Assessment of Hyperspectral, Multispectral, Radar, and Digital Elevation Model data in structural lineaments mapping: A case study from Ameln valley shear zone, Western Anti-Atlas Morocco

Hajaj, Soufiane; Harti, Abderrazak El; Jellouli, Amine

doi:10.1016/j.rsase.2022.100819

Cited by 9 publications

(5 citation statements)

References 55 publications

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“…The study area is known for the existence of several structural features trends, with the particular dominance of the NE-SW trend. The dominance of this feature was demonstrated with multisource remote sensing datasets [39].…”

Section: Geological Sitting Of the Study Areamentioning

confidence: 94%

Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

et al. 2023

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Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth’s surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and effective mapping of various types of lithologies in arid and semi-arid regions. This article discusses the use of machine learning algorithms, specifically Support Vector Machines (SVM), one-dimensional Convolutional Neural Network (1D-CNN), random forest (RF), and k-nearest neighbor (KNN), for lithological mapping in a complex area with strong hydrothermal alteration. The study evaluates the performance of the four algorithms in three different zones in the Ameln valley shear zone (AVSZ) area at eastern Kerdous inlier, Moroccan western Anti-Atlas. The results demonstrated that 1D-CNN achieved the best classification results for most lithological units. Additionally, the LK-SVM demonstrated good mapping results compared to the other SVM models, as well as RF and KNN. Our study concludes that the combination of the CNN and HyMap data can provide the most accurate lithologic mapping for the three selected region, with an overall accuracy of ~95%. However, this study highlights the challenges in identifying different lithological units using remotely sensed data due to spectrum similarities induced by similar chemical and mineralogical compositions. This study emphasizes the importance of carefully considering and evaluating ML and DL methods for lithological mapping studies, then recommends the high-resolution hyperspectral data and DL models for accurate results. The implications of this study would be fascinating to exploration geologists for Mineral Prospectivity Mapping (MPM), especially in selecting the most appropriate techniques for highly accurate mineral mapping in metallogenic provinces.

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Section: Geological Sitting Of the Study Areamentioning

confidence: 94%

Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

et al. 2023

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“…Accurate mapping of faults and surrounding areas is essential to identify potentially productive regions and discover new reservoirs in fractured zones (Jari et al, 2022). Lineaments are linear or curvilinear geological structures associated with various geomorphological or tectonic features (Adiri et al, 2017;Hajaj et al, 2022). Their orientation, density, and connectivity are important factors in their characterization (Hajaj et al, 2022).…”

Section: Geological Factormentioning

confidence: 99%

“…Lineaments are linear or curvilinear geological structures associated with various geomorphological or tectonic features (Adiri et al, 2017;Hajaj et al, 2022). Their orientation, density, and connectivity are important factors in their characterization (Hajaj et al, 2022). Lineament density is used as a hydrodynamic indicator to assess groundwater resources and identify productive zones (Razandi et al, 2015).…”

Section: Geological Factormentioning

confidence: 99%

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Jari,

Bachaoui,

Hajaj

et al. 2023

Front. Water

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Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan-Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating the effectiveness of ensemble learning algorithms in the groundwater potential mapping (GWPM) in arid regions.

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“…In lineament mapping, the linear features can be extracted via manual [10][11][12], automated [11,13,14], or semi-automated procedures [11,15]. Several optical, Radar, and elevation datasets could be applied for extraction lineament, where the high resolution of the used remote sensing product strongly enhances the accuracy of the extracted linear features [16]. The overall signal-to-noise ratio is increased when data is captured using Landsat 8 (Operational Land Imager) OLI, which offers enhanced radiometric precision over a 12-bit dynamic range, which has made Landsat 8 OLI a preferred product for lineament extraction (e.g., [17,18]).…”

Section: Introductionmentioning

confidence: 99%

Lineament Mapping in Batie Area (West-Cameroon) Using Landsat-9 Operational Land Imager/Thermal Infrared Sensor and Shuttle Radar Topography Mission Data: Hydrogeological Implication

Mono,

Bouba,

Ngoh

et al. 2024

RIG

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This study focuses on the mapping of lineaments using remote sensing techniques and Geographic Information Systems. The aim is to carry out a statistical analysis of the lineaments in order to better understand the organization of fracturing in the Batie district, and to identify areas of high fracturing density and their relationship with the hydrographic network. The methodology implemented to achieve these objectives is based on the processing and analysis of Landsat 9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite imagery and Shuttle Radar Topography Mission (SRTM) data covering the study area. After essential pre-processing of data, the following processing techniques were applied: Principal Component Analysis, Sobel Directional Filter with a 3 × 3 matrix and shaded relief of SRTM data with a sun azimuth of 345°, altitude of 45 m and an elevation factor of 3, followed by manual lineament extraction. The results reveal a total of 176 lineaments, ranging in size from 0.002 to 2.461 km, with an average length of 840.66 km. The projection of these lineaments on a Rose diagram shows a predominance of the North East-South-West (NE-SW) direction, which is the direction of the major tectonic fractures that affect the basement of Central Africa. These lineaments are organized into networks and corridors that define large fracture zones or corridors that control the drainage of the study area. The zones with networks show medium and high densities. This fracturing density gives good aquifer and reservoir potential to the study area.

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Assessment of Hyperspectral, Multispectral, Radar, and Digital Elevation Model data in structural lineaments mapping: A case study from Ameln valley shear zone, Western Anti-Atlas Morocco

Cited by 9 publications

References 55 publications

Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Lineament Mapping in Batie Area (West-Cameroon) Using Landsat-9 Operational Land Imager/Thermal Infrared Sensor and Shuttle Radar Topography Mission Data: Hydrogeological Implication

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