Kabral Mogos Asghede , Abazar M.A. Daoud , Musaab A.A. Mohammed , Woldegabriel Genzebu , Kefela Beyene Kiflay , Péter Pecsmány , János Vágó
{"title":"利用多源遥感数据和机器学习模型,为厄立特里亚上梅里布地区开发岩石构造图","authors":"Kabral Mogos Asghede , Abazar M.A. Daoud , Musaab A.A. Mohammed , Woldegabriel Genzebu , Kefela Beyene Kiflay , Péter Pecsmány , János Vágó","doi":"10.1016/j.rsase.2025.101722","DOIUrl":null,"url":null,"abstract":"<div><div>The Upper Mereb catchment area, located on the Southern zone of Eritrea, is a geologically complex region within the Arabian-Nubian Shield (ANS). The area's intricate litho-structural framework presents significant challenges for mineral exploration and groundwater investigations. Traditional geological mapping techniques often struggle to capture the fine-scale structural details necessary for resource assessments in such complex terrains. This study introduces a novel, high-resolution litho-structural mapping approach, integrating Landsat 9 (L9) multispectral data and gravity data with advanced machine learning algorithms, specifically Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The classification results indicate that ANN outperforms SVM, achieving an accuracy exceeding 79 %, demonstrating the effectiveness of machine learning in distinguishing lithological units. Furthermore, detailed field investigations validate the accuracy of the litho-structural map, showing strong correlations with ground-truth data. A key component of this study is the structural analysis of lineament orientations, which provides critical insights into the tectonic evolution of the region. The Pan-African orogeny has significantly influenced the structural framework, with dominant NE-SW compressional forces creations the fracture patterns. The identified lineaments fall into three primary sets: NW-SE extensional fractures, indicative of crustal stretching; NE-SW release fractures, reflecting zones of stress relaxation; and N-S shear fractures, formed under oblique stress conditions. These structural features highlight the region's complex deformation history and provide essential information for understanding subsurface fluid flow and resource potential. This study represents the first comprehensive application of an integrated remote sensing and geophysical machine learning approach to geological mapping in the Upper Mereb area. The results emphasize the potential of hybrid remote sensing and geophysical data fusion for enhancing structural interpretations, offering a powerful tool for mineral exploration, groundwater assessments, and tectonic studies in the Arabian-Nubian Shield.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"40 ","pages":"Article 101722"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a litho-structural map for the Upper Mereb area, Eritrea, using multi-source remote sensing data and machine learning models\",\"authors\":\"Kabral Mogos Asghede , Abazar M.A. Daoud , Musaab A.A. Mohammed , Woldegabriel Genzebu , Kefela Beyene Kiflay , Péter Pecsmány , János Vágó\",\"doi\":\"10.1016/j.rsase.2025.101722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The Upper Mereb catchment area, located on the Southern zone of Eritrea, is a geologically complex region within the Arabian-Nubian Shield (ANS). The area's intricate litho-structural framework presents significant challenges for mineral exploration and groundwater investigations. Traditional geological mapping techniques often struggle to capture the fine-scale structural details necessary for resource assessments in such complex terrains. This study introduces a novel, high-resolution litho-structural mapping approach, integrating Landsat 9 (L9) multispectral data and gravity data with advanced machine learning algorithms, specifically Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The classification results indicate that ANN outperforms SVM, achieving an accuracy exceeding 79 %, demonstrating the effectiveness of machine learning in distinguishing lithological units. Furthermore, detailed field investigations validate the accuracy of the litho-structural map, showing strong correlations with ground-truth data. A key component of this study is the structural analysis of lineament orientations, which provides critical insights into the tectonic evolution of the region. The Pan-African orogeny has significantly influenced the structural framework, with dominant NE-SW compressional forces creations the fracture patterns. The identified lineaments fall into three primary sets: NW-SE extensional fractures, indicative of crustal stretching; NE-SW release fractures, reflecting zones of stress relaxation; and N-S shear fractures, formed under oblique stress conditions. These structural features highlight the region's complex deformation history and provide essential information for understanding subsurface fluid flow and resource potential. This study represents the first comprehensive application of an integrated remote sensing and geophysical machine learning approach to geological mapping in the Upper Mereb area. The results emphasize the potential of hybrid remote sensing and geophysical data fusion for enhancing structural interpretations, offering a powerful tool for mineral exploration, groundwater assessments, and tectonic studies in the Arabian-Nubian Shield.</div></div>\",\"PeriodicalId\":53227,\"journal\":{\"name\":\"Remote Sensing Applications-Society and Environment\",\"volume\":\"40 \",\"pages\":\"Article 101722\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Applications-Society and Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352938525002757\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938525002757","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Development of a litho-structural map for the Upper Mereb area, Eritrea, using multi-source remote sensing data and machine learning models
The Upper Mereb catchment area, located on the Southern zone of Eritrea, is a geologically complex region within the Arabian-Nubian Shield (ANS). The area's intricate litho-structural framework presents significant challenges for mineral exploration and groundwater investigations. Traditional geological mapping techniques often struggle to capture the fine-scale structural details necessary for resource assessments in such complex terrains. This study introduces a novel, high-resolution litho-structural mapping approach, integrating Landsat 9 (L9) multispectral data and gravity data with advanced machine learning algorithms, specifically Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The classification results indicate that ANN outperforms SVM, achieving an accuracy exceeding 79 %, demonstrating the effectiveness of machine learning in distinguishing lithological units. Furthermore, detailed field investigations validate the accuracy of the litho-structural map, showing strong correlations with ground-truth data. A key component of this study is the structural analysis of lineament orientations, which provides critical insights into the tectonic evolution of the region. The Pan-African orogeny has significantly influenced the structural framework, with dominant NE-SW compressional forces creations the fracture patterns. The identified lineaments fall into three primary sets: NW-SE extensional fractures, indicative of crustal stretching; NE-SW release fractures, reflecting zones of stress relaxation; and N-S shear fractures, formed under oblique stress conditions. These structural features highlight the region's complex deformation history and provide essential information for understanding subsurface fluid flow and resource potential. This study represents the first comprehensive application of an integrated remote sensing and geophysical machine learning approach to geological mapping in the Upper Mereb area. The results emphasize the potential of hybrid remote sensing and geophysical data fusion for enhancing structural interpretations, offering a powerful tool for mineral exploration, groundwater assessments, and tectonic studies in the Arabian-Nubian Shield.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems