Lithological Mapping Research Articles

Entrainment maps considering hydrological conditions for mass movement runout modelling: Application to debris-flow bulking at Pizzo Cengalo

Debris flows entrain sediments and water along their flow path and grow significantly in size. Because the entrainment process isn’t well understood and data is rare, hazard and risk assessment with numerical models is challenging. It is known, however, that both for debris flows and rock avalanches, interstitial pore water in flow path substrate can cause increases in pore water pressures when overridden by the flow, which enhances erosion. The entrained water likely also plays a role in the process transition from rock avalanche to debris flow, like in the Pizzo Cengalo event in 2017. We present a framework for producing entrainment maps serving as an input for runout modelling, here illustrated using RAMMS. The entrainment maps consist of spatially-distributed entities with properties such as max erosion depth, and soil water content inferred from land cover and lithology maps. This study serves as a basis for producing duration curves of subsurface water available for entrainment and include it into the entrainment maps. Such hydrologically-informed entrainment maps will be useful to assess the probability of certain runout distances.

Mapping the critical level of water infiltration areas in the western of Mijen sub-district, Semarang City, Central Java

Semarang City as the capital of Central Java Province is the main destination for urbanization in the Central Java region, which causes population density in Semarang City to increase, resulting in the development of land functions that can interfere with water infiltration areas. The western sub-district of Mijen, Semarang City, which is located on the border of Semarang City with Kendal Regency, and Semarang Regency, has also become a place for land use development due to the dense urban area. From these problems, research was conducted on the criticality of water infiltration areas in the western part of Mijen Subdistrict in order to be taken into consideration by the government to make policies. This research uses primary data in the form of soil and lithology mapping, and secondary data in the form of land use and slope, which are then verified in the field. The data were analyzed using the AHP (Analytical Hierarchy Process) method by creating a pairwise comparison matrix on each parameter, and subparameter, which was then overlay. The western part of Mijen Sub-district has a good level of criticality of water infiltration areas located in parts of Wonoplumbon Village, Ngadirgo Village, Tambangan Village, and a small part of Cangkiran Village. Naturally normal conditions and starting to be critical are relatively evenly distributed throughout the research area. Slightly critical, critical, to very critical conditions are located in the center of Wonoplumbom Village, north of Ngadirgo Village, and south of Cangkiran Village, and Bubakan Village.

Lithological mapping of ophiolitic rocks from southern part of the Sivas Basin (Turkey) using ASTER imagery

The east-west extended Sivas Basin in central-eastern Anatolia is a foreland basin that formed after the obduction of the Tethyan ophiolite during the late Cretaceous and is also a north-verging fold-and-thrust belt. The basement rocks of the basin represent mainly sedimentary (Mesozoic platform-type carbonates) and late Cretaceous Divriği ophiolitic complex. As the ophiolitic rocks are affected by intense tectonic processes, field-based mapping studies require long processes and costs. The study tests to reveal the lithological features of the ophiolitic complex outcropping around the Ulaş district of the Sivas Province using remote sensing methods and techniques. Due to arid climatic conditions and rare vegetation cover in the region, almost all outcrops of the basin rocks can be separated by spectral enhancement methods easily. Band ratio (BR), spectral indices (SI), decorrelation stretch (DS), principal component analysis (PCA), and support vector machine (SVM) on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used in this research. BR, PCA, DC, and SI techniques clearly distinguish the Divriği Ophiolitic Complex from the basement and cover sedimentary rocks. SVM distinguishes the chromite-bearing dunites from the other ophiolite-related rock units. According to image analysis performances, it has been observed that the rocks of the Divriği ophiolitic complex can be differentiated in more detail compared to a 1/100.000 scaled geological map of the region.

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

Improvement of Lithological Mapping Using Discrete Wavelet Transformation from Sentinel-1 SAR Data

Lithological mapping using dual-polarization synthetic aperture radar (SAR) data is limited by the low classification accuracy. In this study, we extract ten parameters (backscatter coefficients and polarization decomposition parameters) from the Sentinel-1 dual-pol SAR data. Using 94 mother wavelet functions (MF), a one-level two-dimensional discrete wavelet transform (DWT) is applied to all the parameters, and the suitable MF is screened by comparing the overall accuracy and F1 score. Finally, the lithological mapping of the study area is performed. According to the cross-validation results, DWT can improve the overall accuracy for all MF. Db13 improved the overall accuracy by 6.1% (from 49.5% to 55.6%). The F1 score of granitoids improved by 0.223. Among the five rock units, Grantoids and Quaternary alluvium and sediment with finer gravel can be better differentiated than the other three rock units. The overall accuracy of effusive rocks (marine basic volcanic rocks) is not improved by DWT, but this study confirms the great potential of DWT in lithology classification.

Neuro-Fuzzy-AHP (NFAHP) Technique for Copper Exploration Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Geological Datasets in the Sahlabad Mining Area, East Iran

Fusion and analysis of thematic information layers using machine learning algorithms provide an important step toward achieving accurate mineral potential maps in the reconnaissance stage of mineral exploration. This study developed the Neuro-Fuzzy-AHP (NFAHP) technique for fusing remote sensing (i.e., ASTER alteration mineral image-maps) and geological datasets (i.e., lithological map, geochronological map, structural map, and geochemical map) to identify high potential zones of volcanic massive sulfide (VMS) copper mineralization in the Sahlabad mining area, east Iran. Argillic, phyllic, propylitic and gossan alteration zones were identified in the study area using band ratio and Selective Principal Components Analysis (SPCA) methods implemented to ASTER VNIR and SWIR bands. For each of the copper deposits, old mines and mineralization indices in the study area, information related to exploration factors such as ore mineralization, host-rock lithology, alterations, geochronological, geochemistry, and distance from high intensity lineament factor communities were investigated. Subsequently, the predictive power of these factors in identifying copper occurrences was evaluated using Back Propagation Neural Network (BPNN) technique. The BPNN results demonstrated that using the exploration factors, copper mineralizations in Sahlabad mining area could be identified with high accuracy. Lastly, using the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) method, information layers were weighted and fused. As a result, a potential map of copper mineralization was generated, which pinpointed several high potential zones in the study area. For verification of the results, the documented copper deposits, old mines, and mineralization indices in the study area were plotted on the potential map, which is particularly appearing in high favorability parts of the potential map. In conclusion, the Neuro-Fuzzy-AHP (NFAHP) technique shows great reliability for copper exploration in the Sahlabad mining area, and it can be extrapolated to other metallogenic provinces in Iran and other regions for the reconnaissance stage of mineral exploration.

Lithology mapping with satellite images, fieldwork-based spectral data, and machine learning algorithms: The case study of Beiras Group (Central Portugal)

Geological remote sensing has been an invaluable means to obtain data to perform geological mapping objectively and with high accuracy. However, there is a significant gap in geological cartography information at 1:50 000 scale throughout the territory in mainland Portugal. The lack of geological mapping is reflected in the geological resources and land management information. This investigation’s main objective was to assess the viability of using remote sensing, machine learning and geochemical techniques as proof of concept for the Portuguese context using the Beiras Group (mainland Portugal) as a case study area of 341 km2. Multispectral analysis was carried out in two Landsat-8 images of 2015, one in the winter and the other in the summer. Hyperspectral data were obtained using a 400–1000 nm spectroradiometer applied to 23 rock samples collected in two field campaigns – the first in January and the second in April 2019. Spectral differences were found distinguishing the two main lithological units, where the granites (Granito de Coentral and Granito de Vila Nova) had an increasing wavelength spectra shape throughout the whole VNIR measurements. Geochemical data was carried out using X-ray Fluorescence, where the average quantity of major elements such as Na2O [2.15 %] and CaO [0.41 %] was higher in granites than metasediments: 0.38 % and 0.11 %, respectively. The J48 machine learning algorithm was performed using as input Landsat-8 reflectance data which showed a high success rate in both confusion matrixes (83,72 % and 94,08 %).

Mapping the Groundwater Potential Areas by Using the GIS Method in Kg Samawang, Sandakan, Sabah, Malaysia.

This study aims to study the groundwater potential areas around Kg Samawang, Sandakan by using the Geographic Information System (GIS) method. There are few basic maps and data were used in this study, which is geology, drainage, soil type, lineament, topography, satellite images and rainfall distribution data. A total of seven thematic maps were produced including the lineament density map, drainage density map, landuse map, soil type map, rainfall distribution map, lithology map and topographic elevation map by using the ILWIS 3.0 software. All the thematic maps will be given a weightage based on its compensation to groundwater availability before being integrated based on the Hopkins Linear Equation using the Multi-Class Index Overlay model. This process will produce a forecast map of the groundwater potential at the study area. As a result of the forecast map, it is estimated that 40% of the study area has high potential of groundwater while 60% has medium, low and very low potential. It is hoped that this groundwater potential map can be used for the exploration work in the future.

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.

Towards lithology mapping in semi-arid areas using time-series Landsat-8 data

Previous remote sensing studies have used optical images to map lithology, commonly based on a single-date product to extract spectral diagnostic features or other geology-related features to aid interpretation. Yet, the performance of time-series or multi-date optical images in lithology mapping has rarely been discussed. In this study, we employed time-series (TS) Landsat-8 products to map the lithology in a semi-arid area of Xinjiang, China. First, we extracted TS reflectance from Landsat-8 multispectral data, and then extracted TS surface moisture, greenness, and brightness via tasseled cap transformation (TCT), a method for transforming the spectral bands of optical images into components (brightness, greenness, and moisture) that can be physically interpreted. Moreover, the land surface temperature (LST) was directly extracted from Landsat-8 Collection 2 products. In total, five surface parameters (TS reflectance, TS moisture, TS greenness, TS brightness, and TS LST), were collected. Then, the rank-based non-parametric Kruskal Wallis rank-sum statistical test was applied, which demonstrated a significant difference between the TS surface parameters among different rock units. Furthermore, different combinations of the TS surface parameters were stacked separately and served as different input features for the random forest classifier. Finally, the performance of TS surface parameters in mapping lithology was evaluated. The classification results showed that (1) TS reflectance outperformed single-date reflectance for mapping rock units; (2) the combination of TS brightness, greenness, and moisture gave a comparable classification result, relative to TS reflectance; (3) TS LST performed best when only one single surface parameter was used to map lithology, and (4) the combined use of LST and all three TCT components achieved the highest accuracy (85.26%) with a kappa coefficient of 0.77. Although surface parameters derived from rock units changed inconspicuously or irregularly over time, these variations captured by TS Landsat-8 data enabled the improvement of the classification accuracy. Overall, our study illustrates the great potential and benefits of using TS Lansat-8 data to map rock units in arid domains.

Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager

Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical and geophysical surveys, and/or remote sensing. Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials, prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration. Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost. Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation. In this study, we extend an artificial intelligence-based, unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager (OLI) satellite imagery and machine learning. The novelty in our method includes: (1) knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures; (2) detection of anomalies occurs only in the variable domain; and (3) a choice of a range of machine learning algorithms to balance between explain-ability and performance. Our new unsupervised method detects anomalies through three successive stages, namely (a) stage I – acquisition of satellite imagery, data processing and selection of bands, (b) stage II – predictive modelling and anomaly detection, and (c) stage III – construction of anomaly maps and analysis. In this study, the new method was tested over the Assen iron deposit in the Transvaal Supergroup (South Africa). It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known. To summarise the anomalies in the area, principal component analysis was used on the reconstruction errors across all modelled bands. Our method enhanced the Assen deposit as an anomaly and attenuated the background, including anthropogenic structural anomalies, which resulted in substantially improved visual contrast and delineation of the iron deposit relative to the background. The results demonstrate the robustness of the proposed unsupervised anomaly detection method, and it could be useful for the delineation of mineral exploration targets. In particular, the method will be useful in areas where no data labels exist regarding the existence or specific spectral signatures of anomalies, such as mineral deposits under greenfield exploration.

Digital and global lithologic mapping of the Moon at a 1:2,500,000 scale

Digital and global lithologic mapping of the Moon at a 1:2,500,000 scale

Evaluation of machine learning techniques with AVIRIS-NG dataset in the identification and mapping of minerals

Identifying valuable mineral resources is obligatory for human development and survival due to their scientific and economic implications. Hyperspectral Remote Sensing (HRS) is an efficient technique that empowers us to precisely identify and map altered minerals based on spectral absorption curves in the VNIR and SWIR range of the electromagnetic spectrum. Machine Learning Algorithms (MLAs) are a subclass of Artificial Intelligence (AI) that improves and automate HRS based lithological mapping using spectra based classification approach. The present study evaluates various MLAs in identifying and mapping hydrothermally altered and weathered minerals such as kaolinite, talc, kaosmec, and montmorillonite. The study was performed with the Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) hyperspectral dataset of Jahazpur town of Bhilwara district in the state of Rajasthan, India (75° 9′ 57′' E, 25° 32′ 24″ N). The Spectral Angle Mapper (SAM) algorithm was applied to create a reference mineral distribution map for the target mineral classes. Further, the reference map has been verified with the field validation survey. A total of 828 pixels were identified as end member pixels. The obtained endmember pixels were further used for developing predictive machine learning based models for mapping the mineral resources using eight supervised classifiers, namely Support Vector Machine, k-Nearest Neighbor, Decision Tree, Random Forest, Logistic Regression, Artificial Neural Network, Linear Discriminant Analysis, and Naïve Bayes. The performance of the classifiers was measured using the kappa coefficient, overall accuracy, precision, recall, F1-score, Matthews’s correlation coefficient, and ROC curve. It was observed in the study that the Support Vector Machine outperformed all the classifiers in terms of overall accuracy and AVIRIS-NG data poses an excellent potential for mineral mapping using machine learning based models in a diverse mountainous area.

A new digital lithological map of Italy at the 1:100 000 scale for geomechanical modelling

Abstract. Lithological maps contain information about the different lithotypes cropping out in an area. At variance with geological maps, portraying geological formations, lithological maps may differ as a function of their purpose. Here, we describe the preparation of a lithological map of Italy at the 1:100 000 scale, obtained from classification of a comprehensive digital database and aimed at describing geomechanical properties. We first obtained the full database, containing about 300 000 georeferenced polygons, from the Italian Geological Survey. We grouped polygons according to a lithological classification by expert analysis of the 5456 original unique descriptions of polygons, following compositional and geomechanical criteria. The procedure resulted in a lithological map with a legend including 19 classes, and it is linked to a database allowing ready interpretation of the classes in geomechanical properties and is amenable to further improvement. The map is mainly intended for statistical and physically based modelling of slope stability assessment and geomorphological and geohydrological modelling. Other possible applications include geoenvironmental studies, evaluation of river chemical composition, and estimation of raw material resources. The dataset is publicly available at https://doi.org/10.1594/PANGAEA.935673 (Bucci et al., 2021).

Processing of Multispectral ASTER Data to Delineate Bauxite Abundant Zones and its Geochemical Characterisation Deposited over Deccan Traps of Central India in Mainpat Plateau, Surguja District, Chhattisgarh

Abstract The current study deals with the application of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data to delineate bauxite deposits over Mainpat plateau, Surguja district, Chhattisgarh, India. Application of specific band combinations help to delineate bauxite in the (SWIR) region (2.16-2.33 μm) and visible near infrared (VNIR) band (0.4–1.0 μm) of multispectral ASTER data. Various image processing techniques like atmospheric correction, minimum noise fraction, pixel purity index, n-dimensional visualiser, and spectral angle mapper were applied to ASTER image to delineate bauxite rich zones. The pure pixels were differentiated from the mixed pixels using pixel purity index which were further subjected to n-dimensional visualiser to identify the target end-members. The presence of bauxite was confirmed by comparing the spectral signatures of end-members with in-built spectral plots of United States Geological Survey spectral libraries and categorized as bauxite and ferruginous bauxite. Finally, the end-members were categorised as bauxite and ferruginous bauxite and mapped using spectral angle mapper. The result obtained was further compared with the lithological map provided by Geological Survey of India for accuracy. Then XRF analysis was carried out for the collected samples to confirm the presence of bauxite and ferruginous bauxite. Geochemical analysis also categorized the samples as bauxite and ferruginous bauxite that added accuracy to the results obtained from remote sensing technique. This study might be considered as a time saving eco-friendly exploitation of natural resources and finally the potential of multispectral ASTER data for mapping mineral resources.

Using a Landsat-8 image for lithological mapping of basement rocks at southeast Aswan city, South Eastern Desert, Egypt.

Using a Landsat-8 image for lithological mapping of basement rocks at southeast Aswan city, South Eastern Desert, Egypt.

Geotechnical Mapping for Soil Physical and Mechanical Parameters and Hard Soil Depth in Badung Regency

The infrastructure is important for the great life of a region and most of the country. Therefore, infrastructure must be always in proper condition in its functionality. Furthermore, it must follow the main principle which is infrastructure asset management. In this case, geotechnical mapping is also related to the principles of Infrastructure Asset Management in planning, designing, and feasibility studies of an infrastructure project in Badung Regency, hence the infrastructure projects are better prepared. The research methods include collecting soil investigation data, processing soil investigation data, describing the results of soil investigation data with mapping tools, and geotechnical zoning with statistical analysis. The results obtain geotechnical map of Badung Regency in 2 and 3-dimensional form. The 2D results in the form of a hard soil depth map can be concluded that South Kuta District has a variation depth of 0.4 – 15 meters, North Kuta District has a variation depth of 1.5 – 5 meters, Kuta District has a variation depth of 1.5 – 10 meters, Mengwi District has a variation depth of 1.5 – 10 meters, Abiansemal District has a variation depth of 5-15 meters, Petang District has a variation depth of 5-22 meters. The 3D Zone 1 lithology map can be concluded that Zone 1 area covered with hard soil depth around 5 – 10 meter is dominated by sandy silt, silty sand, sand and a little clayey silt. That area covered with hard soil depth about 1.5 – 5 meter is dominated by clay, clayey silt, sandy silt, silty sand and sand with heterogeneous distribution. The Zone 1 area is dominated by the specific volume saturated value (γsat) range of 1.5 – 2.0 t/m3. The values > 2.0 t / m3 is found in Sading area with hard soil depth of 5-10 meters. The Zone 1 area is dominated by the range of N-SPT values = 0 - 30. The N-SPT values > 30 are found in Sading area with hard soil depth 5-10 meters.

Mapping of mafic-ultramafic rocks in SMUC-SGT, India using ASTER & Sentinel-2A satellite images

The Salem Mafic–Ultramafic Complex (SMUC) is a major complex in southern Granulite Terrane (SGT), in the northern part of the Cauvery Suture Zone (CSZ), India, also known as the Chalk Hills of Salem. The present study aims to review the efficiency of satellite image processing and related techniques to map the Mafic-Ultramafic rocks in Salem Mafic-Ultramafic Complex (SMUC). This mapping is done using atmospherically corrected Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A satellite image data. The processes employed were image enhancement techniques like Band Combinations (BC), Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) to map the various rock types. Band combinations constructed from the output of PCA and MNF in RGB format showed promising results in discriminating various rock units. The Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classifications techniques were used to classify ASTER and Sentinel-2A satellite images for the study area. The classified lithologies showed strong match with the lithology map published by the Geological Survey of India (GSI), other researches, and field investigation. Particularly, SVM classification of Sentinel-2A MNF + DEM showed 90% overall accuracy when compared with field investigation. The study proved that the processing of VNIR and SWIR bands of ASTER and Sentinel-2A Satellite images coupled with DEM and ground investigation has excellent potential in mapping Mafic-Ultramafic rocks in SMUC SGT.

Correcting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France

Active remote sensing by laser scanning (LiDAR) has markedly improved the mapping of a cave environment with an unprecedented level of accuracy and spatial detail. However, the use of laser intensity simultaneously recorded during the scanning of caves remains unexplored despite it having promising potential for lithological mapping as it has been demonstrated by many applications in open-sky conditions. The appropriate use of laser intensity requires calibration and corrections for influencing factors, which are different in caves as opposed to the above-ground environments. Our study presents an efficient and complex workflow to correct the recorded intensity, which takes into consideration the acquisition geometry, micromorphology of the cave surface, and the specific atmospheric influence previously neglected in terrestrial laser scanning. The applicability of the approach is demonstrated on terrestrial LiDAR data acquired in the Gouffre Georges, a cave located in the northern Pyrenees in France. The cave is unique for its geology and lithology allowing for observation, with a spectacular continuity without any vegetal cover, of the contact between marble and lherzolite rocks and tectonic structures that characterize such contact. The overall accuracy of rock surface classification based on the corrected laser intensity was over 84%. The presence of water or a wet surface introduced bias of the intensity values towards lower values complicating the material discrimination. Such conditions have to be considered in applications of the recorded laser intensity in mapping underground spaces. The presented method allows for putting geological observations in an absolute spatial reference frame, which is often very difficult in a cave environment. Thus, laser scanning of the cave geometry assigned with the corrected laser intensity is an invaluable tool to unravel the complexity of such a lithological environment.

Structural lineament mapping of Central-Eastern high atlas, Morocco, using ASAR/Envisat and SAR/sentinel 1B data

The Central-Eastern part of the High Atlas is dominated by carbonate of Mesozoic age which is affected by the Alpine Orogeny. The deformation manifest in area by different geological structures. The analysis of these later lead to their classification in different category for understanding the geodynamic of the area. This study focuses to map structural lineaments in the Central-Eastern High Atlas using two types of Radar data: Synthetic Aperture Radar (SAR)/Sentinel 1B with polarization (VV + VH) and Advanced Synthetic Aperture Radar (ASAR)/Envisat with polarization (VV + HH). This research aims to demonstrate the utility of Radar data for extracting structural lineaments in carbonate deposits. In this work, two techniques of processing data are used: First, the automatic extraction technique, we are employing the Algorithm Line Module of PCI Geomatica program, after the enhancement of images by applying the mean Co-occurrence filter. Second, the manual extraction technique, it was accomplished by applying directional filters with four directions (N00°, N45°, N90° and N135°) in order to highlight linear structures, then trace the detected lineaments. The synthetic maps of structural lineaments are developed by the two techniques. They are validated by using the lithologic maps extracted from Landsat Operational Land Imager (OLI), the previous researches in the study area, shaded relief extracted from ASTER Global Digital Elevation Model (GDEM), and field investigation data. The results present a good correlation between the direction of structural lineaments extracted from the satellite images and the field measurements. Therefore, the directions of lineaments extracted are N-S and E-W, NE-SW and NW-SE, especially the abundance of the direction N-S. This last one is perpendicular with the major faults. The density of structural lineaments in the study area is controlled particularly by the lithological nature and dynamic evolution, where the concentration is high in the Liassic carbonates. The geostatistical and spatial distribution of the structural lineaments led us to conclude that different tectonic factors are responsible for the distribution of lineaments in the surface of the study area. The comparison of two types of radar data shows that Envisat data is more efficient in the manual extraction than Sentinel 1B data. However, Sentinel 1B data is more efficient in the automatic extraction than Envisat data. This work identifies several families of structural lineaments which can be determined the tectonic phases of the Alpine orogeny affected the intracontinental Atlas basin.