth (d) (Michael, 2001).

Figure 5.4: Demonstration of the simple back-projection principle where the data is just smeared out ((a) and (b)) and filtered back-projection principle where the information is convolved by a filter function before back-projecting ((c) and (d)) (Michael, 2001). 5.3. X-ray systems 5.3.1. Basic components In order to develop a system with CT capabilities, basically three components are necessary. A source to generate X-rays, a detector to catch the attenuated X-rays and transform them in a signal and a device that allows either the source-detector system or the object of interest to rotate.

Source Different types of X-ray tubes exist but the working principle of most of them is based on the Coolidge tube (figure 5.5). Electrons are released into vacuum from a heated filament cathode and are accelerated towards the anode in the tube. Once colliding with the matter of the metal anode, X- ray photons are created. The main advantage of the Coolidge tube is the capacity to easily adjust the energy of the produced X-rays. This energy is equal to the kinetic energy of the incoming electrons and hence, a change in the acceleration voltage will lead to a change in the energy of the X-rays. Moreover, an electrostatic focusing cup can be utilised to reduce the focal spot size of the X-rays leaving the tube, leading to the possibility to image at a higher resolution.

5. X-ray computed tomography

Figure 5.5: Schematic overview of the Coolidge X-ray tube (Oak Ridge Associated Universities).

Detector X-rays can be detected in two ways: indirect and direct. Indirect detectors will first convert incoming X-rays to visible light by a scintillator and subsequently that light is measured by the actual detector. This way of measuring is well developed but tends to lead to loss of spectral information. Moreover, scattering of the light within the scintillator causes a limitation of the image resolution. Another drawback is the dependency of the resulting electric signal on the energy of the incoming photon. Nevertheless, indirect detectors are dominantly used in radiography. A direct detector measures incoming photons directly by the creation of an electron-hole pair in a sensitive detector area. The possibility of measuring single X-ray photons and deriving their energy based on the deposited charge, has the potential to improve digital X-ray imaging significantly. However, further development of these types of detectors is necessary before using them to a larger extent. 5.3.2. Medical CT scanner Since it is impossible to spin a human or an animal, the source-detector system of a medical CT scanner has to rotate around the patient (figure 6.2, in methods section). The detector consists of a curved linear array existing out of up to 1000 pixels. As only one detector array is present, the X-ray beam can be presented as fan-shaped. In the newest scanners, up to 32 of these detector arrays are aligned next to each other. Therefore, a conical beam is applied. The entire setup is moved relatively slowly over the sample in order to create a helical scan path. Medical CT scanners have a large focal spot size and hence only a maximal spatial resolution of 250 µm can be reached. This resolution is sufficient for medical purposes, however not for high resolution studies. Due to the large and sensitive detectors, the signal-to-noise ratio and contrast of the images is usually high. Here, the objects are also placed in the middle between source and detector. Consequently, the magnification factor of these objects is 2. 5.3.3. Micro-CT scanner Unlike the medical CT scanner, it is possible with a micro-CT scanner to adjust the source object distance (SOD) in order to obtain a higher resolution. Also, by reducing the electron current in the X- ray tube, the focal spot size can be reduced and leads to a higher resolution. Typically, a current of 300 µA is applied on a micro-focus tube, which is 1000 times smaller than in a medical CT system (Cnudde et al., 2006). A large variety of different micro-CT setups exist, all designed for specific purposes.

5. X-ray computed tomography

Figure 5.6: Setup of the micro-CT system HECTOR at the Ghent University Centre for X-ray Tomography (UGCT).

At the Ghent University Centre for X-ray Tomography (UGCT) a micro-CT system, referred to as HECTOR (High Energy CT Optimized for Research) is frequently utilised for scanning all sorts of objects, including the samples that form the base of this dissertation (figure 5.6). It consists of a mechanical setup with nine motorized axes, a microfocus directional target X-ray source up to 240 kV and a large flat-panel detector. The system can accommodate large samples up to 80 kg, 1 m long and 80 cm diameter and it is also suited for high resolution scans up to maximal 3-4 µm resolution (Masschaele et al., 2013). 5.4. Image quality and artefacts To obtain data about separate grains smaller than for instance 100 µm, it is necessary to make sure the resolution is high enough, so that individual grains are depicted by at least several voxels. In order to comprehend methods to do so, an overview about the resolution and different artefacts that can occur in an 3D dataset is given in this section.

5.4.1. Resolution The spatial resolution depends on the magnification factor M, which is the ratio of the source- detector distance over the source-sample distance. The closer the sample is to the source, the larger the magnification will be and consequently the spatial resolution will be higher too. But also the focal spot size of the X-ray tube, the pixel size of the detector and physical phenomena such as X-ray scattering and interaction between pixels of the detector, control the spatial resolution. Ideally, the pixel size of the detector divided by the magnification of the system define the resolution and the maximal resolution is limited by the spot size of the X-ray source. All these factors come together in the following equation (Vlassenbroeck and Van Hoorebeke, 2009): 푑 1 푅 = + (1 − ) 푠 푀 푀 with R the achievable resolution in the object, s the spot size of the source and d the pixel size of the detector.

5. X-ray computed tomography

Out of the previous equation can be derived that the pixel size of the detector is the limiting the resolution when the magnification is low. On the other hand, for high magnifications, a significantly large spot size can result in blurry pictures. Therefore, the smallest obtainable spot size defines the maximal reachable resolution of the entire setup. In other words, to achieve the smallest resolution and therefore the largest quality of image, it is necessary to make sure that the spot size of the X-ray source is as small as possible and the object is as close to the source as possible (Cnudde and Boone, 2013). A problem comes into play with large objects, since severe image artefacts could appear when the object is not entirely into X-ray beam (Kyrieleis et al., 2011). Consequently, a trade-off between the maximal resolution and the dimensions of the object has to be made.

In other words, the resolution will increase with with a smaller source object distance, but the dimensions of the object limit this distance. This is where subsampling comes into play. By taking samples with smaller dimensions than the entire core, which is 6 cm diameter, the potential magnification M will rise and eventually the spatial resolution will do so as well. 5.4.2. Discretization effects and Partial Volume effects The obtained data is always acquired by a 1D or 2D detector. To gather 3D information, these obtained pixels have to be transformed into 3D voxels. This process is also referred to by the term discretization. The fact that this process is necessary, has a large influence on the possibilities of high- resolution X-ray CT.

Due to the rotational movement of the object, the reconstructed volume is a cylindrical shape with a certain diameter. That diameter is divided into a fixed amount of pixels perpendicular to the rotational axis for computational and technological reasons (Cnudde & Boone, 2013). Also, to avoid artefacts, the dimensions of object of interest should not exceed the dimensions of the cylinder (Kyrieleis et al., 2011). These facts cause a limitation of the achievable voxel size in function of the size of the depicted object.

Figure 5.7: Partial volume effect. A grain will only be distinguished if it is larger than the voxel-size. The µ-value of a single voxel will be an average of the µ-values of the objects or parts of objects within the voxel. The smaller the voxel-size, the better a single object will be recognised, however, the edges will remain blurry as there is no infinite small voxel-size possible.

Features within the scanned object that are smaller than the voxel size cannot be distinguished in reconstructed datasets. Still, the µ-value of the small feature will contribute to the µ-value of the entire voxel. This change of the µ-value to an average value of different features within the voxel is called the partial volume effect (Cnudde & Boone, 2013) (figure 5.7). Also voxels at boundaries between features with a different µ-value will be affected by this effect and the voxel will have an intermediate grey value, leading eventually to a reduction in the accuracy of the data analysis

5. X-ray computed tomography

(Ketcham and Carlson, 2001). Especially when small structures such as mineral grains or pores are analysed, a large error can appear (Kerckhofs et al., 2008). For pushing the method to further limits concerning picturing small grains, it is consequently necessary to reduce the reconstructed voxel size.

5.4.3. Ring artefacts In some of the datasets retrieved for this dissertation, ring artefacts are visible. Due to inaccuracies in the detector, ring artefacts can appear in reconstructed images as circles around the rotational axis. Like the other artefacts, these can be filtered out as well. Nevertheless, filtering the artefacts out fully is a challenge. 5.5. Data visualisation After reconstructing the volume, it can be visualised by 3D rendering techniques and subsequently different cuts through the volume can be made to investigate different properties. Features with a certain grey value, for example, can be retained while others are pictured transparent, which can be useful to picture the location of different phases within the object. Also, large structures, such as major cracks in the object or a network of large pores, can be easily shown and assessed qualitatively. However, when a quantitative assessment of properties is necessary, it is essential to analyse the volume with proper computer algorithms. 5.6. 3D data analysis The true strength of three dimensional imaging lies in the possibility to extract quantitative information about three dimensional structures or objects. To analyse a 3D dataset, several steps have to be respected. A first step is, if required, apply a filter operation on the data to filter out noise. Too much filtering is, however, not beneficial as the edges within the images are blurred, geometrical changes can occur and even complete structures can be removed. In a second phase of analysing, the filtered dataset is 3D phase segmented, based on the attenuation coefficients of the different voxels. This means that one or multiple thresholds are set on the dataset in order to create a binary dataset, wherein only the voxels with the desired µ-value are withheld. Furthermore, binary operations can be performed on the segmented data. To optimise the dataset for a later separation of the different objects, it could be useful to apply, for instance, an ‘erosion’, a ‘dilation’, a ‘fill holes’ and/or a ‘remove small spots’ operation. To break unwanted connections between multiple objects in the 3D dataset, a watershed separation function (Vincent and Soille, 1991) is most of the times performed. Eventually, the separated objects are labelled and in a last step, quantitative information can be derived for the thresholded data. Examples of quantitative information are porosity between grains, shapes of grains, minerals present, different phases present but most importantly for this dissertation also grain sizes and grain 3D orientations.

3D imaging basically uses a very large dataset of 2D images and aligns the latter in an appropriate way to make sure any slice through the object can be made. This results in rather large datasets. Also, a significant amount of 3D parameters can be derived from a 2D image, but most of the structural parameters are calculated using measurement operations in the three-dimensional data set. For these two reasons, a computer with a large RAM and GPU is required to do this kind of processing. For this research 3D volumes of 2000x2000x7500 voxels were obtained and this results in datasets of more than 60 GB wherein millions of grains are included.

6. Methods

In order to determine the paleoflow directions of the 1601 megaturbidite, Vermassen (2015) did several measurements on sediment cores retrieved from Lake Lucerne. This dissertation concentrates on one specific core (LU14-12) gathered in that campaign. Therefore, some analyses were also done on this core and therefore briefly considered here. Especially previously obtained X- ray computed tomography data from this core is relevant, but also the orientation of the core using natural remnant magnetization is highlighted. 6.1. Previous work 6.1.1. Coring On July 26th 2014, twenty-eight short-cores were collected on Lake Lucerne by R/V Thallassa, a boat owned by the EAWAG institute (Kastanienbaum, Switzerland). By using the dataset obtained by Schnellmann et al. (2002), the location and the depth of the megaturbidite could derived and consequently the coring locations could be determined beforehand. It was made sure that the core locations covered a grid over the complete spatial extent of the turbidite and were placed on, or near to, the seismic lines (Schnellmann et al., 2002). In this way, the sediment cores could be easily related to the seismic data (figure 6.1).

Figure 6.1: The grid of locations for the 28 short-cores taken by Vermassen (2015) and the grey seismic lines from Schnellmann et al. (2002). Six profiles (yellow lines) were retained. Locations are referred in Swiss coordinate system CH1903. Figure from Vermassen (2015).

At the location of the megaturbidite, the Vitznau basin is about 150 m deep. Gravity cores are taken by lowering an empty liner in a controlled manner until approximately 10 m above the lake floor. After this, the core is released and it accelerates to a quasi free-fall velocity caused by its weight, leading to penetration of the sediments when it hits the lake bottom. During lowering and the free- fall, a lid at the top of the liner is left open, so water can run through. After going through the sediment, the lid closes and consequently a vacuum is created in the top of the core. When the core

6. Methods

is pulled up, the sediment will stay in the liner due to this vacuum. When the core is taken on board, a cap is quickly placed on the bottom of the liner to prevent sediment from running out. To reach the depth of the megaturbidite, extra weight was required. For most of the cores (including LU14-12) a total weight (liner and weights) of about 30 kg was sufficient. To make the cores easier to handle for transport and future analysis, they were cut into pieces of maximum 1.5 m. For this dissertation, only sediment core LU14-12-G-2-2-A is of importance.

6.1.2. Measurements on closed core - Medical X-Ray Computed Tomography Some advantages convinced Vermassen (2015) to scan the cores before they were opened. The reason involves the geometry. A ‘D’-shaped object, like a split core, is a lot more sensitive to errors and artefacts than a cylindrical object, such as a closed core. Moreover, after medical CT scanning, sedimentological features can be seen and analysed and the core can be eventually opened along the plain with the best representation of these sedimentological features. Another evident advantage is that twice as much data is retrieved from scanning a closed core compared to a split one.

The scanning was performed at the Ghent University Hospital with a Siemens medical CT scanner (SOMATOM Definition Flash; Siemens AG, Healthcare sector, Erlangen, Germany)(figure 6.2). With a detector resolution of 512x512 pixels and a field of view of 7.5, an across-core pixel size of ~0.25 mm was obtained. The down-core spatial resolution is determined by the slice thickness, which was 0.6 mm in this case. A voltage of 100 kV was applied with 100 mAs and 0.55 pitch. Theoretically, this setup should allow the identification of separate grains above 0.5 mm diameter (coarse grained sand) in the reconstructed volume. However, practically, those grains will have to be separated at least 0.5 mm from each other in a muddy matrix to be visible as an individual grain. In the end, the purpose is to see sedimentary structures that are not visible with on normal photographs. The processing and analysis of these scans was done with Octopus Analysis 2.0. software (Brabant et al., 2011) and VGStudio 2.0 ©.

Figure 6.2: Sediment core placed in the medical X-ray CT scan system in the Ghent University Hospital (Vermassen, 2015)

6. Methods

- Micro X-ray Computed Tomography Vermassen (2015) performed micro CT experiments at the Centre for X-ray Tomography of Ghent University (UGCT) with HECTOR. In order make sure the cores could rotate and to succeed the scanning procedure, they were cut in shorter sections. Specific cores were selected for micro CT by the results of the medical scans, including the LU14-12 core. A voltage of 100 kV with a target power of 10 W was applied to scan the entire core. A voxel size of 35 µm was obtained. To know what side of the cores were front and to have an adequate orientation, the cores were carved as a mark.

Moreover, a Region of Interest (ROI) scan was done on two cores, LU14-12 inclusive. A ROI scan focuses on a part of the object, with the purpose of imaging that specific part with a higher resolution. 130 kV was the voltage applied for these scans, with a target power of 20 W. A volume with a reconstructed voxel size of 10 µm was derived and the images were afterwards processed and analysed in Octopus Analysis 2.0. (Brabant et al., 2011) and VGStudio 2.0 ©.

6.1.3. Orientation To determine the orientation of core LU14-12-G, a U-channel of was taken at 19 – 174 cm core depth. This subsample was examined for detrital natural remnant magnetization in the Laboratoire des Sciences du Climat et de l’Environment (LSCE). Under gravitational settling conditions, grains tend to settle according to Earth’s magnetic field present at that moment. When deposits are reworked, the remnant magnetism of those grains will differ from the magnetic field (Vermassen, 2015). Consequently, an absolute orientation of the core can be derived and information about reworking can be retrieved. 6.2. Subsampling As proven by Vermassen (2015) a voxel size of 20 µm is not enough to clearly visualize isolated grains within the sedimentary record of Lake Lucerne. As explained earlier in this dissertation (section 5.4.), when a grain is smaller than the reconstructed voxel size, it will not be distinguished in the reconstructed volume. The same counts for boundaries between mineral grains, mud and organic material, which will be blurred extensively when the resolution is not high enough. Also, whenever the space between two grains is smaller than the reconstructed voxel size, the boundaries between two grains will not be visible. Within the sediment core of interest, the packing of the grains is rather close. Hence, a higher resolution is necessary to distinguish the pore space and allow to split separate grains easily in a later phase of processing.

As the maximal achievable resolution is dependent on the magnification, which is limited by the sample size, the dimensions of the latter have to be reduced. Therefore, it is essential to subsample the core. In a first stage the region of interest within the 6 cm wide core was sampled into a smaller U-channel of 2x2x11 cm. Afterwards the U-channel was subsampled with straws, to reach the smallest subsample dimensions possible. This proposed workflow is depicted in figure 6.3. To check whether or not straw samples are a good size to do analysis on and determine a workflow for that analysis, test straws were also taken in two different sediment cores.

6. Methods

Figure 6.3: Workflow of the different steps of taking subsamples and CT-scanning. First, a scan of the area of interest in the core was done, together with an ROI scan of higher resolution. Afterwards, the core was opened and a U-channel subsample was taken and scanned both normally and ROI. Last, straws were inserted in the top of the U-channel.

6. Methods

6.2.1. Lucerne and Eklutna Straws Two straws of 4 mm diameter were pushed into interesting spots of two different sediment cores. One core being the Lucerne core LU14-12-G-2-2-A (previously described), that will be further worked on, and the other one coming from Eklutna Lake, Alaska. The reason for taking subsamples in different cores is the grain-size distribution within those, determined in earlier research (Vermassen, 2015, E. Vandekerckhove, RCMG, respectively).

The main goals of taking and analysing these test straws is to see which grain sizes can be determined using µCT and develop a workflow applicable on the rest of the reconstructed volumes of other subsamples. However, to broaden the knowledge on the technique and its applicability in grain size determination, it is necessary to define the grain-size range specifically for the spot where the straw subsample was taken. Also, analysis will show partly whether or not grains are oriented within the straw sample.

The straws were pushed in gently without blocking the way for outflowing air on top, to make sure the straw does not push away the underlying sediment. Once the straws reached a satisfying depth, they were labelled and pulled out, also really gently.

6.2.2. U-channel In order to preserve as much as possible of the core for future research and to keep the subsample small and feasible for scanning, a 11 cm long U-channel was pressed into the part of interest of the megaturbidite (22 – 33 cm core depth) (figure 6.4). Since it was necessary to cut off the sediment at the open end of the channel with a thread, this was pushed down into the core with the U-channel, at its top end. After cutting the sediment through, the U-channel was carefully lifted out manually to prevent any sliding of the wet sediment. Afterwards, it was fully closed off with plastic foil to avoid dehydration and stored at 4 °C.

Figure 6.4: The U-channel just after it was taken out of the core.

6. Methods

6.2.3. Straw subsamples The U-channel was subsampled with the same kind of straws that were used to take subsamples immediately out of the core (section 6.2.1.). While the U-channel was in its holder on the platform, ready to be scanned, 5 straws were pushed in one by one through the free surface at the top. While pushing in the first straw, the depth of the straw within the sediments was checked by taking X-ray photos at some points during the pushing. The deepest point should be the first organic rich layer. Since the pieces of organic matter are mostly larger than the diameter of the straw, it is hard or even impossible to push through those layers without totally deforming the sediment. 6.3. X-ray CT 6.3.1. Scanning - Lucerne and Eklutna test straw subsamples To perform the scans, the straws were fixed on a thin pencil core, which are placed in a custom-made holder attached to the scanner. On one hand, this was done to make sure the entire setup has a stable basis and would not move during the scanning process. On the other hand, the rotational movement of the object close to the X-ray source is not hindered by the dimensions of the base. In other words, a full rotation could be made, as close to the source as possible.

Both, the Lucerne and Eklutna straw, were scanned with a magnification (M) of 63 leading to a voxel size of 2.02 µm. The applied accelerating voltage is 60 kV with a target power of 2 W. 3601 projections were made with an illumination time of the detector of 1 s for each projection.

- U-channel The dimensions of the U-channel do not allow a same approach as for the straws. To proceed to the largest magnification possible, a holder was especially designed. The channel was placed in the holder, which was directly fixed to the rotation platform of the CT-scanner.

The subsample was scanned with a magnification (M) of 13 leading to a voxel size of 15.2 µm. The applied accelerating voltage is 160 kV. An aluminium filter was installed in front of the spot source to avoid beam hardening artefacts. 3001 projections were made with an illumination time of the detector of 2 s for each projection.

A zoom-scan of the top 2 cm of the U-channel was also done. For this a magnification of 39 was obtained and a voxel size of 5 µm was retrieved. The tube voltage was also 160 kV. 3001 projections were made with an illumination time of the detector of 2 s for each projection.

- U-channel-straw subsamples The original plan was to scan the U-channel with the five straws in it, in order to check the deformation that the pushing caused within the U-channel. Afterwards, the straws would be labelled, oriented within the U-channel and taken out gently. The straws would then be scanned and 3D reconstructed. However, the first X-ray image of the straws in the U-channel showed a complete failure in the subsampling method (figure 6.5). Due to the compression and deformation caused by the procedure, this subsampling method is not reliable to derive orientations.

6. Methods

Figure 6.5: X-ray CT reconstructed volume of the scan done after the straws were pushed in the U-channel. When reaching a certain depth in the U-channel, the straws do not take up anymore sediment and deform all the sediments next to and underneath them.

6.3.2. Processing and analysis The first process that is necessarily done when images are gathered, is to reconstruct them into a volume and perform a first rough image enhancement. For this cause, the reconstruction tools in the Acquila software package were used, developed by XRE. To produce a better distinguishability between the multiple phases, further processing was done. Parameters for the several processing steps were derived by trial and error. Eventually, a better insight regarding the influence of the parameters on the images is also obtained and could be used for later processing of other image sets. Afterwards the reconstructed volume was ready to be analysed, which means derive parameters, about e.g. size or orientation of mineral grains, from the dataset. Both of the latter steps were done with the Avizo 9.0 3D Software package, developed and provided by FEI (figure 6.6).

Avizo offers the possibility to load any dataset in a workflow the user has set out. In this dissertation, a lot of CT-data is subject to analysis and therefore the capability to let the data run automatically through a certain workflow is beneficial. Moreover, the parameters of different steps within that workflow can be kept the same for all the analysed datasets but also adapted to the requirements of the user when necessary, which is also an advantage. These two facts, together with the availability, determined the choice for Avizo 3D software and not another one as for example Octopus Analysis (Inside Matters). The necessity to develop a certain workflow to gain a lot of data in the shortest amount of time possible, had a part in the decision to take test samples. The picked workflow is given in the ‘results’ section.

The purpose of the analysis is to derive parameters of grains that could indicate grain size as well as grain-orientation. In the Avizo Manual, a list of parameters that the software can derive from the 3D data is available. In there, different parameters can be used to derive grain-size distributions. As for the orientation, Euler angels (, , ), also known as tilt, pitch and roll, can be calculated and values for trend and plunge can derived therefrom, using an algorithm (written by J. Van Stappen (PProGress)). The trend indicates the orientation of the longest axis of an object. The plunge is the angle of that line with a horizontal plain.

6. Methods

Figure 6.6: Snapshot of the Avizo lay-out. In the middle is the visualisation window, the upper left shows the workflow. The window on the bottom left is were different parameters can be filled in and last, the window on the right is where worksheets with parameters of the grains will appear.

Since a flow only orients grains with an elongated shape rounded, only those that exceed a certain value for elongation are considered for grain orientation. The parameter elongation is calculated by using eigenvalues of the three different axis (Avizo 9.0 manual). An eigenvalue is a decimal number with which a particular eigenvector has to be multiplied to get from its standard length towards its current length. In other words, the software starts from a standard round ball with axis with the same length. To shape the ball into a certain grain, every axis is multiplied by a number, or eigenvalue, unique for every axis. The elongation is defined as the ratio of the eigenvalue of the intermediate axis and the eigenvalue of the longest axis. Hence, a round or disc-shaped (oblate) object will have an elongation close to 1 and a bladed or prolate shaped grain will obtain a value closer to 0.

To generate the rose charts of the data, as well as all the other graphs in this dissertation, GrapherTM 11 (Golden Software) was employed. Final formatting of the graphs and drawings of the explanatory schemes were done with CorelDraw X7 (CorelTM).

Next to analysing all of the sedimentary features that could be derived from CT, it is necessary to go through slices of the obtained object in order to retrieve information about sedimentary structures that computed analysis has missed. The human eye and mind are specialised in recognising structures that are not logical to computers. Especially shapes of macrostructures, for instance ripples, erosional surfaces, lineation, lamination, etc. are not seen by this software.

6.3.3. Orienting CT-derived data towards the magnetic north Eventually, after all the data concerning orientation of grains is retrieved, it is necessary to couple the orientations of the grains within the U-channel back to the position of the entire core, and finally to the true north at the time of deposition. Therefore, it is essential to be aware at all-time how exactly a subsample is taken and which side of the subsample the computer takes as zero during analysis. Hence, the core should be geographically oriented. The latter was done by Vermassen (2015) by measuring the natural remnant magnetization. In a next phase, the obtained orientations within the U-channel has to be linked to this true north. The workflow, from retrieving the core to deriving absolute flow orientations within the sediments, is depicted in figure 6.7.

6. Methods

Figure 6.7: Workflow to determine the absolute orientation of the grains in a sediment core, starting from the left drawing. After retrieving the core and cutting it open, it is necessary to orient it towards the true north using remnant magnetism of the particles settled by only gravity force. One has to be aware at all time of the directions of the cutting, the orientation of the subsamples, the orientation of the objects during CT scanning. Eventually when the grains are oriented within the scan setup, one can link it back to the position in the core and afterwards to the absolute north at time of deposition. 6.4. Laser-diffraction grain-size analysis Some grain-size information was already available (Vermassen, 2015), but in order to check the measurements and to produce a one-by-one comparison between the grain-size distribution obtained by the X-ray CT data and the distribution retrieved by laser diffraction, additional samples were retrieved. A sample was taken adjacent to the test straw sample location in the LU14-12 core at 25 cm core depth. The sample was suspended in 10 ml of water. Before the analysis, the organic matter within the sample was removed by adding 2 ml of hydrogen peroxide and leaving it on a hot plate of 120 °C for 5 minutes. To be certain that aggregates disintegrate and therefore prevent the measurement of a too coarse grain-size distribution, 1 ml of calgon ([NaPO3]6) was added.

The device used to perform the laser-diffraction grain-size analysis is the Malvern Mastersizer 3000, with a Hydro MV (medium volume) dispersion unit. Two different lasers are used to measure, a red and a blue one. Since blue light has a shorter wavelength, it is able to measure smaller particles than the red light. Both of light beams are diffracted while propagating through a lens with flowing water and suspended particles. The intensities of these scattered light beams are measured by a series of detectors within the device, and finally, those intensities lead to a particle size distribution. A curve of the distribution can be immediately made by using GrapherTM.

7. Results

7.1. Test Straws 7.1.1. µCT images and processing One of the goals of the two test straws was to develop a workflow for the analysis of all the subsequent scan data. An applicable workflow has to result in a good conservation of the shapes of the grains and other phases within the dataset. Hence, the effect that one or several filter algorithms will have on the shape and orientation of the grains and the organic matter should be carefully considered. The voxel size of both, the Eklutna and Lucerne straw reconstructed volumes, is 2.2 µm (figure 6.1 and figure 6.2).

Lucerne test straw

Figure 6.1: A horizontal slice (left) and a vertical slice (right) through the unprocessed reconstructed volume of the Lucerne straw. Three phases can be recognised here. The dark organic material, the grey mud clasts and matrix and the pale mineral grains. Within the grains, phases with two different µ-values can be considered.

Since the amount of noise in the test straw of the Lucerne core was considerable, a Non-Local Means (NLM) filter was applied on the reconstructed images. Afterwards the filtered data was segmented or thresholded. In a first phase only the grains were retained in a binary dataset. An ‘erosion’ operation, ‘remove small spots’ operation and a ‘dilation’ operation was done respectively in order to remove remaining noise. Also voxels with an average µ-value between the mineral grain phase and another phase, such as organic material or mud (partial volume effect), are deleted from the binary data set in this process. Afterwards a separation operation was executed to separate grains attached to adjacent ones. The parameters used to separate are found with trial and error and can vary significantly for different datasets. For this dataset, a marker extent of 3 was chosen, together with a neighbourhood value of 24. This resulted in a rather nice separation of the grains. However, some single grains were divided into multiple and multiple grains sometimes remain as one. Nevertheless, these parameters were evaluated as the best for the dataset. A last step in analysing the data, is labelling of the single grains, so the software knows which parameters suit which grain.

7. Results

Exactly the same was done for the organic matter, only the parameters used to execute the binary operations and the separation are different. Since the grey-value of organic matter lies quite close to the grey-value of the matrix and the mud clasts, a lot of unwanted voxels are taken up into the binary dataset when the organic matter is thresholded. Therefore, all of the binary operations (erosion, remove small spots and dilation) were set heavier. This means that a lot more voxels at the edge will disappear first (erosion), more loose voxels will be removed (remove small spots) and afterwards, more voxels will be added to the eroded objects (dilation). Eventually this creates objects that are different in some extent to the real branches and leaves in the dataset. This does not influence the orientation of the object, but volumetric parameters are slightly adjusted. However, to isolate them, these binary steps are necessary to complete, if not, the individual organic materials cannot be separated properly. For separation, a marker extent of 4 was used, combined with a neighbourhood value of 18. Due to the likeliness of the mud clasts and the matrix between the grains, it is impossible to separate individual mud clasts. All steps of the procedure explained above were snapshotted and can be found in figure 7.3.

Different parameters are attributed to the labelled grains by performing a label analysis. The parameters of most importance for both grain size and grain-orientation are equivalent diameter, length, width, breadth, 3D volume, elongation, trend and plunge. By doing this, the grains that are elongated can be picked out of the complete dataset and be analysed separately. Here, the objects with an elongation of less than 0.3 are retained for analysis of the orientation of the grains.

Eklutna straw

Figure 6.2: Slices through the unprocessed reconstructed volume of the Eklutna straw. On the left hand a horizontal plane is shown. The straw was taken at the transition between fine sand and mud and this can be seen herein, as the right side of the circle tends to have more mud clast-like features and a smaller grain size. Remaining ring artefacts are seen on this cut- through. The image on the right is a vertical cut-through of the straw, going from the left to right of image on the left.

Although the amount of noise in this sample can also be considered as rather high, the homogeneity of the sample prevents to use filter operations. Since filters tend to blur edges, it would not be beneficial to the dataset. Separate grains would merge as boundaries between them would fade away. Hence no filter was applied on the reconstructed volume.

7. Results

The same workflow was applied as for the Lucerne straw, only the operational parameters were chosen differently. Since the µ-values of the grains and the matrix is rather similar, it was hard to separate grains with the same parameters for thresholding, binary operations and separating as used for the Lucerne straw. Especially thresholding the pictures was not an easy task and, for this reason, two different thresholds were applied on the dataset: a low one so that grains and edges with a lower density are also a part of the dataset, and a higher one, so only the rather dense grains or denser grain-cores are retrieved. Afterwards, the two binary datasets were processed in the same way for both of the different thresholds. In this manner, it is possible to check how the grain size and orientation is influenced by the threshold chosen by the user. Unlike the Lucerne sediments, not much organic matter was found in the straw coming from the Alaskan deposits. Therefore, this material was not investigated in the latter. Images for the different steps executed on the high threshold data are shown in figure 6.4.

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Figure 7.3: The different steps of processing the reconstructed images of the Lucerne straw and coming towards 3D data for single objects. (a) is a reconstructed image on which an NML filter was applied. (b, c, d, e and f) are all snapshots from the processing of the mineral grains found in the straw, (g, h, i, j and k) represent the processing steps for organic matter. The result from thresholding the object µ-value is (b) and (g). After doing binary processes (erosion, remove small spots and dilation), the data appears as in (c) and (h). Eventually the grains are separated and labelled (d)(i). The colours are given randomly to the objects, to make clear what is separate and what not. When a volume rendering of the data is done, volumes (e) and (j) are produced. If the objects with an elongation smaller than 0.3 are only retained, a volume like (f) and (k) is created.

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Figure 7.4: (a) an unprocessed slice through the Eklutna volume. If only the densest grains or parts of grains are thresholded (b) and processed (c), a good separation of the grains can be made and they can be labelled (d). (e) and (f) are volume renderings of, respectively all the grains and the grains with an elongation value of 0.3.

7.1.2. Grain size Lucerne straw The laser-diffraction grain-size measurements on the sample taken on the Lake Lucerne core resulted in a trimodal distribution (figure 7.5). The smallest peak, or mode, is found at 0.5 – 0.6 µm, the second one at 4 – 5 µm and the last mode appears at approximately 70 µm.

To test the limits of the Malvern device, a small experiment was done that is in fact not too relevant to the goal of this dissertation. However, it is worth mentioning. Since it was not known how large in quantity the sample had to be, to have good results, three different amounts of sample were taken and ran through the device. All of them resulted in a different laser obscuration value, going from a large obscuration (32%) for the heavy sample and a small obscuration (10%) for the sample that had the least particles in it. The distributions coming from two lightest samples, with therefore the lowest obscuration, were consistent with each other. The 32% obscuration resulted into a fourth mode in the large particle range and a broader peak for the smaller particles. Therefore, these results were not withheld to continue the research regarding grain-size distribution.

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Figure 7.5: The grain-size distribution for the three sample taken from the same layer in core LU14-12. The only difference between the samples is their weight, or amount of particles. When analysed in the Malvern Mastersizer 3000, this results in different laser obscuration values, since the laser is more blocked when more particles are present in the device at the same moment.

The volumes reconstructed from the CT images can deliver different parameters of single grains (figure 7.6). To analyse the grain-size distribution the equivalent diameter was retained, just as maximum diameter and the minimum diameter and the intermediate diameter. The maximum and minimum diameter were used to calculate an average diameter. The CT derived curves can be divided into two parts. The first part contains objects smaller than 15 µm. All of the curves, created by different parameters, display peaks in the distribution between 1 and 10 µm, with their largest peaks at about 2 - 3 µm. One can derive from this value, that these peaks are generated by small objects consisting of only 1 (2 µm peak) or several (4 µm peak, 6 µm peak, etc.) voxels.

In the second part of the curves, for objects bigger than 15 µm, the different parameters all cause a bimodal distribution. For the equivalent diameter, the modes of these peaks appear at 20 and 55 µm. The modes of the peaks produced by the average diameter are found at 30 and 70 µm. However, the parameter that seems to fit the most with the laser diffraction data is the intermediate diameter. With the mode of the coarsest peak at 60 µm and the largest objects near 240 µm, these parts of both curves fit well. The second peak of the intermediate diameter, with mode at 28 µm. For all CT derived distributions, this part is inconsistent with the laser diffraction distribution and demands for an explanation.

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Figure 7.6: The grain-size data derived from the laser-diffraction analysis (Malvern) compared with the equivalent diameter data gathered through analysis of the µCT data. The intermediate diameter is the same as the length of the intermediate axis through a grain and the average diameter is the length of the longest and the shortest axis divided by two.

Eklutna Straw Earlier research (data E. Vandekerkhove, RCMG) on the Eklutna core derived a grain-size distribution (Malvern Mastersizer 3000) that contains generally smaller grains than the ones sampled from the Lucerne core. A broad peak with mode around 7-8 µm is present (figure 7.7), together with a smaller broad peak in the fraction between 0.1 and 1 µm.

As the 2 µm peak largely exceeded the rest of the distribution, it was left out for both curves. This way, the most important part of the distribution curves is shown clearly. Depending on how the two or multiple pixels are in contact with, the diameter of the object will be slightly different. The diameter of an object with voxels meeting at corners, will be larger than the diameter resulting from voxels that have an entire edge or even an entire plane in common. Going to larger grain sizes, the curve tends to flatten out. This is the reason why the modes of the peaks do not appear right above multiples of 2.2 µm, which is the voxel size. The distribution for all the grains (low µ-value) shows a high peak at 3-4 µm. The distribution of the high µ-value grain size data is somewhat different, with a smaller peak at 3-4 µm than the other curve and a broader peak with mode at 8-9 µm. When the laser-diffraction data is combined with the µCT data one can see that the coarsest part fits more or less, but the correlation is completely lost looking at smaller grain sizes (figure 7.7).

From the curves from both samples can be derived that a minimal amount of pixels is necessary within an object to properly contribute to the grain-size distribution curves. Object smaller and only slightly larger than the voxel size will result in noise in the lower grain-size ranges. This is displayed by multiples of the dimensions of the reconstructed voxel sizes. Here, as the reconstructed voxel size is 2.2 µm, unwanted peaks, or noise, appears at 3 µm, 4.5 µm, 6 µm etc. For the Lucerne straw, a limit value under which these unwanted peaks arise, can be put at approximately 15 µm. Still, the major peak at 20 µm that is not found in the Malvern data has to be explained. This also implicates that an object has to larger than at least 7 – 8 times the voxel size before the results coming from analysing the labels of the object become trustworthy. If this is the case, the dataset of the Eklutna test straw is

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mostly compromised, as most of the grains have a size lower than 15 µm. For other datasets, this means that an assessment of the wanted resolution, compared to the size of the objects of interest, is necessary.

Figure 7.7: the grain-size distribution determined by the equivalent diameter derived from µCT-data. Both of the datasets, all of the grains (low µ-value) and the dense grains (high µ-value), are considered here.

7.1.3. Orientation Grains tend to orient their longest axis in the direction of the flow present at the time of deposition. To determine a general grain orientation, all the grains that have an elongation value smaller than 0.3 are employed. In order to investigate whether or not this picked elongation is enough to show a clear orientation, also only grains with an elongation of 0.2 were separated and analysed. To present this data graphically, it is necessary to show the emphasis lays on orientations but the orientations of the single grains have to be presented in a cumulative way as well. Therefore, rose charts are particularly helpful. The circular axis defines the orientation; the straight horizontal axis indicates how many grains are present within an interval of five degrees. Consequently, trends in possible grain-orientation can be detected.

The Lake Lucerne straws (figure 7.8) show a significant orientation of 145°- 325° for the 0.2 and 0.3 elongation for both the mineral grains and the organic material. The Eklutna straw (figure 7.9) shows a main grain orientation of 45° -225° for both, all of the grains and the denser part of the grains. An absolute orientation could not be established since the straws were not oriented when they were taken in the cores.

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Figure 7.8: The orientation of the grains and the organic matter in the Lucerne straw for two different elongation values.

Figure 7.9: The grain-orientation for two different threshold values in the Eklutna straw. The threshold for the upper part of the figure was set towards higher grey-values and therefore higher densities. Hence, less grains were retained. For both thresholds, the orientation of the longest axis of the grains was determined and depicted here on the right side of the pictures.

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7.2. U-Channel 7.2.1. µCT images and processing The U-channel was scanned and reconstructed with a voxel size of 15.2 µm (figure 7.10). After failing to process the entire U-channel at once due to the extremely large dataset (over 60 GB), it was chosen to divide the U-channel in subvolumes for analysis. The deposits consist of several sand layers, derived from turbidite pulses, interbedded in layers rich of mud and organic material. It seems reasonable that every layer could have an orientation of its own. Hence it was chosen to split up the U-channel in individual subvolumes, each containing a sand layer or an organic rich layer.

The processing the different subvolumes was done in the same steps as previously described for the Lucerne test straw. Only the NLM filter was not applied on the U-channel images and it was necessary to adjust the separation parameters depending on the subvolume and grain size inside that subvolume. The organic material present in the U-channel mostly consists of leaves and small branches. Those elongated and flat objects tend to pile up and touch each other frequently in the organic rich layers. The organic material in the Lucerne test straw was separated from each other in most cases and therefore, analysis of separate leaves and branches was rather easy. In this dataset, this will be less straightforward. After several attempts to get the organic matter out of the U- channel properly, one had to conclude that it is impossible to do the separation automatically in this dataset. It could be done manually but this extremely time-consuming. Therefore, the organic rich interlayers were not analysed. Similar to the Lucerne test straw, it was also not impossible to retrieve information out of the mud clasts, due to their similarity with the matrix.

The original purpose was to do a region of interest (ROI) scan of the ‘seiche’ deposits present in figure 7.11f, however another area of 0.8 cm height and diameter was scanned with a voxel size of 5 µm before the inserting of the straws. These images were processed using the same workflow as the U-channel with, again, an adaptation for the separation parameters. The grains were analysed as well as the organic material, since the material is positioned loosely from each other, and separation is possible (figure 7.11c). The images also were subject to ROI artefacts. For this reason, only the centre of the ROI was used for analysis. Another ROI of all the seiche deposits were done after the straws were inserted, but due to the induced deformation, these ROI scans were not subject for analysis. Nevertheless, it is demonstrated that obtaining a reconstructed voxel size of 5 µm is possible on the U-channel.

Figure 7.11: (a) unprocessed slice in the reconstructed volume of the ROI scan taken at the location marked in figure 7.10. The blue squares indicate a subvolume taken out of the reconstructed volume to analyse. (b) are the processed grains, (c) is processed organic matter.

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Figure 7.10: general overview of the U-channel. The sand-rich parts of the channel are interesting to do further analysis on regarding orientation and grain size and are therefore marked and magnified. Zoom-in (f) is divided in multiple layers, since this part of the U-channel is characterised by alternating organic rich and organic poor layers possibly related to the seiche movement of the lake water.

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7.2.2. Grain size In order to evaluate the capability of µCT to determine grain-size distributions accurately, a comparison is made with the distributions determined by Vermassen (2015) (figure 7.12). First the sample locations of this author were linked to the location in the U-channel. Afterwards the grain size data derived from the µCT-data of approximately the same spot, is combined with laser- diffraction data obtained by Vermassen (2015). The grain sizes are expressed as equivalent diameters in the CT data.

The laser-diffraction grain-size data (figure 7.12) shows three main peaks. The fourth peak, appearing at large grain sizes, is due to the fact that the organic matter was not removed for these analyses. In the sand layers of the stacked turbidite, there is clearly an influence of coarser material as the peak is higher for the first three sample locations. As the stacked turbidite goes over into the seiche deposits, the coarser peak loses importance and the mode of the peak shifts from approximately 100 µm to a smaller grain size of approximately 50 µm. On the other hand, the peak with mode around 5 µm becomes gradually more important but, unlike the mode of the coarse grain peak, the mode remains at the same location throughout the U-channel. Also the fraction smaller than 1 µm becomes more important in the seiche deposits.

Again, comparable to the test samples, sharp peaks appear in the µCT distribution at smaller grain sizes. For the first five distributions (figure 7.12), the equivalent diameter distribution has its largest peak at approximately 15 µm, which is the size of one voxel in the reconstructed volume. To clearly show the variability in the curve, these peaks were left out for all distributions in figure 7.12. The second peak is situated around 35 µm, the third around 50 µm. The broadest peak is then found at larger grain sizes with the mode at about 75 µm. The grain-size distribution of the base sand layer shows a bump that is not present in the four others at approximately 200 µm. The analysis of the grain size in the highest part of the U-channel was done on the ROI of the top, with a reconstructed voxel size of 5 µm. Similar peaks as in the previously discussed distributions appear in the lowest grain-size range, but, due to the smaller voxel size, at smaller values of 5 µm, 20 µm, 25 µm and 35µm. Overall, the modes in the larger grain-size range by both methods correlate rather well for all graphs.

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Figure 7.12: A photo of the core section LU14-12-G-2-2-A is placed on top with the sample spots for grain size determination depicted as yellow dots. The same dots reappear in the U-channel, which is here divided in the sandy base layer (yellow), the rest of the stacked turbidite (grey) and the sediments deposited by the seiche movement (orange). The grain-size distribution determined with the Malvern is shown underneath the image of the U-channel. The bottom curves are grain-size distributions derived from µCT data (green) combined with a part of the laser-diffraction derived grain sizes. The dotted lines indicate at which grain size the µCT data shows inconsistencies, as peaks appear in the CT data that are not seen in the laser-diffraction grain-size data. The distribution at the most right is derived from the ROI with a 5 µm. The others are derived from the general scan of the U-channel (15 µm).

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7.2.3. Orientation - Remnant magnetism Vermassen (2015) obtained a curve with the variation of the declination through the entire core LU14-12-G (figure 7.13). After retrieval, the core was split in three sections and each of them were opened with a certain orientation that seemed appropriate to Vermassen (2015). Section 1 and section 3 were opened with the same orientation, core section 2 was opened with 90° difference. It was important that at all time, it was known which half was taken for analysis of the remnant magnetism. To determine the declination of the remnant magnetism, a U-channel subsample was collected. The cap of the U-channel was considered as 0° and continues clockwise when looking at the top. Vermassen (2015) corrected the results for declination so that the values fit with the declination of the second core section and therefore subtracted 90° of the declination values of the other core sections.

Figure 7.13: Photos of the different sections, medical CT images, coloured CT images and interpretation depositional conditions are given from left to right respectively. The corrected declination curve for core LU14-12-G obtained by natural remnant magnetization is the black line. This curve is corrected for the different orientations used to split the cores, by subtracting 90° to the declination value of section 1 and 3. Interpretations and a division in different depositional units are done by Vermassen (2015). Figure from Vermassen (2015).

Of most importance to derive the absolute orientation of the core is the declination of the magnetic minerals within the sediments that are deposited only by the influence of gravity. These are mainly

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found in Unit V, section 2, and lead to a declination of 210°-220° for the true north. The core section discussed in this dissertation is the section 3, which contains the base of the megaturbidite. As section 3 was split with an orientation perpendicular to the orientation with which core section 2 was split, 90° must be added to that declination to retrieve the orientation of the true north in section 3. As a result, the true north is oriented towards approximately 300° for the section discussed in this dissertation.

- Grains according to position U-channel Immediately after scanning the U-channel, it was not known which direction would operate as the zero direction of the grain orientation. Therefore, during analysis, some grains with an elongation lower than 0.1 were picked out and their orientation relative to the U-channel was further examined. Moreover, the grain-size analysis pointed out that grains with a size below 80 µm show unwanted peaks. It is not known if the orientation data is biased as well and, therefore, only grains above 100 µm are withheld. The direction of the grains is defined by a dip, strike if one sees the object as a plane and by trend and plunge if one considers the objects as lines. Since the orientation of the longest axis is demanded, the latter parameters are retrieved. The value given for the trend of an object in this dataset is the direction the most upper part of the inclined elongated grain points at. The plunge is then the inclination between the longest axis of the object and a horizontal plane. By looking into those elongated single grains, it was found that the cap of the U-channel represents the zero orientation. Looking to the top of the U-channel, with the cap at the bottom, 90° is found on the left side, 180° on the top side and 270° on the right side of the U-channel.

- Grains according to true north Now that the core has an absolute orientation and it is known how the U-channels are oriented relative to each other, it is possible to derive the magnetic north in the U-channel that was subject to the µCT and possible grain alignments can have an absolute orientation. As the declination of 300° was derived for the north, it implies that 120° in the µCT U-channel becomes the north and will get a value of 0°. This results in 240°, 330°, 60° and 150° as new values for 0°, 90°, 180° and 270° respectively (figure 7.14). To apply this on the acquired datasets, 240° was added to all of the values for the trend. If the adding resulted in a value larger than 360°, 360° was subtracted from this value.

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Figure 7.14: Top view of a cut-through of the core with the U-channels prepared for remnant magnetism determination (upper) and µCT (lower). The orientations of the U-channels for their own setup is given by the black numbers. As the north was determined, the µCT U-channel can be oriented absolutely and the black orientation numbers can be changed in the purple ones.

Now the absolute orientation of the core is derived, the direction of the grains within sand layers can be linked to it. The results are shown in following figures (7.15 – 7.21). In figure 7.15 the trend of the grains relative to the µCT setup is given as well. For every subvolume used to make the figures, multiple rose charts were made with each a different grain-elongation threshold. The threshold chosen for the figures in this dissertation is determined by which thresholds gives the best projection of the overall orientation. Within one figure, the threshold is however the same for all rose charts. In other words, sometimes a grain-elongation of less than 0.3 was used to make the figures, for others an elongation smaller than 0.2, depending on which elongation threshold gave the best orientation peak. Moreover, only grains above 100 µm were retained. As the µCT-derived grain size data showed odd peaks below this value, it is not certain if values for grains that are much smaller than 100 µm can be trusted and therefore it was chosen not to derive orientation for smaller objects.

Eventually after analysing all the sand layers, two main directions seem to appear. Layer a, c and e show an overall NE-SW direction, while layer b and d are oriented more or less NW-SE. The seiche deposits have undergone the same analysis as the sand layers underneath. Moreover, the organic material present within the layer was distributed well enough within the sediment to distinguish separate objects and hence, organic material could be analysed. A first analysis was done on entire layers f1-7 (figure 7.20). To see whether or not orientations change within the different laminae as well, subdivisions have been made and analysed for orientation (figure 7.21).

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Figure 7.15: The base sand layer contains three layers with fining upward. Therefore, they were analysed separately. The grains of the base (1), have a NE-SW orientation, sand layer 2 likewise. The third subvolume was inconsistent. The trend, or the direction of the longest axis within the µCT setup, is given first, Only the grains with an elongation smaller than 0.3 were retained.

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Figure 7.16: Analysis of the second part of the stacked turbidite results in an absolute orientation for the bottom part of NW-SE, grains in the middle layer tend to have the same orientation and the orientation of the upper layer is more scattered. The uncertainty for all of these layers is however large. Grains with an elongation smaller than 0.2 were used for this figure.

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Figure 7.17: The total orientation of the sand layer depicted here was inconclusive and therefore, it was decided to split the layer up in subvolumes of 1 mm height (dashed boxes). Eventually, the data of subvolumes that seem to have similar components of orientation were put together. This resulted in boxes of 2mm height (full white boxes). The base and middle of the layer has an NE-SW orientation, the top has a lot of scatter and is rather inconclusive. The grains with an elongation smaller than 0.3 were retained.

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Figure 7.18: Also this sand layer was divided into two subvolumes as the orientation of the total layer showed a rather large scatter. However, both the base and the top of the layer have a NW-SE orientation. The elongation threshold here is 0.2.

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Figure 7.19: As there is a layer rich of organic matter between two sand-rich layers, these were also considered separately. The bottom layer shows a NE-SW orientation, while the top layer is inconclusive. An experiment was done with the bin-sizes of the rose-charts in order to gain information about the top part of this sand layer. However, not enough data was retrieved from this layer to gain useful information.

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Figure 7.20: Zoom on the sediments deposited by seiche movement. The sediments are divided into units that are rich in organic matter (OM) and units with a smaller amount of organic material. The different layers were subject to analysis of the orientation. The strike of the grains and organic matter is given here, which is 90° less than the trend, however it gives a good overview of how well the objects are oriented. To come to an absolute orientation for this data 30° has to be subtracted from the strike values.

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Figure 7.21: The seiche deposits were divided into several units (f1-f7) by looking to the amount of organic material. Depending on how large the single unit is, a subvolume with fixed dimensions was placed one or multiple times within a unit (a-c). The orientations of the grains and the organic material (OM) enclosed by the volume were analysed and here given with rose charts.

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7.2.4. Sedimentary structures The high detail obtained by µCT scans could reveal sedimentary structures that cannot be seen when a lower voxel size is acquired (figure 7.22). The transition between the background sediment and the base of the turbidite seem to have either ripples or erosional features. For this reason, a horizontal slice was also taken through the shift from mud to sand (figure 7.22a). Since all of the sediment in the U-channel is slightly tilted, horizontally deposited sedimentary features will appear gradually and smeared out over the screen while scrolling through the dataset. This was also the case for the transition. However, the wavy structures in figure 7.22a appeared and disappeared following the orange lines. Therefore, these lines indicate the crests and troughs of the sedimentary structures and their direction is running from 179° to 349°. Therefore, it was also chosen to take vertical slices of the reconstructed volume with this direction (figure 7.22c) and the perpendicular one (figure 7.22d). As the true north is situated at 120°, the result for converting the direction to an absolute orientation is an NE - SW direction.

Figure 7.22: a) Horizontal slice through the transition between the background sediment and the base of the megaturbidite. The orange lines indicate in which way the wavy structures move when one looks at the slices on top and beneath the projected one. The black lines are the slices taken for profiles c and d. The arrows reflect the point of view for profiles c and d. b) Horizontal slice through the first alignment of mud clasts encountered going from bottom to top in the megaturbidite. The longest axis of elongated mud clasts and organic material are given by the orange lines. Both a and b are oriented within the µCT setup. c) Vertical slice taken from 169° to 349°, following the orange lines in both, a and b. d) Vertical slice from 79° to 259°, perpendicular to the orange lines in a and b.

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A large amount of small mud clasts is present in the dataset and can occur aligned and even imbricated from some points of view. However, the µ-value of the mud clasts is intermediate between the grains and the organic matter and is the same the value for the matrix and therefore, data about separate mud clasts could not be obtained by analysing µCT data as was done for grains. By taking a horizontal slice through the layer of mud clasts, a rough estimation of the overall orientation of the possible imbrication can be sometimes made. For the first mud clast layer above the base of the megaturbidite this is the case (figure 7.22 c and d). As a horizontal slice was taken through this mud-clast rich layer (figure 7.22b), most of the seemingly elongated ones have an orientation of approximately 160°-340°, which is roughly the same as the earlier determined wavy structures at the border of the turbidite. Hence, the two profiles taken can also provide information about the orientation of the imbrication. In figure 7.22c, an imbrication is visible, pointing towards the right side of the profile, while this is not the case for figure 7.22d. Therefore, the orientation of the imbrication is 340°-350°, which can be translated into an absolute direction towards the SW.

More mud clast lineations appear in the dataset, but horizontal slices through those layers give no solid answer about the direction of the more elongated ones. In the vertical slices, however, some seemingly imbricated layers can be distinguished (figure 7.23). All of the imbricated mud clasts appearing in profile c, appear to have the same direction as the mud clast layer previously discussed. Moreover, the imbrication does not seem to occur in profile d, which confirms also an SW orientation for the indicated layers. The other mud-clast rich layers lacked convincing traces for imbrication in both profiles.

The upper part of the profiles through the stacked turbidite (Figure 7.23) is characterised by a thick layer rich of mud and organic material, which consists of flat leaves and longer round branches. In profile c, the leaves seem to be bended around round features. This does not occur in profile d, where the leaves look more or less flat and the twigs are also more extended in this direction. Therefrom, one can derive that the elongated twigs are mostly oriented parallel to profile d, as they appear as round features in profile c. This is also stated by the bending of the leaves around the elongated twigs. The elongated twigs are therefore mostly oriented 80° - 260° relative to the µCT setup of the U-channel or NW – SW relative to the true north.

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Figure 7.23: two slices through the complete stacked turbidite section depicting several sedimentary structures. The profiles are taken as mentioned in figure 7.22. Profile c shows signs of an imbrication of both mud clasts and grains in some layers indicated with arrows, whereas imbrication seems to lack in the same layers in profile d. The leaves in the top of profile c appear bended around twigs. The same area in profile d seems to have straight leaves.

8. Discussion

8.1. Evaluation of µCT in sedimentological research Vermassen (2015) argued that µCT has a high potential to solve sedimentological questions, especially when the resolution can be increased. However, no real workflow was developed for his attempt to derive grain orientations, nor to validate the method. In the next section an overview is given about what works and what not regarding sampling, analysing grain-size distributions and grain orientations.

8.1.1. Proposed workflow The workflow proposed in this dissertation has led to a resolution of 5 µm, which is the resolution limitation on the entire U-channel using the Hector system. The key to this workflow and acquired resolution is producing a small object in order to gain resolution. Therefore, subsampling is a necessary step. Moreover, it is the most important factor for succeeding the research, since it is mainly the only step in the process that could go wrong. Unlike X-ray CT, which is not destructive and can be repeated at all times, subsampling a certain area of a core cannot be repeated. Whenever subsampling with the aim to analyse structures fails, neither that part subsampled object, nor the sediment right next to it, is fit for analysis. Consequently, it is important to assess the subsample methods used for the purpose of this dissertation.

Use of U-channels For the Lucerne core, subsampling the sediments with a U-channel was possible without disturbing the internal structure of the sediment too much. Thus, for this kind of sediment, one can state that this method of subsampling works fine. Still, cutting the U-channel out of the middle of a core remains risky. To make sure that the sediment sticks to the plastic, the water content has to be sufficient to create a force of adhesion. A water content that is too high, however, will possibly result in a sliding out of the sediments. Moreover, cores with particles ranging from a millimetric to centimetric scale, should not be subsampled with a U-channel. Similar to what happened with the straws, large objects will be dragged along while pushing in the U-channel causing the sedimentary structures in the U-channel to possibly deform. For these reasons, it needs to be assessed whether or not this kind of sampling is possible in other cores, with different kinds of sediment. Based on a medical overview scan, one could estimate whether or not taking a U-channel will succeed. If the region of interest in the core does not seem to fit within the requirements for subsampling with a U- channel, other solutions have to be considered.

The quality of X-ray CT scan performed on the U-channel is very high. Hence, the shape of the object, other than the manufacturing of the sample holder, does not provide any limitations regarding the quality of the images, neither for the handling of the sample. The U-channel can be closed of properly in order to prevent degradation or deformation and thus, rescanning the object in case of a problem, is possible.

Use of straws For the core of interest, inserting straws is simply not a good manner to reduce the sample size and preserve the internal structures at the same time. Pushing the straws into the sediments will cause large grains and organic material at the border of the straw entrance to be dragged down. This will deform both the sediment inside and outside the straw. Elongated flat organic matter will even clog the entrance entirely. Pushing the straw even further will deform a large amount of the sediments

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underneath it and, eventually, none of the structures will be useful to do analysis on. In cores with only fine-grained particles, this method of subsampling could yet be helpful.

Hence, other methods have to be considered to sample sediments in core LU14-12 and retain the structures therein. However, one can raise the question if smaller grains will even be oriented and if retrieving an even higher resolution than the 5 µm, obtained by the region of interest scan on the U- channel, will provide a lot more information. As seen for sand layers with a considerable mud-clast content, the presence of larger objects will influence the orientation of smaller ones. The 2 µm scans delivered a lot of information concerning grain size and shape, still, the improvement of the higher resolution on information about the orientation needs to be studied.

8.1.2. µCT and grain size In every grain-size distribution curve derived from µCT data, peaks appear in the lowest grain-size range. These peaks are artefacts derived from noise or from small grains with dimensions not much larger than the retrieved voxel-size and are consequently troublesome for grain-size analysis. What those peaks do reveal, is the amount of voxels an object has to contain in order to be valid in the grain size reconstructions. This can be done while looking at the distributions of the test samples. For both, the Lucerne and Eklutna test straws, unwanted and excessively high peaks occur below 10 – 15 µm, which is equivalent 5 – 8 times the acquired resolution, respectively. If these values are extended to the grain-size distributions of the 5 µm resolution and 15 µm resolution scans, grains below 25 – 40 µm and 75 – 120 µm respectively, cannot be used to develop a grain-size distribution.

Now, looking at the graphs for the latter scans (figure 8.1), this theory seems to fit as most of the peaks occur beneath or within this threshold range and never exceed the upper limit of 8 times the resolution. Although, a lot can depend on the scan parameters, the reconstruction parameters, the parameters used to threshold, filter and separate the data and the characteristics of the dataset itself. If only grains with a size exceeding 120 µm are withheld for the 15 µm resolution scans, almost no grains are left to analyse for other parameters, such as orientation. For this reason, the range of 5 to 8 times the resolution suggested here, is merely a guess for the 2 µm datasets. It is necessary to assess the value for the grain size threshold separately for every dataset coming from separate scans and/or that are analysed with different parameters.

In the higher grain-size range, the distribution of the Lucerne test straws (figure 7.6, in results) show a significant resemblance to the laser-diffraction grain-size distribution. Only the peak at 20 - 30 µm in the Lucerne test straw needs to be explained. This can result from either an over separation of the large grains, whereby pieces of large grains are considered to be smaller grains, or an under separation of small grains, whereby small grains are taken as one big grain. Although, the latter cannot be the case as one would expect also assemblages resulting in bigger grains than given by the Malvern distribution. In case of the intermediate diameter, for example, it seems highly unlikely that bigger grains have formed due to under separation because both, the Malvern and the intermediate diameter curve fit well for the largest grains. Another possibility is that the peak is an actual peak in grain size, not seen by the Malvern.

The fact that the curve for the equivalent diameter displays a smaller grain size compared to the other parameters, in every part of the distribution, also needs to be discussed. The equivalent diameter is equal to the diameter of a sphere with the same volume as the object and is consequently not prone to variations in grain elongation. Grains measured by the Malvern are randomly oriented. Accordingly, the Malvern data is an average result of all grain elongations. The average diameter, or the average between the longest and the smallest shape axis, fits therefore

8. Discussion

rather well. The intermediate diameter, or the width of the particle, which is the size of the intermediate shape axis of the particle, fits even better with the Malvern data. Since the equivalent diameter does not fit with the latter three, one can conclude that the grains are all more or less elongated and that round grains are quite rare.

At the end of the discussion regarding grain-size data, it has to be claimed that all the CT-derived data is linked to the laser-diffraction data of the Malvern. The grain-size distribution plots created by the latter method are based on different physical parameters of the grains than CT and are also no ground-truth of the reality. The resemblance between the results of the methods in the higher grain- size range is however a good validation for both. For bigger grains in a clearly distinguishable matrix, one could consider the CT-data as even preferable, as more accurate data concerning the shapes of the grains can be derived. Additionally, µCT can also deliver grain-size evolutions rather fast. As volumes are scanned, the evolution of the distribution can be seen when the sizes of the particles are plotted against their position in the scan.

Figure 8.1: Grain-size distributions derived from different scans: The Lucerne straw (2 µm), the ROI scan of the U-channel (5 µm) and the full U-channel scan (15 µm, random subvolume taken). The arrows indicate the undesired peaks in the lower grain-size ranges of each distribution.

8.1.3. µCT and grain orientation A first assessment of the grain orientation was done in the test straws. The rose charts of the strike tend to lead to the conclusion that there is definitely a certain orientation captured by the grains. However, the straws are taken parallel to the layers of the cores. This implicates that the shortest shape axis is more or less perpendicular to the direction of sampling and the longest shape axis should vary in direction within the straw. The straw was scanned as it was taken, meaning that the straw was placed on the rotational stage of the CT device with its longest axis pointing up. All this to say that the orientations derived from these scans are difficult to interpret, as most of the elongated grains in the dataset all point up or down relative to the horizontal plane (figure 8.2). The strike values derived are thus not the true strike values relative to a false horizontal plane. The strike values could be converted, but this would not be an easy task. The straw was also not oriented whilst sampling, so therefore, any possible relative orientation of grains or organic matter was lost. If µCT-

8. Discussion

scan was available from the original sample where the straws were taken out of, a comparison could be made and the utility of taking straw subsamples straight out of a core could be evaluated more.

Figure 8.2: Sampling with straws parallel to the stratification results in a grain orientation that is biased after scanning and is necessarily transformed to a true horizontal plane, which is equal to the plane of deposition.

Subsampling in U-channels overcomes the problem described in the previous paragraph, because the sedimentary structures are scanned in their original position. The only difficulties encountered are no longer due to the way of sampling the sediments but to an overflow of information for software to handle. Due to the immensely high amount of grains, the memory and GPU tend to overload and consequently even false values were attributed to objects. In order to overcome this problem, it is necessary to take subvolumes of a several ten thousand grains and reduce the amount of parameters that are analysed.

By reducing the volume, the grains with a sufficient low elongation value, to observe a preferred orientation, will be reduced as well. It is essential to question the integrity of the results of very small subvolumes. Peaks that potentially appear in the rose charts are potentially not a reflection of the flow direction. For some subvolumes within the seiche movements (figure 7.20, in results), analysis brought to light that less than one hundred grains or organic particles are present to determine orientations. Peaks in the rose charts, consisting of 10 particles, are then taken as the preferential orientation. This signal could also be coincidence and for this reason, the subvolume analyses of the seiche deposits (figure 7.20, in results) are doubted.

In the sand layers of the U-channel, a pronounced direction of grain orientation is present. A good validation is the presence of imbricated mud clasts in those layers. Hence, a cautious assumption is made that the method is able to discover the main direction of paleoflows. Nevertheless, a better result would have been obtained if one is able to apply rotational algorithms on the U-channel dataset, so every layer could be analysed in a perfect horizontal position. This way, also the dip or

8. Discussion

plunge of an object would become important. The trend of a particle is defined by the direction to which the upper end of the longest shape axis points. For a perfect horizontal analysis plane, imbrication would therefore be noticed in the rose charts and an absolute flow direction could be derived straight from the grains. In a tilted dataset, however, noticing imbrication is dependent on the angle that the horizontal plane makes with the layer of interest and hence, it is not always visible.

To verify the method further, a comparison can be made with sediments that are oriented absolutely with the anisotropy of magnetic susceptibility method (Rees, 1983). Moreover, X-ray CT could even deliver information on small scale whether or not the axes of anisotropy are equal to the shape axes for all sorts of grain sizes and minerals. Comparing the two methods could only be constructive for both. 8.2. Flow history determination 8.2.1. Flow model In order to assess the capabilities of the µCT, it is necessary to have an idea about the source of the pulses in the lower unit of the megaturbidite. Vermassen (2015) suggested six possible sources of slope failure (figure 8.3). However, two of them are most likely to have caused density flows that have reached the position of core LU14-12. The author argued based on grain-size analysis and mapping that the pulse at the base of the turbidite is almost certainly coming from the Rietsort source, in the northeast of the basin. Furthermore, several pulses could have originated from that source. Also a density flow coming from the east or southeast (Vitznau or Vitznau 2) could have contributed to the sediments in the core of interest.

Figure 8.3: Bathymetry map with seismic profiles and the core locations of Vermassen (2015), together with the six source areas proposed (ellipses). The yellow mark depicts the core location of LU14-12, the core of interest in this dissertation. The red dots are cores of highlighted in the discussion. Figure modified from Vermassen (2015)

8.2.2. µCT: sedimentary structures and fabric Stacked turbidite Those orientations can be compared to the orientations derived by µCT. It is logical to start with the bottom of the megaturbidite unit and, hence, the boundary between the background sedimentation

8. Discussion

and the first sand layer. Since these structures have formed in very fine clay, it is rather unlikely that these can be considered as wave ripples. Therefore, these are possibly erosional lineations caused by the passing density flow. The lineations on this transition are oriented NE-SW and thus the flow has to come from either one of these directions. Although, it should be questioned if the small scale of the U-channel is sufficient to incontestably argue that these lineations are a result of erosion, and not just a reflection of natural occurring roughness on the bedding plane.

While looking at the grains of first turbidite pulse, without considering the orientation analysis, possible imbrication is visible on a slice taken with a NE-SW direction (figure 8.4). As this was not seen on the slice with a perpendicular direction, and the grains tend to be inclined with their top towards the SW, the density flow that deposited the first sand layer was most likely coming from the northeast. Also the mud clasts present in the top of the first sandy layer seem show an imbrication towards the southwest, confirming the theory by Vermassen (2015) that the first layer originated from a source located in the northeast of the Vitznau basin, i.e. Rietsort.

Figure 8.4: The left panel is a horizontal slice through the U-channel, with the vertical profiles on the right hand depicted by the white lines. The layer of interest Is the mud-clast rich layer about the sandy base of the turbidite. The orange lines were drawn to indicate elongation of the mud clasts in a horizontal view.

Also in some other mud-clast rich layers higher up in the sequence, mud clasts tend to have an imbrication with more or less the same orientation as the first mud layer discussed in the previous paragraph. The clearest imbrications are the ones on the top of the sand-layer c and e. For this reason, one can argue that these pulses possibly have the same direction as the first sand layer. Unlike for the latter, this is however not supported by imbrication seen in the sand itself. Also, the possible imbrication within several mud-clast rich layers remains undetermined as well. Hence, this approach is helpful for some layers, but an entire assessment of the paleoflow history within the megaturbidite cannot be given by visually analysing the CT images.

The organic matter covering the sand layers consists of elongated branches, relatively large compared to the rest of the data set, leaves and mud. In a NE-SW cross-section of that organic-rich layer, the leaves appear to be bended around black circular objects. The latter are cross-sections through a branch perpendicular to the longest shape axis of a branch. These elongated branches are seen in the NW-SE slice, where the leaves appear to be not bended. By these two facts, a NW-SE orientation is derived for the branches. However, the behaviour of branches relative to an applied current is not well known. A certainty is that elongated organic material tends to orient either

8. Discussion

parallel or perpendicular to the flow direction (Reineck and Singh, 1973). Hence, a flow direction cannot be derived as the flows coming from other sources are largely perpendicular to each other.

Seiche deposit Looking to the grain size of the sand, the mud-clast content and the organic content, Vermassen (2015) divided the sediments related to seiche movements into laminae (figure 8.5). He noticed laminations of alternating mud-clast and organic content and explained the deposition of these layers by a theory based on the behaviour of an oscillating movement of a water body. However, the author did not consider the entire sequence of layers discussed in the following paragraphs. Only what is considered here as the base of the ‘seiche sequence’ was noticed by Vermassen (2015). Since µCT done in this study could deliver higher detail, it is necessary to discuss all the features seen in the U-channel.

Figure 8.5: The division of the seiche deposits in different units. This separation indicates when a velocity threshold for a certain particle (grain, mud clast, organic material) to settle, is reached. A lot, however, depends on the size and density of a particular particle.

To explain the alternation of mud-clast rich and poor layers, Vermassen (2015) turned to the fluid dynamics created by an oscillating water body. Right after the earthquake and the deposit of the stacked turbidite, the water movement caused by the seiche was rather strong, possibly erosive, and most of the particles had to stay in the suspension cloud. A particle is able to settle when the velocity drops under a certain threshold. Slowing down the current can be caused by two processes: (i) the current in the centre of the basin is lower when the standing wave is at one of its outer maxima and (ii) the amplitude of the movement will eventually fade over time. Process (i) can be defined in other words to make it more visual. For instance, when all the water is at one side of the basin, there is a short moment of standstill with a zero velocity. When the water the water then returns, the bottom current velocity will linearly go to a maximum, before it fades again to (close to) zero. Vermassen (2015) explained the sand layers as a pulse of high velocity during the seiche movement and the mud-clast rich layers to a decrease in velocity of the bottom current due to the first process

8. Discussion

This theory can be tested by bearing these two processes in mind while looking at the µCT images. Although Vermassen (2015) made a separation between laminae of alternating mud/organic content, the process of deposition is easier explained if the sequence is divided into units (figure 8.5).

The first 3 units (I, II and III) are separated from a sandy base on and stop when the next sandy layer comes in. The sandy bases of unit I and unit II are deposited while the current, induced by the seiche movement, is at its strongest point. This means that only the coarsest sand particles are able to settle in the basal layers. As the flow velocity decreases, because of process (i) described in the previous paragraph, some mud clasts are capable to escape the suspension and settle. The last velocity threshold is reached for organic particles, which only settle when the current speed is close to zero. These fragments are therefore found on top of these first two units. Unit III is somewhat thicker and contains sharper transitions between laminations therein. Since the amplitude of the seiche movement has already faded over time, the velocity of the bottom current is not as strong anymore as for Unit I and II. This results in a large amount of particles that is able to settle. A relatively thick sand layer is deposited almost out of traction, then follows a mixed sand mud layer, as the velocity threshold for mud clast deposit is reached. Unit III ends in a layer almost completely consisting of mud clasts and organic fragments, as the result of, again, a moment of a velocity close to zero.

The other units are somewhat more difficult to explain. The division between unit IV and V was made owing to the sudden appearance of organic material on the top of unit IV. As this material is considered to be the least heavy, it only gets deposited in a slow flow regime. However, if the sequences seen below unit IV are followed, unit IV and V could be derived from one single pulse. Similar to the top of unit III, unit V consists of a large amount of mud clasts, causing the rather dark appearance on the µCT images. On the other hand, pulses of sand grains are seen in unit V, which indicates either a non-deposition of the mud clasts in the sand-rich layers or a non-deposit of sand in the mud-clast rich layers. This could indicate individual pulses, and therefore the sediment of unit V was marked as a single unit. All of this to say that unit IV and V could originate from the same pulse, or unit V consists of several pulses. Nevertheless, the flow regime at the bottom of a basin is quite complex and the sandy layers in unit V could be due to turbulence in the flow or another factor linked to flow regimes. Unit VI is again sandier, which indicates the start of a new pulse. Although, to be certain about the latter, the section should have been longer.

Generally, the sequence of fining up within one unit and a gradually thickening of the different units (figure 8.5), can find a perfect explanation in the deposition theory proposed here. Therefore, it can be assumed to be deposited out of a seiche movement. Another argument for this is derived from the grain-size data, derived from Vermassen (2015), where a gradual decreasing in particle size is noticed for the sand grains and the mud content increases (figure 8.6a).

8. Discussion

Figure 8.6: (a) All the grain-size distributions determined by Vermassen (2015) for core LU14-12. A gradual fining upward of the fine sand fraction is seen throughout the seiche deposits and is marked with the transparant arrow. Moreover, a relative increase of the mud is also present. An apparent gap between the sand puls peaks and the seiche peaks is indicated by the black arrow. (b) The same grain-size distributions are given for LU14-12, completed with the distribution of a sand layer in LU14-06 and LU14-18.

The sediment between the first pulse of the seiche movement and the last pulse of the stacked turbidite, or unit 0, is still not explained. It is likely that this unit indicates a transition between a depositional regime dominated by turbidites and a regime mostly influenced by the seiche movement. Still, no grain-size data about this unit was obtained, nor any sedimentary features can be recognised, so the depositional history of this layer remains a mystery. However, between the seiche and the turbidite grain sizes, there appears to be a lack of sediment, indicated in figure 8.6a, by a gap between grain-size distributions of the seiche layers and the stacked turbidite. If the grain- size distribution of unit 0 would have been determined, maybe the gap can be filled. Or, a density flow coming from another source area than the turbidite pulses below could have a finer grain-size distribution to start with, and could be deposited by influence of both, density flow and seiche movement.

An argument for the latter theory comes from cores LU14-06 and LU14-18 (Vermassen, 2015) (figure 8.6b). The first core, LU14-06, was taken at the most eastern edge of the campaign area. The core incorporated a sandy base, mass-flow deposits and a sandy layer on top of the mass flow deposits. Since the core was taken in the eastern side of the basin, the mass-flow deposits come from the Vitznau source. Vermassen (2015) dedicated the presence of the lower sand to density flows coming from north-eastern sources, the upper sand layer has originated from a density flow coming from the east or southeast. Core LU14-18, retrieved in the southeast of the campaign area, has a thick sand layer compared to other cores in the neighbourhood. Due to its proximity to the south-eastern source (Vitznau 2), it is more likely that this source has contributed a lot to the thickness of the sand layer. The likelihood that the origin of the upper sand layer in LU14-18 lies in the south-eastern source is rather large. When one looks to the grain-size distributions of the discussed sand layers of both cores, it fits with the distributions of the seiche movement in core LU14-12 (figure 8b). For this reason, together with a lack of sedimentary structures and no lamination, one can argue that unit 0 is deposited by a mixture of a density current coming from the southeast and the oscillating movement of the water body. This theory, however, has to be confirmed by obtaining more data from other cores.

8. Discussion

8.2.3. µCT: grain orientation analysis In the previous section, all of the discussed items were based on visual analysis of the µCT data. Combined with other proxies, such as grain-size analysis, a lot of information can be retrieved concerning the depositional history and flow regime. Nevertheless, computed analysis of the absolute orientation of grains can lift the argued theories to a higher level of certainty and this will be discussed in the following paragraphs.

Stacked turbidite Some arguments already have been given to assume that the sandy base layer was deposited by a density flow coming from the northeast. To validate the µCT method proposed in this dissertation, this direction should match the orientation derived from analysis of the single grains. Still, since the analysed layers are tilted, the determination of the plunge will certainly be invalid. This results in a false peak at one side of the rose charts, as the trend is defined by the direction in which the upper point of the grain points towards. Therefore, merely a general direction can be derived rather than a source area. Then again, the source areas are separated in a way that the directions will lead to an assessment of the source area of a certain pulse. A solution is to apply a correction on the orientation parameters derived from µCT. The parameters should be transformed to a new virtual plane, horizontal to the plane of deposition. This involves a rotation and tilt of the horizontal plane of analysis and could be executed in further research.

Figure 8.7: The sand layers of the stacked turbidite with their absolute orientations given as rose charts. Layers (a) and (b), separately analysed and depicted in the results, are here considered as one large basal layer and therefore, only one rose chart is projected.

The rose charts for the base of every sand layer is depicted in figure 8.7. The base of the first sand layer showes a peak to the NE, which is consistent with the assumed source area. However, going up in the layer, until the first mud layer, the data on the rose charts becomes gradually more scattered. The bases of the other sand layers also indicate a NE-SW direction, except for sand layer (b) and (d).

8. Discussion

Sand layer (b) is separated from the base sand layer (a) by the first mud-clast rich layer and was therefore considered as an individual pulse. Nevertheless, this pulse is also characterised by the presence of mud clasts within the sand layer. Since the mud clasts are considerably larger than the surrounding grains, it is not ruled out that these have influenced the fabric of the grains. Hence, determining a clear grain orientation linked to the paleoflow is maybe not possible. Moreover, a NW- SE trend was spotted while, as discussed earlier, the mud clasts have a NE-SW trend. This could indicate that grains have a tendency to orient perpendicular to the flow in the presence of mud clasts. Or, that the flow regime changes, whereby mud clasts are able to deposit in an imbricated way, but grains are deposited with their longest shape axes perpendicular to the flow direction. The latter is however contradicting Shor et al. (1984). These authors argue that grains will orient perpendicular to the flow direction only in a relative fast flow. It is not natural to assume that a mix of mineral grains and mud clasts gets deposited in a faster flow regime than the pure sand below. For this reason, one can assume that the presence of mud clasts influences the grain orientation to be more or less perpendicular to the flow and not, or in a smaller extent, the flow regime. This is confirmed by Reineck and Singh (1973), who argued that features on the bedding, like ripples or the presence of larger particles, tend to influence the orientation of the grains more that the flow direction itself. Moreover, as the main direction is even perpendicular to the imbrication of the mud clasts, it can be questioned if the combination of the flow direction and the presence of larger objects always lead to perpendicular deposition of smaller grains relative to the flow direction. An answer could be that it is due to the turbulence that is created at the lee side of the mud clast, but this is hypothetical and needs to be tested. Since the orientation of the mud clasts in sand layer (b) and the grains of sand layer (a) is the same, it is further on referred to as one pulse. Also in figure 8.7, the two layers are taken as one and the perpendicular grain orientation of sand layer (b) is not depicted.

Also the grains of sand layer (d), the second rose chart (going from left to right in figure 8.7), have another orientation. Like in sand layer (b), mud clasts are present, but the ratio mud clasts/grains is much lower here compared to layer (b). Moreover, derived NW-SE orientation of the grains is more pronounced in the rose charts of layer (d). Consequently, a large influence of the mud clasts on the grains is more questionable. A reason for the NW-SE direction could be either a suddenly faster flow regime or a pulse directly from that direction. Since there is a chance that a density flow arrives from the east (Vitznau pulse) or the southeast (Vitznau 2 pulse), the latter explanation seems more possible. If cores that show clear pulses coming from right next to location LU14-12 and closer to the possible source areas could be CT imaged on a resolution obtained here, and analysed for orientation afterwards, a clear explanation for the source of sand layer d could be retrieved.

So, uncertainties concerning the behaviour of the density flows and the grains therein remain and questions about this matter need to be solved before coming to a real understanding of this succession of sand, mud clasts and organic material. On the other hand, the matching orientations extracted from the grain analysis and the visual analysis of the µCT are very promising.

Seiche deposit Only the lowest three units, discussed as seiche units above, are depicted in here and analysed for grain orientation. The reason for this is that not enough insight regarding the depositional processes was available previous to the analysis. However, the rose charts can deliver a better understanding of the deposits. Layers f2 and f3 together are equal to unit I of the seiche deposits. Therefore, when the two rose charts are combined, a stronger signal in a NW-SE direction will be produced for the OM. The grains, however, do not correlate with each other and show random scatter in the combined

8. Discussion

rose chart. Again, this could be due to a significant amount of mud clasts and organic material in the dataset of f3. The scatter in the grain orientations is not only appearing in unit I. Unit II is equal to laminae f4 and f5 and for those, a combination of the grains results again in random scatter. The organic matter points out an EW direction, however, the rose chart is not as clear as for unit I. A better combination is retrieved for f6 and f7 (lower part unit III), where grains and organic material line up with an ENE-WSW and a NE-SW orientation respectively. All the previous in depicted in figure 8.8.

Figure 8.8: the different layers analysed can be taken together into units of a sandy base and a mud-clast rich top. The absolute orientations of those units are displayed in the rose charts at the bottom of the figure, with the mineral grains on top and the organic matter at the bottom. Unit 0 and unit III run further out of the range of the picture, however, those areas are not analysed.

A different orientation within units I, II and III, points out that a seiche wave causes particular currents on the bottom of the lake that are not explained easily. One could think that a standing wave would cause only one distinguishable orientation of the organic material and the grains, as the wave goes back and forth across the basin. Nevertheless, the flow dynamics on the bottom of the lake are more complex and are related to the basin morphology, the seiche amplitude and the directions of potential mass flows or density flows.

Mulder et al. (2009) also derived orientations for a seiche movement in a megaturbidite in Cretaceous rocks (Pyrenees). The authors studied the orientations of the longest shape axis of quartz and calcite minerals through thin sections. The flow direction at the base of the seiche sediments

8. Discussion

matched the directions of the density flow at the bottom of the megaturbidite. The results obtained in this dissertation can be compared to those of Mulder et al. (2009). Unit III has the same orientation as most of the sand pulses in the turbidite and therefore, this result can count as consistent with Mulder et al. (2009). The different orientation of the organic material in unit II and II can also be explained by the behaviour of the organic particle in different flow regimes. In unit I, the flow velocity was larger than in unit III and therefore, an OM orientation perpendicular to the direction of the flow is possible. For unit III, the velocity has already decreased and grains, as well as OM, could be oriented in the direction of the propagation of the seiche wave. Unit II can then be seen as a ‘mixture phase’ between the two outer ends. All this also implicates that the propagation of the standing wave has an orientation equal to the orientation of the density flow(s) coming from the NE. Nonetheless, this all remains rather theoretical and more research on the flow behaviour of seiche movements and the particle behaviour within a flow is needed to confirm this.

8.2.4. Combination of visual and computational analysis A final model for the sand pulses in the base of the megaturbidite is given in figure 8.9, whereby sand pulse 1, 2 and 4 are considered to come from the north-eastern sources, confirmed by the imbrication of mud clasts embedded in the sand and the grain orientations of the sand grains. The mud clasts present in the third pulse, however, did not show any orientation or imbrication. Although it could be present, visual analysis just cannot distinguish any sedimentological reason to assume a direction for that pulse. The grain orientation, on the other hand, did show a preferred orientation in an NW-SE direction. As mentioned in previous sections, this could be due to the presence of the mud clasts. Hence, it is questionable whether or not this pulse was coming from a source in the eastern or south-eastern part of the Vitznau basin.

A possible explanation for the succession of laminae in the upper part of the U-channel can be given by deposition out of an oscillating water body or seiche movement. More power to this theory is given by analysis of the orientation of the organic material present in the different pulses. However, the uncertainty of the behaviour of organic matter in a flow is rather large. For this reason, the proposed theory needs to be tested on other proxies and further research of the influence of seiche movements on sediments is necessary.

8. Discussion

Figure 8.9: the orientations for the mud clasts and grains combined for all the sandy pulses lead to this concluding figure with possible flow directions. The previously mentioned sand layers (a) and (b) are taken as one pulse in this figure. The orientation of the third sandy layer is not confirmed, nor denied, by the mud clasts and is therefore considered as questionable and indicated with a question mark.

9. Conclusion and future outlook

The proposed workflow in this dissertation leads to satisfying results. Only the last phase of subsampling the U-channel with straws of 4 mm diameter was not possible without deforming both the sediment inside and outside the straws. Nevertheless, the X-ray CT scans led to a resolution of 15 µm, and the it can even be pushed to 5 µm, for a U-channel of 2 cm by 2 cm. With other sample techniques to obtain smaller samples, a higher resolution can be achieved. Since the results described in this dissertation can be realised in the timespan of a master’s dissertation, X-ray micro computed tomography certainly has the potential to unravel sedimentary structures and fabrics no other technique can, in a large quantity, over a reasonable amount of time.

Determining a grain-size distribution is considered to be possible, taking into account resolution limitation. Logically, the higher the resolution, the better the grain size can be determined. When a certain threshold related to the resolution is reached, several unwanted peaks appear and the data in the lower range of the distribution is considered as biased. The data above the threshold seems to depict a rather good representation of the true grain-size distribution. Not the equivalent diameter gave the best result, but the width, or the length of the intermediate shape axis of the object, displayed the best fit with the laser-diffraction data. This can be explained by the averaging of all the shape axes by laser-diffraction grain-size analysis. As the distribution was considerably different than for the equivalent diameter, one can conclude that most of the grains are elongated in shape. These results also illustrate the added value of µCT data, as much more grain-size parameters can be determined, enabling more reliable and in depth analysis.

The workflow also guides to good results for orientation of grains until U-channel level. If there is a preferable direction, rose charts, obtained by analysing separate layers, tend to show it and moreover, they confirm the initially hypothesised directions of Vermassen (2015). Several problems due to the excessive data were encountered, but these were mostly resolved by reducing the volumes of analysis. Nevertheless, small volumes of analysis had consequences for the orientation data. Since the amount of grains taken into consideration decreases, the signal to a preferred direction will decrease as well. To improve the signal, more grains could be taken into consideration. Because the space of analysis is limited, this can be done by either take less elongated grains into account, or decrease the voxel size, so smaller grains can be taken up in the datasets as well. The former will lead to a wider spreading of preferred direction, which is undesirable, while the latter is only obtained by region of interest scans or retrieving smaller samples. As earlier mentioned, deriving a higher resolution by using straws is not possible. Furthermore, one could wonder if there is a necessity to proceed into higher resolutions. The likelihood that smaller grains will orient as good as grains of a size exceeding 100 µm, does not seem that large in this dataset. Nevertheless, in other datasets, where the small grains are not influenced by the presence of larger grains, it could be profitable to go to higher resolutions.

Wondering if imaging the smaller particles is beneficial, leads to the main problem of the technique, which is the uncertainty on the behaviour of different particles under different flow regimes. This is not only the case for the orientations presented here, but a general issue regarding determining flow histories. A better understanding could be derived from µCT imaged sediments of known composition with a more or less known orientation and flow regime. For example, organic material tends to orient itself either parallel or perpendicular to the flow direction, and the results of this dissertation showed that sand grains will possibly orient perpendicular to the flow in presence of larger objects, such as mud clasts. Therefore, also investigating what the influence of features on the

9. Conclusion and future outlook

bedding, such as ripples or larger objects, is on the orientation of fine sand grains, could be beneficial for the second aim of this dissertation.

That second goal was to use the grain orientations to verify, and if possible improve, the flow model determined by Vermassen (2015). For this purpose, the base of megaturbidite in core LU14-12-G was divided into two major parts. The lower unit consists of 4 sand pulses, coming from density flows, interbedded by layers almost entirely existing of mud clasts and organic matter. The lowest, the second and the fourth pulse most likely came from a source in the northeast of the basement, indicated by both the orientation of the grains and imbrication of mud clasts. This is also largely confirmed by the model of Vermassen (2015). Doubts remain, on the other hand, for the orientation of the third layer. The orientation of the grains points out that the flow comes from, or goes towards, the southeast. However, a considerable amount of non-imbricated mudclasts is present in the layer and could have influenced the orientation of the sand grains. If the direction of the orientation is correct, the layer most likely comes from the Vitznau (east) or Vitznau 2 (southeast) source.

The second part of the sediments, a more fine-grained unit with lamina, was connected to a seiche movement by Vermassen (2015). With help of the obtained high resolution, different units, with a fining upward, are seen in the deposits, and the depositional setting could more or less be derived, confirming that the deposit is owing to a seiche movement of the water body. In contrast to the orientations of the organic material, those of the grains were scattered and of no use. The twigs in the lowest section of the seiche deposit are NW-SE oriented, but going up higher in the sequence, the orientations get more mixed, to eventually end up with a clear NE-SW orientation. Organic matter tends to orient perpendicular to the flow direction in fast flow regimes and parallel to it in slow regimes. If this is taken as valid, it could be concluded that the currents induced by the seiche movement have the same orientation as the density flows that occurred before.

From all the previous, one could conclude grain orientation appears to be a good proxy for determining paleoflow of density flows in Lake Lucerne after the 1601 A.D. earthquake. Especially in combination with other proxy data, such as grain size analysis. However, the method to derive paleoflow histories can be sensitively enhanced. To study the paleoflow more precisely, for instance, an analysis of the grain orientations through CT data should be executed prior to subsampling with meaning to determine the grain-size distribution. This way, different pulses can be detected exactly and the locations of interest for grain-size analysis can be picked out more precisely for sampling. A more accurate flow model will be the result of this combination.

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