role of orogen parallel trending faults …geo.geoscienze.unipd.it/sites/default/files/anna...

Scuola di Dottorato in Scienze della Terra, Dipartimento di Geoscienze, Università degli Studi di Padova – A.A. 2014-2015

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ROLE OF OROGEN PARALLEL TRENDING FAULTS IN THE DEFORMATION OF ANDEAN FORELAND

Ph.D. candidate: ANNA TRAFORTI, I course

Tutor: Prof. DARIO ZAMPIERI, Co-Tutor: Prof. MATTEO MASSIRONI, GIULIO DI TORO Cycle: XXX

Abstract

Orogen parallel strike-slip faults play ambiguous role in the evolution of orogenic belts and are classically affected by geometrical complexities. Most of them nucleate on pre-existing anisotropies and are characterized by ambiguous kinematics since they are forced to accommodate different strains with time. N-S faults in the Andean back-arc and foreland are well representative of such faults. The timing and kinematics of Cumbre de Gaspar-Nono fault (CGN) in the Andean foreland has been investigated during my first year of research. Remote sensing analysis on multispectral sensor images and medium to high resolution Digital Elevation Models, along with meso-structural fieldwork analysis and stress inversion methods, reveal a complex polyphasic deformation, dominated by a transtensional palaeostress regime supporting a sinistral strike-slip component along the CGN fault. If the proposed kinematics is proved to be true, the role of the strike-slip component in the Andean foreland deformation must be reconsidered. Introduction

Orogen parallel strike-slip faults are generally strongly controlled by inherited structural and rheological anisotropies, display a very complex geometry in plan view and are often associated to ambiguous polyphase kinematics. All these factors make their role in the present-day seismicity of orogenic belts very difficult to be understood. One of the main goals of my PhD research is to define the role of orogen parallel strike-slip faults in the overall evolution of orogenic belts. The N-S trending faults in the Andean cordillera back-arc and foreland are perfect candidates to study such peculiar structures. Hence, after having finalized my master degree follow-ups on the Andean N-S strike-slip faults of Laguna Blanca (Traforti et al. 2015), I have shifted my interest on the Andean foreland slope, trying to define the role of N-S trending faults in the deformation of Eastern Sierras Pampeanas, in order to verify whether strike-slip deformation of undulated steep N-S faults might account for observed basins and confined prominent ranges. The Eastern Sierras Pampeanas of central Argentina are composed of a series of basement-cored ranges, uplifted and tilted during the Neocene, as result of the easternmost continental deformation related to the flat-slab subduction of the Nazca Plate beneath the South American Plate. The principal ranges are bounded along their western flanks by N-S, east-dipping reverse faults. The metamorphic basement of late Proterozoic to early Palaeozoic age exposed in this ranges is composed by anatectic rocks associated with gneisses and schists mainly of sedimentary origin (Bonalumi et al., 1999). Moreover, the basement was intruded by small plutons of granitic, tonalitic and dioritic composition of early to middle Palaeozoic age, and more extended peraluminous granitoids, associated to the Devonian-Carboniferous boundary, e.g. Achala batolith (Bonalumi et al., 1999) (Figure 1). During this first year, my study was focussed on the Sierras de Córdoba (central region of Eastern Sierras Pampeanas) and in particular, on the Cumbre de Gaspar-Nono fault system (CGN), which bound the western flank of Sierra Grande de Cordoba (Figure 1C). This N-S trending fault exhibits along strike sinuosity, which is spatially related to basin area and topographic high (Nono basin and Cumbre de Gaspar respectively) (Figure 2A). The CGN fault system shows evidence of neotectonic activity as it is associated to the deformation of Pliocene sediments (Kraemer et al., 1993; Costa et al., 2014) and to local crustal seismicity events (with hypocentre depth from 5 and 25 km), which cluster along its structure (Richardson et al., 2012; Richardson et al., 2013). The kinematics and time of deformation of the CGN fault system have been investigated by means remote sensing analysis on multispectral sensor images and medium to high resolution Digital Elevation Models, along with meso-structural fieldwork analysis and stress inversion methods. Concurrently, the presence of well-preserved fault zones in the study area gave us the opportunity to characterize these structures (fault core and damage zone) from a spectroscopic point of view. The grain


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size reduction and oxide/clay-rich mineralogical composition of fault rocks greatly influence their reflectance spectra. Therefore, fault rock spectra have been employed in image classification techniques, as a new possible means for the remote sensing identification of fault zones.

Figure 1

(A) Sierras Pampeanas general location; (B) WPS = Western Sierras Pampeanas, NSP = Northern Sierras Pampeanas, ESP = Eastern Sierras Pampeanas; (C) main geological features of Sierras de Cordoba, white rectangle indicates the Cumbre de

Gaspar-Nono (CGN) fault system. Methods

Cumbre de Gaspar-Nono fault system I performed a remote sensing analysis on multispectral sensor images (ASTER, Landsat 8 OLI/TIRS) and on medium to high resolution Digital Elevation Model (SRTM , ASTER GDEM and Alos AW3D Jaxa DEM), aimed to assess the regional fault pattern and the geological and geometrical complexity of CGN fault system. After the pre-processing phase (radiometric calibration, Short-Wave InfraRed – SWIR – bands resampling to 15 m/pixel and Fast Line of sight Atmospheric Analysis of Spectral Hypercubes – FLAASH – atmospheric correction for ASTER images; Landsat Calibration for Landasat 8 images), the processing of satellite images was carried out by classical procedures: image sharpening of Landsat 8 OLI/TIRS and directional filters applied to ASTER data. The processed images resulted thus more suitable for the detection of main tectonic lineaments, fault zones and fracture patterns. Concurrently, several hillshade models were created from the DEMs in order to emphasize lineament features according to their strike, by using different solar azimuths and elevation angles. I proceeded with the mapping of lineament features at different scales (1:30.000 and 1:100.000) from Landsat 8 OLI/TIRS and ASTER processed images, DigitalGlobe products (ArcGis World Imagery tool) and DEM hillshade models. From the resulting lineament maps the related rose diagrams were extracted. The remote sensing interpretation, checked during the field surveys, was essential in the identification of key sectors, on which perform the meso-structural analysis. The latter was aimed to characterize the CGN fault system along its southern part, where the Nono basin is located (Figure 2A). It appears to be a 30-Km-long to 8-km-wide basin with sigmoidal shape in map view, developed on the western side of the Achala batholith. Fault-slip data


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(strike and dip of fault planes and slickenlines) and Riedel-type kinematic indicators (Tchalenko, 1970; Petit, 1987) were collected on measure sites located along the CGN fault system on the western margin of Nono basin (Figure 2B). The measured fault-slip data were used to retrieve local principal palaeostress axes through stress inversion methods. The software used for the inversion was WinTensor 5.8.2 (Delvaux et al., 1997; Delvaux & Sperner, 2003), which allows the application of both classical inversion methods (e.g. Right Dihedra method) and an iterative inversion procedure defined as Rotational Optimization method (Delvaux & Sperner, 2003).

Figure 2

(A) Cumbre de Gaspar-Nono (CGN) fault system; (B) Location of meso-structural measure sites along the western margin of Nono basin (base maps are hillshade models of Alos AW3D Jaxa DEM).

Fault rocks analysis and remote sensing image classification Preliminary analysis of fault rock spectra was carried out on samples of two well preserved fault zones in the Northern Sierras Pampeanas and Southern Puna plateau. From the first fault zone (Farallòn Negro) a sample of mineralized cataclasite was collected; from the second fault zone (South of Vicuña Pampa Caldera) two samples of fault gouge were collected (characterized by different degree of alteration). Bidirectional reflectance spectra of fault rock samples were acquired by means of a Field-Pro Spectrometer mounted on a goniometer (IAPS-INAF, Roma), with a spectral range of 0,350 µm - 2,50 µm and a spectral resolution of 3 nm in the VIS (VISible light) and 10-12 nm in the NIR (Near InfraRed). I acquired ten reflectance spectra on the massive cataclasite sample (both on mineralised and non-mineralised surface) and four reflectance spectra for each of the fault gouge samples, maintaining their original grain size. The reflectance spectra of each samples were then averaged, in order to retrieve one spectral signature representative of the internal variability of the sample. Similarly, I acquired one reflectance spectrum for each samples reduced at a grain size of < 150 µm. This allowed me to compare


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the spectral signature of the powdered samples (diameter < 150 µm) to the mineral signatures in the USGS spectral library (typically retrieved from powdered minerals). The mineralogical composition of the mineralized cataclasite was measured by means of X-ray diffraction (University of Padova) both on powdered sample and on mineralised sample surface. In the same way, Powder X-ray diffraction was applied to define the mineralogical composition of fault gouges. The latter were also subjected to a granulometric analysis (University of Padova). The characterization of fault rock samples was fundamental during the following image classification procedures aimed to identify fault zones remotely on the basis of their spectral characteristics. In particular, I applied Spectral angle Mapper (SAM) classification (Kruse et al., 1993), which classifies the image pixels by defining the spectral similarity between a reference spectrum and a test spectrum. The test spectrum is calculated for each pixel and the reference spectrum is defined by the operator. The similarity between the spectra is calculated as the angle between vectors in a n-D space, where n is the number of bands. Smaller angles represent closer matches to the reference spectrum. This classification is particularly efficient as it is insensitive to illumination and albedo effects, which do not affect the angle between the vectors, but only their length. In order to define which spectral signatures is more suitable to be used in the identification of fault zones by means of SAM technique, I performed this classification on ASTER images using different spectral signatures as reference spectrum. The adopted reference spectra were: (i) the spectral signatures of samples with their original characteristics (original grain size for fault gouges and massive surfaces for mineralized cataclasite); (ii) the spectral signatures of the powdered samples (diameter < 150 µm); (iii) USGS spectral library signatures of the fault rock minerals (defined by XRD) and (iv) the spectral signatures derived from ROI (Region Of Interest) directly drown on ASTER images on the studied fault zones. Preliminary results and discussions

Cumbre de Gaspar-Nono fault system The remote sensing analysis of CGN fault system and nearby area was focused on the Nono basin sector. It reveals the presence of three main lineament systems trending at N-S, E-W and WNW-ESE. The N-S lineament system appears to be more prominent and continuous and it frequently cuts both the E-W and WNW-ESE trending lineaments. In some case, these N-S lineament features have been interpreted as sub-vertical faults, probably associated to a sinistral strike-slip component, due to the presence of associated Riedel-type structures. The CGN fault system, bounding the western margin of Nono basin, is morphologically highlighted by a tens of meters high scarp with a regular curvilinear shape, concave towards the east. NW-SE and NE-SW crosscutting fracture systems seem to be spatially related to the CGN fault in this sector. Field observations and measurements performed during the meso-structural analysis showed that the western margin of Nono basin is affected by a polyphasic deformation, dominated by normal faulting. The neotectonic activity of the fault system is constrained by crosscut Quaternary fluvial sequences and gravitational taluses. The first sector analysed is located in the southern part of the basin, where the CGN fault trends NNW-SSE (Figure 3B). Here, a local transpressional palaeostress regime with a NNW-SSE maximum principal stress axis (σ1 ≈174/18) was derived from secondary E-W reverse and NW-SE dextral faults (Figure 3C). This regime can be associated to the Pleistocene, as the E-W reverse faults affect fluvial sediments of the Las Rabonas Formation (Pleistocene age) (Figure 3A). In the same sector, a local extensional axis trending at ENE-WSW (σ3 ≈086/05) was also retrieved for N-S high angle normal faults (Figure 3D). In the central sector of the basin western margin, where CGN fault trends N-S (Figure 4B), a local extensional palaeostress tensor was calculated on N-S normal faults, with the minimum principal stress axis trending at NE-SW (σ3 ≈ 041/01), (Figure 4C). This regime was associated to the Pliocene, as the analysed fault zone is characterized by Pliocene deposits dragged in the fault breccia (Figure 4A). In the northern part of the basin (i.e. CGN fault trending at NNE-SSW) a clear local transtensional palaeostress was retrieved for NNE-SSW oblique-slip faults, with the minimum principal stress axis trending at NW-SE (σ3 ≈ 300/11), (Figure 5).


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Figure 3

(A) E-W reverse fault affecting fluvial sediments of Pleistocene age (Las Rabonas Formation); (B) measure sites located in the southern part of the Nono basin (i.e. CGN fault trending NNW-SSE) and associated palaeostress tensor; (C) local

transpressional palaeostress regime (σ1 ≈174/18) derived from secondary E-W reverse and NW-SE dextral faults, during the stress inversion procedure (Rotational Optimization Method); (D) local extensional axis trending at ENE-WSW (σ3 ≈086/05)

retrieved for N-S high angle normal faults (PTB Method). Fault rocks analysis and remote sensing image classification The spectra of sample with its original characteristics (original grain size for fault gouge and massive sample surface for mineralized cataclasite) are characterized by higher band depths, lower albedo and lower continuum slope, compared to the spectra of powdered fault rock samples (< 150µm) (Figure 6). Therefore, the measured spectra are strongly affected by the sample grain size. The effects of grain size reduction on reflectance spectra is well documented by several authors and can be summarized as follows: (i) reflectance decreases with increasing grain size (e.g. Adams and Filice, 1967; Ross et al., 1969; Gaffey et al., 1993; Hapke, 1993); (ii) continuum slope increases with grain size reduction, up to a given size under which the slope start to decrease (e.g. Starukhina and Shkuratov, 1996; Harloff et al. 2001); (iii) there is an optimal particle size for each absorption feature that maximizes its band depth (e.g. Clark and Lucey, 1984; Salisbury et al., 1991; Gaffey et al., 1993; Hapke, 1993); (iv) for bulk surfaces the absorption features are weaker and the continuum slope more negative than that of the powders spectrum (Harloff et. al., 2001). The differences between the spectra of original samples and the powdered ones are significant, as demonstrated by the results of SAM classification on ASTER images (Figure 7 and 8). Indeed, SAM technique based on reflectance spectra of samples with their original characteristics provided a good classification of the fault zones, in terms of both length and width (Figure 7C and 8C). On the contrary, extremely poor results were provided by SAM classification based on powered fault rock spectra (Figure


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7D and 8D). Equally, the fault zones were wrongly classified by SAM technique based on USGS spectral library signatures of the fault rock minerals (Figure 7E and 8E). Therefore, powders spectra, as well as spectral library signatures, are less representative than the spectra of original samples in the remote sensing identification of fault zones. This can be quantified considering the maximum threshold angle, chosen in each SAM classification, which provides a measure of the spectra similarities between the reference spectrum and the test spectrum. Smaller angles, which represent closer matches to the reference spectrum, were defined for SAM classification based on the spectrum of the samples with their original grain size (Figure 7 and 8). However, it is worth of note how SAM classification based on ROI spectral signatures gave the better results in terms of close fault matching and small threshold angle (Figure 9). This is due to the fact that ROI spectral signatures are extracted directly from the image and therefore have been acquired with the same spectral sensor and under the same acquisition geometry and environmental condition of the whole image.

Figure 4

(A) Normal fault zone characterized by Pliocene deposits dragged in the fault breccia; (B) measure site located in the central part of the Nono basin (i.e. CGN fault trending N-S) and associated palaeostress tensor; (C) local extensional palaeostress

tensor (σ3 ≈ 01/041), derived from N-S normal faults, during the stress inversion procedure (Rotational Optimization Method).


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Figure 5

(A) Normal faults affecting granitoids of the Achala Intrusive Complex; (B) measure site located in the northern part of the Nono basin (i.e. CGN fault trending NNE-SSW) and associated palaeostress tensor; (C) local extensional palaeostress tensor (σ3 ≈ 300/11), derived from NNE-SSW oblique-slip faults, during the stress inversion procedure (Rotational Optimization

Method).

Figure 6

Bidirectional reflectance spectra of fault rock samples acquired with Field-Pro Spectrometer. (A) Reflectance spectra of powdered (< 150 µm) mineralized cataclasite (red) and massive mineralized cataclasite (blue); (B) reflectance spectra of

powdered (< 150 µm) fault gouge (red) and fault gouge sample with original grain size (blue). Black lines represent spectra resampled at ASTER sensor resolution.


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Figure 7

(A) ASTER image (RGB = 321) of the studied fault zone in Farallòn Negro, yellow star indicates the location of mineralized cataclasite sample; (B) geological map of the study area; (C) result of SAM classification technique based on massive fault

rock sample; (D) result of SAM classification technique based on powdered fault rock sample (< 150 µm); (E) result of SAM classification technique based on USGS spectral library signatures of the fault rock minerals. At the top of figure (C), (D) and (E) the reference spectra used in each SAM classification are reported, black lines represent the resampled spectra at ASTER

sensor resolution. Conclusions and future work

The performed remote sensing techniques provided a valid instrument to assess the structural framework of the study area. During the next year, a detailed morphometric analysis will be carried out on the Alos AW3D DEM (5 m of spatial resolution), in order to reveal the eventual influence of active faults on river morphology and quaternary landforms. The results of stress inversion procedure along with field observations reveal that the CGN fault system was subjected to a complex polyphasic deformation. The calculated palaeostress tensors in the Nono basin sector show a dominant extensional component, which exhibits a progressive rotation of the minimum principal stress axis (σ3) from ENE-WSW to NW-SE direction associated to the variation in strike of the main fault. This is in agreement to what it is expected at the margin of an extensional basin associated to a transcurrent fault. In order to disentangle the deformational history of the CGN fault system further meso-structural analysis will be necessary, also including the northern sector of the CGN fault (Cumbre de Gaspar). In this perspective, during next semester, a second field survey will be performed in the studied area. Concurrently, I will visit the University of San Juan (Argentina) to analyse the crustal seismicity associated to the Sierras the Cordoba. This will be carried out in collaboration with Patricia Alvarado (University of San Juan and CONICET institute) and INPRES (Instituto Nacional de Prevención Sísmica). The characterization of crustal seismicity will allow me to better constrain the active deformation in the area.


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In the application of spectral matching algorithms (e.g. SAM) for the identification of fault rocks, great attention must be paid to the choice of reference spectra. In particular, powders spectra are less representative than spectra of original samples for those features, like fault zones, which are characterized by grain size reduction processes. During the next year, I will perform a spectral characterization of a fault zone affecting monomineralic host rocks. In this way, I will try to define the variation of the reflectance spectrum strictly due to the progressive grain size reduction observed from the damage zone to the fault core. With this purpose, I have already collected 40 samples along two transect perpendicular to the Campo Imperatore fault zone (CIFZ, Abruzzo, Italy) which crosscuts a mono-lithologic dolostone. In the next future, I will perform bidirectional reflectance spectra measurement both on fault rock samples in the laboratory (IAPS-INAF, Roma) and in situ, using the same instrument (Field-Pro Spectrometer). Subsequently, on the bases of those measurements, I will apply spectral matching algorithms (e.g. SAM) on satellite images in order to define the fault-rock distribution associated to the CIFZ. Considering the width of CIFZ (≈ 300 m), I will use multispectral images with high spatial resolution such as WordView3 products.

Figure 8

(A) ASTER image (RGB = 321) of the studied fault zone in the South of Vicuña Pampa Caldera, yellow star indicates the location of fault gouge samples (characterized by different degree of alteration); (B) geological map of the study area; (C)

result of SAM classification technique based on fault rock samples with original grain size; (D) result of SAM classification technique based on powdered fault rock samples (< 150 µm); (E) result of SAM classification technique based on USGS

spectral library signatures of the fault rock minerals. At the top of figure (C), (D) and (E) the reference spectra used in each SAM classification are reported, black lines represent the resampled spectra at ASTER sensor resolution.


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Figure 9

Comparison between the (A) result of SAM classification based on ROI spectral signatures and the (B) result of SAM classification based on massive fault rock sample (Farallòn Negro fault zone).


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References

ADAMS, J.B., & FILICE, A.L. 1967. Spectral reflectance 0.4 to 2:0 µm of silicate rock powders. J. Geophys. Res. 72, 5705–5715.

BONALUMI, A., MARTINO, R., BALDO, E., ZARCO, J., SFRAGULLA, J., CARIGNANO, C., & KRAEMER, P. 1999. Programa Nacional de Cartas Geológicas de la República Argentina Hoja Geológica 3166-IV Provincias de Córdoba, La Rioja y San Luis Villa Dolores.

CLARK, R.N., & LUCEY, P.G. 1984. Spectral properties of ice-particulate mixtures and implications for remote sensing. J. Geophys. Res. 89 (B7), 6341–6348.

COSTA, C. H., MASSABIE, A. C., SAGRIPANTI, G. L., BRUNETTO, E., & COPPOLECCHIA, M. 2014. Neotectonica. Relatorio del XIX Congreso Geológico Argentino- Cordoba, 2014.

DELVAUX, D., MOEYS, R., STAPEL, G., PETIT, C., Levi, K., MIROSHNICHENKO, A., RUZHICH, V., SANKOV, V. 1997. Paleostress reconstructions and geodynamics of the Baikal region, Central Asia. Part II: Cenozoic rifting. Cloetingh, S., Fernandez, M., Munoz, J.A., Sassi, W., and Horvath, F. (Editors), Structural controls on sedimentary basin formation. Tectonophysics 282: 1-38.

DELVAUX, D., SPERNER, B. 2003. Stress tensor inversion from fault kinematic indicators and focal mechanism data: the TENSOR program. New Insights into Structural Interpretation and Modelling (D. Nieuwland Ed.). Geological Society, London, Special Publications 212, 75-100.

GAFFEY, M. J., MCFADDEN, L. A., NASH, D., & PIETERS, C. M. 1993. Ultraviolet, visible, and near-infrared spectroscopy: Laboratory spectra of geologic materials. In Remote Geochemical Analysis: Elemental and Mineralogical Composition (C. M. Pieters and P. J. Englert, Eds.), pp. 43–78. Cambridge Univ. Press, Cambridge

HAPKE, B. 1993. Theory of Reflectance and Emittance Spectroscopy. Cambridge Univ. Press, Cambridge.

HARLOFF, J., & ARNOLD, G. 2001. Near-infrared reflectance spectroscopy of bulk analog materials for planetary crust. Planetary and Space Science, 49, 191–211.

KRAEMER, P., TAUBER, A., SCHIMIDT, Ch., & RAME, G. 1993. Análisis cinemático de la “Falla de Nono” Evidencias de actividad neotectónica, Valle San Alberto, Provincia de Córdoba. 12º Congreso Geológico Argentino y 2º Congreso de Explotación de Hidrocarburos, Actas 3, 277–281.

KRUSE, F. A., LEFKOFF, A. B., BOARDMAN, J. B., HEIDEBRECHT, K. B., SHAPIRO, A. T., BARLOON, P. J., & GOETZ, A. F. H. 1993. The Spectral Image Processing System (SIPS) – Interactive Visualization and Analysis of Imaging Spectrometer Data: Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 145 - 163.

PETIT, J. P. 1987. Criteria for the sense of movement on fault surfaces in brittle rocks. Journal of Structural Geology 9, 597-608.

RICHARDSON, T., GILBERT, H., ANDERSON, M., & RIDGWAY, K. D. 2012. Seismicity within the actively deforming Eastern Sierras Pampeanas, Argentina. Geophysical Journal International, 188, 408–420.

RICHARDSON, T., RIDGWAY, K. D., GILBERT, H., MARTINO, R., ENKELMANN, E., ANDERSON, M., & ALVARADO, P. 2013. Neogene and Quaternary tectonics of the Eastern Sierras Pampeanas, Argentina: Active intraplate deformation inboard of flat-slab subduction. Tectonics, 32(3), 780–796.

ROSS, H.P., ADLER, J.E., HUNT, G.R. 1969. A statistical analysis of the reflectance of igneous rocks from 0.2 to 2.65 µm. Icarus 11, 46–54

SALISBURY, J.W., WALTER, L.S., VERGO, N., D’ARIA, D.M. 1991. Infrared (2.1–25 µm) Spectra of Minerals. John Hopkins University Press, London.

STARUKHINA, L.V., SHKURATOV, Y.G. 1996. Particle size dependence of spectral slope and the depth of absorption bands — implication for lunar soils. Abstracts of the 24th Vernadsky-Brown Microsymposium in Moscow, pp. 90–91.

TCHALENKO, J. S. 1970. Similarities between shear zones of different magnitudes. Geological Society of America Bulletin 81, 1625–1640.


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TRAFORTI, A., MASSIRONI, M., & ZAMPIERI, D. 2015. Geo-structural map of the Laguna Blanca basin (Southern Central Andes, Catamarca, Argentina). Journal of Maps, (May), 1–12.

SUMMARY OF ACTIVITY IN THIS YEAR

Courses: N. SURIAN, F. FERRARESE: “Corso di GIS avanzato”. Departement of Geosciences, University of Padova P. STARK, L. SALMASO, L. CORAIN: “Statistics for Engineers”. Department of Mechanical Engineering, University of Padova A. BISTACCHI: “Tecniche per la modellazione 3D di aree di affioramento”, University of Milano Bicocca M. BORG: “Consolidating skills in English: a multimedial approach”, Department of Geosciences, University of Padova J. ANGEL: “Scientific Communication” Department of Geosciences, University of Padova, 21/10/2014 (still in progress) Communications: TRAFORTI, A., MASSIRONI, M., ZAMPIERI, D., CARLI, C. (2015). Remote sensing analysis for fault-zones detection in the Central Andean Plateau (Catamarca, Argentina). In: European Geosciences Union General Assembly 2015. Vienna, Austria. Publications: TRAFORTI, A., MASSIRONI, M., & ZAMPIERI, D. (2015). Geo-structural map of the Laguna Blanca basin (Southern Central Andes, Catamarca, Argentina). Journal of Maps, (May), 1–12.

Teaching activities: Teaching assistant: 25 hours, “Rilevamento Geologico 1”, Laurea Triennale in Scienze Geologiche (a.a. 2014/2015).

Other:

Training for the application of stress inversion methods at the Geological Survey of Norway (Trondheim, Norway) under the supervision of Giulio Viola (June 2015).

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