Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region

Chile’s mining industry, a global leader in copper production, faces challenges due to increasing volumes of mining waste, particularly Waste Rock Dumps (WRD) and Leaching Waste Dumps (LWD). The National Service of Geology and Mining (SERNAGEOMIN) requires assessment of the physical stabi...

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Main Authors: Gabriel Hermosilla, Gabriel Villavicencio, Giovanni Cocca-Guardia, Vicente Aprigliano, Manuel Silva, Juan Carlos Quezada, Pierre Breul, Vinicius Minatogawa, Jaime Morales
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10843715/
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author Gabriel Hermosilla
Gabriel Villavicencio
Giovanni Cocca-Guardia
Vicente Aprigliano
Manuel Silva
Juan Carlos Quezada
Pierre Breul
Vinicius Minatogawa
Jaime Morales
author_facet Gabriel Hermosilla
Gabriel Villavicencio
Giovanni Cocca-Guardia
Vicente Aprigliano
Manuel Silva
Juan Carlos Quezada
Pierre Breul
Vinicius Minatogawa
Jaime Morales
author_sort Gabriel Hermosilla
collection DOAJ
description Chile’s mining industry, a global leader in copper production, faces challenges due to increasing volumes of mining waste, particularly Waste Rock Dumps (WRD) and Leaching Waste Dumps (LWD). The National Service of Geology and Mining (SERNAGEOMIN) requires assessment of the physical stability (PS) of these facilities, but current methods are hindered by data scarcity and resource constraints. This study proposes a simplified evaluation methodology using first-order parameters from open-access data. By integrating Geographic Information Systems (GIS) and Artificial Intelligence (AI)—utilizing models like YOLOv11 and convolutional neural networks—we automate the detection and characterization of WRD and LWD from satellite imagery, extracting critical parameters for PS assessment. This approach reduces analysis time and minimizes human error. Validated in the Antofagasta Region, Chile’s primary mining area, we identified and evaluated 70 WRD and 54 LWD. The results demonstrate the effectiveness of prioritizing deposits based on potential risk, enhancing SERNAGEOMIN’s capacity for supervision. The successful application suggests scalability to other mining regions and adaptability to different facility types, including tailings storage facilities. This work offers a practical tool to improve safety and risk management in the mining industry, addressing critical challenges in PS evaluation under current regulatory constraints.
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language English
publishDate 2025-01-01
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spelling doaj-art-29de8397e1574783ade36c8935e7805a2025-01-25T00:02:44ZengIEEEIEEE Access2169-35362025-01-0113144531447010.1109/ACCESS.2025.353085610843715Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta RegionGabriel Hermosilla0https://orcid.org/0000-0002-0674-2254Gabriel Villavicencio1https://orcid.org/0000-0002-5342-0063Giovanni Cocca-Guardia2https://orcid.org/0009-0006-4962-0659Vicente Aprigliano3Manuel Silva4https://orcid.org/0009-0005-0900-4117Juan Carlos Quezada5https://orcid.org/0000-0001-6164-7949Pierre Breul6https://orcid.org/0000-0003-1231-3496Vinicius Minatogawa7https://orcid.org/0000-0002-7441-7242Jaime Morales8https://orcid.org/0000-0001-5722-7781Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileEscuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileEscuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileEscuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileEscuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileICUBE, UMR 7357, CNRS, INSA de Strasbourg, Strasbourg, FranceDépartement Génie Civil, Polytech Clermont, Institut Pascal UMR CNRS 6602, Université Clermont Auvergne, CEDEX, Clermont Ferrand, FranceEscuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileEscuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileChile’s mining industry, a global leader in copper production, faces challenges due to increasing volumes of mining waste, particularly Waste Rock Dumps (WRD) and Leaching Waste Dumps (LWD). The National Service of Geology and Mining (SERNAGEOMIN) requires assessment of the physical stability (PS) of these facilities, but current methods are hindered by data scarcity and resource constraints. This study proposes a simplified evaluation methodology using first-order parameters from open-access data. By integrating Geographic Information Systems (GIS) and Artificial Intelligence (AI)—utilizing models like YOLOv11 and convolutional neural networks—we automate the detection and characterization of WRD and LWD from satellite imagery, extracting critical parameters for PS assessment. This approach reduces analysis time and minimizes human error. Validated in the Antofagasta Region, Chile’s primary mining area, we identified and evaluated 70 WRD and 54 LWD. The results demonstrate the effectiveness of prioritizing deposits based on potential risk, enhancing SERNAGEOMIN’s capacity for supervision. The successful application suggests scalability to other mining regions and adaptability to different facility types, including tailings storage facilities. This work offers a practical tool to improve safety and risk management in the mining industry, addressing critical challenges in PS evaluation under current regulatory constraints.https://ieeexplore.ieee.org/document/10843715/Artificial intelligenceclosure plangeographical information systemsmine waste storage facilitiesphysical stability assessmentSentinel-2 satellite imagery
spellingShingle Gabriel Hermosilla
Gabriel Villavicencio
Giovanni Cocca-Guardia
Vicente Aprigliano
Manuel Silva
Juan Carlos Quezada
Pierre Breul
Vinicius Minatogawa
Jaime Morales
Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region
IEEE Access
Artificial intelligence
closure plan
geographical information systems
mine waste storage facilities
physical stability assessment
Sentinel-2 satellite imagery
title Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region
title_full Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region
title_fullStr Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region
title_full_unstemmed Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region
title_short Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region
title_sort simplified physical stability assessment of chilean mine waste storage facilities using gis and ai application in the antofagasta region
topic Artificial intelligence
closure plan
geographical information systems
mine waste storage facilities
physical stability assessment
Sentinel-2 satellite imagery
url https://ieeexplore.ieee.org/document/10843715/
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