Seismic-Reflection and Seismic-Refraction Imaging of the South Portuguese Zone Fold-And-Thrust Belt

$Seismic-Reflection and Seismic-Refraction Imaging of the South Portuguese Zone Fold-And-Thrust Belt$

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 362

Seismic-Reflection and Seismic-Refraction Imaging of the South Portuguese Zone Fold-and-Thrust Belt

CEDRIC SCHMELZBACH

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This thesis is based on the following papers, which are referred to in the text by their Roman numerals:

I Schmelzbach, C., C. Juhlin, R. Carbonell, and J. F. Simancas (2007), Prestack and poststack migration of crooked-line seismic reﬂection data: A case study from the South Portuguese Zone fold belt, southwestern Iberia, Geophysics, 72(2), B9–B18. II Schmelzbach, C., J. F. Simancas, C. Juhlin, and R. Carbonell (2007), Seismic-reﬂection imaging over the South Portuguese Zone fold-and-thrust belt, SW Iberia, submitted to Journal of Geophysical Research. III Schmelzbach C., C. A. Zelt, C. Juhlin, and R. Carbonell (2007), P- and SV -velocity structure of the South Portuguese Zone fold- and-thrust belt, SW Iberia, from traveltime tomography, submitted to Geophysical Journal International.

Reprints were made with permission from the publishers.

Additional publications written during my stay at Uppsala University but not included in this thesis:

• Schmelzbach C., H. Horstmeyer, and C. Juhlin (2007), Shallow 3D seismic-reflection imaging of fracture zones in crystalline rock, Geophysics, in press. • Schmelzbach C., H. Horstmeyer, and C. Juhlin (2006), High-resolution 3-D seismic imaging of the upper crystalline crust at a nuclear-waste disposal study site on Ävrö Island, southeastern Sweden, 76th Annual International Meeting, Society of Exploration Geophysicists, Expanded Abstracts, 1396– 1400. • Schmelzbach C., A. G. Green, and H. Horstmeyer (2005), Ultra-shallow seismic reflection imaging in a region characterized by high source-generated noise, Near Surface Geophysics, 3(1), 33–46. • Schmelzbach C., and C. Juhlin, (2004), Oskarshamn site investigations: 3D processing of high-resolution reflection seismic data acquired within and near the array close to KAV04A on Ävrö, 2003, SKB AB (Swedish Nuclear Fuel and Waste Management Co.), P-04-204.

Contents

1 Introduction ...... 9 1.1 Motivation and General Objective ...... 9 1.2 Outline of the Thesis ...... 10 2 Background Information on the South Portuguese Zone and the IBER- SEIS Profile ...... 11 2.1 Geological Overview ...... 11 2.1.1 Lithostratigraphy of the South Portuguese Zone ...... 11 2.1.2 South Portuguese Zone Structural Image ...... 12 2.1.3 Geodynamic Evolution ...... 13 2.2 Previous Investigations ...... 14 2.3 The IBERSEIS Profile ...... 15 3 Physical Background ...... 19 3.1 Seismic-Wave Propagation ...... 19 3.1.1 The Wave Equation ...... 19 3.1.2 Describing Wave Propagation by Rays ...... 20 3.1.3 Partitioning at an Interface ...... 21 3.1.4 Seismic Resolution ...... 21 3.2 Petrophysical Aspects ...... 22 3.2.1 Mineralogy ...... 23 3.2.2 Crack-Like Porosity ...... 23 3.2.3 Fractures and Faults ...... 25 3.2.4 Stochastic Analysis of Geophysical Log Data ...... 25 3.2.5 Implications for Seismic Imaging in Crystalline Environ- ments ...... 27 4 Review of Seismic-Reflection Techniques ...... 29 4.1 Applications ...... 29 4.2 Seismic-Reflection Data Acquisition ...... 30 4.3 Challenges in Hard-Rock Seismic-Reflection Data Processing . 31 4.3.1 Crooked-Line Acquisition Geometries ...... 31 4.3.2 Effects of the Near-Surface Layer ...... 33 4.3.3 Dip-Moveout Corrections, Prestack, and Poststack Mi- gration ...... 35 5 Review of Seismic-Refraction Techniques ...... 41 5.1 Applications ...... 41 5.1.1 Algorithms and Inversion Approaches ...... 41 5.1.2 S-Wave Traveltime Inversion ...... 42 5.1.3 Seismic-Refraction vs. Seismic-Reflection Acquisition . . 42 5.2 First-Arrival Traveltime Tomography ...... 43 5.2.1 Forward Modeling ...... 44 5.2.2 Solving the Inverse Step ...... 44 5.2.3 Assessment of the Solution ...... 45 6 Summary of Papers ...... 49 6.1 Paper I: Prestack and Poststack Migration of Crooked-Line Seismic Reflection Data: A Case Study from the South Por- tuguese Zone Fold Belt, Southwestern Iberia ...... 49 6.1.1 Motivation ...... 49 6.1.2 Methods ...... 49 6.1.3 Conclusions ...... 50 6.2 Paper II: Seismic-Reflection Imaging over the South Portuguese Zone Fold-and-Thrust Belt, SW Iberia ...... 52 6.2.1 Motivation ...... 52 6.2.2 Methods ...... 52 6.2.3 Results ...... 53 6.2.4 Conclusions ...... 55 6.3 Paper III: P- and SV-Velocity Structure of the South Portuguese Zone Fold-and-Thrust Belt, SW Iberia, from Traveltime Tomography ...... 55 6.3.1 Motivation ...... 55 6.3.2 Methods ...... 55 6.3.3 Results and Conclusions ...... 56 7 Conclusions and Outlook ...... 59 7.1 General Conclusions ...... 59 7.2 Outlook ...... 59 7.2.1 Prestack Migration of Crooked-Line Data ...... 60 7.2.2 Analysis of Converted, Shear, and Surface Waves ...... 61 7.2.3 Waveform Tomography ...... 63 8 Summary in Swedish: Avbildning av väck och överkastnings bältet Södra Portugisiska Zonen med seismisk reflektion och seismisk refraktion ...... 67 8.1 Syfte av avhandlingen ...... 67 8.2 Seismisk reflektions processering av data med krokiga insamlingslinjer (Artikel I) ...... 67 8.3 Geologisk tolkning av reflektioner och diffraktionsenergi (Ar- tikelII)...... 67 8.4 Hastighets tomografi med användande av första ankomster från P- och SV-vågor (Artikel III) ...... 68 8.5 Slutsats ...... 69 Acknowledgments ...... 71 Bibliography ...... 73 Abbreviations

General terms 1D One-dimensional 2D Two-dimensional 3D Three-dimensional

Geophysical terms CMP Common midpoint DMO Dip-moveout NMO Normal moveout P-wave Primary (compressional, longitudinal) wave PSTM Prestack time migration rms root-mean square S/N Signal-to-noise ratio S-wave Shear or secondary (rotational, equivoluminal) wave

SV -wave Vertically polarized shear wave

Geological terms CIZ Central Iberian Zone IPB Iberian Pyrite Belt Moho Mohoroviciˇ c´ discontinuity OMZ Ossa-Morena Zone SPZ South Portuguese Zone VMS Volcanogenic Massive Sulﬁde

1. Introduction

1.1 Motivation and General Objective The South Portuguese Zone fold-and-thrust belt (SPZ) represents the southernmost part of the Iberian massif on the South-Western Iberian Peninsula. It is commonly acknowledged that the Iberian massive forms one of the best exposed fragments of the Variscan orogeny in Western Europe (Variscan orogeny: 480 – 290 Ma; see e.g., Matte, 1986). Hence, the South-Western Iberian Peninsula has attracted a broad scientific interest, as the area offers the opportunity to study the Variscan orogeny in space and time, for example, evoking the SW Iberia interdisciplinary scientific program promoted by EUROPROBE (Ribeiro et al., 1996). Magmatic activity was intense within the SPZ during Early Carbonifer- ous times resulting in world-class massive sulphide deposits being developed within the Iberian Pyrite Belt (IPB) unit of the SPZ (Carvalho et al., 1999; Sáez et al., 1999). Detailed knowledge of the Variscan structure with depth is important for any palinspastic reconstructions, and, consequently, is essential for ore exploration in the IPB area. However, current geological models of the SPZ are primarily based on surface geological mapping (e.g., Soriano and Casas, 2002). Only after the acquisition of the IBERSEIS deep seismic- reflection profile (Simancas et al., 2003; Carbonell et al., 2004) has detailed depth information become available. Previous seismic investigations of the Variscan Iberian massif structure involved mainly seismic wide-angle and refraction experiments aiming at resolving the SPZ structure at large scale (e.g., Mueller et al., 1973; Diaz et al., 1993; González et al., 1998). In contrast, the recently recorded IBERSEIS deep-seismic reflection profile, filling a gap in the deep-seismic imaging of the Variscan Belt in Europe, provided, for the first time, a complete and detailed crustal-scale image of the Iberian Variscan Belt (Simancas et al., 2003; Carbonell et al., 2004). Although the seismic-reflection processing by Siman- cas et al. (2003) revealed impressive images of the crustal structure, e.g., the mid-crustal Iberseis Reflective Body (Carbonell et al., 2004), it did not always provide crisp images of the shallow (< 5 km depth) upper crust. The aim of the seismic-reflection and seismic-refraction analysis of the IBERSEIS data set presented in this thesis was to resolve the thin-skinned tectonics of the shallow SPZ crust in more detail than the previous seismic- reflection processing by Simancas et al. (2003), in order to document the

9 lithostratigraphy with depth and to provide images that allow a deeper insight into the structure of the IPB.

1.2 Outline of the Thesis Before presenting the investigations that from the basis of this thesis, a short introduction to the investigation area (Chapter 2) and to relevant seismic and petrophysical aspects is provided (Chapter 3). Then, I briefly review hard- rock seismic-reflection and seismic-refraction studies and present some key challenges of hard-rock seismic surveying and discuss data processing issues (Chapter 4 and Chapter 5). The principal investigations of this thesis are summarized in Chapter 6. Crisp imaging of the complex crystalline subsurface requires applying migration before stacking. To tackle the problem that data acquired along crooked survey lines violate underlaying assumptions of 2D seismic-reflection imaging, I present and compare three crooked-line prestack migration schemes (Pa- per I). Paper II summarizes the seismic-reflection re-processing of the SPZ- part of the IBERSEIS data employing the crooked-line prestack migration scheme developed in Paper I. The observation of high-amplitude diffracted energy motivated the development and application of a modified Kirchhoff- summation scheme to enhance diffracted energy at the expense of specular reflections. The interpretation of the final seismic-reflection sections provided new insights into the structure of the SPZ upper crust and revealed bands of extensive, previously unrecognized dikes. However, the seismic-reflection sections do not resolve the shallowest around 500 m, as the earliest potential reflections are overwhelmed by source-generated noise. In order to fill the gap between the shallowest observed reflections and the surface, first-arrival P- and SV -wave traveltimes were inverted for velocity models (Paper III). Finally, conclusions and ideas for further developments are presented in Chapter 7.

10 2. Background Information on the South Portuguese Zone and the IBERSEIS Proﬁle

2.1 Geological Overview In Portugal and Spain, the Variscan Belt of Western Europe is represented by the Iberian massif, the southern branch of the Iberio-Armorican Arc, one of the best exposed fragments of the European Variscan basement (see Figure 2.1 for location). The Iberian massif is considered to have resulted from the amal- gamation of three different terranes, the South Portuguese Zone (SPZ), the Ossa-Morena Zone (OMZ), and the Central-Iberian Zone (CIZ), during the Variscan orogeny (480–290 Ma). The CIZ autochthonous block was part of Gondwana, while the other terranes have been ascribed to Laurentia or other intervening micro continents (Matte, 2001). However, considerable debate persists concerning the details of the tectonic models attempting to explain the evolution of the Variscan orogeny (see Ribeiro et al., 1995; Simancas et al., 2002, and references therein).

2.1.1 Lithostratigraphy of the South Portuguese Zone The SPZ represents the southernmost unit of the Iberian massif and includes the Iberian Pyrite Belt (IPB), a world-class metallogenic province of volcanogenic massive sulﬁde (VMS) deposits (Carvalho et al., 1999, Figures 2.1 and 2.2). Generally, the IPB lithostratigraphic sequence of the exposed metasedimentary and metaigneous rocks is divided, from oldest to youngest, into the following three major units: the Phyllite-Quartzite group (PQ), the Volcano-Sedimentary Complex group (VSC), and the Flysch group (Schermerhorn, 1971; Carvalho et al., 1999). Upper Devonian sedimentary rocks of the PQ group outcrop in the core of the anticlines (see e.g. the La Puebla de Guzman antiform on Figure 2.2); they are of terrigenous origin and were deposited on the platform of a continental margin. The VSC group, overlying the PQ group is of Lower Carboniferous age. The VSC group consists of a heterogeneous assemblage of bimodal volcanic rocks interbedded with metasediments (e.g., Boulter, 1993). This volcanic suite hosts the VMS deposits. On the basis of the geochemistry of the maﬁc volcanics, an extensional tectonic context is generally assumed for the Early

11 Figure 2.1: Zones of the Iberian Massif and location of the IBERSEIS seismic line. The IBERSEIS line crosscuts the South Portuguese Zone (SPZ), the Ossa-Morena Zone (OMZ) and the Central Iberian Zone (CIZ) in the southwestern part of the Iberian Variscides. The line was designed to be perpendicular to the main geological structures, mainly the two sutures separating these three zones. Figure and caption adapted from Simancas et al. (2003).

Carboniferous time (e.g., Thièblemont et al., 1998). Alternatively, Onézime et al. (2003) interpreted the magmatism in the framework of a volcanic arc related to subduction. The extensional context seems more probable considering the geochemistry and the overall tectonic evolution of SW Iberia in Early Carboniferous time (F. Simancas, personal communication, 2007). The volu- minous felsic crustal-derived magmatism (Thièblemont et al., 1998), as well as the huge hydrothermal engine inferred from the giant VMS deposits (Sáez et al., 1999), strongly suggest a heat source in the crust, such as mantle-derived maﬁc magmas that intruded into the SPZ. The Middle to Upper Carboniferous aged Flysch group forms the third major lithostratigraphic unit in the IPB. It covers most of the SPZ and ranges in thickness from several kilometers in Portugal to less than 500 m in the eastern parts of the IPB. Thick turbiditic sequences of shale, graywacke and local lenses of intraformal conglomerate and till-like rocks build up this syn- orogenic ﬂysch unit (Oliveira, 1990).

2.1.2 South Portuguese Zone Structural Image From a structural point of view, the SPZ is a foreland fold-and-thrust belt of the Variscan orogeny characterized by a south-west vergent imbricated thrust system (Silva et al., 1990; Soriano and Casas, 2002). The folding and thrust displacement is dated to Middle Carboniferous times, and classically divided into two successive folding episodes: (1) a top-to-the-S-SW thrust tecton-

12 Figure 2.2: Field layout of the IBERSEIS deep-seismic reflection profile drawn on a geological map of the Iberian Pyrite Belt (IPB) unit of the South Portuguese Zone (SPZ). Light-gray Migration line shows the location of the final seismic-reflection sections discussed in Paper I and Paper II of this thesis. Three major lithostratigraphic units are, from oldest to youngest: (i) the Phyllite-Quartzite group, (ii) the Volcano- Sedimentary Complex (hosting massive sulfide deposits), and (iii) the Flysch group. Locations of massive sulfide bodies (after Carvalho et al., 1999) and names of main villages along the profile are marked. Survey site within Spain is shown in the inset. Figure modified from Simancas et al. (2003). Figure corresponds to Figure 1 in Paper II.

ics, nappe emplacement, and mylonitic foliation, and (2) a more conspicuous phase related to regional folding. Ribeiro and Silva (1983) proposed a major decollement at the base of the imbricated complex and interpreted the SPZ in terms of thin-skinned tectonic deformation. The results of the IBERSEIS deep-seismic reﬂection study strongly support the thin-skinned model, suggesting a basal decollement at 12–15 km depth (Simancas et al., 2003, for location of the IBERSEIS proﬁle see Figures 2.1 and 2.2).

2.1.3 Geodynamic Evolution Following Simancas et al. (2003), the IBERSEIS seismic data reveal a practically frozen snapshot of the SW Iberian crust at the end of the Variscan collision (Figure 2.3a). The resolved structures resulted from the cumulative displacements of a continental collision that took place from around 390– 300 Ma. In summary, the main points of the geodynamic evolution as derived

13 from the IBERSEIS seismic images summarized in Simancas et al. (2003) are (Figure 2.3b): 1. The collision between the Ibero-Armorican indentor and a northern conti- nent, Laurussia, started in Early–Middle Devonian times at the OMZ/CIZ boundary (∼420–390 Ma; Figure 2.3b, see also Figure 2.1). Deformation was concentrated to the upper OMZ crust and southernmost CIZ, but seems not to have penetrated into the SPZ. 2. The Earliest Carboniferous time (∼360–350 Ma) was a transient period characterized by extension and magmatism. The tectonic regime was left- lateral transtensional. Bimodal volcanism was abundant in the SPZ, OMZ and CIZ, and led to the formation of the giant sulphide deposits in the IPB. During this time period, a mantle plume is believed to have played a major role in controlling the thermal and stress regimes of the SW Iberian crust. 3. The last stage of the orogeny was of transpressional character and lasted until the blockade of the continental collision (345–300 Ma). The fold-and- thrust tectonics of the SPZ entirely formed during this period of contractive deformation.

2.2 Previous Investigations In the past, geophysical investigations of the Variscan Iberian massif mainly involved seismic wide-angle and refraction experiments (e.g., Mueller et al., 1973; Diaz et al., 1993; González et al., 1998). For the SPZ, González et al. (1998) report high apparent velocity (6.4 km/s) layers at shallow depths (7– 10 km) which they suggest to be related to mafic and ultramafic rocks within the upper levels of the crust. Bouguer anomaly data of the Iberian peninsula (Mezcua et al., 1996) show a prominent positive anomaly across the SPZ and OMZ, indicating an anoma- lous density distribution inside the crust. Fernàndez et al. (2004) modeled the SW Iberian lithosphere along a 1000 km-long transect based on forward finite- element computations that combine heat flow, gravity, geoid anomalies, and absolute elevation. Their most outstanding result is a density excess at midcrustal depths that must be compensated by a mass deficit at deep lithospheric mantle levels in order to fit the observed geoid, gravity, and elevation. Lateral variations of upper and middle crust have been the target of recent magnetotelluric experiments (Almeida et al., 2001; Carbonell et al., 2004; Pous et al., 2004). Over the SPZ, the magnetotelluric surveys indicate shallow conductors (<10 km depth), which may be connected to mineralizations in the Pyrite Belt.

14 Figure 2.3: (a) Crustal architecture of Southwest Iberia, after the interpretation of the IBERSEIS deep reflection seismic profile by Simancas et al. (2003). (b) Interpretation of the Variscan evolution of Southwest Iberia. Subduction in the boundary SPZ/OMZ (with the development of an accretionary prism and ophiolitic obduction) happened at the same time that collision started in the OMZ/CIZ boundary. A transtensional stage during the Tournaisian is related with the extrusion of volcanics, mainly in the SPZ, and the intrusion of a great volume of magma at a midcrustal level of the OMZ (the Iberian Reflective Body in the IBERSEIS seismic profile; Carbonell et al., 2004). Since Visean, transpressional shortening dominated until the end of collision, giving the final architecture of the southern Iberia crust. Figure and caption adapted from Simancas et al. (2003).

2.3 The IBERSEIS Profile The IBERSEIS (SW Iberia deep-seismic reflection) profile provides, for the first time, a complete section at crustal scale of the Variscan Belt in SW Iberia (Simancas et al., 2003). Hence, the IBERSEIS profile complements other deep seismic investigations to study the Variscan Belt in Europe: e.g., in the northern Iberian Massif (e.g., Ayarza et al., 1998), in central Europe (e.g., BIRPS and ECORS, 1986; Anderle et al., 1991), and across the Urals (e.g., Echtler et al., 1996; Carbonell et al., 2000; Friberg et al., 2002). The IBERSEIS deep-seismic reflection profile runs approximately perpendicular to the strike of the main geological units of the SPZ (see Figures 2.1, 2.2, and 2.4). To allow high-resolution imaging at shallow depths and for resolving steep dips, source and receiver-group intervals of 70 m and 35 m, respectively, were used (Table 2.1). Four 22TM Vibroseis trucks generated non-

15 Figure 2.4: Field layout of the IBERSEIS deep-seismic reflection profile drawn on a geological map of the Iberian Pyrite Belt (IPB) unit of the South Portuguese Zone (SPZ). See Figure 2.2 for explanations. G marks the La Puebla de Guzman antiform. Location of tomography profiles P1–P4 discussed in this thesis are plotted as gray lines. Survey site within Spain is shown in the inset. Figure modified from Simancas et al. (2003). Figure corresponds to Figure 1 in Paper III. linear sweeps in the 8−80Hz frequency range. Six 20s long sweeps were used to increase the source energy rather than using a larger number of sweeps per vibration point. Data acquired along the IBERSEIS profile were recorded with an asymmetric 8365 m-long split-spread receiver layout using a minimum of 240 active channels. The overall population density in the survey area is low with an ambient-noise level that was also generally low, resulting in mostly high S/N-ratio data. The analyzed data range and, hence, the employed processing geometries differ between Paper I/Paper II and Paper III. Figure 2.2 displays the extent of the seismic-reflection profile presented in Paper I and Paper II, which covers the southern part of the SPZ. The final seismic-reflection sections discussed in Paper II refer to the Migration line. For the tomographic analysis, almost all IBERSEIS data sampling the SPZ were considered (Figure 2.4). The final velocity models discussed in Paper III are referred to as profiles P1–P4 for the P-wave data, and S1 and S2 for the SV -data. Profiles P1 and S1 as well as S2 and P2 coincide, with S2 being somewhat shorter than P2. Furthermore, the Migration line coincides along most of its length with P2/S2.

16 Table 2.1: Data acquisition parameters of the IBERSEIS deep-seismic reﬂection pro- ﬁle. Table corresponds to Table 1 in Paper I/Table 1 in Paper II.

Source information Energy source Four 22 TM Vibrators Source interval 70 m Sweep frequencies 8 − 80Hz Sweep type Nonlinear Sweeps per vibrator point 6 Sweep length 20 s Receiver information Receiver type 10 Hz natural frequency Station spacing 35 m Station configuration 12 Geophones per string in line Spread type Asymmetric split spread, varying along the profile Offset range Typically -6165 – 2200 m; min.: -9780 m, max.: 8465 m Nominal CMP fold 60 Acquisition system Recording system SERCEL 388 Number of channels 240 (minimum) Field filters Out Recording length 40 s listening time Record length after correlation 20 s Sample rate 2 ms Field processing Sweep correlation Noise suppression Diversity stacking the sweeps

3. Physical Background

3.1 Seismic-Wave Propagation This section provides a brief summary of the theoretical background for seismic-wave propagation that is relevant to the work presented later. The equations presented are mainly adapted from Lowrie (1997) and Aki and Richards (2002), where derivations and in-depth discussions can be found.

3.1.1 The Wave Equation Seismic waves transport energy by elastic displacement and are described by the elastic wave equation in three dimensions propagating through a source- free, isotropic, homogeneous, elastic medium: ∂ 2u ρ =(λ + 2μ)∇(∇ · u) − μ∇ × (∇ × u) , (3.1) ∂t2 where λ and μ are Lame’s constants describing the elastic properties of the medium, ρ is the density, u(x,t) is the displacement vector ﬁeld, x is position, ∇ ∇ = · ∂ + · ∂ + · ∂ t is time, and represents the Nabla operator ex ∂x ey ∂y ez ∂z with ex, ey, ez being unit vectors. Equation 3.1 accounts for both compressional (longitudinal) and transverse (shear, rotational, or equivoluminal) motion and can be separated into a compressional (P-wave) component and a transversal (S-wave) component by deﬁning u = ∇φ + ∇ × ψ. Whereas for compressional motion ∇ × φ = 0 holds, ∇ · ψ = 0 holds for transversal motion, mean- ing the passage of a compressional wave involves no rotation, while shear waves cause no volume changes (dilatation). Hence, P- and S-wave propagation can be described with the scalar wave equations (Lamé’s theorem): ∂ 2φ = V 2∇2φ , (3.2) ∂t2 P ∂ 2ψ = V 2∇2ψ . (3.3) ∂t2 S In terms of elastic parameters, the velocity of the P- and S-waves are given by: λ + μ K + 4 μ = 2 = 3 , VP ρ ρ (3.4) μ = , VS ρ (3.5)

19 = λ + 2 μ μ where K 3 is the bulk modulus and the shear (rigidity) modulus. Poisson’s ratio σ relates these quantities (Sheriff , 2002): λ V 2 − 2V 2 σ = = P S . (3.6) (λ + μ) ( 2 − 2) 2 2 VP VS

3.1.2 Describing Wave Propagation by Rays The eikonal equation approximates the wave equation for harmonic seismic waves in media with elastic properties that vary only slowly relative to the seismic wavelength (high-frequency approximation): 1 (∇T)2 = , (3.7) V(x) where T is the is the traveltime of the wavefront and V(x) is the local wave speed. Equation 3.7 establishes the equivalence of treating seismic wave propagation by describing the wavefronts or the ray paths. However, ray theory may fail for finite-frequency signals traveling through highly heterogeneous media. The passage of a wave through a medium and across interfaces where the medium properties change can conveniently be explained by Huygens’ principle, stating that all points on a wavefront can be regarded as point sources for the production of new spherical waves; the new wavefront is the tangential surface of the secondary wavelets (e.g., Lowrie, 1997). Likewise, the phenomenon of diffraction can be explained by Huygens’ principle; a point obstacle hit by an incident plane wave acts as secondary source and scatters diffracted energy in all directions, as opposed to a plane that reflects the incident wave front as a plane wave. Often, it is sufficient to describe the Earth to have a multi-layered structure with numerous thin layers, each characterized by a constant velocity. Travel paths for both reflected and refracted rays within each layer obey Snell’s law: sinα i = const , (3.8) Vi where αi is the angle the ray makes with the normal at the ith interface, and Vi is the velocity within the ith layer. Likewise, the Earth can be represented by cells/blocks of constant velocity as done in most traveltime-inversion approaches. The traveltime t of a seismic ray in a continuous velocity medium is given by the integral: 1 t = dl , (3.9) L(V) V(x) where L is the ray path. Equation 3.9 is non-linear since the integration path depends on the velocity V(x). Fermat’s principle states that the integration is performed along the ray path that yields the shortest traveltime.

20 3.1.3 Partitioning at an Interface Seismic waves hitting a planar interface where the seismic (acoustic) impedance: Z = ρV (3.10) changes are partly reﬂected, refracted, and conversions between P- and S- waves is possible. For the normal-incidence case, the amplitudes AR for the reﬂected wave, in terms of the amplitude AI of the incident wave are given by:

A Z − Z ρ V − ρ V RC = R = 2 1 = 2 2 1 1 , (3.11) AI Z2 + Z1 ρ2V2 + ρ1V1

The amplitude ratio RC is called the reﬂection coefﬁcient. At non-normal incidence, the Zoeppritz equations describe the energy partitioning as a function of angle of incidence (e.g., Yilmaz, 2001).

3.1.4 Seismic Resolution Resolution relates to how close two points can be while they are still discernible as individual points. Resolution of seismic-reflection data can be assessed in the vertical and horizontal direction. Energy reflected from points located within the first Fresnel zone arrives at the receiver with all phases being within no more than a half-cycle and, hence, interfere more or less constructively; these points are indiscernible and contribute all to the observed reflection. In terms of the dominant wavelength λd, the radius of the first Fresnel zone for a spherical wave at depth z0 is given by (Sheriff and Geldart, 1995): λdz0 V t0 r1 = = , (3.12) 2 2 fd where V is the velocity, t0 = 2z0/V is the two-way traveltime, and fd = V/λd is the dominant frequency. The first Fresnel zone width is often taken as an estimate of the lateral resolution of seismic-reflection data before migration. For the IBERSEIS data, the first Fresnel zone radii vary between ∼900–1300 m and ∼1800–2600 m at 1 s and 4 s, respectively, for frequencies of ∼20–40 Hz. Migration tends to collapse the Fresnel zone to approximately the dominant wavelength in the direction of migration (Yilmaz, 2001), but the presence of noise and migration-velocity errors affect migration adversely and may significantly decrease the lateral resolution. When the dimensions of a reflector are somewhat smaller than the size of the first Fresnel zone, its response is essentially that of a point diffractor (Sheriff and Geldart, 1995). Generally, vertical (or temporal) resolution of seismic-reflection data can be quantified by the Rayleigh quarter-wavelength criteria (e.g., Kallweit and Wood, 1982). Two spikes of equal amplitude and equal polarity convolved

21 with a zero-phase ﬁnite-bandwidth wavelet having a dominant wavelength λd are discernible if they are separated by a distance of at least:

λ dz = d . (3.13) 4

If the two spikes have opposite polarity, the amplitude of the composite wavelet reaches a maximum at a separation of λd/4, which is referred to as the tuning thickness. However, vertical resolution and tuning phenomena also depend on the shape of the wavelet and the ambient noise. For the IBERSEIS data, signal frequencies of the processed, stacked data range within ∼20–40 Hz, thus, provide wavelengths of ∼150–300 m for an average velocity of 5800 m/s. Consequently, the vertical resolution or tuning thickness is ∼35–70 m. For refraction-seismic tomography, Williamson (1991) and Williamson and Worthington (1993) argue that, to a first approximation, the smallest features that can be expected to be recovered in ray-based tomography have dimensions in the order of the first Fresnel Zone. Therefore, seismic-refraction results represent heterogeneities averaged within a zone of: √ r ∼ Lλ , (3.14) where L is the propagation distance and λ is the wavelength. For the IBER- SEIS data, the wavelengths are in the few hundreds of meters range and the travel paths range between several hundreds of meters and few kilometers, accordingly, the first Fresnel zone size is in the range of several hundreds to few kilometers.

3.2 Petrophysical Aspects Seismic-wave propagation depends on the in-situ elastic properties, i.e. the bulk modulus K, the shear modulus μ, and the density ρ of the probed rock formations (see also Equations 3.4–3.6, 3.10, and 3.11). In crystalline rocks, controlling factors of the elastic properties range from small-scale rock properties such as mineralogy and crack-like porosity to large-scale formation properties such as fracture zones. The intention of the following section is to provide a short description of the phenomena that are relevant for the understanding of wave-propagation in crystalline rocks and the interpretation of seismic-reﬂection and refraction results. In-depth discussions of rock properties are found e.g. in Cramichael (1989) and Schön (1996), while studies focussing on elastic properties of crystalline rocks are e.g. Christensen (1996) and Salisbury et al. (2003).

22 3.2.1 Mineralogy The most important factor in determining the overall elastic properties of a crystalline rock is the mineral composition. Theoretical mixing rules allow the overall elastic properties to be calculated and, hence, the seismic velocities of a particular polymineralic rock type can be determined within some bounds (Schmitt et al., 2003). These theoretical bounds are generally in good agreement with the observed range of intrinsic velocities for crystalline rocks. Figure 3.1 shows compilations of measured VP- and VS-velocities for a wide range of crystalline rocks (Salisbury et al., 2003). Generally, both the VP and VS velocities tend to increase with density from felsic to mafic rocks, and increase with increasing metamorphic grade along the Nafe-Drake curve (Lud- wig et al., 1970). Most significant differences between√VP and VS are (1) the ve- ∝ / locity range for VS is smaller than√ for VP,asVS VP 3 (e.g., Lowrie, 1997); (2) whereas generally VP/VS ∝ 3, there are some prominent exceptions. For example, quartz-rick rocks have anomalously high VS-velocities. Determin- ing the composition of a rock based only on knowledge of its VP-velocity is difficult because of the similarity of VP for many common crustal rocks (see Figure 3.1; Christensen and Mooney, 1995). Hence, measuring both VP and VS may provide some additional constraints to identify rock types based on seismic-velocity measurements (Christensen, 1996).

3.2.2 Crack-Like Porosity Crack-like porosity of crystalline rocks is due to microcracks that are very small in size (few μm) and are very oblate in shape (e.g., Schmitt et al., 2003). They are produced by e.g. the release of confining pressure during unloading of the crust. Even though crack-like porosity of crystalline rocks may be in the order of 1% or less, it can have a dramatic influence on the elastic properties and, hence, the seismic velocities. If cracks are dry, both the bulk modulus K and the shear modulus μ are reduced, whereas the density ρ is hardly affected by the low porosity (Popp and Kern, 1994). As a consequence, both VP and VS are reduced but the VP/VS may not change significantly. In contrast, water saturation increases the bulk modulus of slightly cracked rocks to almost the bulk modulus of the uncracked medium while having little effect on the shear modulus. Consequently, the relationship between VP and VS can become strongly non-linear depending on the degree of fluid saturation. At low pressures, as found within the upper crust, much of the crack-like porosity remains open and causes lowering of the seismic velocities. However, crack-like porosity decreases with increasing pressure and the elastic moduli increase. As a consequence, seismic velocities depend non-linearly on pressure (or depth; Meglis et al., 1996). Likewise, the attenuation of seismic waves traveling through crystalline rocks depends on the crack-like porosity and exhibits an opposite

23 Figure 3.1: (a) Seismic VP-velocity versus density at a conﬁning pressure of 200 MPa for common crystalline rocks. (b) Seismic VS-velocity versus density at a conﬁning pressure of 200 MPa for common crystalline rocks. Ellipses in (a) and (b) have areas corresponding to standard deviations of density and velocities found in Salisbury et al. (2003). Light-gray background indicates Nafe-Drake curve. Lines of constant acoustic impedance are superimposed for reference. Figure and caption adapted from Salisbury et al. (2003).

24 pressure/depth relation as the seismic velocities (Meglis et al., 1996). It is generally assumed that cracks are closed at conﬁning pressures of 200–300 MPa (corresponding to depths of 6–8 km) above which the pressure-velocity relationship normally becomes linear.

3.2.3 Fractures and Faults Large-scale fracture zones may result from cataclastic shear deformation in the upper brittle crust or ductile shear deformation at great depths resulting in laminated mylonites. Porous and fluid-filled fracture zones influence seismic- velocities by reducing both the elastic moduli and density, and, thus, often produce a significant reduction in the seismic impedance (see Equation 3.10). Salisbury et al. (2003) argued, based on the time-average relationship of Wyl- lie (Wyllie et al., 1956), that a fracture zone with 10% water-filled porosity will cause a 16% impedance reduction at the contact with the surrounding intact rock, as illustrated on Figure 3.1a. In contrast, mineral-sealed fractures would be expected to have a slightly higher acoustic impedance than the surrounding rock. Zones of laminated mylonites exhibit increased seismic velocities in the plane of foliation and anisotropy. Christensen and Szymanski (1988) presented a case for reflections from the Brevard Fault zone, where significant P-wave reflectivity is attributed to compositional layering, anisotropy, and construc- tive interference of seismic waves. Furthermore, sheet-like intrusions may control the formation of fracture zones, as they could act as planes of weakness within the otherwise intact rock. Hence, the observed reflectivity of fracture zones (or intrusions) may be controlled by both fractured rock and lithological contrast (e.g., Juhlin and Stephens, 2006).

3.2.4 Stochastic Analysis of Geophysical Log Data Borehole logging bridges the gap between high-resolution laboratory measurements and low-resolution surface-based seismic experiments. Geophysi- cal log data represent densely spaced in situ measurements of various petrophysical properties. For example, sonic logs provide high-resolution seismic- velocity measurements along borehole walls. The convolutional model provides a link between well-log measurements of seismic velocity and density and seismic data. A seismic trace s(t), where t is the time variable, can be represented by the convolution (denoted by ∗)of an embedded (basic seismic, equivalent) wavelet w(t) with a series of time- dependent reﬂection coefﬁcients r(t) derived from the sonic and density logs (using Equation 3.11) plus random noise n(t) (Sheriff , 2002):

s(t)=w(t) ∗ r(t)+n(t) . (3.15)

25 Strictly speaking, a seismogram represents the convolution of the embedded wavelet with the Earth’s impulse response that includes also multiple reflections of all types (e.g., Yilmaz, 2001). Statistical analyses of sonic-log data from a wide variety of upper crusts revealed surprisingly similar statistical properties (Holliger, 1996a); sonic log power spectra uniformly scale as 1/ f over a wide range of spatial frequencies f , which is indicative for band-limited scale-invariant (spatially self- affine or fractal) behavior known as flicker noise (Holliger and Goff , 2003). Although the cause of the fractal nature of the sonic log data in low-porosity crystalline rocks is not well understood, a number of studies suggests that brittle fault structures could be important (Holliger, 1996b; Jones and Holliger, 1997; Holliger and Goff , 2003). However, Bean (1996) showed that also compositional changes can lead to sonic logs scaling as 1/ f . The von Kármán family of autocovariance functions C(h) has proven to be a versatile model for describing a wide range of band-limited self-affine fractal phenomena (see e.g., Goff and Jordan, 1988; Holliger and Goff , 2003): ν σ 2 h h ( )= ν , C h ν−1 K (3.16) 2 Γ(ν) ah ah where h is the lag vector, ah is the correlation length in the direction of the lag vector, σ is the standard deviation, Γ is the gamma function, and Kν is the modified Bessel function of the second kind of order 0 ≤ ν ≤ 1. The outer limits of the scale invariance are controlled by ah, which is for this reason referred to as the scale parameter. Evidence for the success of this stochastic approach to characterize the crust are the similarity of synthetic models to field data (e.g., Holliger, 1996a). Generally, sonic logs are assumed to be the sum of a deterministic, depth- dependent part, describing the ’large-scale’ trend and a stochastic part of the remaining ’small-scale’ fluctuations (see e.g., Holliger, 1996a). Goff and Hol- liger (1999) and Holliger and Goff (2003) observed that the residual velocity fluctuations of the KTB sonic logs can be represented by the sum of four separate stochastic processes with von Kármán autocovariance functions having different scale parameters of ∼1000 m (σ=167 m), ∼50 m (σ=161 m), ∼7m (σ=145 m), and ∼0.5 m (σ=205 m; see Equation 3.16). Their analyses showed that the two largest scale parameters are related to lithological variations, whereas the third scale is probably related to fracturing. The process with the shortest scale parameter could be clearly associated with the low-pass filtering effect of the finite-length of the logging instrument (e.g., Holliger, 1996a; Holliger et al., 1996). The interpretation of Holliger and Goff (2003) is consistent with the findings of a similar analysis of the KTB data using a different approach by Jones and Holliger (1997) that fracturing and compositional variations have dominant effect on velocity fluctuations at short and long scales, respectively. Considering Holliger (1996a)’s observation of the uniformity of upper-crustal

26 sonic-log velocity fluctuations, Jones and Holliger (1997) argue that the interpretation of this phenomenon in terms of fluid-filled cracks (e.g., Leary, 1991; Holliger, 1996a,b) could be representative for the upper crust in general.

3.2.5 Implications for Seismic Imaging in Crystalline Environments Reflection Strength and Geometry of Reflecting Interfaces Reflection coefficients (Equation 3.11) must exceed a minimum value in order to give rise to detectable reflections with this value being somewhere between 0.01 (Meissner and Rabbel, 1999) and 0.06 (Salisbury et al., 2003). Following Salisbury et al. (2003), a minimum reflection coefficient of 0.06 (corresponding to an impedance contrast of 2.5 × 105g/cm2s; Equation 3.10; see also Figure 3.1) implies that reflections between different felsic rocks are unlikely to produce detectable reflections (e.g., granitic bodies often appear unreflec- tive on seismic-reflection sections). In contrast, contacts between felsic and mafic rocks, or between fracture zones and intact rock likely generate strong reflections. However, the detectability of seismic reflections is also governed by other factors such as the ambient noise level. Interfaces must be rather continuous and exceed in size the first Fresnel zone to give rise to specular reflections (Equation 3.12; Sheriff and Geldart, 1995). Therefore, it may be difficult to infer reflectivity from borehole observations using the convolutional model (Equation 3.15), which implies a horizontally stratified Earth with laterally extending layers of constant properties. Crystalline rocks have often been subjected to several phases of deformation and/or metamorphism leading to a complex subsurface geometry with short and steeply dipping interfaces. The complex subsurface geometry has two consequences for seismic-reflection imaging: (1) resolving reflections and diffractions of conflicting dips requires migration before stack (e.g., Dere- gowski, 1986); (2) seismic-reflection profiling along 2D lines may be affected by out-of-plane effects (Zaleski et al., 1997; Ayarza et al., 2000; Drummond et al., 2004). As a consequence, most reflections from lithological interfaces within crystalline rocks may be too weak to exceed the ambient noise level. Therefore, the two main sources of seismic reflections in crystalline rocks are (1) fracture zones and (2) mafic dikes. Both fracture zones and intrusions are associated with large impedance contrasts and are laterally continuous and, hence, show larger geometrical dimensions than the intercalated and often heavily folded and deformed lithological bodies. Numerous studies confirm that the strongest observed reflections indeed correlate with either fracture zones (e.g., Green and Mair, 1983; Juhlin and Stephens, 2006) or dikes (e.g., Juhlin, 1990). However, it is still difficult if not impossible to identify the cause of a seis-

27 mic reﬂection in crystalline environments without borehole information (e.g., Harjes et al., 1997).

Scattering of Seismic Waves Seismic-reflection profiling has proven to be very successful in sedimentary basins where the Earth structure can be approximated by a superposition of a long-wavelength background model and short-wavelength reflectors of subhorizontal layers with strong impedance contrasts. Statistical sonic-log analyses (e.g., Holliger, 1996a) and statistical descriptions of geological and petrophysical data (e.g., Holliger and Levander, 1992, 1993; Levander et al., 1994), however, indicate that crustal heterogeneities span a wide range of spatial scales. Scattering of seismic waves is most efficient if the wavelength of a primary seismic wave λ with wavenumber k = 2π/λ has the same scale order as the scale of a heterogeneity a, i.e. when ka ≈ 1. The scattered wavefield may be very complex between the extremes of an essentially homogenous medium (ka 1) and a very complex medium (ka 1). Depending on the frequency content of the seismic wavefield and the length of the travel path, scattering can be weak (single scattering dominates) and strong (multiple scattering dominates). Consequently, the validity of ray theory and weak scattering commonly underlaying seismic imaging and interpretation techniques need to be addressed (e.g., Levander and Holliger, 1992). Scattering can introduce considerable noise into seismic-reflection data from crystalline environments that may even be enhanced by standard processing (Gibson and Levander, 1990). Furthermore, the random inhomogeneities cause focusing and de-focusing effects, introducing amplitude and phase distortions (Gibson and Levander, 1988). If seismic experiments fall into the strong-scattering domain, the seismic-reflection images may be severely distorted (Levander et al., 1994). Holliger (1997) demonstrated with finite-difference simulations, that the transition from weak to strong scattering can occur within the crustal-seismic frequency bandwidth for moderately deep reflectors, hence, possibly leading to deterioration of high-frequency upper-crustal seismic-reflection images. The fact that the highest frequencies in the crustal-seismic bandwidth can fall into the strong scattering regime possibly explains the observation that low-pass frequency filtering improves the lateral continuity of deep seismic data (Levander et al., 1994).

28 4. Review of Seismic-Reﬂection Techniques

In the following section, I briefly discuss applications and principal difficul- ties associated with seismic imaging in crystalline environments. Since the commercial use of seismic techniques began in the 1920’s, seismic methods have mostly been applied for hydrocarbon exploration (Sheriff and Geldart, 1995). Somewhat later, seismic-reflection methods started to be used for near- surface investigations in the 1950’s (e.g., Allen et al., 1952). It was not until the late 1970’s and the beginning of the 1980’s when the potential of seismic- reflection surveying in crystalline environments was recognized (e.g., Green and Mair, 1983). In the following, seismic profiling has proven to be a reliable and valuable tool for imaging complex subsurface structures in hard-rock environments at different scales.

4.1 Applications Seismic-reﬂection investigations in hard-rock terranes cover a wide range of applications:

• Large-scale crustal studies: e.g. BIRPS and ECORS (BIRPS and ECORS, 1986), COCORP (e.g., Oliver, 1982; Potter et al., 1987), LITHOPROBE (e.g., Clowes et al., 1984; Cook et al., 1999), DEKORP (e.g., Meissner and Bortfeld, 1990; Anderle et al., 1991; Rabbel and Gajewski, 1999), BABEL (BABEL Working Group, 1990; Korja and Heikkinen, 2005), IBERSEIS deep seismic-reﬂection proﬁle (Simancas et al., 2003), or investigations in the Urals (e.g., Echtler et al., 1996; Juhlin et al., 1998; Carbonell et al., 2000; Tryggvason et al., 2001; Friberg et al., 2002); • Regional-scale investigations: e.g. Juhlin and Pedersen (1987); Milkereit and Green (1992); Wu et al. (1995); Roy and Clowes (2000); Goleby et al. (2002); Eaton et al. (2003); Goleby et al. (2004); Tryggvason et al. (2006); • Mineral exploration: e.g. Singh (1984); Perron and Calvert (1998); Adam et al. (2000); Milkereit et al. (2000); Pretorius et al. (2000); Eaton et al. (2003); Hammer et al. (2004); • Detection of fracture zones: e.g. Green and Mair (1983); Juhlin (1990); Kim et al. (1994); Juhlin (1995); Juhlin and Palm (1999); Bergman et al. (2002); Schmelzbach et al. (2007).

29 Seismic-reflection investigations of the large-scale crustal structures down to the crust-mantle boundary (Mohoroviciˇ c´ discontinuity) at around 40 km depth (Christensen and Mooney, 1995) have long been primarily of academic interest. Hard-rock seismic-reflection studies to image the upper crust have gained increased attention in recent years as its potential for (1) mineral exploration (e.g., Milkereit et al., 1996; Pretorius et al., 2000; Salisbury et al., 2000; Eaton et al., 2003) and (2) mapping fracture zones at nuclear-waste disposal site studies was recognized (e.g., Green and Mair, 1983; Juhlin and Stephens, 2006; Schmelzbach et al., 2007). Since the IBERSEIS profile crosses one of the arguably most important and largest volcanogenic massive sulfide (VMS) metallogenic provinces in the world and its acquisition parameters are similar to parameters of regional-scale investigations at mining camps, the IBERSEIS data acquisition and processing can be compared with ore-deposit studies such as Tryggvason et al. (2006).

4.2 Seismic-Reflection Data Acquisition Because the acquisition of seismic data involves discrete sampling of the seismic wavefield in time and space, 1D and 2D (spatial) aliasing criteria have to be considered. Whereas the sampling of data in time with more than two samples per cycle, i.e. below the Nyquist frequency, is technically easily ac- complished, 2D aliasing is more often a problem. In order to avoid spatial aliasing, seismic data with a maximum frequency fmax, a maximum event dip φmax, and a minimum medium velocity Vmin need to be sampled with a maximum trace spacing Δx (Yilmaz, 2001): v Δx ≤ min . (4.1) 4 fmax sinφmax Events with steeper dip or higher frequency will be spatially aliased. The 2D aliasing criteria dictates the trace spacing for un-aliased CMP-stacked seismic sections, but also applies to trace spacing of prestack data where slow- traveling surface waves can be aliased. For example, using typical values for the IBERSEIS data, to image a 45◦ dipping structure in a medium with with Vmin = 4500 m/s and fmax = 40 Hz, a maximum CMP spacing of around 40 m, corresponding to a receiver interval of maximum 80 m, is necessary (Compare with a receiver spacing of 35 m employed during the IBERSEIS data acquisition; see Table 2.1). Furthermore, in designing seismic-reflection surveys in hard-rock environments, it is necessary to accommodate the following requirements: • Generally, hard-rock seismic data show a low S/N ratio, hence, the acquisition of high-fold data is necessary to increase signal strength by stacking; • Enough near-source traces to image the shallowest features and long offsets for stacking-velocity determination of the deepest events and imaging steeply dipping interfaces are required;

30 • Imaging deep and steeply dipping reﬂectors requires the acquisition of long seismic lines.

4.3 Challenges in Hard-Rock Seismic-Reflection Data Processing The acquisition of common-midpoint (CMP) data has become standard in seismic-reflection surveying (Mayne, 1962). Traces are sorted by their common source-receiver midpoint and, in case of horizontal reflectors, energy recorded by these traces is reflected from subsurface points laying directly below the CMP. Stacking will increase the strength of the coherent reflections embedded in random noise. Simple CMP stacking may fail to enhance reflections at the expense of random noise and may not provide crisp images if: • reflectors dip and events of different dip cross as stacking velocities are dip-dependent; • the data is affected by coherent noise (e.g., source-generated noise); • reflections do not align along hyperbolic summation paths due to static shifts, then stacking will act as high-cut filter (Marsden, 1993a); • a straightforward trace midpoint to CMP sorting is not possible as in the case of crooked-line data. These issues are discussed in the following section. Other aspects that may lead to failure of simple CMP stacking are e.g. when the small-spread assumption of hyperbolic NMO fails due to long offsets or when velocities change significantly in the lateral direction (Yilmaz, 2001).

4.3.1 Crooked-Line Acquisition Geometries If 2D seismic-reflection surveys are forced to follow existing winding roads for logistical and economic reasons, crooked-line acquisition geometries result. These recording geometries challenge standard 2D seismic reflection processing designed for straight-line geometries and require adapted crooked-line processing (e.g., Paper I; Wu, 1996). The areal distribution of sources and receivers of crooked-line acquisition geometries results in trace midpoints covering an area surrounding the survey line. For the seismic-reflection data processing, a straight or curved processing line (or a series of line segments) is usually traced through the cloud of midpoints, CMP bins are constructed, and those traces whose midpoints fall within a bin are gathered to a CMP ensemble. For example, CMP bins can extend in the lateral (crossline) direction either perpendicular to the processing line or parallel to the dominant strike of the subsurface structures (Sheriff and Geldart, 1995). Wu (1996) demonstrated that binning the trace midpoints along one (or several) straight lines is superior to slalom-line binning in providing a uniform CMP fold and offset distribution. Furthermore, straight-line

31 binning yields a processing line that overall better satisﬁes the assumptions underlaying 2D processing than slalom-line binning. The areal distribution of sources and receivers yields ray paths sampling a 3D volume and reﬂections from a dipping interface in a constant-velocity medium follow the 3D traveltime equation (Yilmaz, 2001):

4h2(1 − sin2 φ cos2 θ) t2 = t2 + , (4.2) 0 V 2 where θ is the azimuth angle between the structural dip direction and the direction of the profile, φ is the dip angle, t is the two-way traveltime from the source to the reflection point on the dipping reflector and back to the receiver, t0 is the two-way zero-offset time associated with the normal-incident ray path at the midpoint location, and V is the medium velocity. The expres- sion sinφ = sinφ cosθ represents the apparent dip and illustrates the combined effect that the interface dip and the source-receiver azimuth have on the traveltime. Traveltime deviations introduced by crooked-line acquisition geometries can be split into short-wavelength crooked-line effects that lead to misaligned reflections in CMP gathers, and into long-wavelength distortions due to an acquisition line or line segments running oblique to the general dip of the subsurface structures.

Crossdip Corrections If the source-receiver azimuths, and hence θ in Equation 4.2, vary from trace to trace within one CMP gather, reflections from a dipping interface do not align along a single hyperbolic moveout curve and degrade the stack. A number of researchers have addressed the issue of traveltime variations deteriorat- ing stacked sections and have proposed schemes to correct for the misalignment of reflections to improve the stack quality. Generally, the apparent dip is split into an inline and a crossline component and then the applied corrections are a function of the distance between the CMP bin center and the actual trace midpoint. Larner et al. (1979) estimated a time-variant crossdip correction simultaneously with residual-static corrections. Du Bois et al. (1990) applied an independently derived dynamic (time-variant) correction to traces within a CMP bin to flatten NMO-corrected reflections before stacking. Wu et al. (1995) computed a static crossdip correction for data portions or the entire stack and judged the stack-quality im- provement visually. Common for all schemes is that the crossdip correction term is CMP-consistent and corrects for a linear misalignment of the NMO-corrected traces in a CMP gather. Source-receiver azimuth variations within a CMP gather are assumed to be very small, implying that the traveltime distortions are solely due to the midpoint spread. Furthermore, none of the schemes account for the effects that the reflector dip in conjunction with varying

32 source-receiver offsets has on the traveltime. Thus, these approaches may not be appropriate in combination with DMO or prestack migration. Alternatively, Nedimovi´c and West (2003a) derived a processing ﬂow involving NMO corrections, DMO corrections and cross-dip moveout corrections, which essentially are CMP-based time-dependent linear-moveout corrections in the crossline direction for optimum stacking.

Oblique Lines If survey or processing lines run oblique to the dip of the main structures, reﬂector dips appear shallower than they actually are (Figure 4.1). Lynn and Deregowski (1981) projected stacked crooked-line data onto a line thought to be the dip direction of the 2D target horizon before poststack migration. The CMP projection routine proposed in Paper I and illustrated in Figure 4.1 can be seen as extension of the Lynn and Deregowski (1981) procedure for prestack migration.

3D Processing of Crooked-Line Data As crooked-line surveys sample a 3D subsurface, the seismic-reflection data can be migrated into a 3D volume (e.g., Nedimović and West, 2002, 2003b). However, because of the missing sampling in the crossline/perpendicular direction, crooked-line seismic-reflection data may be too sparse for 3D prestack or poststack migration and resultant images may be significantly affected by migration artifacts such as cross-line migration smiles and difficult to inter- pret.

4.3.2 Effects of the Near-Surface Layer Hard-rock terranes are often covered by one or several layers of heterogeneous unconsolidated sediments or weathered rock with generally high attenuation and low velocities resulting in a high velocity contrast at the boundary to the crystalline rock (Stümpel et al., 1984). These near-surface layers can have dramatic effects on the recorded seismic wavefield by (1) causing significant traveltime delays (static shifts) and (2) leading to strong source-generated noise contamination of the recorded wavefield.

Refraction-Static Corrections Low velocities of roughly 500–2000 m/s (Stümpel et al., 1984) with highly variable layer thickness delay the reflected wavefield when traveling through the near-surface. Thus, the application of refraction-static corrections (e.g., Hampson and Russell, 1984; Cox, 1999), which compensate for the time delays, is usually crucial to preserve high frequencies when processing seismic- reflection data from high-velocity crystalline environments (e.g., Juhlin, 1995; Wu et al., 1995; Adam et al., 2000; Schmelzbach et al., 2007).

33 Figure 4.1: Projection of CMPs from the CMP binning line onto the straight migration line. (a) Plan view of a portion of the IBERSEIS profile before CMP projection. (b) CMPs are projected perpendicular from the CMP binning line (dark gray line) onto the straight migration line (black line). (c) Synthetic-data stacked section with CMPs located on the CMP binning line. The unmigrated model reflector 5 with dip direction parallel to the migration line is overlaid (black line). For display purposes, reflector 5 is drawn 50 ms below its actual position. The CMP binning line changes orientation at CMP 1903, marked by the dashed line. The apparent dip of reflection A originat- ing from reflector 5 changes at the kink. (d) Synthetic-data stacked section after CMP projection. Reflection A and model reflector 5 are practically coincident as the projection plane and the reflector dip are parallel. Smeared energy (B) is possibly due to source-receiver azimuths not parallel to the orientation of the CMP binning line. Figure corresponds to Figure 4 in Paper I, see Paper I for details of synthetic data.

Source-Generated Noise Source-generated noise (i.e., direct, refracted, guided, and surface waves; Robertsson et al., 1996a,b; Roth et al., 1998) arises mostly from the interactions of the seismic wavefield with the complex near-surface region (Levander, 1990). Varying free-surface and layer interface topography cause scattering and mode conversions. Furthermore, the low-velocity layer bounded by a high impedance contrasts may be an efficient waveguide. High attenuation controls the amplitude decay and frequency depletion of the source-generated noise with increasing traveltime (Holliger and Robertsson, 1998). Depending on the properties of the near-surface layer, coherent source-generated noise can dominate the earliest recorded portion of the seismic wavefield.

34 Source-generated noise may interfere with signals and, in particular, can seriously overwhelm shallow reflections. Without appropriate suppression of source-generated noise during seismic processing, spurious coherent events, which can be misinterpreted as reflections, may be generated on final stacked sections. Generally, top-mute functions eliminate first-arrival wavetrains and surface-waves can, generally, efficiently be separated from reflections by frequency filtering (see e.g., Wu et al., 1995, Paper II). However, guided waves and direct SV -wave trains may have similar dominant frequencies as reflections, hence the data has to be transformed to a domain where signal and noise are separable (Ulrych et al., 1999; Yilmaz, 2001). Examples of noise separation in domains other than the t −x domain in hard-rock seismic investigations are filtering in the f − k domain (Juhlin, 1995) or processing in the τ − p (Radon) domain (Schmelzbach et al., 2007), or wavelet-based approaches (Welford and Zhang, 2004). Even though the near-surface layer can introduce severe problems in crystalline seismic-reflection processing, neither refraction-static corrections nor source-generated noise were a major obstacle in processing the IBERSEIS data. First of all, the study area exhibits a low and fairly smooth topography and it is likely that the near-surface layer is thin and homogeneous compared to e.g. surveys from Scandinavia or Canada. Figure 4.2 displays a typical IBERSEIS source gather characterized by distinct P-wave and SV -wave first arrivals and high-amplitude low-frequency surface waves (events a, bcon Figure 4.2a). Deconvolution and frequency band- pass filtering successfully suppressed the surface-wave phases and most of the SV -first arrivals. Subsequent application of top-mute functions cancelled the P-wave first arrivals, leaving reflections within the 20–40 Hz frequency band (events d on Figure 4.2c,d).

4.3.3 Dip-Moveout Corrections, Prestack, and Poststack Migration Reflectors in hard-rock environments are often short, steeply dipping, and be- have rather like point scatterers than specular reflectors. Hence, the recorded wavefield is characterized by crossing reflections and diffractions. Seismic migration is an inverse operation involving rearrangement of recorded seismic information so that reflectors and diffractors are plotted at their true position. Commonly, seismic-migration algorithms make use of some approximated form of the scalar wave equation (see Equation 3.2). The aim of the following section is to briefly present dip moveout (DMO) corrections, prestack and poststack (zero-offset) migration schemes, and demonstrate the equivalence of employing either DMO plus zero-offset migration or prestack migration in the frame of time migration (as opposed to depth migration). The equivalence of these schemes was exploited in Paper I

35 Figure 4.2: (a) Raw source gather recorded at station number 848 (corresponding to CMP 1458). The various arrivals are interpreted as follows: a – Linear P-wave trains with an apparent velocity of ∼ 5000 m/s, b – SV-wave trains with an apparent velocity of ∼ 2900 m/s, c – surface-wave trains with an apparent velocity of ∼ 1800 m/s. (b) RMS Power spectrum of (a) plotted up to Nyquist frequency of 125 Hz. Signal energy is concentrated within the 10–40 Hz band. (c) As for (a) after application of the preprocessing (see Paper II), which reveals a band of high-amplitude events d between 1 and 2 s travel time. (d) RMS Power spectrum of (c). For display purposes, only every second trace is shown in (a) and (c) and each trace is normalized with respect to its RMS-amplitude value. Figure corresponds to Figure 2 in Paper II.

to ﬁnd a suitable prestack migration scheme for the crooked-line IBERSEIS data, which was later applied in Paper II. Generally, seismic rays traveling through a medium of varying velocity are bent as required by Snell’s law (Equation 3.8). If velocity changes are only depth-dependent, the velocity structure can be represented by layers of constant velocity and Dix’s equation (Dix, 1955) allows the approximation of bending rays by straight rays. Thereby, the 1D velocity layering from the surface to a particular depth point is replaced by one rms-velocity value Vrms. The time-migration routines discussed here are all based on this straight-ray approximation that generally also holds for small lateral velocity variations (Yilmaz, 2001).

Kirchhoff Poststack (Zero-Offset) Migration Huygens’ secondary source principle forms the basis of diffraction- summation methods like Kirchhoff migration (e.g., Schneider, 1978; Yilmaz, 2001). The signature of a Huygens’ secondary point source is a hyperbola in

36 the t − x plane. Huygens’ secondary sources do not emit energy uniformly in all directions but their waveforms include an obliquity (directivity) factor, and a wavelet shaping factor, which both ensure correct amplitude cancellation of interfering waveforms. Diffraction summation that includes obliquity, wavelet shaping factor and also a spherical-spreading correction is termed Kirchhoff migration. The 2D output image Pout (x0,z = Vτ/2,t = 0) at an output subsurface location (x0,z), where t is the input time, τ is the event time in the migrated position, and V is the medium velocity, is computed from a 2D zero-offset waveﬁeld Pin(x,z = 0,t) recorded at the surface (z = 0) by summation over a spatial aperture (Yilmaz, 2001): Δ θ √ = x √cos ω ∗ . Pout π ∑ Pin (4.3) 2 x Vrmsr

2 2 Here, Vrms is the rms velocity at the output point, r = (x − x0) + z is the distance between input (x,0) and output (x0,z) location (the hyperbolic trajectory), Δx is the discretization interval (e.g., the CMP interval), and the asterisk denotes convolution.√ Equation 4.3 includes an obliquity factor√ cosθ, a spherical spreading term Vrmsr, and a wavelet shaping factor ω. In order to enhance diffracted energy at the expense of specular reﬂections, I modiﬁed Equation 4.3 in Paper II by replacing the standard weighted summations of data by coherency estimates based on semblance computations (Neidell and Taner, 1971):

t+mΔ N 2 1 ∑ ∑ = gti S = t i 1 , (4.4) T t+mΔ N ( )2 N ∑t ∑i=1 gti where gti is the amplitude of trace i at time t and N is the number of traces. The semblance ST denotes the ratio of the total energy of the stack within a gate of length (1 + mΔ) to the sum of the energy of the component traces within the same time gate. For reflections, only a few, but high-amplitude, samples along the hyperbolic trajectory contribute to the final migrated output sample. In contrast, a large number of traces record energy from a point diffractor, which scatters energy in all directions. Replacing the standard weighted summations along the hyperbolic trajectory by semblance computations will accentuate diffracted and suppress reflected energy.

Dip-Moveout (DMO) Corrections and Prestack Time Migration (PSTM) The following discussion will focus on kinematic (geometric) aspects (as opposed to dynamic/amplitude behavior) of dip-moveout (DMO) corrections and prestack time migration (PSTM) of common-offset sections . Conventional NMO corrections and CMP stacking fail to preserve conﬂict- ing dips since stacking velocities are dip-dependent (Levin, 1971), with the optimum stacking velocity Vstk being a function of the dip φ: Vstk = Vrms/cosφ.

37 Furthermore, reflector dip introduces reflection-point dispersal (Deregowski and Rocca, 1981). Thus, poststack migration of CMP stacked seismic sections may not produce crisp images in the presence of conflicting dips (Yilmaz and Claerbout, 1980). In contrast, the combination of NMO and DMO corrections maps nonzero-offset data to zero-offset (Deregowski, 1986; Yilmaz, 2001). Subsequently, a common-offset section mapped to zero-offset can be migrated either before or after stacking using a zero-offset migration algorithm. The effect of DMO and PSTM processing in the common-offset t − x domain is illustrated on Figure 4.3. The traveltime to a point scatterer (or reflection point on a dipping interface) D on a common-offset section with a source-receiver offset 2h is marked by T at the midpoint between source S and receiver R on Figure 4.3a. Normal-moveout corrections using the (dip- independent) medium velocity V map the energy at T to Tn (Bancroft, 2000): 4h2 T 2 = T 2 − . (4.5) n V 2 Subsequent DMO corrections map the energy to Td along the DMO-ellipse. The DMO-ellipse is restricted by the horizontal boundaries of the source and receiver and its kinematic equation is given by (Bancroft, 2000):

2 2 x + t = , 2 2 1 (4.6) h Tn where x is the distance along the CMP axis and t is time. Dip-moveout corrections spread energy along the DMO ellipse only to dips less then 45◦. After DMO corrections, the common-offset section is approximately equivalent to a zero-offset section (Yilmaz, 2001). Final poststack migration of the DMO-corrected common-offset section maps the energy at Td to its true position D along a semi-circular trajectory. Equation 4.6 gives the impulse response associated with integral DMO corrections (e.g., Deregowski and Rocca, 1981; Yilmaz, 2001) and is used here for demonstrating the DMO ellipse, whereas an f -k common-offset DMO routine (Hale, 1984) was applied in Paper I and Paper II. In contrast, prestack migration maps the energy at T directly to D, thereby spreading energy along an ellipse with a semi-major axis of VT/2 (corresponding to half the total ray length between S and R) and with the source and receiver positions at the foci of the ellipse (Figure 4.3; Bancroft, 2000):

2 2 4x + t = . 2 2 2 1 (4.7) T V Tn Hence, NMO, DMO plus zero-offset migration of the common-offset section (or zero-offset migration of the DMO-corrected CMP stacked section) is equivalent to direct prestack migration for constant velocities, but this equivalence may also be valid within the bounds of velocity variations judged to be

38 Figure 4.3: (a) Common-offset section recording of a scatter point located at D (gray dot marks spike amplitude). Horizontal axis is distance x along the CMP-axis, vertical axis is time (while no difference is made between recording time and event time in the migrated position), S and R mark source and receiver position, respectively, which are separated by a source-receiver offset of 2h. T marks the original recording time, NMO corrections map the spike amplitude at T to Tn, DMO corrections to Td. Final zero-offset migration map the energy to its true position D. (b) as (a) except showing the prestack migration ellipse, which maps energy at Tn directly to D (NMO corrections placing energy at Tn is an inherent step in the prestack migration routine). The migration path from (a) is plotted in light gray and illustrates the equivalence of NMO, DMO, plus zero-offset migration, and prestack migration for constant velocity. Paths are exact only for constant-velocity media. Figure modiﬁed from Bancroft (2000). acceptable for time migration (i.e., where the rms-velocity assumption is ap- plicable; Yilmaz, 2001). While migration procedures including DMO corrections are practical solutions to the problem of conﬂicting dips with different stacking velocities, prestack migration is a more rigorous processing scheme.

5. Review of Seismic-Refraction Techniques

The use of seismic tomography began in the 1970’s (e.g., Aki and Lee, 1976; Aki et al., 1976) and is nowadays a very popular technique for imaging subsurface structures at all scales. In this section, I review applications of active- source traveltime tomography and discuss some relevant theoretical aspects of ﬁrst-arrival traveltime tomography.

5.1 Applications Active-source seismic-refraction experiments have been used extensively in a wide range of applications:

• Imaging the crustal and upper mantle structure (<400 km) using so called ’Peaceful Nuclear Explosions’ (PNE), e.g. Nielsen et al. (1999, 2003); • Continental and oceanic crust studies: e.g. Zelt and Smith (1992); BABEL Working Group (1993); Gohl and Pedersen (1995); Riahi et al. (1997); Zelt and Barton (1998); Darbyshire et al. (1998); Day et al. (2001); Rawlinson et al. (2001); Thybo et al. (2003); Zelt et al. (2006b); • Investigating the shallow crust for environmental and engineering studies, mining or petroleum exploration: e.g. White et al. (1992); Hughes et al. (1998); Lanz et al. (1998); Morey and Schuster (1999); Bauer et al. (2003); Bergman et al. (2004, 2006); Zelt et al. (2006a); Heincke et al. (2006); Flecha et al. (2006); Marti et al. (2006); • Mapping the top of the basement or estimating refraction-static corrections for seismic-reﬂection processing based on simple plane or irregular layer models: e.g. Hampson and Russell (1984) and reviews in Marsden (1993a,b); Sheriff and Geldart (1995); Cox (1999); Yilmaz (2001).

5.1.1 Algorithms and Inversion Approaches A number of algorithms for interpreting active-source seismic-refraction data have been developed, with the most widely used ones being inversion schemes by Hole (1992), Zelt and Smith (1992), Zelt and Barton (1998), and Tryggva- son et al. (2002). Traveltime inversion schemes involve either a ’pure’ tomographic approach, inverting only the ﬁrst-arrival travel times (e.g., Hole, 1992; Zelt and Barton, 1998), or seismic-refraction and wide-angle reﬂection travel-

41 times are inverted simultaneously (e.g., Zelt and Smith, 1992). The first, ’pure’ tomographic approach provides a continuously varying velocity field. In contrast, including reflection traveltimes allows for resolving both interfaces and velocity variations within layers, e.g. inverting PmP-traveltimes to resolve the Moho discontinuity structure (e.g., Riahi et al., 1997). In Paper III, the simplest, i.e. most featureless, model appropriate for the assigned data errors in accordance with Occam’s principle of minimum structure (Constable et al., 1987) was sought employing the Zelt and Barton (1998) inversion approach. Thereby, the simplest model is equated with the smoothest model (e.g., Scales et al., 1990).

5.1.2 S-Wave Traveltime Inversion Using shear (S)-wave traveltimes, S-wave velocity models can be derived employing the same modeling algorithms and methods as mentioned above for P-wave traveltime inversion. Recording both vertically (SV ) and horizontally- polarized (SH ) shear waves requires three-component measurements, which are standard in Earthquake studies for simultaneous inversion of P- and S- wave arrival times (e.g., Eberhart-Phillips, 1990; Tryggvason et al., 2002). In contrast, three-component receivers are less commonly used in active-source experiments (e.g., Gohl and Pedersen, 1995; Darbyshire et al., 1998). Occa- sionally, SV -waves are observed on vertical-receiver seismograms, as in the case of the IBERSEIS data (see even b on Figure 4.2a), and SV -wave ﬁrst- arrival times can be inverted for S-wave velocity models (e.g., White et al., 1992; Bauer et al., 2003)

5.1.3 Seismic-Refraction vs. Seismic-Reflection Acquisition Ray paths of refracted waves are curved when traveling from a surface source to a surface receiver through the Earth, which has a velocity that primarily increases with depth. Therefore, the source-receiver offsets generally have to exceed the penetration depth for a seismic-refraction experiment. In contrast, near-vertical seismic-reflection experiments employ generally smaller source-receiver offsets than the target depth and, consequently, seismic-reflection offsets are shorter than offsets for seismic-refraction measurements. Even though the inversion of first-arrival traveltimes extracted from near-vertical seismic-reflection data can only resolve the subsurface to a limited depth, traveltime tomography can provide complementary velocity information about the shallowest subsurface, which is usually difficult or impossible to image with seismic-reflection data (e.g., Schmelzbach et al., 2005). Source-generated noise often dominates the shallowest portions of the observed wavefield masking potential very shallow reflections (see Chapter 4.3.2).

42 In the context of high-resolution seismic-reflection studies to map fracture zones, Bergman et al. (2004, 2006) resolve the cover thickness with tomographic methods, and White et al. (1992) invert P- and S-wave first-arrival traveltimes along a seismic-reflection profile crossing the Kapuskasian Uplift.

5.2 First-Arrival Traveltime Tomography The first-arrival traveltime tomography method of Zelt and Barton (1998) is used in Paper III to resolve a minimum-structure velocity model of the shallow crust along the SPZ-part of the IBERSEIS line. While many aspects of the inversion scheme and its application are discussed in Paper III, some additional background information about the inversion approach and the employed algorithms is given here. First-arrival traveltime tomography depends on the general principles of inverse theory. Geophysical inverse theory is discussed in e.g. Menke (1989), Parker (1994), and Tarantola (2005), while e.g. Nolet (1987b) and Rawlinson and Sambridge (2003) provide reviews on seismic traveltime tomography. Generally, the aim of an inversion scheme is to invert an observed data set dobs (e.g., traveltime measurements) to determine a set of model parameters s representing some property of the subsurface (e.g., the seismic velocity). Data and model parameters are related by some known function dpred = g(s). Hence, theoretical measurements dpred can be predicted for a particular set of model parameters. The difference dobs − g(s) provides some estimate of how well the model parameters s predict the observed data. The tomography inverse problem is then to find a model parameter set that minimizes the difference between observed and predicted data. Following Oldenburg and Li (2005), the inverse problem suffers from the fact that the physical-property distribution is a continuous function with in- finitely many degrees of freedom, but only a finite number of inaccurate data are measurable. As a consequence, the inverse problem is non-unique. Fur- thermore, the inverse problem is unstable in that small data perturbations can map into large structures in the recovered model. Because the inverse problem is non-unique and unstable, it needs to be formulated in a way that includes additional information, which are regularization constraints and/or prior knowledge about the model. Then, the inverse problem is solved as an optimization problem. Essential steps in any inversion scheme are (1) the forward calculation solving dpred = g(s), here computing the first-arrival traveltimes of seismic waves by solving the wave equation; (2) the inversion procedure adjusting the model parameters (here the velocity structure) to better match the observed data, and (3) solution assessment.

43 5.2.1 Forward Modeling The Zelt and Barton (1998) inversion approach uses a modified form of Vidale (1990)’s method to compute first-arrival traveltimes on a uniform velocity- node grid. The Vidale (1990) scheme employes a finite-difference approximation to the eikonal equation (Equation 3.7). Traveltimes are computed progres- sively away form a source on the sides of an expanding cube. Zelt and Barton (1998) modified the method to be also valid in the case of large gradients and velocity contrasts following Hole and Zelt (1995). The modified code further involves a sorting technique ensuring that the expanding cube tracks the shape of the true wavefronts as close as possible. Ray paths, which are an essential input for the inversion step, are found by following the steepest gradient of the time field from a receiver back to the source (Vidale, 1988). Other fast finite-difference eikonal solvers have also been developed e.g. by Podvin and Lecomte (1991).

5.2.2 Solving the Inverse Step Traveltime inversion is a non-linear problem because the ray paths depend on the velocity model (see Equation 3.9). One approach to solve the inverse problem can be formulated as a problem of minimizing an objective function consisting of a data residual term and one or more regularization terms. The objective function O(s) used in Paper III reads as (e.g., Zelt et al., 2006a):

( )=δ T −1δ + O s t Cd t + λ[α( T T + T T )+ s Wh Whs szs Wv Wvs +( − α)( − )T T ( − )] , 1 s s0 Wp Wp s s0 (5.1) where δt is the traveltime misfit vector, s is the model slowness vector, s0 is the starting slowness, Cd is the data covariance matrix containing the estimated picking uncertainties, Wh and Wv are the horizontal and vertical roughness matrices (i.e., second-order spatial finite-difference operators normalized by the prior slowness), respectively, and Wp is the perturbation weighting matrix; the perturbation weighting matrix is a diagonal matrix of the reciprocal starting slowness values, measuring the relative perturbation of the current model from the starting model. The trade-off parameter λ controls the overall regularization, while the relative weight of the vertical and horizontal smooth- ing are controlled by the parameter sz. Additionally, the parameter α governs the relative weight of the second-derivative and perturbation regularization, which is a modification to the original Zelt and Barton (1998) method (e.g., Zelt et al., 2006a). The first term in Equation 5.1 measures the traveltime misfit weighted by the data covariance matrix Cd. The second and third term in Equation 5.1 may be seen as a combination of two different regularization frameworks: Occam’s principle of minimum structure and a Bayesian approach. The regularization

44 approach based on Occam’s principle of minimum structure (Constable et al., 1987) seeks a solution with the least structure necessary to fit the data. The rea- soning behind applying Occam’s principle is that because of the ill-posedness of the inverse problem, we can find arbitrarily complicated models that fit the data, but it is not possible to make the model arbitrarily simple. In a Bayesian inversion approach, the knowledge and incorporation of a priori information assumes great importance. The effect of the third term is to encourage solutions that are close to the starting model s0. The goal of the inversion approach is to find a set of model parameters s that minimizes the objective function in Equation 5.1. We are only able to minimize O(s) in one step if the objective function is a quadratic function; under that condition, taking the gradient of O(s) will yield a linear equation system that can be solved. However, the optimization problem is non-quadratic in that the forward problem is non-linear. Thus, an iterative procedure is required and model (and ray paths) are updated over a series of iterations: sn+1 = sn + δs. To minimize the objective function at each iteration, Equation 5.1 is expanded in a Taylor series at the point of the current model sn. The derivative of the Taylor series expansion with respect to the model perturbation δs, ignoring second- and higher-order terms, is set equal to zero and solved for the model perturbation δs (Gauss-Newton equation; for details see e.g., Rawlinson and Sambridge, 2003; Oldenburg and Li, 2005). Thereby, a partial-derivative (sensitivity) matrix G = δg(s)/δs is required with elements Gij = lij, where lij is the ray segment length of the ith ray in the jth cell from the forward computation. Zelt and Barton (1998) solve the resultant matrix equation of dimension M × M iteratively with the LSQR-variant of the conjugate-gradient algorithm (e.g., Paige and Saunders, 1982; Nolet, 1987a).

5.2.3 Assessment of the Solution The main motivation of model assessment is to determine in what specific ways the solution is non-unique. Following Zelt (1999), methods assessing models can be separated into two different classes: (1) indirect model assessment techniques that involve deriving resolution measures from the final model without computing additional models; (2) direct model assessment methods attempt to derive alternative models that fit the real data equally well. Indirect assessment techniques are e.g. presenting model statistics (e.g., Ta- bles 2 and 3 in Paper III) or ray diagrams (Figures 8–12 in Paper III). Further- more, resolution and covariance matrices can be computed (e.g., Tarantola, 2005) for a quantitative assessment of model quality (e.g., Darbyshire et al., 1998). However, the applicability of linear assessment theory for an iterative solution of a non-linear problem may be less meaningful (e.g., Rawlinson and Sambridge, 2003; Kalscheuer and Pedersen, 2007).

45 Checkerboard Tests and Spatial Resolution (Indirect Assessment Methods) Checkerboard tests are a commonly applied indirect assessment technique to estimate the spatial resolution in traveltime tomography (e.g., Day et al., 2001). These tests involve the inversion of synthetic traveltimes computed for a model consisting of the final model (or background model) and a superimposed alternating pattern of positive and negative anomalies (e.g., Zelt, 1998). The final model (or background model) is then used as the starting model. Finally, the difference (or semblance) between the recovered model and the checkerboard model is an indication for the resolvability of features of a specific size. Figure 5.1 displays a series of checkerboard tests run for Profile P2 discussed in Paper III. One limitation of checkerboard tests arises from the fact that the ray coverage depends on the velocity distribution. Using small and sinusoidal anomalies reduces the difference between the ray paths for the final model and the ones for the checkerboard model (Zelt, 1998). Furthermore, averaging the result of a number of checkerboard patterns will reduce the de- pendance on the ray paths. Inverting for a set of differently sized checkerboard patterns allows a spatial resolution estimate to be obtained (see Figure 5.1). From a theoretical point of view, to a first approximation, the smallest features that can be expected to be recover in ray-based tomography have dimensions in the order of the first Fresnel Zone (see Equation 3.14 and Chap- ter 3.1.4). As a result, heterogeneities within one Fresnel zone are averaged, but overlapping Fresnel zones from different source-receiver pairs may allow the resolution of details that are smaller than the Fresnel zone’s dimension (Zelt et al., 2006a). Direct model assessment techniques aim at finding alternative models that fit the data equally well, and, thus, allow to better estimate the absolute bounds on a model parameter. One example of an alternative model that satisfacto- rily explains the SV -wave traveltime data inverted in Paper III is presented in Figure 5.2. Whereas identical P-wave models were used for computing the VP/VS-ratio in Figure 5.2a and b, two different SV -wave models, which both fit the data equally well, were employed. Figure 5.2b indicate that the shallow layer of high VP/VS-ratio is not required by the data.

46 Figure 5.1: (a)–(d) Proﬁle P2 recovered checkerboard anomaly patterns for four anomaly sizes: 2, 2.5, 3, and 4 km, respectively. The boundaries of the true checkerboard pattern are overlaid. Interval of thick and thin contour lines is 5 and 2.5%, respectively, with negative-valued contour lines drawn dashed. (e)–(h) Semblance between true and recovered patterns in (a)–(d). Contour interval is 0.1, the 0.7 contour, indicating good resolution, is white. Depth is measured from sea level, vertical exaggeration is 1 : 3. Figure corresponds to colored Figure 16 in Paper III.

Figure 5.2: (a) Ratio of P to SV -wave velocity along proﬁle P2/S2. (b) Alternative / VP VSV -ratio based on a SV -data inversion using the P-wave ﬁnal model of P1 scaled by 1/1.858 as starting model. Interval of thick and thin contour lines in (a) and (b) is 0.05 and 0.025, respectively. Contour lines for 1.95 and higher are white. Depth is measured from sea level, vertical exaggeration is 1 : 3. Figure corresponds to colored Figure 18 in Paper III.

6. Summary of Papers

6.1 Paper I: Prestack and Poststack Migration of Crooked-Line Seismic Reflection Data: A Case Study from the South Portuguese Zone Fold Belt, Southwestern Iberia 6.1.1 Motivation The SPZ seismic-reflection images of Simancas et al. (2003) indicate a complex shallow crust (depth <15 km) with reflectors of limited lateral extent and varying dips of up to 50◦. Nevertheless, the original seismic-reflection processing only involved CMP stacking and poststack migration, which are not able to provide crisp images in the presence of conflicting dips with different stacking velocities (Levin, 1971). Dip-moveout (DMO) corrections, in combination with zero-offset migration, or prestack migration are necessary for correct imaging of conflicting events (e.g., crossing reflections and diffractions; Deregowski, 1986; Milkereit et al., 1994; Yilmaz, 2001). However, crooked- line acquisition geometries, such as the survey geometry of the IBERSEIS line, may violate underlaying assumptions of 2D prestack and poststack imaging routines, requiring adapted crooked-line imaging schemes. Here, we compare three different crooked-line prestack migration schemes with the aim to decide upon a reliable imaging procedure to resolve the shallow SPZ crust.

6.1.2 Methods We tested the following three crooked-line prestack migration procedures, which include a CMP-projection scheme, presented in a later paragraph, on synthetic and the IBERSEIS ﬁeld data (thereafter referred to as schemes 1–3):

1. NMO corrections, DMO corrections, CMP stacking, CMP projection, and poststack time migration; 2. NMO corrections, DMO corrections, CMP projection, zero-offset time migration of the common-offset gathers, and CMP stacking; 3. CMP projection, prestack time migration in the common-offset domain, and CMP stacking.

Because seismic velocities in hard-rock environments are usually observed to vary only weakly in vertical and horizontal directions, these velocity variations commonly are within the bounds acceptable for time (as opposed

49 to depth) migration. Thus, migration of DMO-corrected stacked sections (scheme 1) or common-offset sections (scheme 2) is largely equivalent to full prestack migration of nonzero-offset data (scheme 3; Yilmaz, 2001). While migration procedures including DMO corrections are practical solutions to the problem of conflicting dips, prestack migration is a more rigorous processing scheme. First, the data preprocessing (e.g., residual-static corrections) and DMO corrections (for scheme 1 and scheme 2) were performed along six straight CMP-binning line segments that follow the actual acquisition geometry as closely as possible (e.g., Wu et al., 1995; Wu, 1996). However, the same dipping reflector will show different apparent dips if imaged on differently oriented CMP-binning line segments. In order to reduce the influence of changes in apparent dip and obtain a more consistent representation of the subsurface, we projected„ in a second step, the CMP-binning line segments onto a single straight line, here referred to as the migration line. Crucial steps of the CMP projection scheme are: 1. Definition of the straight migration line oriented parallel to the dip direction of the main geological units and deviating as little as possible from the CMP binning line. 2. The original CMP coordinate system is translated and rotated to a new CMP coordinate system with its origin at the beginning of the migration line and its x-axis oriented parallel to the migration line. The original CMP coordinates (x,y) are transformed into new CMP coordinates (x ,y ), where the x value is proportional to the distance from a CMP to the beginning of the migration line and the y value corresponds to the perpendicular distance from a CMP to the migration line. 3. The y values of all CMPs are set to zero, corresponding to a translation of a CMP from its original position along a perpendicular line onto the migration line. 4. The projected traces are sorted into 17.5m wide bins (corresponding to the original CMP bin size) with traces falling within the same bin being stacked. We projected either common-offset or stacked sections after the DMO- application, but before zero-offset migration (scheme 1 and scheme 2), or before prestack migration (scheme 3) onto the migration line. An essential property of the projection scheme is that the original source-receiver offset is maintained. The projection scheme is based on the assumption of a 2.5D subsurface, implying that no lateral changes are present over the projection distance.

6.1.3 Conclusions Where the proﬁle is relatively straight, we obtain high quality synthetic and ﬁeld-data images using the three imaging routines, with the full prestack mi-

50 Figure 6.1: Resultant synthetic data time sections for CMP 900 to 2000. (a) Receiver layout (dark gray dots), trace midpoint positions (light gray dots), CMP binning line (dark gray line), and migration line (black line) in plan view. (b) Source-receiver offset distribution with CMP. Color indicates the source-receiver azimuth. (c) Data after DMO corrections, CMP projection, and migration after stacking. (d) Data after DMO corrections, CMP projection, and zero-offset migration of common-offset gathers before stacking. (e) Data after CMP projection and prestack time migration. For display purposes, traces in (c), (d), and (e) are scaled with respect to the mean absolute amplitude of the respective section and subsequently multiplied by a factor of 5. Figure corresponds to Figure 4 in Paper I.

51 gration routine (scheme 3) providing the most coherent field-data sections. In contrast, both the resultant synthetic and field-data seismic sections suggest that the applied full prestack migration routine (scheme 3) may be more prone to migration artifacts than DMO and zero-offset migration where the profile is significantly crooked (artifact F on Figure 6.1e). These observations illustrate the extra sensitivity of direct prestack migration to the acquisition geometry, in particular to source-receiver azimuth variations as observed between CMP 1600 and 1700 on Figure 6.1a. We conclude that the imaging procedures involving DMO and CMP projection are the most appropriate ones to process the IBERSEIS reflection- seismic data considering the crooked-line geometry. In particular, the scheme involving DMO corrections, CMP projection, and zero-offset migration of the common-offset gathers before stacking (scheme 2; see Figure 6.1d) offers an alternative procedure to prestack time migration (scheme 3; see Figure 6.1f) for migrating significantly crooked-line data.

6.2 Paper II: Seismic-Reflection Imaging over the South Portuguese Zone Fold-and-Thrust Belt, SW Iberia 6.2.1 Motivation Seismic-reflection images of the shallow SPZ crust (depth <15 km) may provide crucial information to extend surface-mapped geological features to depth and to develop models of the SPZ tectonic history. Whereas the original IBERSEIS seismic-reflection processing aimed at imaging the entire crust (Simancas et al., 2003; Carbonell et al., 2004), this processing attempt did not always provide crisp images of the complex shallow crust. Thus, we reprocessed an ∼35 km-long portion of the IBERSEIS seismic data set crossing the SPZ with the goal to obtain new, higher-resolution sections than the previous processing effort.

6.2.2 Methods A key issue of the applied seismic-reflection imaging was to preserve high frequencies, which are important for successful imaging in high-velocity crystalline environments. Thus, special attention was paid to trace editing, static corrections, and filter design (e.g., deconvolution). In contrast to the original processing by Simancas et al. (2003) that included only poststack migration, we employed the crooked-line prestack migration routine developed in Pa- per I for crisp imaging of conflicting dips (i.e., DMO, CMP projection of the common-offset gathers, zero-offset migration of the common-offset gathers, and CMP stacking).

52 Prominent diffraction patterns are observed in the stacked sections. In order to analyze the sources of the diffracted energy, we developed and employed a diffraction-imaging scheme consisting of a modified conventional zero-offset Kirchhoff-migration routine (see e.g., Schneider, 1978; Yilmaz, 2001) that enhances diffractions at the expense of specular reflections. The standard weighted summation along hyperbolic trajectories was replaced by semblance computations (Neidell and Taner, 1971), which accentuate diffracted energy while suppressing reflected energy at the same time. High semblance values on the resultant diffraction section correspond to the apices of diffraction hyperbolae. The frequency content of a seismic section may be altered by various effects, such as tuning caused by thin layers (Milkereit et al., 1994). Thus, it is instructive to compare frequency-filtered stack panels. Here, we compared a low-frequency (20–30 Hz) with a high-frequency (30–40 Hz) stack panel to identify frequency-dependent reflectivity.

6.2.3 Results Altogether, five seismic facies were identified on the migrated data based on their different reflection characteristics, marked as A, B, C, D, and E on Fig- ure 6.2. Considering the diffraction section and the frequency filtered stack panels, the observed features on Figure 6.2 and Figure 6.3 were interpreted as follows: • Seismic unit A: This quasi-transparent unit corresponds to the Flysch group. Weak reflectivity of this sequence of sediments may be evidence for either very fine layering with respect to the seismic wavelength (i.e., layer thickness significantly smaller than the tuning thickness of 35–70 m), obliteration of layering by compaction, or the complex geometry of the turbidite units. • Seismic unit B: This highly reflective seismic facies correlates with the ore-bearing Volcano-Sedimentary Complex (VSC) group. Small-scale contrasts between the felsic and mafic components within the highly heterogeneous VSC group likely produce the observed reflectivity pattern of strong sub-horizontal to moderately dipping reflections. The high-frequency stack reveals tuning effects which indicate thin layering in the range of the tuning thickness of 35–70 m, which may additionally add to the prominent appearance of unit B on the seismic section. • Seismic units C and D: The Phyllite-Quartzite (PQ) group coincides with seismic unit C, whereas a change in amplitude characteristics with depth may indicate a second unit D located below the PQ, possibly consisting of a sequence of unknown Lower Paleozoic metasediments. • Seismic unit E: High amplitudes and a prominent band of high-semblance dots on Figure 6.3 were interpreted as bands of extensive dike swarms,

53 Figure 6.2: Dip-moveout-corrected, CMP projected, migrated and stacked section ranging from CMP 450–2100 with geological interpretation overlaid. Mapped surface geology and major fault zones are plotted on top (see Figure 2.2 for orientation). The various facies were interpreted as follows: A – Flysch group, B – Volcano-sedimentary complex (VSC), C – Phyllite-Quartzite group, D – Paleozoic metasediments, E1 and E2 – Dike swarms, F – Major fault zones; interpreted faults are marked as black lines. I and II are boundaries between seismic facies A and B, and B and C, respectively. Section plotted 1:1 for a velocity of 5800 m/s, T marks the surface topography. Figure corresponds to Figure 4 in Paper II.

Figure 6.3: Diffraction section ranging from CMP 450–2100 with high-semblance values (≥ 0.0065) plotted as black spots. Identiﬁed seismic facies and faults are indicated, see Figure 6.2 for explanations. Section plotted 1:1 for a velocity of 5800 m/s, T marks the surface topography. Figure corresponds to Figure 5 in Paper II.

54 which were deformed to short, discontinuous reﬂectors/diffractors during the orogeny. • Numerous faults cut through all seismic units with some faults extending down to a crustal decollement at 10–15 km depth. In particular, shallow faults are more coherent on the low-frequency (20–30 Hz) stack panel than on the high-frequency (30–40 Hz) stack panel, due to either tuning effects of ∼50–75 m thick layers or high-frequency absorption effects. High- semblance spots within seismic unit B tend to align along interpreted faults, where reﬂectors are truncated.

6.2.4 Conclusions Overall, the reprocessed seismic-reflection sections show vivid images of the foreland fold-and-thrust belt. Key findings are (1) the mapping of the highly- reflective VSC group, which can be traced through almost the entire processed section; (2) the identification of the extensive bands of dikes, characterized by distinct diffraction patterns. The existence of these deep VSC equivalents may be of importance for the understanding of the huge thermal activity assumed in Early Carboniferous times that is strongly related to the ore formation; (3) an impressive image of a fault and thrust system cutting all units and merging into the middle crust.

6.3 Paper III: P- and SV-Velocity Structure of the South Portuguese Zone Fold-and-Thrust Belt, SW Iberia, from Traveltime Tomography 6.3.1 Motivation Source-generated noise masks the earliest potential reflections on the IBER- SEIS data and limits the shallowest observed signals on the seismic-reflection section to depths >500 m (see Figure 6.2; Paper II). Here, we invert P- and SV -first arrival traveltimes for the smoothest minimum-structure models with the aim to image the shallowest few hundreds of meters along the SPZ-part of the IBERSEIS line and fill the gap between the mapped surface geology and the seismic-reflection section.

6.3.2 Methods Clear P-wave ﬁrst arrivals are observed on almost all Vibroseis source gathers along the ∼60 km-long SPZ-part of the IBERSEIS line. A total number of 66,696 P-wave picks with an assigned uncertainty of 8 ms were inverted. SV - wave ﬁrst arrivals could be picked along an around 25 km long section in the south of the investigation area, where topographic changes are low and the

55 subsurface geology is known to be more homogeneous than in the northern part of the investigation area. In total, 15,501 SV -arrival times with an assigned uncertainty of 16 ms were analyzed. We employed the first-arrival regularized inversion method of Zelt and Bar- ton (1998) in which a combination of data misfit and model roughness is min- imized to provide the smoothest velocity model appropriate for the assigned data errors. Extensive tests with different starting models and inversion parameters were performed to find a minimum structure model in accordance with Occam’s principle (e.g., Constable et al., 1987) to avoid interpreting small- scale features on final models that are not warranted by the data. Because the IBERSEIS data were acquired along a crooked survey line, we tested two different crooked-line geometry schemes. For all calculations, the entire data set was split into four segments with each segment having a roughly straight-line acquisition geometry. Then, either the forward and the inverse calculations were performed using data with sources projected onto a straight line and receiver positions derived from the source-receiver offset (2D inversion). Alternatively, forward modeling involving 3D ray tracing through a laterally homogeneous model provided the ray paths but the inversion was performed in 2D (2.5D inversion). A comparison of the final models from both the 2D and 2.5D approach showed no significant differences. Hence, we used the computationally simpler and faster 2D approach. The reliability of the final models was assessed on the basis of the traveltime residuals, the ray coverage, and a comparison of coincident velocity- depth profiles. Checkerboard tests revealed a resolution of 2–3 km and 3–4 km throughout most of the P- and SV -models, respectively. We used two ways to compute the VP/VS-ratio, (1) by dividing the independently derived final P and SV -models, and (2) using a scaled final P-image as starting model for the SV -traveltime inversion and subsequently determine the VP/VS-ratio.

6.3.3 Results and Conclusions The P-wave data reveal rather homogeneous structures in the south, coincid- ing with the Flysch unit mapped at the surface (Figure 6.4 and Figure 6.5). Velocities range between 4.5 km/s at the surface and 5.25 km/s or higher at 350 m depth below surface. A prominent low-velocity zone (<4. km/s at the surface and around 4.5 km/s or less at 350 m depth below surface) character- izes the La Puebla de Guzman antiform in the center of the area, where the Phyllite-Quartzite group is exposed. Short-wavelength variations characterize the northern part of the investigation area, with the Flysch unit characterized by high velocities (4.5 km/s at the surface and 5.25 km/s or higher at 350 m depth below surface) and the Volcano-Sedimentary Complex showing low velocities (<4.5 km/s at the surface, ∼5 km/s at 350 m depth below surface). / The VP VSV -ratios determined for the southern part, where the Flysch unit out-

56 Figure 6.4: (a) and (b) Perspective 3D views of the P-data preferred ﬁnal models and the geological map (see Figure 2.2). The various lithological units are: A – Flysch group,B–Volcano-Sedimentary Complex (VSC),C–Phyllite-Quartzite group, D – Paleozoic metasediments Z – Cenozoic cover; major faults are marked. Final models are clipped at crossing points, interval of the thick and thin contour lines is 0.5 km/s and 0.25 km/s, respectively. Depth is measured from sea level, vertical exaggeration is 1 : 3. Figure corresponds to Figure 19 in Paper III.

/ crops, range between around 1.8 and 1.9, with low VP VSV -ratios interpreted to indicate less fractured rocks, e.g. between xP4 = 4 km and xP2 = 10 km on / Figure 6.5. In contrast, high VP VSV -ratios suggest increased porosity due to intense fracturing.

57 Figure 6.5: Comparison with coincident seismic-reflection profile (see Paper II) and mapped geological section along the seismic-reflection profile. (a) P-data final images P1 and P2 projected onto the seismic-reflection section. Interval of the thick and thin contour lines is 0.5 km/s and 0.25 km/s, respectively. h and j mark prominent faults. (b) SV final images S1 and S2 projected onto the seismic-reflection section. Interval of the thick and thin contour lines is 0.5 km/s and 0.25 km/s, respectively, with isoveloc- / ity contours for 2.5 km/s and higher plotted in white. (c) VP VSV -ratio for P1/S1 and P2/S2 (see (a) and (b)) projected onto the seismic-reflection section. Interval of thick and thin contour lines is 0.05 km/s and 0.025 km/s, respectively. Crossover positions, at which final models were clipped, are indicated on (a)–(c). Seismic units on (a)– (c) are interpreted as follows: A – Flysch group,B–Volcano-Sedimentary Complex (VSC),C–Phyllite-Quartzite group; h and j mark major faults, T marks the topography. Depth is measured from sea level, sections plotted 1 : 1. (d) Sketch of P1 and P2 (black lines) and seismic-reflection line (gray line). Figure corresponds to Figure 20 in Paper III.

58 7. Conclusions and Outlook

7.1 General Conclusions A principal objective of this thesis was to image the shallow crust of the SPZ using seismic-reflection and seismic-refraction methods. Crooked-line geometry data challenges standard 2D seismic-reflection processing, in particular migration routines, requiring adapted processing sequences (Paper I). The developed crooked-line prestack migration routine revealed vivid images of the SPZ fold-and-thrust belt, and the application of diffraction imaging complemented the seismic-reflection sections (Paper II). Source-generated noise overwhelms the shallowest reflections and limits the seismic-reflection section to a minimum depth of ∼500 m. Tomographic inversion of P- and SV -wave first-arrival traveltimes provided velocity models filling the gap between the surface-geological data and the shallowest observed reflections (Pa- per III). The final seismic-reflection and seismic-refraction images compare favorably with the mapped surface geology. Furthermore, they provide detailed insights into the SPZ and revealed e.g. prominent, previously unrecognized mafic dikes, that may be related to the hydrothermal activity assumed in Early Carboniferous times. The developed seismic-reflection tools (i.e., the crooked-line prestack time migration sequence and the diffraction imaging) may well be suited for other hard-rock seismic-reflection investigations such as regional-scale surveys to map host rocks of ore deposits. Commonly, source-generated noise obscures the shallowest reflections and traveltime tomography may be able to provide complementary velocity models of the shallowest subsurface.

7.2 Outlook Building on the accomplishments of this thesis, I first outline in this chapter alternative ways to prestack migrate crooked-line data. Usually, seismic- reflection processing aims at enhancing primary reflections while viewing other wave types as noise (e.g., refracted arrivals, surface waves, converted waves). However, these inevitably recorded events may provide additional useful information and some potential processing methods are discussed. Fi- nally, I present some ideas concerning further resolution improvements of the tomographic images obtained in Paper III.

59 7.2.1 Prestack Migration of Crooked-Line Data Surveys forced to follow winding roads that result in crooked-line acquisition geometries are common in hard-rock seismic investigations. The DMO, CMP- projection, and zero-offset migration scheme presented in Paper I allowed for a reliable prestack migration of the only slightly crooked IBERSEIS data. This scheme may work well for other minor crooked lines or line segments. However, it is based on the assumption that source-receiver azimuth variations are small and only the general orientation of the line has to be corrected for. If lines are severely crooked and source-receiver azimuths vary considerably, the schemes presented in Paper I my not provide crisp images (see artifact B on Figure 4.1c,d). As outlined in Chapter 4.3.1, both the line orientation in relation to the dip direction and the interface dip affect the 3D reflection traveltime (see also Equation 4.2). A potential crooked-line prestack migration scheme can aim at resolving the full 3D subsurface geometry (e.g., Nedimović and West, 2002, 2003b). However, the 3D subsurface coverage of crooked-line data is often too poor to justify such an approach. Alternatively, one can assume that the general dip direction (or the dip of a particular horizon) is known. Then, migration schemes can be designed to include the true areal source and receiver distribution to image a 2.5D (laterally homogeneous) subsurface. Based on these ideas, the following existing prestack migration approaches could be adapted for the migration of crooked-line data: • Azimuth-moveout (AMO) corrections: The AMO operator proposed by Biondi et al. (1998) transforms 3D prestack narrow-azimuth data from multiple marine streamers into effective common-azimuth data. As crooked-line data resemble feathered marine data with midpoints and azimuths spread within an narrow range, source-receiver azimuths of crooked-line data could be oriented in the assumed dip direction by applying AMO corrections and subsequently any 2D prestack migration algorithm can be used for imaging. • AMO corrections could be approximated by forward 3D DMO and inverse 2D DMO corrections (Canning and Gardner, 1996). In fact, this procedure corrects for the effect that the azimuth θ between the structural dip and the profile direction in Equation 4.2 has on the stacking velocities. Alter- natively, 3D DMO corrections could be applied to the data and subsequent 2D zero-offset migration would approximate a full 2D prestack migration. • 2.5D prestack migration: Gray et al. (1999) discussed a modified 2D prestack migartion routine that computes 3D ray paths through a 2.5D subsurface. Their scheme implicitly includes an AMO correction. • Full 3D (Kirchhoff) prestack migration onto a 2D line: For example Bancroft et al. (1998)’s equivalent-offset migration (EOM) uses the areal source-receiver geometry. EOM was successfully applied to crooked-line data by Geiger (2001).

60 7.2.2 Analysis of Converted, Shear, and Surface Waves

Apart form P-P-reflections, converted P-SV -reflections and potentially also SV -SV -reflections are recordable with vertical geophones, but are usually dis- carded in standard P-wave seismic-reflection processing. Likewise, source- generated noise components are suppressed as they obscure reflections. These inevitably recorded ’noise’, however, may contain complementary information on the subsurface elastic properties. In particular, the analysis of these wave types may allow for determining the S-wave velocity-depth distribution. S-velocity, and VP/VS-ratio or Poisson’s ratio models can significantly improve lithological discrimination (e.g., Christensen, 1996; Bauer et al., 2003) and rock-property characterization (e.g., Paper III; Marti et al., 2006).

Seismic-Reflection Processing of Converted and Shear Waves When a P-wave hits a planar interface at non-normal incidence angle, energy can be reflected and refracted as P- and as converted SV -waves (see also Chapter 3.1.3; Yilmaz, 2001). The resultant variations in amplitudes of P- and SV -reflection with angle of incidence depend on changes in the elastic properties and density across an interface and, when measured, may allow inferring on the nature of the reflector. Figure 7.1 shows the reflection coefficient versus angle of incidence curves for the two most common reflectors in a crystalline environment: (1) a contact between the (felsic) host rock and a mafic body, and (2) a fracture zone (Ayarza et al., 2000). The amplitude curves suggest that considerable converted waves are produced at these interfaces and that relative changes in amplitudes could serve as a tool to separate the causes of reflections (e.g., Juhlin, 1990). Seismic-reflection processing of P-SV -reflections has the potential to provide additional structural and amplitude information that could help to identify sources of reflectivity. Furthermore, converted-wave processing provides an S-wave velocity model. Major complications in converted- wave processing arise from the asymmetric geometry of the P-SV -reflection paths. Shear waves respond to a different combination of elastic properties and density than P-waves. Although vertical impact or point sources generate little shear-wave energy (e.g., Kahler and Meissner, 1983; Fertig and Krajew- ski, 1989) or a fraction of the emitted P-wave energy can be converted to SV -wave energy in the vicinity of the source (e.g., Fertig, 1984), careful processing could enhance SV -reflections that would complement the P- and P- SV -reflection images. As shear-wave analyses likely have a great potential, three-component seismic recording could contribute to hard-rock seismic surveying.

61 Figure 7.1: Reflection coefficient versus angle of incidence for two possible seismic reflectors in a volcano-sedimentary host rock (VP = 6150 m/s, VS = 3420 ms, density 3 3 ρ = 2.88g/cm ): (a) a mafic sill (VP = 6900 m/s, VS = 3630 ms, ρ = 3.0g/cm ) and 3 (b) a fluid-filled fracture zone (VP = 5400 m/s, VS = 2700 ms, ρ = 2.7g/cm ). Num- bers on curves refer to VP/VS-ratio. Figure and caption adapted from Ayarza et al. (2000).

Surface-Wave Inversion Rayleigh waves are primarily sensitive to the S-wave velocity of the subsurface. Several recent studies have focused on analyzing the dispersive character of Rayleigh waves to derive S-wave velocity versus depth models (Xia et al., 1999; Park et al., 1999; Xia et al., 2003; Beaty et al., 2002) or estimate attenuation factors (Xia et al., 2002). Full-waveform inversion of surface-wave multichannel data as an alternative to dispersion analyses may soon be possible (Forbriger, 2003a,b). Prominent surface waves are observed on the IBERSEIS source gathers (Event c on Figure 4.2, or event c on Figure 2 in Paper III). Alternative S- velocity depth functions derived from Rayleigh-wave inversion could con- strain the large-scale structure of the SV -velocity models computed in Pa- per III.

62 7.2.3 Waveform Tomography Ray-based inversion methods consider only a very limited portion of the recorded signal, namely the onset time of the first-arriving waves. As a consequence, these methods suffer from a number of inherent limitations. In particular, because the resolution of traveltime tomography scales with the size of the first Fresnel zone (see Equation 3.14 and Chapter 3.1.4) they can only resolve structures that are relatively large and smooth with respect to the dominant wavelength of the signal (Williamson, 1991; Williamson and Worthington, 1993). In contrast to traveltime tomography, waveform tomography uses the seismic waveforms themselves and its numerical methods are based on the full wave equation (Tarantola, 1984; Pratt, 1999). By considering the detailed waveforms and correctly accounting for wave propagation effects in the inversion process, resolution of the velocity images can significantly be improved compared to traveltime tomography. Waveform tomography has the potential to provide images in the wavelength or even sub-wavelength scale (Pratt, 1990). Accordingly, the resolution of waveform tomography can increase by an order of a magnitude compared to traveltime tomography. Brenders and Pratt (2007) demonstrated the potential resolution improvements of waveform tomography in comparison to traveltime tomography in a study using synthetic data (see also Hole et al., 2005). The employed synthetic Earth model is described in detail by Hole et al. (2005) and displayed in Figure 7.2a. A typical crustal cross-section with stochastic variations at different scales, including low-velocity sediments (red), crystalline crust (orange through light blue), and upper mantle (dark blue), is shown. First-arrival traveltime tomography (Zelt and Barton, 1998; Zelt et al., 2003) resolved accu- rately average velocities at wavelengths of ∼10 km horizontally and by ∼2km vertically of the upper crust. In comparison, waveform tomography (Pratt, 1999) using the traveltime tomography model in Figure 7.2b as a starting model provided images with spatial resolution of ∼1 km in the center of the model. Furthermore, the waveform tomography resolved a low-velocity zone between 190 and 215 km along the model which was invisible to traveltime tomography model. Complex geological structures have been imaged in recent studies using waveform tomography, e.g. frequency-domain waveform tomography was applied to wide-angle data acquired in the axial zone of the southern Apennines fold-and-thrust belt (Operto et al., 2004; Ravaut et al., 2004). Successful application of full waveform tomography crucially depends on a robust initial long-wavelength velocity image, usually obtained from traveltime tomography, such as the velocity models derived in Paper III (see e.g. Figure 6.4). Hence, it would be interesting to continue the tomographic study performed in Paper III by applying waveform tomography to the high-quality IBERSEIS data. However, the crookedness of the acquisition line, which did

63 Figure 7.2: P-wave velocity models for (a) the true model used to compute the synthetic data, (b) the starting model for subsequent waveform tomography using all 141,729 hand picked ﬁrst arrivals, and (c) the ﬁnal model derived from waveform tomography. The blind test recovered the model at a spatial resolution on the order of 1 km within the central and near-surface portions of the model. Figure and caption adapted from Brenders and Pratt (2007).

64 not affect the quality of the low-resolution travetime tomography, may be an obstacle for high-resolution waveform inversion.

8. Summary in Swedish: Avbildning av väck och överkastnings bältet Södra Portugisiska Zonen med seismisk reﬂektion och seismisk refraktion

8.1 Syfte av avhandlingen Den Södra Portugisiska Zonen (SPZ) utgör det södra väck och överkastnings bältet i den iberiska variskiska bergskedjebildningen och innehåller rika fyndigheter av malm. Iberiska variskiska bergskedjen är en del av Europas variskiska bältet och har varit i fokus av ﬂera undersökingnar, till exempel EUROPROPE projektet. IBERSEIS djup-seismiska data, som är en del av EUROPROBE, tilllåter för första gången en avbildning av hela jordskorpan av södra iberia med hög upplösning. Den här avhandlingen behandlar seismisk reﬂektions och refraktions processering av en del av IBERSEIS-projektets djup-seismiska data och syftar till att med hög upplösning avbilda den övre jordskorpan i SPZ (mindre än runt 15 km djup).

8.2 Seismisk reﬂektions processering av data med krokiga insamlingslinjer (Artikel I) En jämförelse mellan olika avbildningsprocedurer för att analysera seismiskt data från krokiga insamlingslinjer visade att ett processeringsschema bestående av ’dip-moveout’ (DMO) korrekturer, projektion av ’common midpoints’ (CMP) och migration av ’common-offset gathers’ gav de mest koherenta bilderna med avseende på insamlingsgeometrin. ’Prestack time migration’ (PSTM) producerade lika koherenta bilder var linjen är mindre krokig men bildarna blev felaktiga var insamlingsgeometrin är mer krokigt.

8.3 Geologisk tolkning av reflektioner och diffraktionsenergi (Artikel II) På grund av god korrelation med ytgeologiska data kunde fyra geologiska grupper med olika reflektionskarakteristik identifieras: den 0–2 km djupa Flysch gruppen från övre Karbon, det mycket reflektiva upp till 5 km djupa

67 vulkanisk-sedimentära komplexet (’Volcano-Sedimentary Complex’, VSC) och två djupa paleozoiska metasedimentära grupper, med den grundare ’Phyllite-Quartzite’ (PQ) gruppen exponerad i La Puebla de Guzman antiformen. Tydlig diffraktionsenergi analyserades med en modifierad Kirchhoff avbildningsalgoritm som inkluderar koherens beräkningar vilka förstärkar diffraktionsenergi medan reflektionsenergi försvagas. Hög reflektivitet och tydliga diffraktioner utmärker långa band av intrusioner på 6–12 km djup, vilka möjligen är relaterade till den höga hydrotermala aktivitet som ledde till bildandet av den malmbärande vulkanisk-sedimentära gruppen.

8.4 Hastighets tomograﬁ med användande av första ankomster från P- och SV-vågor (Artikel III) Signalgenererat brus (det vill säga direkta, refrakterade, och ytvågor) döljer signaler på grundare djup än cirka 500 m i den seismiska sektionen. Första ankomster från P- och SV -vågor inverterades för att avbilda hastighetsstrukturen i den översta skorpan. Inversionen syfte på att bestämma dem hastighetsmodeller som har minst struktur för att förklara dem observerade först ankomster. Två olika sätt att behandla den krokiga insamlingslinjen testades: en hel två-dimensionel model var strålvägar och inversionsberäkningen är utförd i två dimensioner. Alternativt, strålvägar kan beräknas i tre dimensioner men inversionsberäkningen bara i två dimensioner. En jämförelse mellan de två metoder visade att den krokiga insamlingslinjen har ingen större effekt och beräkningarna kan göras helt två dimensionelt. Modellernas kvalität testades på olika sätt, till exempel med ’checkerboard’ tester som visade att att SV -vågsmodeller har en mindre upplösngingsförmåga än P-vågsmodeller på grund av mindre antal data och en större osäkerhet. Modellerna korrelerar på generellt sätt väl med ytgeologiska data och ger höga (högre än 5,25 km/s) och enhetliga P-vågshastigheter för Flysch gruppen i södra SPZ. En kropp med tydligt låg P-vågshastighet (ungefär 4,5 km/s) framträder där PQ gruppen bildar kärnan i La Puebla de Guzman antiformen. P-våghastigheterna varierar mest i norra SPZ, där Flysch gruppen uppvisar höga hastigheter (högre än 5,25 km/s) medan den vulkanisk-sedimentära (VSC) gruppen har intermediära hastigheter (ungefär / 5 km/s). Låga VP VSV kvoter (ungefär 1,8) beräknade för södra delen av proﬁlen tolkas som orsakade av mindre deformerat berg från Flysch-gruppen, / medan höga VP VSV kvoter (ungefär 1,9) visar på uppsprucket berg.

68 8.5 Slutsats Avbildningsprocedurer för att analysera seismiskt data från krokiga insamlingslinjer som utveklades i den här avhandlingen kan väl användas för an- dra reflektionsseismiska undersökningar lika som undersökningar med syfte till att avbilda malmbärande strukturer. Signalgenererat brus döljer vanligtvis dem grundaste reflektionssignaler och hastighets tomografi av första ankomster kan vara ett utmärkt instrument att avbilda ytnära strukturer.

Acknowledgments

While working on PhD-project I have been supported by many people. I express my gratitude to all who helped me making this research project a success. First of all, I would like to thank my supervisor Chris Juhlin for many con- structive discussions, support, and comments on papers and this thesis, and for giving me considerable freedom during these years to follow my interests. I also would like to thank my second supervisor Laust B. Pedersen for having accepted me as a PhD student in the Geophysics Program. I appreciate their belief in my abilities to undertake this work. I would like to thank the IBERSEIS partners Ramon Carbonell and J. Fer- nando Simancas for their cooperation. I especially appreciated J. Fernando Simancas instructive comments on geological aspects of SW Iberia and for- mer geophysical investigations in the area. I would like to express my sincere gratitude towards Alan Green and Hein- rich Horstmeyer, ETH Zurich (Switzerland), for numerous discussions and advice during a three-months stay at the AUG-group of the Geophysics Insti- tute. I am very grateful towards Alan Levander and Colin Zelt, Rice University Houston (USA), for their warm welcome, sharing their broad experience, and generous ﬁnancial support I received during a six-months stay at the Earth Science Department. At Uppsala University I would like to thank all fellow students, colleagues, and friends I met during my PhD-student time. I will not try to write down a full list of names, since I do not want to miss anybody. We spent many hours together in the ’Geocenter’ during coffee breaks and on conference or ﬁeld trips. All your help, support, and friendship made my time truly enjoyable. As people from almost all over the world gather in Uppsala, I learned a lot about different cultures and views on life. I believe this is one of the most valuable experiences I can take with me from my PhD-student time. I am especially thankful to Stefan Knöß for his friendship and helping me with his broad technical/computer knowledge. Pálmi Erlendsson and Kristín Jónsdóttir spent many very enjoyable hours with me in Sweden and Iceland, and I am truly thankful for their friendship. While writing this thesis, Björn Lund kindly assisted me with the Swedish summary and Thomas Kalscheuer as well as Jens Tronicke commented on parts of the thesis. I very much appreciate their help.

71 Outside the Department, I would like to thank Claudia for having offered me a room to stay during most of my time in Uppsala; I realy felt home at Tryffelvägen. I am also very thankful to Uwe and Ulrike for their friendship and their help while I was away from Uppsala. I am truly indebted to my parents, my brother David, and Sibylle for their encouragement, support, and love. Without their steady motivation and end- less patience — wherever and whenever I carried out the research for this thesis—Iwould not have been successful in reaching my goals. This thesis is dedicated to them.

April 2003 – October 2007

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