Interpretation of Sedimentological Processes of Coarse-Grained

Open Geosci. 2017; 9:525–538

Research Article Open Access

Éva Farics*, Dávid Farics, József Kovács, and János Haas Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis https://doi.org/10.1515/geo-2017-0040 Received March 10, 2017; accepted June 12, 2017 1 Introduction

Abstract: The main aim of this paper is to determine the A coarse-grained clastic bed-set occurs at the base of the depositional environments of an Upper-Eocene coarse- Upper Eocene marine succession in the Buda Hills, Trans- grained clastic succession in the Buda Hills, Hungary. First danubian Range, Hungary (Fig. 1a). The oligomictic and of all, we measured some commonly used parameters of rarely monomictic conglomerate beds contain dominantly samples (size, amount, roundness and sphericity) in a dolomite pebbles, but pebbles of volcanic rocks are also much more objective overall and faster way than with tra- common locally. Other rock types are also present spo- ditional measurement approaches, using the newly de- radically. Due to the rather poor exposure conditions, the veloped Rock Analyst application. For the multivariate sedimentological structures of the basal beds are rarely data obtained, we applied Combined Cluster and Discrimi- visible. Therefore, the composition of the clastic material nant Analysis (CCDA) in order to determine homogeneous and the size and shape of the clasts may serve as a ba- groups of the sampling locations based on the quantita- sis for the determination of both the source area of the tive composition of the conglomerate as well as the shape clasts and the interpretation of the transport and deposi- parameters (roundness and sphericity). The result is the tional processes they were subject to. Coarse-grained clas- spatial pattern of these groups, which assists with the in- tic successions may be deposited in various sedimento- terpretation of the depositional processes. According to logical environments forming alluvial, glacial, fluvial, and our concept, those sampling sites which belong to the erosional sedimentary sequences. For the characterisation same homogeneous groups were likely formed under simi- of these rocks, plenty of petrography and textural parame- lar geological circumstances and by similar geological pro- ters (composition of the grains, grain size, roundness and cesses. sphericity) are available, and these can then be analysed In the Buda Hills, we were able to distinguish various sed- with statistical methods. The main aim of this paper is the imentological environments within the area based on the interpretation of the depositional conditions of the Eocene results: fan, intermittent stream or marine. basal conglomerates of the Buda Hills, and the determina- Keywords: homogeneous groups, Rock Analyst application of the source of the volcanic clasts—by applying a spe- tion, roundness, composition, depositional environments, cial grouping method for various clast parameters, mea- coarse-grained succession, Buda Hills sured using our newly developed IT application. In many cases the suggested parameters for the de- scription of roundness and sphericity (e.g. Szádeczky- *Corresponding Author: Éva Farics: Eötvös Loránd University, Department of Physical and Applied Geology, H-1117 Budapest, Kardoss’ (1933) CPV (C=concave, P=planar, V=convex) [1]) Pázmány Péter stny. 1/C, Hungary, are poorly defined and their measurement in coarse and MTA-ELTE Geological, Geophysical and Space Science Research grained siliciclastic rocks is rather subjective. To improve Group, H-1117 Budapest, Pázmány Péter stny. 1/C, Hungary, E-mail: the precision of the measurements, we redeveloped an [email protected] earlier version of the Rock Analyst application (Győrfy Dávid Farics: was a student in Engineering Information Technology at the Budapest University of Technology and Economics, Budapest, 2015 [2]), leading to major improvements (see Section 3.3). Hungary. This application is now available at http://faricseva.web. József Kovács: Eötvös Loránd University, Department of Physical elte.hu/. and Applied Geology, H-1117 Budapest, Pázmány Péter stny. 1/C, For the data obtained using the Rock Analyst ap- Hungary plication, we applied a multivariate statistical grouping János Haas: MTA-ELTE Geological, Geophysical and Space Science method, Combined Cluster and Discriminant Analysis Research Group, H-1117 Budapest, Pázmány Péter stny. 1/C, Hungary

Open Access. © 2017 Éva Farics et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. 526 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas

(CCDA) (Kovács J. et al. 2014 [3]) for the interpretation of zircon U-Pb dating) was encountered in the dolomite in the sedimentological processes of the coarse-grained clas- well Budaörs-1 [12]. The higher part of the Triassic suc- tic successions. CCDA combines linear discriminant anal- cession is made up of cherty dolomite and limestone of ysis and cluster analysis and its aim is to find not only a basin facies as well as coeval platform dolomites and similar, but even homogeneous groups of sampling loca- limestones [13–16] (Fig. 1b). Bauxitic clay locally occurs tions based on the multivariate samples. The result is a on the karstified surface of the Triassic carbonates [9]. The spatial pattern which shows the relationship of the sam- Triassic carbonates are overlain by an Upper Eocene suc- pling sites (homogeneity or difference and in particular the cession starting usually with conglomerate beds. Magyari rate of the latter). This method has been used successfully (1994) [17] distinguished two types of the basal conglomer- for hydrological and hydrogeological interpretation [3–5] ates: Type 1 which contains exclusively Triassic dolomite and biological analysis [6]. In this paper, CCDA is used in a and chert clasts and Type 2 which contains remarkable sedimentological problem. According to our opinion, the amounts of volcanic clasts along with dolomite and chert sampling sites which form a homogeneous group on the pebbles. The andesite clasts yielded Carnian U-Pb ages (I. basis of different criteria indicate deposition under simi- Dunkl, personal communication). lar geological circumstances and by the same geological processes. A sedimentological model based on this concept might be more accurate and objective than former 3 Materials and methods ones owing to the consistency of our approach. More details about CCDA, along the meaning of homogeneity can About 20-25 hand-sized conglomerate samples were col- be found in Section 3.4. lected from the volcanic clast-bearing Upper Eocene basal Several studies have discussed the formation of the conglomerate successions (Fig. 1b): Apáthy Rock - Stone maximally five meter thick volcanic clast-bearing Up- Gate (AR), Fenyőgyöngye quarry (FQ), Hunyad Peak (HP), per Eocene coarse-grained clastic succession of the Buda János Hill - Virág Valley (JH), Kő Hill of Budaörs (KH), Látó Hills. Early researchers thought that the Eocene volcanic Hill (LH), Róka Hill quarry (RH), Tündér Rock (TR) and Út material had been accumulated on the karstified surface of Hill (UH). Triassic carbonates (mostly dolomites) prior to transgres- The following parameters of the conglomerates were sion, and this was preserved in the fractures and depres- determined in every outcrop using the Rock Analyst appli- sions of the basement [7]. Later, some of the researches cation (see Section 3.3): thought that the clasts were formed and transported via i) the size of each clast; erosion [8], while others proposed a terrestrial deposi- ii) the quantitative composition of the clast types in the tional environment [9, 10]. Farics et al. (2015) [11] pro- conglomerates; posed a simple transport model: redeposition of the vol- iii) the roundness and the sphericity of each clast. canic clasts from south to north (i.e. from Budaörs to Róka The parameters of 500 randomly selected clasts of ev- Hill) during the latest Cretaceous to Priabonian period, ery sampling sites were measured. The investigation of prior to the onset of the marine sedimentation. However, the thin sections of the whole conglomerate was neces- this interpretation was based on field observations only, sary, as the preparation of small (2-8 mm) clasts indi- which suggested a northward decreasing trend in the size vidually mostly from the silica-cement is hardly possible. of the andesite clasts. In the present study, along with the Moreover, microscopic investigation of thin sections was clast size, further parameters (e.g. percentage of the com- needed for the exact determination of rock types. Based ponents, shape of the clasts) are also considered. Conse- on the results of Farics et al. (2015) [11], the following quently, the previous simple model is significantly refined clast types were distinguished: dolomite, andesite, vari- and modified. ous acidic volcanic rocks (including dacite-rhyolite tuff, ig- nimbrite and rhyolite) and chert (Fig. 2). 2 Geological setting of the study area 3.1 Roundness definitions

Various methods have been proposed for the measurement The uppermost Anisian to lowermost Carnian Budaörs of the roundness of clasts. Most of these methods (Went- Dolomite is the oldest formation cropping out in the Buda worth 1921 [18], Cailleux 1952 [19]) only take into account Hills (Fig. 1b). An andesite dike of Carnian age (based on Interpretation of sedimentological processes of coarse-grained deposits Ë 527

Figure 1: Geological setting of the study area: a., simplified Pre-Cenozoic map of the Transdanubian Range showing the location ofthe study area (and places mentioned in the article) (after Haas and Budai 2014 [41]), 1 – Pre-Cenozoic rocks on the surface, 2 – Paleozoic- Middle Triassic formations, 3 – Upper Triassic formations, 4 – Jurassic-Cretaceous formations; b., geology map of the Buda Hills showing location of the study outcrops (base map of Budai and Gyalog 2010 [42]), 1 – Budaörs Dolomite, 2 – Hauptdolomite, 3 – Dachstein Lime- stone, 4 - Csővár Limestone - Mátyáshegy Formation, 5 – Eocene formations, 6 – Oligocene formations, 7 – Miocene formations, 8 – Quater- nary formations, 9 – Tectonic elements, 10 – Border of town.

the less rounded sides of the clasts. In contrast, the methods of Wadell (1932) [20] and Szádeczky-Kardoss (1933) [1] take into consideration the whole surface of the clasts for the calculation. We used both of the latter methods for the measurement of roundness. Wadell (1932) [20] defined a Wr roundness index as follows: ∑︀N ri /N Wr i=1 = R (1)

where ri is the radius of curvature of the i-th clast corners, R is the radius of the largest inscribed circle, and N is the number of clast corners measured. Only those clast corners are taken into account where r < R (Fig. 3). This definition describes a certain thin section ofa clast. In practice, one takes a random plane in a con-

Figure 2: Macroscopic photos of the volcanic clast-bearing Upper glomerate, leading to a thin section in which then all of Eocene basal conglomerate in the Buda Hills: a. and b., Kő Hill (KH); the clasts are measured. For the better interpretability of c., Apáthy Rock - Stone Gate (AR); d., Fenyőgyöngye quarry (FQ); e., the Wr roundness index, values were grouped by Pow- Róka Hill quarry (RH); f., János Hill-Virág Valley (JH). ers (1953) [21] into very angular, angular, subangular, subrounded, rounded and well rounded (see later Fig. 5). 528 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas

ity arise. Wadell’s (1932) [20] projection sphericity Ψ’ (hereinafter sphericity) is defined via

Ψ′ = d/D (3)

where d is the diameter of a circle with an equal area to that of the clast and D is the diameter of the smallest circle circumscribing the clast. Riley’s (1941) [22] projection sphericity R (hereinafter sphericity) is defined as follows:

R = ∂/D (4)

where ∂ is the diameter of the largest inscribed circle and Figure 3: The clasts are separated on the cut surface of the macro- D is the diameter of the smallest circumscribing circle. scopic sample by curves, and Wadell’s roundness values are cal- culated by the Rock Analyst application. Yellow lines – andesite clasts, red lines – acidic volcanic clasts, green lines – dolomite clasts, purple circle – the largest inscribed circle, blue circle – cur- 3.3 The Rock Analyst application vature of the clast corner. The Rock Analyst application had already been used by Győrfy (2015) [2] and was further developed for this project. The CPV method of Szádeczky-Kardoss (1933) [1] is The program is available in the Appendix. It is of dual ben- an alternative way of measuring the roundness of clasts efit: on the one hand, the program facilitates the measure- based on the concave, convex and planar segments of their ment of the parameters, while on the other hand, it re- surfaces. In contrast to Wadell’s definition, which char- duces the error of the measurements and makes the pro- acterizes roundness with a single value, the CPV method cess more objective. uses three values for the same purpose, namely the ra- For using this application, we first have to import the tio of the three curvature elements (C=concave, P=planar, scanned image of a cut surface of a siliciclastic rock and/or V=convex) of the perimeter. The results of CPV analyses a thin section. Then we assign the clast types and mark can be displayed on a triangle diagram (see later Fig. 6 for the boundaries of the analysed clasts (Fig. 3). The largest an example). While Szádeczky-Kardoss (1933) [1] defined diameter (mm), perimeter (mm), area (mm2), quantitative in which thin sections one has to measure these CPV val- composition, Wadell’s roundness, CPV values, Wadell’s ues exactly, in practice—as with Wadell’s method—we take sphericity and Riley’s sphericity are automatically calcu- a random plane in a conglomerate, leading to a thin sec- lated based on the scanning resolution (DPI - dot per inch, tion in which then all of the clasts are measured. the number of the pixels per 2.54 cm) and displayed in a table. Summarized results for various size boundaries are also available. Specifically, a summary of roundness and 3.2 Sphericity definitions sphericity values (average, standard deviation, minimum, maximum, median and quartiles) per clast type and also Wadell (1932) [20] defined sphericity as the ratio of the in each size fraction is displayed. The application is also clast surface area to the surface area of a sphere having capable of presenting the results of CPV measurements on the same volume. Unfortunately, the measurement of this triangle diagrams per clast types and/or clast size frac- index is very difficult. An alternative is the operational tions. sphericity Ψ, which is given by the following formula: √︃ Vc clast Ψ = 3 ( ) (2) 3.4 CCDA method Vscs (smallest circumscribing sphere)

where Vc is the clast volume and Vscs is the volume of the Combined cluster and discriminant analysis (CCDA, smallest circumscribing sphere. Sphericity values range Kovács J. et al., 2014 [3]) applies two widely known and from 0 (non-spheroidal) to 1 (perfect sphere). applied methods, HCA (hierarchical cluster analysis [23]) In practice, if only a two-dimensional thin section is and LDA (linear discriminant analysis [24, 25]) in order to available, the following two options of projection spheric- find homogeneous groups of sampling sites. CCDA can be Interpretation of sedimentological processes of coarse-grained deposits Ë 529 applied in settings where one has multiple observations Hence, one can take the decision rule based on whether from different origins and wants to determine whether the difference value, d = ratio − q95, is positive or negative. these origins are significantly different from each other If d ≥ 0, we have significant differences, while in the case or can actually be treated as homogeneous based on of d < 0, groups can be treated as homogeneous. Each the measured parameters. Hence, for each observation grouping possibility obtained by HCA is investigated this (which can be a sample or measurement) we must have way, and the grouping with the highest difference value a label describing the origin of the observation (which is considered as optimal. The sub-groups of this grouping is usually the name of the sampling site where the mea- then always have to be investigated iteratively until all surement was taken or where the sample was collected difference values are negative and thus the grouping from). Regarding the components, HCA divides data into under consideration can be treated as homogeneous. a hierarchy of clusters, where at the lowest level each Hence, homogeneity in our paper is referred to as a case, item belongs to its own cluster and at the highest level all where groups cannot be distinguished from each other items belong to the same cluster. The resulting tree-like using linear functions/hyperplanes in a space any better structure, called dendrogram, can be cut at different than at random. As a typical case, even if the point clouds heights, resulting in different grouping possibilities of the very much overlap, once their centres lie sufficiently apart data. LDA defines linear functions that separate labelled from each other, they become distinguishable by the CCDA observations of different classes in an optimal fashion procedure, leading to a positive difference value, such subject to specific statistical metrics [26]. These two that we would not consider them as being homogeneous. methods are combined in CCDA as follows: first of all, a In these cases, the groups could at most be regarded basic grouping of the sampling sites has to be found. This as similar, the degree of similarity being described by can be achieved by taking the means of the observations the difference value; the closer this is to zero, the more at each sampling location and then applying HCA with similar the groups. The details about CCDA can be found Ward’s method [27] and squared Euclidean distances. in Kovács J. et al. (2014) [3] and the corresponding “ccda” While this is the standard way of implementation in the R package in Kovács S. et al. (2014) [28]. “ccda” R package in Kovács S. et al. (2014) [28], one could also take a different, e.g. expert-based grouping of the sampling sites. The idea then is to take the original point 4 Results clouds of the measurements in the parameter space and try to separate them using LDA. For a given grouping, In our case, the CCDA method was applied to compare one can take the discriminant functions as a decision each conglomerate of the Upper Eocene succession on the rule to classify the original observations. Comparing the basis of the following: the quantitative composition and predicted class labels with the true class label eventually the shape parameters (roundness and sphericity) of the leads to a percentage value describing the ratio of correctly andesite and dolomite clasts. As a result, homogeneous classified observations (the so called “ratio” value) bythe groups of sampling sites were determined. In order to linear plane [3]. The latter is a measure for the separability make the results of the CCDA analysis more comprehen- of the groups within that specific grouping, which tells sible, we start this section with some descriptive statistics something about the similarity of groups. Similarity is about size, composition and shape. useful in many cases, e.g. [29, 30]. Nonetheless, if the goal is to find not only similarly behaving sampling sites, but even homogeneous groups of sampling sites, one has to 4.1 Descriptive statistics find even the most minute differences. This is the ultimate goal of CCDA. The idea is to compare the goodness of a Table 1 presents the maximum sizes of andesite, dolomite certain grouping with the goodness of a random grouping. and chert clasts in all outcrops. The andesite clasts are the The random grouping is obtained by randomly permuting biggest at sites KH and UH (maximum 20 cm) while the the class labels of the individual observations. One does smallest clasts were found at sites LH and JH (maximum many of these random groupings and each time evaluates 3 cm). The maximal size of the dolomite clasts is 15 cm the percentage of correctly classified cases by LDA. The at sampling sites JH, KH, LH and UH. The chert clasts are 95% quantile of these percentage values is called q . 95 the biggest at site UH (maximum 20 cm). The conglomer- If ratio > q , then significant differences between the 95 ates are usually poorly sorted, while at FQ and TR poorly groups occur—as the ratio of correctly classified cases by LDA is better than in 95% of the random groupings. 530 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas to moderately sorted. The most abundant size fraction is that of the dolomite clasts. The judgment of both spheric- the one between 2-8 mm in each of the outcrops. ity measures is very similar, which does not come as a sur- prise, since their correlation at the individual sampling Table 1: The maximum size of the clast types in the outcrops. sites ranges from 0.94 to 0.97. The sphericity of the various acidic volcanic and the chert clasts is similar to the Maximum size (cm) Sampling sites andesite and dolomite clasts (Table 3). Andesite Dolomite A. volcanic Chert AR 15 12 5 10 FQ 6 7 3 4 HP 4 8 1 2 4.2 Results of the CCDA analyses JH 3 15 - 4 KH 20 15 10 10 LH 3 15 - 5 While a first impression about the similarity of some sam- RH 10 6 - 10 TR 6 8 2 4 pling sites based on measured parameters—in particular UH 20 15 10 20 based on the plots (Fig. 5 and 6)—can be obtained, this is by no means objective and cannot be carried out if the number of sampling sites is large. Moreover, similarity Dolomite clasts are the most abundant clastic compo- does not necessarily mean homogeneity, the latter being nent in all outcrops (ca. 45-94%, Fig. 4a). Volcanic clasts the case when different sampling sites can no longer be are the most common in RH, KH, UH and AR (ca. 34-39%), distinguished. and they are rare in JH and LH (ca. 4-6%, Fig. 4a). Com- pared to the other outcrops, the amount of the chert clasts is extremely high at RH (ca. 18%, Fig. 4a). There are some 4.2.1 The quantitative composition of the clast types outcrops (e.g. KH, AR, UH) where volcanic clasts are abundant in the bigger size fractions (Fig. 4b), while at some We grouped the outcrops according to their quantitative other outcrops (e.g. JH, LH) volcanic clasts are also absent composition using the CCDA method. First, we had to de- almost completely in the bigger than 8 mm size fraction rive multivariate observations about composition that are (Fig. 4c). suitable for this kind of analysis. For each of the nine ex- Based on the Wadell and the CPV roundness defini- amined sites, we randomly partitioned the 500 clasts into tions, andesite clasts are the most angular (except at JH 25 non-overlapping blocks, each with 20 clasts. Each of and LH) and dolomite clasts are the most rounded in the these blocks led to a single multivariate observation by cal- samples (Fig. 5 and 6). Various acidic volcanic rock and culating the quantitative composition of the dolomite and chert clasts are likely to be more rounded than the an- the andesite clasts in the given 20 observations. We did not desite clasts, but less rounded than the dolomite clasts take into consideration the chert and sandstone clasts, be- (Table 2); however, this statement is taken as somewhat cause their proportion often took „0%”. In this way, we ob- less certain due to the small number of samples from these tained twenty-five observations for each sampling site. classes. Dolomite clasts are the most rounded in FQ and In accordance with the methodology of CCDA, we first TR, but they are everywhere subrounded by Power’s clas- produced a basic grouping determined by the averages of sification (Fig. 5 and 6). Andesite clasts are poorly rounded these observations. On the basis of the dendrogram ob- in KH, UH and AR, more rounded in TR, FQ, HP and RH and tained (Fig. 7a), the classification of sampling sites into most rounded in JH and LH (Fig. 5 and 6). The two round- 1,2,..., or even 9 groups was possible. We denote these ness definitions (Wadell and CPV) give very similar results groupings by GR1,...,GR9 (GR=group). The nine resulting to each other, in particular if the fraction of convex seg- difference values (d1,...,d9) described how much the re- ments of the perimeter (i.e. V/perimeter) is compared with spective grouping is better compared to a random group- Wadell’s roundness score. There are only a few differences ing. Grouping GR5 was selected as the optimal one as this in their judgement of roundness (Table 2). one had the highest difference value 5(d =11.11%) (Fig. 7b Based on the sphericity definitions of Wadell and Ri- and c). According to these results, we could objectively dis- ley, dolomite clasts are the most spheroidal in FQ and TR, tinguish five sub-groups (SG=sub-group) with CCDA in its and the least spheroidal in LH (Table 3), but overall, differ- first iteration (Fig. 7). Sub-groups contain the following ences are rather small. Andesite clasts are more spheroidal sampling locations: SG1={JH, LH}, SG2={HP}, SG3={TR, in FQ, JH, LH, RH and TR, and less spheroidal in AR, HP, FQ}, SG4={RH} and SG5={KH, AR, UH}. The question was KH and UH (Table 3), but the differences are again small. then, whether these sub-groups are homogeneous. Hence, The sphericity of the andesite clasts is not much lower than sub-groups SG1,...,SG5 still had to be examined sepa- Interpretation of sedimentological processes of coarse-grained deposits Ë 531

Figure 4: The quantitative composition of the conglomerates: a., the area percentage of each clast type compared to all clasts in every outcrop; the area percentage of each clast type compared to all clasts within the various size fractions at b., KH and c., JH.

Table 2: The average (A) and the standard deviation (S) of roundness measures (using Wadell’s (1932) definition and Szádeczky-Kardoss’ (1933) V/perimeter score) of the clast types in all outcrops; A. volcanic = acidic volcanic.

Wadell’s (1932) roundness Szádeczky-Kardoss (1933) - V/perimeter S. sites Andesite Dolomite A. volcanic Chert Andesite Dolomite A. volcanic Chert A S A S A S A S A S A S A S A S AR 0.28 0.1 0.41 0.1 0.34 0.12 0.37 0.11 0.36 0.15 0.7 0.17 0.54 0.16 0.55 0.19 FQ 0.33 0.11 0.46 0.1 0.47 0.09 0.4 0.11 0.49 0.17 0.81 0.14 0.76 0.15 0.73 0.18 HP 0.32 0.1 0.4 0.11 0.34 0.1 0.35 0.11 0.49 0.18 0.77 0.16 0.71 0.2 0.63 0.16 JH 0.37 0.09 0.42 0.1 - - 0.37 0.1 0.71 0.17 0.76 0.18 - - 0.65 0.21 KH 0.28 0.08 0.43 0.1 0.38 0.1 0.37 0.1 0.35 0.13 0.74 0.17 0.59 0.2 0.59 0.2 LH 0.37 0.1 0.42 0.11 - - 0.38 0.1 0.65 0.22 0.73 0.19 - - 0.65 0.21 RH 0.32 0.09 0.37 0.12 - - 0.38 0.11 0.58 0.18 0.73 0.19 - - 0.74 0.2 TR 0.32 0.1 0.46 0.1 0.41 0.09 0.43 0.1 0.54 0.16 0.84 0.14 0.78 0.17 0.77 0.14 UH 0.28 0.08 0.43 0.1 0.4 0.09 0.38 0.08 0.33 0.11 0.69 0.19 0.56 0.13 0.59 0.15

rately. As SG2={HP} and SG4={RH} cannot be further sub- considered as homogeneous. Then SG3 and SG5 were anal- divided, they form separate groups alone. For SG1, CCDA ysed iteratively in the same way. The results indicated that suggested that no more division is needed, because of a the group {TR, FQ} is homogeneous and that the group negative difference value (d=−8.33%) for the case of di- {KH, AR, UH} is also homogeneous, as in both cases we vision into two groups. A negative difference value indi- had negative difference values when these were divided cates that the considered grouping is not significantly bet- into further groups. As an overall result, five homogeneous ter than a random one. Hence, the group {JH, LH} can be groups were obtained (Fig. 8a). 532 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas

Figure 5: The result of Wadell’s (1932) roundness measure with the classification of Powers (1953): va - very angular, a - angular, sa - subangular, sr - subrounded, r - rounded, wr - well rounded; a., dolomite clasts, b., andesite clasts. Andesite clasts are the most angular in AR, KH and UH and the most rounded in JH and LH. Dolomite clasts are more rounded than andesite clasts.

Table 3: The average (A) and the standard deviation (S) of projection sphericity measures (using Wadell’s (1932) and Riley’s (1941) definitions) of the clast types in all outcrops; A. volcanic = acidic volcanic.

Wadell’s (1932) sphericity Riley’s (1941) sphericity S. sites Andesite Dolomite A. volcanic Chert Andesite Dolomite A. volcanic Chert A S A S A S A S A S A S A S A S AR 0.69 0.09 0.76 0.08 0.74 0.08 0.73 0.09 0.51 0.11 0.59 0.12 0.55 0.11 0.55 0.12 FQ 0.73 0.09 0.79 0.09 0.76 0.09 0.75 0.08 0.55 0.12 0.63 0.13 0.61 0.13 0.58 0.1 HP 0.7 0.09 0.76 0.09 0.78 0.07 0.75 0.09 0.51 0.11 0.59 0.12 0.62 0.1 0.59 0.13 JH 0.76 0.09 0.76 0.09 - - 0.77 0.08 0.59 0.12 0.6 0.12 - - 0.6 0.13 KH 0.7 0.09 0.76 0.09 0.73 0.1 0.77 0.07 0.52 0.13 0.6 0.12 0.54 0.14 0.59 0.1 LH 0.76 0.09 0.74 0.09 - - 0.73 0.11 0.61 0.13 0.57 0.13 - - 0.56 0.16 RH 0.75 0.09 0.76 0.09 - - 0.74 0.1 0.59 0.13 0.59 0.13 - - 0.56 0.14 TR 0.74 0.08 0.79 0.08 0.78 0.08 0.76 0.06 0.56 0.11 0.64 0.13 0.64 0.11 0.58 0.1 UH 0.7 0.09 0.76 0.08 0.7 0.1 0.74 0.08 0.52 0.13 0.59 0.12 0.5 0.13 0.57 0.12

4.2.2 The shape of the andesite and dolomite clasts roundness measures), but as discussed before, correla- tions between the same type of measures are high—which We grouped the outcrops according to the shape (round- could distort the analysis. Hence, a single type of roundness and sphericity) of the andesite and dolomite clasts ness measure (C/perimeter and V/perimeter) and one type with the CCDA method. For this analysis we used three of sphericity measure (the one by Riley) were finally se- parameters: Riley’s sphericity values, the C/perimeter lected for the CCDA analysis. In the following, we will refer and the V/perimeter values. A third value (P/perimeter) to these selected parameters as the shape parameters. As from the CPV values was left out, as from two values sphericity and roundness can be measured for every indi- (C/perimeter and V/perimeter) this already follows. The vidual clast, each clast led to a single multivariate observa- reason why the CPV values instead of Wadell’s round- tion that was used in the analysis. In the case of andesite, ness measure were preferred for the analysis is its higher about 130 clasts were taken from each sampling site (at level of complexity and thus, its ability to capture differ- JH, LH, TR and FQ additional andesite gravels were col- ences: while Wadell’s measure is a single value, CPV-based lected and measured to have approximately equal number roundness provides multiple values for the characteriza- of samples from each site), while in the case of dolomite, tion of roundness. Regarding sphericity, Riley’s measure about 200 clasts were taken from each sampling site. was selected as it is the more recent one. CCDA can han- First we investigated the andesite clasts. At first we dle even more parameters so that other measures could produced a basic grouping of the outcrops, resulting be included in the analysis (e.g. Wadell’s sphericity and in a dendrogram (Fig. 9a). With a difference value of Interpretation of sedimentological processes of coarse-grained deposits Ë 533

While SG1,...,SG3 still had to be examined separately, sur- prisingly all of these sub-groups remained as homogeneous groups, no further split was necessary. Hence, as an overall result, four homogeneous groups were obtained (Fig. 8c). Should someone be only interested in the roundness of the clasts, the analyses could be carried out in CCDA without the sphericity measure, i.e. only with the C/perimeter and V/perimeter scores. For the current study, the homogeneous groups obtained would be exactly the same as described above in the analysis together with the sphericity measure.

5 Interpretation and discussion

The area of the Buda Hills became a terrestrial environment probably during the terminal Cretaceous. Then in- Figure 6: The roundness of dolomite and andesite clasts based on Szádeczky-Kardoss’ (1933) CPV-based definition in triangle dia- tense karstic erosion took place under hot and humid cli- grams. The coloured areas are based on multiple measurements of matic conditions from the end of the Cretaceous to the Pri- different clasts with 5% outliers left out to make a clear picture. abonian, which resulted in an indented surface morphol- ogy with significant relief differences (i.e. karst terrain). It was followed by deposition of terrestrial deposits into the 21.18%, GR , a grouping consisting of SG ={AR, KH, UH}, 3 1 depressions of the paleotopography. SG ={FQ, HP} and SG ={JH, LH, RH, TR} was selected as 2 3 Since volcanic clast-bearing Upper Eocene conglom- the optimal one (Fig. 9b). As the sub-groups might not be erate bed-sets are usually poorly exposed, sedimentary homogeneous, SG ,...,SG still had to be examined sep- 1 3 structures (normal gradation) could be observed only at arately. As the result of the exploration, group SG ={AR, 1 one locality (Kő Hill, Budaörs). This structure may occur KH, UH} turned out to be homogeneous because of neg- both in deposits of alluvial fans and braided rivers. How- ative difference values when splitted further (d=−0.22% ever, subsequent processes significantly modified the orig- and d=−5.34%). For SG , CCDA suggested a further sub- 2 inal sedimentary structures (subsequent tectonic defor- division into two groups {FQ} and {HP} with a positive dif- mations, neptunian dyke, etc.). Consequently, the sedi- ference value (d=0,45%). Hence, {FQ} and {HP} each form mentary structures do not provide a suitable base for the a group individually. Regarding SG , CCDA suggested its 3 interpretation of the paleoenvironments and sedimentary further subdivision into two groups {JH, LH} and {RH, TR} processes. with a positive difference value (d=12,02%). {JH, LH} and That is why we attempted to use the measured clast {RH, TR}then had to be further investigated. The first group parameters and the spatial pattern of the distribution of turns out to be homogeneous, because it has a negative dif- the clast properties for an interpretation of the sediment ference value (d=−5.91%) if divided into two. For {RH, TR}, transport and deposition. Although the measured param- CCDA suggested a further subdivision into two groups, eters of the clasts show some differences among the stud- {RH} and {TR} with a positive difference value (d=4.08%). ied outcrops, the CCDA method pointed out several homo- Hence, {RH} and {TR} each form a group individually. As geneous groups of sampling sites. The spatial pattern of an overall result, six homogeneous groups were obtained homogeneous groups assists in the interpretation of the (Fig. 8b) for the shape parameters of the andesite clasts. geological processes that played a crucial role in the for- We investigated the dolomite clasts as well. First, we mation of the conglomerates. It can be assumed that the produced a basic grouping of the outcrops (Fig. 10a). GR 4 measured parameters of rocks classed to the same homo- was selected as the optimal one with a difference value of geneous group were formed under similar geological cir- 4.83%. We were able to distinguish objectively four sub- cumstances and by the same geological processes. It is groups with CCDA in the first iteration (Fig. 10b): SG ={AR, 1 worth mentioning that based on three different parameters KH, UH}, SG ={FQ, TR}, SG ={HP, JH, LH} and SG ={RH}. 2 3 4 (quantity and shape of the andesite and dolomite clasts) 534 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas

Figure 7: Results for the quantitative composition of the gravels: a., dendrogram representing the basic grouping; b., the summarized results of CCDA for groupings GR1,...,GR9 ; c., difference values for the grouping 5of GR . We were able to distinguish objectively five sub- groups based on the quantitative composition of the clasts with CCDA in the first round. These sub-groups had to be further investigated for homogeneity.

Figure 8: Homogeneous groups obtained by CCDA: a., for the quantitative composition of the clasts; b., for the shape parameters of andesite clasts and c., for the shape parameters of dolomite clasts. The same colours mark sampling sites belonging to the same homogeneous groups.

Figure 10: Results for the shape parameters of the dolomite clasts: Figure 9: Results for the shape parameters of the andesite clasts: a., dendrogram representing the basic grouping; b., the summa- a., dendrogram representing the basic grouping; b., the summarized results of CCDA for groupings GR ,...,GR . We could distin- rized results of CCDA for groupings GR ,...,GR . We could distin- 1 9 1 9 guish objectively four sub-groups regarding the shape parameters guish objectively three sub-groups regarding the shape parameters of the dolomite gravels with CCDA in the first round which then also of the andesite gravels with CCDA in the first round which were then remained homogeneous during the further investigations. further investigated so that in the end six homogeneous groups were found. Interpretation of sedimentological processes of coarse-grained deposits Ë 535 the CCDA method classed several successions—although of-slope fans. They are slightly less rounded than in Group they are located 6-10 km apart from each other—to homo- 1, indicating that their transport distance was probably geneous groups (Fig. 8). slightly shorter than that of Group 1. Group 1 (KH, UH, AR; Fig. 8) can be interpreted as Based on the characteristics described above and the small fanglomerate deposits. The following textural and geological setting, we can assume that these volcanic petrographical characteristics constrain this interpreta- clasts derived from volcanic sources of rather limited sur- tion: the conglomerate is poorly sorted; there is a small silt- face exposure. In contrast, the outcrops of dolomites (and to sand-size ground mass; it contains high amounts (35- locally cherty dolomites) were widely extended, providing 39 area%) and relatively large volcanic—predominantly large amounts of dolomite and chert clasts all along the andesite—clasts; the andesite clasts are relatively poorly valleys. In light of the relevant publications on the sedi- rounded with some large angular rock fragments; and mentation of modern alluvial fans [33–36] we summarize high amounts (55-60 area%) and relatively large dolomite the major characteristics of the potentially analogous fans clasts of moderate roundness. The weaker roundness and as follows. In the case of a steep slope, the size of the sphericity values of the andesite clasts compared to those debris is reduced by orders of magnitude within a maxi- of the dolomite clasts is not surprising in the same envi- mum 5-10 km distance, meanwhile the roundness of the ronment, because the hardness of dolomite is less than debris increases. Andesite clasts are maximally 20 cm in the hardness of strongly silicified andesite [31]. These fan- size, and they are less rounded at KH, UH and AR; there- glomerates were deposited in the proximal zone of small fore we suppose essentially local sources of these clasts. fans formed along the toe of slopes [32]. It means that in this area, andesite debris may have been JH and LH form a unique homogeneous group (Group accumulated on fans formed at the foot of slopes where 2) based on the quantitative composition and the shape of the source rocks were exposed in the Early Eocene. In the the andesite and the dolomite clasts (Fig. 8). HP forms a area of the Buda Hills, Middle-Late Triassic andesite rocks single homogeneous group (Group 3) on the basis of the have been exposed only in the well Budaörs-1 [11, 12, 37– quantitative composition and the shape of the andesite 39]. Based on similar petrographic features (porphyric pi- clasts, but forms a homogeneous group with JH and LH lotaxitic texture with labradorite/basic oligoclase plagio- based on the shape of the dolomite clasts (Fig. 8). Based clase, hypersthene and a few augite, biotite phenocrysts) on the amount and shape of the andesite clasts, these two as well as age data (Carnian), the andesite intersected in groups are interpreted as deposits of intermittent streams this well may have been the source rock most of the an- transporting and redepositing clasts along the valleys, desite clasts [8, 11]. Although we do not know of any an- with clasts originating from andesite clast-bearing fans. desite rock (neither on surface, nor in a borehole) close to The following sedimentological characteristics refer to this the AR, the results of our studies suggest the presence of a transport mechanism: the conglomerate is poorly sorted, local andesite source, most probably an outcrop of a dike. but the scarce andesite clasts are small and much more There are two occurrences (TR and FQ) forming a ho- rounded than in Group 1. The andesite clasts have sim- mogeneous group (Group 4) based on the percentages ilar petrographic characteristics (they are strongly silici- of the components and the shape of the dolomite clasts, fied) in every sampling site. Consequently, changes in the but they do not form a homogeneous group based on the amount, size and shape of the andesite clasts depend pre- shape of the andesite clasts (Fig. 8). According to our in- dominantly on their transport distance. Stream transport terpretation, the peculiar characteristics of these conglom- reduced the size and abundance of andesite clasts, and erates may reflect marine sediment redistribution of pre- increased their roundness and sphericity. Based on the viously deposited terrestrial sediments after inundation roundness of andesite clasts, Group 3 was deposited in of the area when the former continental slopes became the proximal zone and Group 2 was deposited in the dis- abrasional rocky coasts. A part of the previously accumu- tal zone of a supposed intermittent stream. The deposition lated terrestrial sediments would have been redeposited in the distal zone is indicated by the most rounded an- under marine conditions and mixed with sediments. This desite clasts at sites JH and LH. At HP,these clasts are more is evidenced by the presence of rounded to well-rounded rounded than in Group 1, but less rounded than in Group and smaller dolomite pebbles with traces of boring or- 2. The transport distance may have been somewhat shorter ganisms at FQ and TR. However, their deposition could here than that at JH and LH. In Groups 2 and 3, the rela- hardly have happened in the active zone of the abrasion tively abundant, large, and less rounded, less spheroidal since the andesite clasts show poorer roundness than that dolomite clasts which occur together with andesite debris of the dolomite clasts. Moreover, these conglomerates are suggest that they were derived also from nearby small toe- poorly to moderately sorted and clasts are embedded in a 536 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas silt- to sand-size matrix. According to our interpretation, Acknowledgement: This study was funded by the Na- dolomite clasts were transported from higher level abra- tional Research, Development and Innovation Office (Hun- sion terraces by gravitational mass movement to the site garian National Science Fund (OTKA)) under grant num- of deposition at the foot of the submarine slopes. ber K 113013 (to L. Fodor). We thank the three anony- RH we can hardly fit into our model. Namely, the con- mous reviewers for their careful reading of our manuscript glomerate from RH differs from the other conglomerates and their many insightful comments and suggestions. We of the Buda Hills based on grouping with CCDA. The cal- thank Solt Kovács for his suggestions regarding the statis- culation creates a separate homogeneous group (Group 5) tical analysis. This work was supported by the European based on the quantitative composition, and the shape of Union, co-financed by the European Social Fund: EFOP- the andesite and dolomite clasts (Fig. 8). Most probably, 3.6.1.- 16-2016- 00004. the volcanic rock debris was derived from the Middle Tri- assic succession occurring on the northern side of a major regional fault zone, the Nagykovácsi Line [40]. In this belt, Abbreviation definition the Middle Triassic volcanic rocks were probably exposed on the surface prior to the Eocene transgression. AR: Apáthy Rock – Stone Gate CCDA: combined cluster and discriminant analysis CPV: Szádeczky-Kardoss’ CPV method (C=concave, 6 Conclusions P=planar, V=convex) FQ: Fenyőgyöngye quarry HCA: hierarchical cluster analysis Due to the scarcity of observable sedimentary structures, HP: Hunyad Peak statistical methods (i.e. descriptive statistics of the textu- GR: Group ral parameters using our newly developed Rock Analyst JH: János Hill – Virág Valley application, and the spatial pattern of these parameters KH: Kő Hill of Budaörs obtained through CCDA) provided the most suitable tool LDA: linear discriminant analysis available for the interpretation of the provenance, trans- LH: Látó Hill port processes and depositional conditions of an Eocene q95: 95% quantile of the percentages for the random coarse grained clastic succession in the area of the Buda groupings Hills. In this way, various depositional environments (ter- RH: Róka Hill quarry restrial toe-of-slope fans, intermittent streams and abra- SG: Sub-group sional rocky coast) could be distinguished. TR: Tündér Rock According to our hypothesis, during the terminal Cre- UH: Út Hill taceous to Priabonian period the study area was a karstic terrestrial environment with significant relief differences. Mostly Triassic formations were eroded at this time. Out- crops of dolomites (and locally cherty dolomites)—i.e. the A Appendix sources of dolomite clasts—were widely extended, but the volcanic clasts (mostly andesite) were derived from vol- The Rock Analyst application and the user manual are canic sources of rather limited surface exposure (as dikes available at the following website: http://faricseva.web. intruded into dolomite). During the long continental pe- elte.hu/ riod prior to the Late Eocene transgression, small fans were formed at the foot of slopes which supplied intermittent streams. Those fans, which were located close to References the andesite source rocks, along with dolomite (and chert) clasts, contained significant amount of andesite clasts. [1] Szádeczky-Kardoss E., Die Bestimmung des Abrollungs grades. These clasts may have been transported and redeposited Zentralblatt für Mineralogie, Geologie und Paläontologie, Abt. by intermittent streams along the valleys. After the marine B, 1933, 389-401 inundation of the area in the Late Eocene, a part of the pre- [2] Győrfy É., Mid-Miocene coarse-clastic sequences in the Dráva viously accumulated terrestrial sediments reworked under Basin: petrography and provenance. Bulletin of the Hungarian Geological Society, 2015, 145/1, 23-44 (in Hungarian with En- marine conditions and mixed with sediments. glish summary) [3] Kovács J., Kovács S., Magyar N., Tanos P., Hatvani I.G., Anda A., Interpretation of sedimentological processes of coarse-grained deposits Ë 537

Classification into homogeneous groups using combined clus- [18] Wentworth C.K., Quantitative studies of the shapes of pebbles. ter and discriminant analysis. Environmental Modelling & Soft- Mester of Science thesis, State University of Iowa, Iowa, USA, ware, 2014, 57, 52-59 1921 [4] Kovács J., Kovács S., Hatvani I.G., Magyar N., Tanos P., Kor- [19] Cailleux A., Morphoskopische Analyse der Geschiebe und Sand- ponai J., Blaschke A.P., Spatial Optimization of Monitoring Nwt- korner und ihre Bedeutung für die Paleoklimatologie. Geologis- works on the Examples of a River, a Lake-Wetland System and che Rundschau, 1952, 40, 11-19 a Sub-surface Water System. Water Resources Management, [20] Wadell H., Volume, shape and roundness of rock particles. Jour- 2015, doi: 10.1007/s11269-015-1117-5 nal of Geology, 1932, 40, 443-451 [5] Tanos P., Kovács J., Kovács S., Anda A., Hatvani I.G., Optimiza- [21] Powers M.C., A new roundness scale for sedimentary particles. tion of the monitoring network on the River Tisza (Central Eu- Journal of Sedimentary Petrology, 1953, 23, 117-119 rope, Hungary) using combined cluster and discriminant anal- [22] Riley N.A., Projection sphericity. Journal of Sedimentary Petrol- ysis, taking seasonality into account. Environmental Monitor- ogy, 1941, 11/2, 94-97 ing and Assessment, 2015, 187:575, doi: 10.1007/s10661-015- [23] Day W.H.E., Edelsbrunner H., Eflcient algorithms for agglomer- 4777-y ative hierarchical clustering methods. Journal of Classification, [6] Bánfi R., Pohner Zs., Kovács J., Luzics Sz., Nagy A., DudásM. 1984, 1, 7-24 et al., Characterisation of the large-scale production process of [24] Duda R.O., Hart P.E., Stork D.G., Pattern Classification. Wiley- oyster mushroom (Pleurotus ostreatus) with the analysis of suc- Interscience Publication, 2000 cession and spatial heterogeneity of lignocellulolytic enzyme [25] McLachlan G., Discriminant Analysis and Statistical Pattern activities. Fungal Biology, 2015, 119, 1354-1363 Recognition. Wiley-Interscience Publication, 2004 [7] Hofmann K., A Buda-kovácsi hegység földtani viszonyai. Annals [26] Gross D.S., Atlas R., Rzeszotarski J., Turetsky E., Christensen J., of the Hungarian Royal Geological Institute, 1871, 1, 199-273 (in Benzaid S. et al., Environmental chemistry through intelligent Hungarian) atmospheric data analysis. Environmental Modelling & Soft- [8] Horváth E., Tari G., Middle Triassic volcanism in the Buda Moun- ware, 2010, 25/6, 760-769 tains. Annales Universitatis Scientarium Budapestinensis de [27] Ward J.H., Hierarchical Grouping to Optimize an Objective Func- Rolando Eötvös Nominatae Sectio Geologica, 1987, 27, 3-16 tion. Journal of the American Statistical Association, 1963, [9] Kósa G., Mindszenty A., Mohai R., Eocene alluvial fan prograd- 58/301, 236-244 ing over a highly dissected palaeokarst surface built up by Up- [28] Kovács S., Kovács J., Tanos P., Combined Cluster and Discrim- per Triassic dolomites. New details on the early Palaeogene evo- inant Analysis: package ‘ccda’ in R 1-6, 2014, http://cran.r- lution of the Buda-Hills. Bulletin of the Hungarian Geological project.org/web/packages/ccda/ccda.pdf Society, 2003, 133/2, 271-285 (in Hungarian with English sum- [29] Hatvani I.G., Kovács J., Kovácsné Sz.I., Jakusch P., Korponai J., mary) Analysis of long-term water quality changes in the Kis-Balaton [10] Horváth A., Kázmér M., Eocene brackish-water algal deposits in water protection system with time series-, cluster analysis and Budapest, Hungary. IAS 7th Regional Meeting on Sedimentol- Wilks’ lambda distribution. Ecological Engineering, 2011, 37/4, ogy, Krakow, Poland, 1986, pp. 85 629-635 [11] Farics É., Józsa S., Haas J., Petrographic features of lava rock [30] Hatvani I.G., Clement A., Kovács J., Kovácsné Sz.I., Korponai J., and tuff clast-bearing sedimentary rocks at the base of the Up- Assessing water-quality data: the relationship between the wa- per Eocene succession in the Buda Hills. Bulletin of the Hungar- ter quality amelioration of Lake Balaton and the construction of ian Geological Society, 2015, 145/4, 331-350 (in Hungarian with its mitigation wetland. Journal of Great Lakes Research, 2014, English summary) 40/1, 115-125 [12] Haas J., Budai T., Dunkl I., Farics É., Józsa S., Kövér Sz. et [31] Mohs F., Grundriß der Mineralogie (two volumes, 1822 and al., The Budaörs-1 well revisited: Contributions to the Triassic 1824). Arnoldschen Buchhandlung, Dresden (translated to En- stratigraphy, sedimentology, and magmatism of the southwest- glish by Wilhelm Ritter von Haidinger as Treatise on Mineralogy ern part of the Buda Hills. Central European Geology, 2017, DOI: in 1825 and published by Constable & Co. Ltd. of Edinburgh) 10.1556/24.60.2017.008 [32] Miall A.D., The Geology of Fluvial Deposits. Sedimentary Facies, [13] Wein Gy., A Budai-hegység tektonikája. Geological Institute of Basin Analysis, and Petroleum Geology. Springer, Berlin, Hei- Hungary, Budapest, 1977 (in Hungarian) delberg, New York, London, Paris, Tokyo, Hong Kong, 1996 [14] Balogh K., Correlation of the Hungarian Triassic. Acta Geologica [33] Ferguson R., Hoey T., Wathen S., Werritty A., Field evidence Hungarica, 1981, 24/1, 3-48 for rapid downstream fining of river gravels through selective [15] Kozur H., Mock R., New Middle Carnian and Rhaetian conodonts transport. Geology, 1996, 24, 179-182 from Hungary and the Alps. Stratigraphic importance and tec- [34] Hoey T., Ferguson R.I., Controls of strength and rate of down- tonic implications for the Buda Mountains and adjacent areas. stream fining above a river base level. Water Resources Re- Jahrbuch Geologischen Bundesanstalt, 1991, 134, 271-297 search, 1997, 33/11, 2601-2608 [16] Haas J., Korpás L., Török Á., Dosztály L., Góczán F., Hámorné [35] Surian N., Downstream variation in grain size along an Alpine Vidó M. et al., Upper Triassic basin and slope facies in the Buda river: analysis of controls and processes. Geomorphology, Mts.—based on study of core drilling Vérhalom tér, Budapest. 2003, 43, 137-149 Bulletin of the Hungarian Geological Society, 2000, 130/3, 371- [36] Sklar L.S., Dietrich W.E., Foufoula-Georgiou E., Lashermes B., 421 (in Hungarian with English summary) Bellugi D., Do gravel bed river size distributions record chan- [17] Magyari Á., Late Eocene Transpression in the Budaörs Hills. Bul- nel network structure?. Water Resources Research, 2006, 42/6, letin of the Hungarian Geological Society, 1994, 124/2, 155-173 W06D18. (in Hungarian with English summary) [37] Kubovics I., Mesozoic magmatism of the Transdanubian Mid- 538 Ë Éva Farics, Dávid Farics, József Kovács, and János Haas

Mountains. Acta Geologica Hungarica, 1985, 28, 141-164 [41] Haas J., Budai T., Stratigraphic and facies problems of the Up- [38] Kubovics I., Szabó Cs., Harangi Sz., Józsa S., Petrology and per Triassic in the Transdanubian Range Reconsideration of old petrochemistry of mesozoic magmatic suites in Hungary and problems on the basis of new results. Bulletin of the Hungar- adjacent areas—an overview. Acta Geodaetica, Geophysica et ian Geological Society, 2014, 144/2, 125-141 (in Hungarian with Montanistica Hungarica, 1990, 25/3-4, 345-371 English summary) [39] Harangi Sz., Szabó Cs., Józsa S., Szoldán Zs., Mesozoic Igneous [42] Budai T., Gyalog L., Geological Map of Hungary for Tourists Suites in Hungary: Implications for Genesis and Tectonic Set- (1:200 000). Geological Institute of Hungary, Budapest, Hun- ting in the Northwestern Part of Tethys. International Geology gary, 2010 Review, 1996, 38, 336-360 [40] Fodor L., Magyari Á., Fogarasi A., Palotás K., Tertiary tectonics and Late Palaeogene sedimentation in the Buda Hills, Hungary. A new interpretation of the Buda Line. Bulletin of the Hungar- ian Geological Society, 1994, 124/2, 129-305 (in Hungarian with English summary)