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

Gabriel Hermosilla, Gabriel Villavicencio, Giovanni Cocca-Guardia, Vicente Aprigliano, Manuel Silva, Juan Carlos Quezada, Pierre Breul, Vinicius Minatogawa, Jaime Morales

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Gabriel Hermosilla, Gabriel Villavicencio, Giovanni Cocca-Guardia, Vicente Aprigliano, Manuel Silva, et al.. Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region. IEEE Access, 2025, 13, pp.14453 - 14470. 10.1109/ac cess.2025.3530856 . hal-04920182

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Received 10 December 2024, accepted 12 January 2025, date of publication 16 January 2025, date of current version 23 January 2025.

Digital Object Identifier 10.1109/ACCESS.2025.3530856

RESEARCH ARTICLE

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

GABRIEL HERMOSILLA<sup>1</sup>, GABRIEL VILLAVICENCIO<sup>2</sup>,

GIOVANNI COCCA-GUARDIA<sup>1</sup>, (Member, IEEE), VICENTE APRIGLIANO<sup>2</sup>,

MANUEL SILVA<sup>1</sup>, (Member, IEEE), JUAN CARLOS QUEZADA<sup>3</sup>,

PIERRE BREUL<sup>4</sup>, VINICIUS MINATOGAWA<sup>5</sup>,

AND JAIME MORALES<sup>5</sup>

Corresponding author: Gabriel Hermosilla (gabriel.hermosilla@pucv.cl)

This research was funded and supported by the Vice-Rectorate for Research, Creation, and Innovation (VINCI) at Pontificia Universidad Católica de Valparaíso (Chile), through the Associative Research Project under grant: 039.301/2024, Centennial Project 2024 under grant: 039.310/2024, and by the Chilean National Agency of Research and Development (ANID) through Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) under Grant 1240573, and ANID Doctorado Nacional 2023-21232328.

**ABSTRACT** 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.

**INDEX TERMS** Artificial intelligence, closure plan, geographical information systems, mine waste storage facilities, physical stability assessment, Sentinel-2 satellite imagery, YOLOv11.

## I. INTRODUCTION

The mining industry in Chile, a world leader in copper production, generates a significant amount of mining waste, which is expected to progressively increase in the future. By 2026, Chile is projected to generate approximately 1,000

The associate editor coordinating the review of this manuscript and approving it for publication was Andrea Rey.

million tons of mining waste annually [1]. These materials are disposed of and stored in large structures known as Mine Waste Storage Facilities (MWSF).

Among these waste types are Waste Rock Dumps (WRD) and Leaching Waste Dumps (LWD), both of which are expected to increase in size and quantity in the near future. To date, more than 792 WRDs and LWDs zones have been identified [2], distributed across mining operations in regions

<footnote n="1">¹Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2374631, Chile</footnote>

<footnote n="2">²Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile</footnote>

<footnote n="3">³ICUBE, UMR 7357, CNRS, INSA de Strasbourg, 67084 Strasbourg, France</footnote>

<footnote n="4">⁴Département Génie Civil, Polytech Clermont, Institut Pascal UMR CNRS 6602, Université Clermont Auvergne, CEDEX, 63000 Clermont Ferrand, France</footnote>

<footnote n="5">⁵Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362854, Chile</footnote>
I, II, III, IV, V, MR, VI, VII, and XI of Chile, reaching the following sizes: i) WRDs vary between 50 and 500 m in height, with projections up to 1,000 m in large mining projects, occupying thousands of hectares (Figure 1.a); ii) LWDs have heights ranging from 10 to 120 m and occupy areas covering hundreds of hectares (Figure 1.b).

To date, these types of MWSF have demonstrated adequate mechanical behavior under static and seismic conditions. However, in Chile, mining companies are legally obligated to assess and ensure their physical stability (PS), even after closure, to safeguard human life and health and to protect the environment, in accordance with national legislation [5], [6]. A potential failure in the PS of these facilities could result in environmental disasters, significant material damage, and even loss of human life, as has been reported in other mining countries [7], [8].

The National Service of Geology and Mining (SERNAGEOMIN), a government entity responsible for the supervision and oversight of MWSF, faces a growing challenge due to the need to ensure the long-term PS of these mining facilities. To guide companies and inspectors in assessing PS, SERNAGEOMIN issued the “Methodological Guide for the Evaluation of the Physical Stability of Residual Mining Facilities” [9], hereinafter referred to as the PS Guide. This tool standardizes and regulates the procedures for evaluating the PS of MWSF. The evaluation process outlined in the PS Guide requires a range of physical parameters and environmental data, including both the design project and the closure plan for these facilities [5], [10]. Additionally, information from periodic monitoring conducted during their operation and construction phase is essential. Using this data, the PS Guide employs a matrix analysis to assess stability condition for various potential failure mechanisms. However, since its official adoption in 2018, the effective implementation of this evaluation and oversight tool has faced several technical barriers. These include the lack of a national MWSF registry, insufficient number of inspectors, difficulties in obtaining critical geotechnical parameters (e.g. foundation soils for MWSF), and stringent administrative requirements imposed by national regulations.

In this context, to address the current challenges associated with applying the PS Guide, this work proposes a simplified evaluation methodology. This methodology uses first-order parameters to estimate the global PS condition of WRD and LWD, using state-of-the-art technologies such as geographic information systems (GIS) and artificial intelligence (AI). GIS enables the integration and analysis of multiple layers of geospatial data, such as geology, topography, seismicity, and precipitation, complemented by specific geotechnical data on the waste [11], [12]. AI facilitates the automatic detection and geometric characterization of these deposits, reducing analysis times and minimizing human errors. This methodology, including proposed matrices and operational approach, will be validated through a case study in the Antofagasta Region, a key mining area in Chile with major operations

(e.g., CODELCO and Antofagasta Minerals), to demonstrate its effectiveness. By integrating geospatial data layers and AI-based detectors, the methodology characterizes WRD and LWD and assesses critical stability parameters. This enables the development of a prioritization ranking for MWSF based on PS levels, facilitating effective supervision and oversight by SERNAGEOMIN. Furthermore, the methodology has potential applications in other mining regions and for different types of MWSF, such as tailings storage facilities (TSF).

## II. RELATED WORK

The evaluation of the PS of MWSF has been a fundamental topic in research, due to the significant risks these facilities represent for human safety and the environment. Traditionally, this evaluation relies on a series of factors and variables considered in geotechnical modeling to analyze possible failure modes (e.g., slope instability, static liquefaction, among others) during the design phase, as well as requiring periodic in-situ monitoring of each variable. However, economic resource constraints and the large scale of modern mining operations demand more efficient and scalable approaches. The following are some relevant studies in this field.

### A. EVALUATION OF MWSF PS

In recent years, remote sensing techniques have transformed the evaluation of the PS of MWSF in engineering. For instance, satellites such as Sentinel-2 and Landsat have played a crucial role in providing high-resolution images and enabling continuous monitoring of the large areas occupied by MWSF. A recent study utilized the Google Earth Engine platform to process satellite images, optimizing information extraction from the deposits through image processing techniques and cloud cover reduction [13]. Another study demonstrated how machine learning algorithms can estimate parameters such as moisture content, facilitating operational periodic control in thickened tailings deposits [14].

In addition, remote monitoring technologies such as synthetic aperture radar (SAR) and interferometric radar (InSAR) have gained prominence in recent years for evaluating the PS of MWSF. These techniques enable the monitoring of surface deformation, which can serve as an early warning indicator for potential physical instability scenarios. Another example is the use of the Small Baseline Subset (SBAS) technique to monitor surface deformations in mining areas [15], [16]. These techniques have proven to be useful tools to complement classic geotechnical evaluations, offering continuous and real-time information.

### B. ARTIFICIAL INTELLIGENCE IN THE DETECTION AND ANALYSIS OF MWSF

The integration of AI in the detection and analysis of MWSF has achieved remarkable progress in recent years, particularly for TSF. Object detection and segmentation models such as YOLO and Faster R-CNN have significantly improved the
<figure><img src="image_2.png" /><figcaption>FIGURE 1. MWSF under construction, located in the northern regions of Chile. a) Waste rock dumps (WRD), El Soldado, Nogales, Chile [3]. b) Leaching waste dumps (LWD), BioCobre, Copiapó, Chile [4].</figcaption></figure>

automatic identification of these mining facilities through satellite imagery [2], [17]. These models not only identify TSF with high precision but also enable the extraction of geo-metric and geotechnical parameters related to PS evaluation. Advances in deep learning have automated processes that previously required manual inspections, thereby increasing the efficiency of periodic monitoring.

Moreover, techniques for generating synthetic data have been developed to complement real datasets and improve the ability of AI models to learn and generalize across different scenarios. A recent example includes the use of generative adversarial networks (GANs) for creating synthetic data and parameters used in training regression models to estimate PS [18]. These innovations are facilitating a more automated and continuous analysis of TSF, a critical step toward improving risk management and mitigation for such mining facilities.

### C. APPLICATIONS OF GIS IN MINING

The use of GIS has been fundamental for management and planning in the mining sector. GIS facilitates the integration and analysis of essential geospatial data, such as topography, geology, and hydrology, enabling informed decision-making regarding the location of new facilities and the environmental management of mining operations. In the context of PS evaluation, GIS provides a platform for overlaying multiple data layers, helping to identify critical spatial patterns influencing the PS of MWSF.

Several studies have implemented GIS methods to monitor, evaluate, or predict environmental or physical risks associated

with TSF. For instance, [19], [20] applied GIS to identify historical and spatial patterns of incidents at such mining facilities and their relationship with climate variables, such as rainfall. In this regard, [20] used satellite images and GIS techniques to assess the risk level of TSF based on factors such as proximity to urban areas and the type of ore extracted. With an environmental focus, [21] applied a GIS-based method to assess the level of sediment storage and transport in rivers impacted by mining activity in Bolivia. [19] developed a GIS methodology to predict failures, using the Brumadinho case in Brazil as an example.

The combination of AI and GIS is proposed as an innovative strategy to improve the detection and evaluation of MWSF. GIS integrates multiple geographical data layers, while AI models analyze large volumes of satellite data, automating the identification of potential risks, obtaining critical parameters for PS evaluation, and optimizing resource management. This integrated approach prioritizes deposits with higher instability risks, offering a scalable solution to enhance safety in remote or hard-to-access areas. The methodology presented in this study can be adapted for TSFs, as long as the key parameters from the PS guidelines that define the physical stability of these mining facilities are included.

### III. PHYSICAL STABILITY OF WRD AND LWD

For the evaluation of the PS of WRD and LWD, SERNA-GEOMIN as the supervisory body of the State of Chile, establishes that mining companies must use the classification system outlined in the PS Guide [9]. The objective is to
facilitate progressive and safe closure processes for these types of MWSF. In general terms, the PS Guide was developed based on the framework of the systems initially proposed by [7] and later modified by [8] but including technical adaptations and criteria applicable to the context of the Chilean mining industry.

Currently, the PS Guide requires the evaluation of 20 factors or parameters that define PS, organized into 8 evaluation matrices (Table 1). To reflect the degree of importance on the PS of WRD and LWD, each factor is assigned a condition and a numerical rating, defined based on the analysis of the mechanical behavior observed in these types of MWSF [9]. The sum of these numerical ratings allows WRD and LWD to be evaluated and classified in terms of their PS level for the failure mechanisms being assessed: slope instability/foundation instability and static liquefaction. A higher numerical rating corresponds to a lower PS level and a greater associated failure occurrence potential (FOP) (Figure 2). It is important to note that this evaluation system was defined by a committee of national and international experts and subsequently applied in various national mining operations for calibration.

Although this classification system uses various parameters from technical reports that mining companies are required to submit to SERNAGEOMIN (such as the design project and closure plan), and basic operational control of these MWSF (geometric configuration, construction background, instrumentation and monitoring, stability performance, among others), it has been evidenced that this information is limited or, in many cases, inaccessible due to poor management and lack of updates of the required information. This has hindered the effective application of the PS Guide for both mining companies and SERNAGEOMIN.

This context has affected SERNAGEOMIN's supervisory role, particularly in the case of WRD and LWD, facing various obstacles: i) the absence of a national registry for these types of MWSF; ii) a shortage of inspectors nationwide; iii) the large number of variables required to evaluate PS, many of which lack quantifiable information; iv) technical and economic difficulties in obtaining critical parameters, such as soil conditions, geometric configuration, and geotechnical characteristics; v) strict administrative requirements imposed by current national regulations. These challenges highlight the need for a more efficient and updated system that enhances both supervision and compliance with safety standards in the management of MWSF.

Given the limitations of the PS Guide for evaluating the PS of WRD and LWD, we propose a simplified system to overcome data scarcity by using open-source information. This proposal relies on geospatial data such as surface geology, active faults, seismicity, topography, and precipitation. In addition, it considers general knowledge of the geomechanical properties of WRD and LWD [11], [12], complemented by advanced GIS and AI technologies for automated data processing, while generally maintaining the evaluation criteria defined in the PS Guide [9].

## IV. PROPOSED PS EVALUATION USING SIMPLIFIED MATRICES

The proposed simplified system, developed with the participation of expert engineers from SERNAGEOMIN, reorganizes the eight original matrices from the PS Guide into four key matrices. These matrices focus on foundation conditions, geometric configuration, geomechanical quality of the material, and the regional setting of the deposits (Figure 3). The excluded matrices (M3: constructive background; M5: instrumentation and monitoring; M7: stability performance; and M8: degree of implementation of measures to ensure PS in the operation and closure stages) rely on data that, to date, are not regularly recorded or monitored during the construction and operation phases of these types of mining facilities. Thus, simplifying and adapting the PS Guide is essential to enable more frequent and accessible evaluations, especially for MWSF that lack records and are not regularly monitored.

Each matrix uses open-access data, enabling scalable and practical implementation. The sum of the scores from each matrix results in the Physical Global Stability Index (PSGI), a qualitative measure that facilitates the prioritization of deposits for SERNAGEOMIN supervision, focusing on those with the highest potential risk. The simplified matrices are presented below.

### A. MATRIX M1': FOUNDATION CONDITIONS

Foundation conditions include key factors for the PS of WRD and LWD, especially for instabilities associated with foundation failures, such as rotational failure, non-circular rotation, wedge failure, base translation, liquefaction, and toe failure [7], [8], [9]. In this context, the parameters included in this matrix are related to the characteristics of the foundation soil: overburden type and topography.

The foundation soil types of WRD and LWD, in a first approximation, can be defined based on their geological origin (rock and residual or sedimentary soils). A complementary parameter, widely used to define seismic site characterization, is the stiffness upper 30 m of the soil profile through an equivalent shear wave velocity $Vs_{30}$ [22]. The foundation soil topography has been linked to the average overall foundation slope angle ($\alpha$) and the foundation shape of the area where the WRD and LWD are located, as shown in Figure 4.2a and Figure 4.2b, respectively. The parameter $\alpha$ corresponds to the angle between the toe of the WRD and LWD slopes and their upper intersection with the natural terrain.

The structure of matrix M1' is presented in Table 2. To obtain the evaluation parameters contained in matrix M1', the following open-source layers were used: i) Geomechanical characteristics of foundation soil: geological map of Chile, scale 1:1,000,000 [23] and Vs30: Global Vs30 mosaic map viewer [24]; ii) foundation soil slope and shape foundation/MWSF typology: ground slope (slope geoprocessing of digital elevation models [25], and open-access satellite images (Sentinel-2).
<table><caption>TABLE 1. Evaluation matrix for PS assessment.</caption><thead><tr><td>ID</td><td>Matrix</td><td>Factor or attribute</td></tr></thead><tbody><tr><td>M1</td><td>Foundation conditions</td><td>Geomechanical characteristics of foundation soil, shape foundation, and MWSF typology.</td></tr><tr><td>M2</td><td>Geometric configuration</td><td>Overall height, overall fill slope angle, maximum thickness lift, and maximum overall thickness.</td></tr><tr><td>M3</td><td>Constructive background</td><td>Construction method and loading rate.</td></tr><tr><td>M4</td><td>Material quality and in-situ state</td><td>Particle size distribution, Atterberg limits, and in-situ state condition.</td></tr><tr><td>M5</td><td>Instrumentation and monitoring</td><td>Piezometrics levels, drainage systems, accelerometers, and inclinometers.</td></tr><tr><td>M6</td><td>Regional setting for the closure condition</td><td>Seismicity and precipitation (rain and/or snow).</td></tr><tr><td>M7</td><td>Stability performance</td><td>PS observed during the operational phase.</td></tr><tr><td>M8</td><td>Degree of implementation of measures to ensure PS in the operation and closure stages</td><td>Verification of works and actions implemented as indicated in the approved closure plan.</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>FIGURE 2. Evaluation scheme of WRD and LWD according to the PS Guide [9].</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>FIGURE 3. Simplified evaluation system of global PS and inspection prioritization of WRD and LWD.</figcaption></figure>

## B. MATRIX M2': GEOMETRIC CONFIGURATION

The geometric configuration of WRD and LWD defines the PSGI level they present, associated with the generation

of the following failure mechanisms: edge slumping, plane failure, and rotational failure [7], [8], [9]. The geometric parameters (Figure 4) are directly related to the shape
<figure><img src="image_5.png" /><figcaption>FIGURE 4. Geometric parameters and typology of WRDs and LWDs. a) Geometric parameters of WRDs and LWDs. b) Basic MWFs types. Adapted from [8].</figcaption></figure>

<table><caption>TABLE 2. Evaluation matrix M1': foundation conditions factors.</caption><thead><tr><th>Factor</th><th>Parameter and condition</th><th>Category</th><th>Ratings</th></tr></thead><tbody><tr><td rowspan="4">Geomechanical characteristics of foundation soil</td><td>Type I: rock and Vs<sub>30</sub> &gt; 900 (m/s)</td><td>Very competent</td><td>0</td></tr><tr><td>Type II: soft or fractured rock, very dense or hard soils and Vs<sub>30</sub> ≥ 500 (m/s)</td><td>Competent</td><td>100</td></tr><tr><td>Type III: dense or very stiff soils and Vs<sub>30</sub> ≥ 350 (m/s)</td><td>Intermediate</td><td>200</td></tr><tr><td>Type III: medium-dense or firm to stiff soils and Vs<sub>30</sub> ≥ 180 (m/s)</td><td>Weak</td><td>300</td></tr><tr><td>Type IV: loose to medium-dense or soft to firm soils and Vs<sub>30</sub> &lt; 180 (m/s)</td><td>Very weak</td><td>400</td></tr><tr><td rowspan="6">Average overall foundation slope angle (α)</td><td>5°</td><td>Flat</td><td>0</td></tr><tr><td>5 – 15°</td><td>Gentle</td><td>50</td></tr><tr><td>15 – 25°</td><td>Moderate</td><td>100</td></tr><tr><td>25 – 32°</td><td>Steep</td><td>150</td></tr><tr><td>&gt; 32°</td><td>Very steep</td><td>200</td></tr><tr><td>No information</td><td>–</td><td>200</td></tr><tr><td rowspan="4">Shape foundation/MWSF typology</td><td>Valley fill or cross valley fill</td><td>Confined</td><td>0</td></tr><tr><td>Sidehill fill</td><td>Moderately confined</td><td>100</td></tr><tr><td>Heaped fill or ridge crest fill</td><td>Unconfined</td><td>200</td></tr><tr><td>No information</td><td>–</td><td>200</td></tr></tbody></table>

and size of these types of MWSF. For simplified evaluation purposes, the proposed matrix M2' includes the following geometric parameters: overall height, volume, overall fill slope angle, and stepped construction (single or multiples lifts), as presented in Table 3. These geometric parameters are obtained from the processing and analysis of satellite images (Sentinel-2). For the single lift slope angle, based on topographic surveys from WRD and LWD design projects, [12] propose a range of 34-39° for WRD. Meanwhile, [11] suggest a range of 33-35° for LWD.

### C. MATRIX M3': GEOMECHANICAL QUALITY OF THE MATERIAL

The geomechanical properties of WRD and LWD, such as gradation (particle size distribution, PSD), plasticity of fines (Plasticity Index, PI, and Liquid Limit, LL), intact strength (Uniaxial Compressive Strength, UCS), and durability (Slake

Durability Index, SDI), among others, are considered key factors influencing overall PS [7], [8]. Although these MWSF are not usually characterized during the construction and operation phases through in-situ and laboratory tests, recently [11] and [12] proposed databases with variation ranges for PSD, plasticity, intact strength, and durability of waste rock and leached ore materials generated by the copper mining industry in Chile. Based on this information, it is possible to globally define the geomechanical quality depending on the type of identified MWSF, which will be used to apply matrix M3' (Table 4).

### D. MATRIX M4': REGIONAL SETTING FOR THE CLOSURE CONDITION

To consider the regional setting where WRD and LWD are located, in the national context for the closure condition of these MWSF, the following parameters have been considered: seismicity, precipitation (rain and/or snow), and active and
<table><caption>TABLE 3. Evaluation matrix M2': geometric configuration factors.</caption><thead><tr><th>Factor</th><th>Parameter and condition</th><th>Ratings</th></tr></thead><tbody><tr><td rowspan="5">Overall height (m)</td><td>&lt; 100</td><td>0</td></tr><tr><td>100 – 250</td><td>50</td></tr><tr><td>250 – 500</td><td>100</td></tr><tr><td>&gt; 500</td><td>200</td></tr><tr><td>No information</td><td>200</td></tr><tr><td rowspan="5">Volume (m³)</td><td>Small</td><td>&lt; 10,000,000</td><td>0</td></tr><tr><td>Medium</td><td>10,000,000 – 100,000,000</td><td>50</td></tr><tr><td>Large</td><td>100,000,000 – 1,000,000,000</td><td>100</td></tr><tr><td>Very large</td><td>&gt; 1,000,000,000</td><td>200</td></tr><tr><td>No information</td><td>200</td></tr><tr><td rowspan="5">Overall fill slope angle (β°)</td><td>Gentle</td><td>&lt; 26</td><td>0</td></tr><tr><td>Moderate</td><td>26 – 34</td><td>50</td></tr><tr><td>Steep</td><td>34 – 39</td><td>100</td></tr><tr><td>Very steep</td><td>&gt; 39</td><td>200</td></tr><tr><td>No information</td><td>200</td></tr><tr><td rowspan="3">Lifts</td><td>With lifts</td><td>0</td><td>0</td></tr><tr><td>Without lifts</td><td>200</td><td>200</td></tr><tr><td>No information</td><td>200</td><td>200</td></tr></tbody></table>

<table><caption>TABLE 4. Evaluation matrix M3': geomechanical quality factors of waste rock and leached ore material.</caption><thead><tr><th>Parameter and condition</th><th>Category</th><th>Ratings</th></tr></thead><tbody><tr><td>Very coarse grained (WRD):<br/>- % fines (passing # 200 sieve; &lt; 0.075 mm) &lt; 10%<br/>- % great than 3" &gt; 50%<br/>- Plasticity: N/A<br/>- Intact strength: very strong to strong (UCS &gt; 100 MPa)<br/>- Durability: very high to high</td><td>High</td><td>0</td></tr><tr><td>Mixed grained (LWD):<br/>- % fines (passing # 200 sieve; &lt; 0.075 mm), between 10-25%<br/>- % great than 3", between 25-50%<br/>- Low plasticity: LL &lt; 35%; PI &lt; 10%<br/>- Intact strength: medium strong (UCS: 50 - 100 MPa)<br/>- Durability: medium high</td><td>Moderate</td><td>100</td></tr><tr><td>No information</td><td>-</td><td>200</td></tr></tbody></table>

<table><caption>TABLE 5. Evaluation matrix M4': regional setting for the closure condition of WRD and LWD.</caption><thead><tr><th>Parameter</th><th>Description</th><th>Condition</th><th>Category</th><th>Ratings</th></tr></thead><tbody><tr><td rowspan="3">Seismicity</td><td rowspan="3">Peak Ground Acceleration (PGA) or A₀, based on seismic zones of Chile (INN, 2012)</td><td>A₀ ≤ 0.2g</td><td>Low</td><td>0</td></tr><tr><td>A₀: 0.2g – 0.3g</td><td>Moderate</td><td>200</td></tr><tr><td>A₀ &gt; 0.4g</td><td>High</td><td>400</td></tr><tr><td rowspan="2">Distance to active and potentially-active continental faults</td><td rowspan="2">Distance from the trace of faults (polygon defined at 300m distance from the fault trace)</td><td>Outside the polygon</td><td>Low</td><td>0</td></tr><tr><td>Inside the polygon</td><td>High</td><td>200</td></tr><tr><td rowspan="6">Precipitation</td><td rowspan="6">Average annual rainfall and/or snowfall</td><td>Rainfall &lt; 100 (mm)<br/>Snowfall &lt; 10 (cm)</td><td>Very low</td><td>0</td></tr><tr><td>Rainfall: 100 – 350 (mm)<br/>Snowfall: 10 – 35 (cm)</td><td>Low</td><td>100</td></tr><tr><td>Rainfall: 350 – 1000 (mm)<br/>Snowfall: 35 – 100 (cm)</td><td>Moderate</td><td>200</td></tr><tr><td>Rainfall: 1000 – 2000 (mm)<br/>Snowfall: 100 – 200 (cm)</td><td>High</td><td>300</td></tr><tr><td>Rainfall &gt; 2000 (mm)<br/>Snowfall &gt; 200 (cm)</td><td>Very high</td><td>400</td></tr></tbody></table>

potentially active continental faults, which form matrix M4' (Table 5). The open-source data layers used for matrix M4' are as follows: i) seismicity: seismic zones of Chile [26]

created in the context of this work using open-source GIS; ii) geological faults: database of active and potentially active continental faults in Chile at a 1:25,000 scale [27];
<table><caption>TABLE 6. ILP in WRD and LWD, based on PSGI and FOP.</caption><thead><tr><th>PSGI</th><th>FOP</th><th>IPL</th></tr></thead><tbody><tr><td>&lt; 600</td><td>Very low</td><td>I: very reduced</td></tr><tr><td>[600 – 1200]</td><td>Low</td><td>II: reduced</td></tr><tr><td>[1200 – 1800]</td><td>Moderate</td><td>III: moderate</td></tr><tr><td>[1800 – 2200]</td><td>High</td><td>IV: priority</td></tr><tr><td>[2200 – 2800]</td><td>Very high</td><td>V: very priority</td></tr></tbody></table>

iii) precipitation: layers from the National Information System of Groundwater of Chile [28].

## E. CALCULATION OF THE PSGI

The PSGI is calculated by summing the scores obtained in each matrix (Eq. 1). The PSGI provides a qualitative approximation of the PS global of the MWSF, linked to FOP and with an inspection prioritization level (IPL) (Table 6). This classification enables the prioritization of inspector actions and resource allocation by SERNAGEOMIN, focusing on WRD and LWD that present the highest PSGI.

$$ \text{PSGI} = \sum_{\text{rating}} \text{M1}' + \sum_{\text{rating}} \text{M2}' + \sum_{\text{rating}} \text{M3}' + \sum_{\text{rating}} \text{M4}' \quad (1) $$

## V. METHODOLOGY

To automate the PS evaluation process using the proposed simplified matrices, remote sensing, GIS, and artificial intelligence techniques will be used. This methodology is organized into several key stages, including:

* Creation of the WRD and LWD database.

* Automatic detection and segmentation of WRD and LWD deposits.

* Integration of GIS layers.

* Application of AI models to the proposed matrices.

* Automatic evaluation of the global PS through simplified matrices.

* Calculation of the PSGI and categorization of WRD and LWD deposits.

Figure 5 illustrates the overall flow of the proposed methodology, applied to the Antofagasta region in Chile, which represents both state-owned and private companies involved in large- and medium-scale mining. The flow shows how PS is evaluated through the detection and segmentation of WRD and LWD using satellite images processed by an AI model. The detected deposits are georeferenced within GIS layers to obtain the parameters needed for evaluation in the simplified matrices, followed by categorization and prioritization.

## A. DATABASE: WRD AND LWD

To date, SERNAGEOMIN does not have an official registry of WRD and LWD. The Atlas de Faenas Mineras, an official SERNAGEOMIN database with information on mining operations approved until 2020, was used to define the search area for WRD and LWD, potentially including nearby MWSF.

<table><caption>TABLE 7. Distribution of the database used for training the YOLOv11 segmentation model.</caption><thead><tr><th></th><th>Total (100%)</th><th>Train (75%)</th><th>Test (25%)</th></tr></thead><tbody><tr><td>Images</td><td>1,733</td><td>1,268</td><td>465</td></tr><tr><td>WRD</td><td>5,206</td><td>3,853</td><td>1,353</td></tr><tr><td>LWD</td><td>2,532</td><td>1,873</td><td>659</td></tr></tbody></table>

Using this approach, images of the mining areas available in the Atlas for the Antofagasta Region were downloaded using the Sentinel-2 satellite API, considering 16 instances between 2023 and 2024 for each mining area at the maximum available resolution of 10 × 10 m/px. Labeling of WRD and LWD was conducted using Label-Studio software [29], with input from a geotechnical expert.

The images were stored in the following filename format to maintain the geolocation of the area: "[x_min,y_min,x_max,y_max] - (date) - band.png", where (x,y) corresponds to (longitude, latitude), "(date)" refers to the date of the mining area, and "band" is the name of the band downloaded from Sentinel-2, which in this case corresponds to RGB. A total of 2,500 images were downloaded, of which 1,733 were labeled after discarding those with clouds or noise. The labeled dataset was divided into a training set (75%) and a test set (25%), ensuring that the first 12 instances of each mining area were used for training, while the last 4 instances were reserved for testing. The details of the downloaded and labeled database are provided in Table 7.

## B. AUTOMATIC DETECTION AND SEGMENTATION OF WRD AND LWD

For detecting and segmenting WRD and LWD, the YOLOv11 model was chosen for its efficiency and accuracy in satellite image analysis [2], [30]. YOLOv11 was selected due to its superior performance in processing high-resolution satellite images, its capability to handle complex object shapes, and its balance between speed and precision, making it well-suited for large-scale mining applications. Fine-tuning was performed using the "YOLOv11x-seg" weights on the labeled database, which allowed the detections to be specifically adapted to the deposits of interest. This fine-tuning was validated using the test set, employing metrics such as precision, recall, and mAP to evaluate model performance.

The results of these metrics, presented in Table 8, indicate that the YOLOv11 model successfully detected all labeled deposits, achieving a precision of up to 0.998 for bounding box detection and 0.995 for segmentation. Despite this high precision, slightly lower recall values indicate some additional false positives, which may be attributed to the presence of visually similar features in the satellite images. These false positives may affect the overall evaluation process by increasing the need for additional verification steps to ensure accuracy. Notably, the model performed exceptionally well in detecting LWD, with mAP50 values of 0.857 for bounding boxes and 0.855 for segmentation. The
<figure><img src="image_6.png" /><figcaption>FIGURE 5. Overview of the Proposed Methodology to automate the PS evaluation of the global PS for WRD and LWD.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 6. Detection and segmentation results of the case study. (a) Unprocessed mining area. (b) Mining area segmented into WRDs and LWDs by YOLOv11.</figcaption></figure>

high precision of YOLOv11 in detecting WRD and LWD ensures high confidence in the automatic evaluation process of global PS. This precision enables the effective integration of segmented masks with GIS elevation and slope layers for a comprehensive analysis of the geospatial characteristics of the WRD and LWD.

## C. INTEGRATION OF GIS LAYERS

Once the YOLOv11 model was trained, it was used to detect and segment WRD and LWD in mining areas of interest,

<table><caption>TABLE 8. Evaluation metrics results of the trained YOLOv11 segmentation model.</caption><thead><tr><th>Class</th><th>Metric</th><th>Precision</th><th>Recall</th><th>mAP50</th><th>mAP50-95</th></tr></thead><tbody><tr><td rowspan="2">All</td><td>bbox</td><td>0.998</td><td>0.608</td><td>0.804</td><td>0.773</td></tr><tr><td>segm</td><td>0.995</td><td>0.606</td><td>0.802</td><td>0.680</td></tr><tr><td rowspan="2">WRD</td><td>bbox</td><td><strong>0.999</strong></td><td>0.614</td><td>0.807</td><td>0.776</td></tr><tr><td>segm</td><td>0.993</td><td>0.610</td><td>0.803</td><td>0.665</td></tr><tr><td rowspan="2">LWD</td><td>bbbox</td><td>0.996</td><td><strong>0.716</strong></td><td><strong>0.857</strong></td><td><strong>0.832</strong></td></tr><tr><td>segm</td><td>0.992</td><td>0.713</td><td>0.855</td><td>0.762</td></tr></tbody></table>

which were then processed alongside GIS layers. The GIS layers used in the matrix evaluation are described in Annex A. These layers have a resolution of 12.5 × 12.5 m/px, which differs from the satellite image resolution of 10 × 10 m/px used in the database. This difference in resolution occurs because GIS layers are typically optimized for regional-scale analysis, whereas satellite images are captured at a finer resolution to provide detailed local information. The detections made by YOLOv11 are adjusted to match the GIS layer size before processing the segmented deposits, which might slightly impact the accuracy of parameter extraction due to scaling adjustments. The detections made by YOLOv11 are adjusted to match the GIS layer size before processing the segmented deposits. This adjustment employs nearest neighbor interpolation, a method that does not affect the extraction of parameters from the GIS layers. It ensures
<figure><img src="image_8.png" /><figcaption>(a) Geological units.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(c) Ground elevation.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(b) Global $V_{S30}$ values.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(d) Ground slope.</figcaption></figure>

<caption>FIGURE 7. Detection and segmentation of WRDs and LWDs in Chuquicamata, South Mine (CODELCO), georeferenced within GIS layers.</caption>
<figure><img src="image_12.png" /><figcaption>**FIGURE 8.** Detection and segmentation of WRDs and LWDs in Chuquicamata, South Mine (CODELCO), georeferenced within GIS layers: a) Seismic zone. b) Precipitations.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**FIGURE 9.** Detection, lift classifier and confinement classifier results shown as: Class, lift presence and typology. (a) LWD, Without lift, Heaped fill. (b) LWD, With lift, Heaped fill. (c) WRD, With lift, Heaped fill. (d) WRD, With lift, Heaped fill. (e) LWD, With lift, Heaped fill. (f) WRD, With lift, Heaped fill.</figcaption></figure>

accurate georeferencing of the surface area occupied by the MWSF under the EPSG:4326 coordinate system.

Thus, the bounding boxes obtained from YOLOv11 detection are used to crop WRD and LWD deposits, preserving their aspect ratio relative to the original image. Preserving the aspect ratio helps maintain the accuracy of georeferencing by avoiding distortions during parameter extraction. The masks resulting from the segmentations performed by YOLOv11 are used to georeference the deposits within the GIS layers to obtain the parameters needed for PS evaluation through the proposed simplified matrices.

## D. APPLICATION OF AI MODELS TO THE PROPOSED MATRICES

To evaluate the proposed matrices, most parameters are obtained from GIS layers. However, matrices M1' and

M2' require visual and geometric parameters, such as shape foundation/MWSF typology (degree of confinement) and the presence of lifts in WRD and LWD. For these cases, two multiclass classification models were trained: one to determine shape foundation/MWSF typology (degree of confinement) and another to identify the presence or absence of lifts (as shown in Figure 4b).

A ResNet18 neural network [31], a variant of the residual network architecture, was used for these classifiers. ResNet18 was chosen due to its ability to mitigate the vanishing gradient problem, its efficiency in training on relatively smaller datasets, and its proven effectiveness in learning complex patterns, making it well-suited for this specific classification task. A new database was generated from the segmented masks of the initial database, manually labeled with an expert's assistance, to train these models. A balanced
<figure><img src="image_14.png" /><figcaption>FIGURE 10. Georeferencing of LWDs and WRDs detected and evaluated in the Antofagasta region. IPL: a) LWD, b) WRD.</figcaption></figure>

set of classes was obtained and divided into training (80%), validation (10%), and test (10%) sets, totaling 5,940 labeled deposits for the presence of lifts and 10,800 MWSF with degree of confinement levels. The class distribution is detailed in Table 9.

The evaluation metrics for the degree of confinement classifier showed outstanding results, with precision ranging from 0.92 to 0.99 and an F1-score from 0.96 to 0.99 across all classes (see Table 10). These metrics indicate the model's high reliability in accurately classifying the degree of confinement, which is crucial for understanding the structural stability of the deposits. For the lift classifier, results also showed high precision and recall, reaching values between 0.96 and 1.00 across all classes and datasets. These results, detailed in Table 11, demonstrate excellent classification capabilities for distinguishing between MWSF with and without lifts.

## E. AUTOMATIC EVALUATION OF GLOBAL PS THROUGH SIMPLIFIED MATRICES

The automatic evaluation of the global PS of WRD and LWD integrates detection, segmentation, and geospatial analysis,

supported by the trained YOLOv11 model (Table 12). The resulting masks are georeferenced within GIS layers to extract parameters for each evaluation matrix. Trained multiclass classifiers are essential for extracting geometric parameters, such as the presence of lifts or the degree of confinement. This automated approach relies on consistent data quality, which may limit accuracy in areas with incomplete or outdated information. Once a WRD or LWD is evaluated, the PSGI is calculated using the following expression:

$$ \text{PSGI} = \sum_{i=1}^{4} \sum_{j=1}^{n_i} m_{ij}^{\text{score}} \quad (2) $$

where $m_{ij}^{\text{score}}$ the score obtained after evaluating factor j within matrix i, and $n_i$ represents the total number of factors corresponding to matrix i for each WRD or LWD detected.

## VI. EXPERIMENTS AND RESULTS

Two experiments were conducted to validate the proposed methodology for evaluating the PS WRD and LWD. The first experiment applied the methodology to a case study at the Radomiro Tomic Division (RTD), Chuquicamata South
<figure><img src="image_15.png" /><figcaption>FIGURE 11. Geological map of Chile, scale 1:1,000,000. Antofagasta region, Chile. SERNAGEOMIN [9].</figcaption></figure>

<table><caption>TABLE 9. Details of the database used to train the lifts classification model and confinement classification model.</caption><thead><tr><th>Multiclass classifier model</th><th>Category</th><th>Total (100%)</th><th>Train (80%)</th><th>Val (10%)</th><th>Test (10%)</th></tr></thead><tbody><tr><td rowspan="2">Lifts classifier</td><td>With lifts</td><td>2,970</td><td>2,376</td><td>297</td><td>297</td></tr><tr><td>Without lifts</td><td>2,970</td><td>2,376</td><td>297</td><td>297</td></tr><tr><td rowspan="3">Confinement classifier</td><td>Valley fill</td><td>2,700</td><td>1,889</td><td>405</td><td>406</td></tr><tr><td>Sidehill fill</td><td>2,700</td><td>1,889</td><td>405</td><td>406</td></tr><tr><td>Heaped fill</td><td>2,700</td><td>1,889</td><td>405</td><td>406</td></tr></tbody></table>

<table><caption>TABLE 10. Evaluation metrics results of the trained confinement classifier model.</caption><thead><tr><th>Metric</th><th>Valley fill</th><th>Sidehill fill</th><th>Heaped fill</th></tr></thead><tbody><tr><td>Precision</td><td>0.920</td><td>0.953</td><td>0.985</td></tr><tr><td>Recall</td><td>0.931</td><td>0.919</td><td>0.990</td></tr><tr><td>F1-score</td><td>0.967</td><td>0.945</td><td>0.980</td></tr></tbody></table>

<table><caption>TABLE 11. Evaluation metrics results of the trained lifts classifier model.</caption><thead><tr><th>Metric</th><th>With lift</th><th>Without lift</th></tr></thead><tbody><tr><td>Precision</td><td>1.000</td><td>0.997</td></tr><tr><td>Recall</td><td>0.997</td><td>1.000</td></tr><tr><td>F1-score</td><td>0.998</td><td>0.998</td></tr></tbody></table>

Mine (Antofagasta region, Chile), involving satellite imagery acquisition using Sentinel-2, segmentation with YOLOv11, and parameter extraction through GIS georeferencing.

Multiclass classification models assessed structural features like the degree of confinement and presence of lifts. The second experiment applied the methodology to all available images from the Antofagasta region to generate a comprehensive PS assessment of WRD and LWD, with the aim of creating a proposed official registry to assist in the inspection and supervision by SERNAGEOMIN.

## A. EXPERIMENT 1: AUTOMATIC EVALUATION OF PS AT THE RTD

Figure 6 shows a satellite image of the mining area under study, along with the corresponding detection and segmentation performed by YOLOv11. The model demonstrated effective detection and segmentation of MWSF in the mining area, accurately delineating the WRD and LWD boundaries and minimizing false positives. For RTD Chuquicamata South Mine, 3 WRD zones and 3 LWD zones were detected, covering a total area of 1327.03 (ha) and 986.53 (ha), respectively.
<table><caption>TABLE 12. Materials required for automatic evaluation of the global PS for WRD and LWD.</caption><thead><tr><th>Matrix</th><th>Factors</th><th>Materials</th><th>Scores</th></tr></thead><tbody><tr><td rowspan="3">M1'</td><td>Geomechanical characteristics of foundation soil</td><td>Mask + GIS Geology layer + GIS V<sub>S30</sub> layer</td><td>m<sup>score</sup><sub>11</sub></td></tr><tr><td>Average overall foundation slope angle (α)</td><td>Mask + GIS ground slope layer</td><td>m<sup>score</sup><sub>12</sub></td></tr><tr><td>Shape foundation and MWSF typology</td><td>Mask + Confinement classifier</td><td>m<sup>score</sup><sub>13</sub></td></tr><tr><td rowspan="4">M2'</td><td>Overall height</td><td>Mask + GIS ground elevation layer</td><td>m<sup>score</sup><sub>21</sub></td></tr><tr><td>Volume</td><td>Mask + GIS ground elevation layer</td><td>m<sup>score</sup><sub>22</sub></td></tr><tr><td>Overall fill slope angle (β)</td><td>Mask + Lift classifier</td><td>m<sup>score</sup><sub>23</sub></td></tr><tr><td>Lifts</td><td>Mask + Lift classifier</td><td>m<sup>score</sup><sub>24</sub></td></tr><tr><td>M3'</td><td>Geomechanical quality</td><td>Mask and material database</td><td>m<sup>score</sup><sub>31</sub></td></tr><tr><td rowspan="3">M4'</td><td>Seismicity</td><td>Mask + GIS seismic zones layer</td><td>m<sup>score</sup><sub>41</sub></td></tr><tr><td>Distance to geological faults</td><td>Mask + GIS active and potentially-active continental faults</td><td>m<sup>score</sup><sub>42</sub></td></tr><tr><td>Precipitation</td><td>Mask + GIS precipitation layer</td><td>m<sup>score</sup><sub>43</sub></td></tr></tbody></table>

<figure><img src="image_16.png" /><figcaption>FIGURE 12. Precipitation mean 2023. Antofagasta region, Chile. SNIAT [28].</figcaption></figure>

Following detection, the segmented masks were geo-referenced within GIS layers, including geological units, active and potentially active continental faults, Vs30 parameter, ground slope, seismic zone, and precipitation layers, to extract specific parameters needed for evaluating each of the proposed matrices (Figures 13 and 12). By integrating these GIS data, a detailed PS assessment of the detected WRD and LWD was performed using simplified evaluation matrices, ensuring that each aspect of the physical environment was accurately considered.

Subsequently, multiclass classification models were used to determine the degree of confinement (shape foundation/MWSF typology) and the presence of lifts in each detected WRD and LWD in the studied mining area. The results are presented in Figure 9. This workflow ensured a consistent evaluation of each WRD and LWD using the proposed matrices, with a summary of the scores obtained for each matrix shown in Table 13. Detailed ratings for each evaluation matrix, along with the resulting PSGI score, FOP, and associated IPL, are also included.

## B. EXPERIMENT 2: APPLICATION OF THE METHODOLOGY IN THE ANTOFAGASTA REGION, CHILE

In the second experiment, the methodology was expanded to cover the entire Antofagasta region, using all available satellite images to conduct a comprehensive assessment of existing WRDs and LWDs. By processing these images, an Excel file was generated containing detailed information on each mining area, including central geolocation and detected WRD and LWD. This Excel file serves as a valuable tool for stakeholders, such as SERNAGEOMIN, to improve monitoring and management efforts by providing centralized, accessible data on deposit locations and characteristics. As SERNAGEOMIN currently lacks an updated registry, our work provides a critical foundation for their oversight efforts.

The generated Excel file also formed the basis for projecting the detected WRD and LWD on a map of the Antofagasta region, offering a clear visual representation of the deposits and their corresponding locations (Figure 10a). Table 14 provides details on the number of detected mining facilities and MWSF, including 70 WRD and 54 LWD deposits, along with their categorization based on FOP and IPL (Figure 10b). This categorization is crucial for prioritizing inspections or interventions, allowing SERNAGEOMIN to allocate resources efficiently and focus on areas of higher risk. By providing an updated registry, our work supports SERNAGEOMIN's efforts to improve their regulatory oversight and management capabilities across the region.
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /><figcaption>FIGURE 13. Antofagasta region, Chile. a) Global Vs30 Mosaic Map Viewer. Heath et al. [24]. b) Digital elevation models. Chile. IDE [25]. c) Slope geoprocessing of digital elevation models. Antofagasta region, Chile. IDE [25]. d) Seismic zones of Chile. Antofagasta region. INN [26].</figcaption></figure>
<table><caption>TABLE 13. Results of the evaluation of global PS for each detected WRDs and LWDs in the case study mining area.</caption><thead><tr><th>Rating</th><th>Mask ID 1</th><th>Mask ID 2</th><th>Mask ID 3</th><th>Mask ID 4</th><th>Mask ID 5</th><th>Mask ID 6</th></tr></thead><tbody><tr><td>m<sup>score</sup><sub>11</sub></td><td>200</td><td>100</td><td>100</td><td>300</td><td>300</td><td>300</td></tr><tr><td>m<sup>score</sup><sub>12</sub></td><td>50</td><td>0</td><td>100</td><td>50</td><td>0</td><td>50</td></tr><tr><td>m<sup>score</sup><sub>13</sub></td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td></tr><tr><td>m<sup>score</sup><sub>21</sub></td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td></tr><tr><td>m<sup>score</sup><sub>22</sub></td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td></tr><tr><td>m<sup>score</sup><sub>23</sub></td><td>50</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>m<sup>score</sup><sub>24</sub></td><td>0</td><td>200</td><td>200</td><td>200</td><td>200</td><td>200</td></tr><tr><td>m<sup>score</sup><sub>31</sub></td><td>200</td><td>200</td><td>0</td><td>0</td><td>200</td><td>0</td></tr><tr><td>m<sup>score</sup><sub>41</sub></td><td>200</td><td>200</td><td>200</td><td>200</td><td>0</td><td>0</td></tr><tr><td>m<sup>score</sup><sub>42</sub></td><td>200</td><td>200</td><td>200</td><td>200</td><td>0</td><td>0</td></tr><tr><td>m<sup>score</sup><sub>43</sub></td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>PSGI</td><td>1300</td><td>1300</td><td>1200</td><td>1350</td><td>1300</td><td>1150</td></tr><tr><td>FOP</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Low</td></tr><tr><td>IPL</td><td>III</td><td>III</td><td>III</td><td>III</td><td>III</td><td>II</td></tr></tbody></table>

<table><caption>TABLE 14. Results of the evaluation of global PS of WRDs and LWDs in the Antofagasta region.</caption><thead><tr><th>FOP</th><th>IPL</th><th>MWFS</th><th>WRD</th><th>LWD</th></tr></thead><tbody><tr><td>Very low</td><td>I: Very reduced</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Low</td><td>II: Reduced</td><td>18</td><td>13</td><td>5</td></tr><tr><td>Moderate</td><td>III: Moderate</td><td>105</td><td>57</td><td>48</td></tr><tr><td>High</td><td>IV: Priority</td><td>1</td><td>0</td><td>1</td></tr><tr><td>Very high</td><td>V: Very priority</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

## VII. CONCLUSION

The present work introduces an innovative solution to the challenges in evaluating the physical stability of WRD and LWD in Chile. The proposal of simplified matrices represents a significant advancement, enabling SERNAGEOMIN to overcome current data and resource limitations. By focusing on first-order parameters obtainable through open-access information, these matrices simplify and streamline the evaluation process without compromising accuracy.

The integration of GIS technologies and AI techniques has proven highly effective in the automatic detection and characterization of WRD and LWD. The developed methodology utilizes detection and classification models, such as YOLOv11 and convolutional neural networks, to analyze satellite images and extract critical parameters for PS evaluation. This approach not only reduces analysis times and minimizes human errors but also allows for significant scalability and adaptability.

The validation of the methodology in the Antofagasta Region, a key mining area in Chile, enabled the identification and evaluation of 70 WRD and 54 LWD. The results obtained demonstrate the capability of the proposed approach to prioritize deposits according to their potential risk, thereby facilitating the supervision and oversight work of SERNAGEOMIN. While this study focused on WRDs and LWDs, future work could explore adapting the methodology to other mining areas and facility types, including TSFs, by incorporating the key parameters outlined in [9] for physical stability.

# APPENDIX
GIS CARTOGRAPHY

See Figures 11–13.

## ACKNOWLEDGMENT

The authors gratefully acknowledge the valuable contribution of SERNAGEOMIN in validating the information used and providing feedback for the evaluation methodology proposed in this work. They also extend their sincere thanks to undergraduate students from the School of Electrical Engineering, Pontificia Universidad Católica de Valparaíso—Rodrigo Pereira, Gonzalo Caballero, and Felipe Aravena—and to geographer Emilio Bustos, who contributed to the downloading and labeling of the database.

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<figure><img src="image_21.png" /></figure>

**GABRIEL HERMOSILLA** was born in Chillan, Chile, in 1982. He received the degree in electronic engineering from the University of La Frontera, Temuco, Chile, in 2007, and the Ph.D. degree in electrical engineering from the University of Chile, Santiago, Chile, in 2012. Currently, he is an Associate Professor with the School of Electrical Engineering, Pontificia Universidad Católica de Valparaíso (PUCV), Valparaíso, Chile. His main areas of research interests include thermal face recognition, pattern recognition, computer vision, and deep learning.

<figure><img src="image_22.png" /></figure>

**GABRIEL VILLAVICENCIO** was born in Valparaíso, Chile in 1977. He received the Ph.D. degree in civil engineering from École Doctorale des Sciences Pour l'Ingénieur, Université Blaise-Pascal, France, in 2009. From 2004 to 2009, he was a Temporary Teaching and a Research Associate with the Civil Engineering Department, Polytech Clermont, France. Since 2010, he has been an Assistant Professor with the Construction and Transportation Engineering, Pontificia Universidad Católica de Valparaíso, and a Geotechnical Engineer with the LEPUCV Laboratory. His research interests include geotechnical engineering applications for tailings storage facilities (TSF), wasted rock dumps, and leaching waste dumps. In addition, in topics such as physical stability TSF, evaluation of liquefaction potential, slope stability, and geotechnical modeling of urban sites. He is an Active Member of the Chilean Geotechnical Society.

<figure><img src="image_23.png" /></figure>

**GIOVANNI COCCA-GUARDIA** (Member, IEEE) was born in Calama, Chile. He received the B.Sc. degree in engineering sciences and the Civil Electrical Engineering degree from Pontificia Universidad Católica de Valparaíso, where he is currently pursuing the M.Sc. and Ph.D. degrees in electrical engineering. His research interests include graph neural networks, electrical power systems, generative AI, and efficient learning AI.

<figure><img src="image_24.png" /></figure>

**VICENTE APRIGLIANO** received the bachelor's degree in geography from UFRJ, the master's degree in transportation engineering from the COPPE, UFRJ, and the Ph.D. degree in human geography from Universität Tübingen, Germany. He was a Postdoctoral Fellow at the Center for Sustainable Urban Development, Pontifical Catholic University of Chile. He is currently a Professor with the School of Construction and Transportation Engineering, Pontificia Universidad Católica de Valparaíso (PUCV), Valparaíso, Chile. His research interests include transport geography, economic geography, urban geography, and urban mobility planning.

<figure><img src="image_25.png" /></figure>

**MANUEL SILVA** (Member, IEEE) was born in Valparaíso, Chile, in 1990. He received the B.S. degree in electronics engineering and the M.Sc. degree in electrical engineering from the School of Electrical Engineering, Pontificia Universidad Católica de Valparaíso (PUCV), in 2022 and 2023, respectively. He is currently pursuing the Ph.D. degree in electrical engineering with the Robotics and Vision Laboratory, PUCV.

His research interests include computer vision, remote sensing, machine learning, and robotics.
<figure><img src="image_26.png" /></figure>

**JUAN CARLOS QUEZADA** was born in Valparaíso, Chile, in 1982. He received the bachelor's degree in construction engineering from Pontificia Universidad Católica de Valparaíso, in 2008, the master's degree from Université Blaise Pascal (Clermont-Ferrand II), France, in 2009, and the Ph.D. degree in mechanics and civil engineering through a collaborative program between Université Montpellier 2, France, and French National Railway Company (SNCF). Following his Ph.D.

studies, he was an Engineer with the Innovation and Research Department, SNCF, Paris, France, until 2014. Then, he joined the LTDS Laboratory, École Centrale de Lyon, France, as a Postdoctoral Researcher, where he focused on the numerical modeling of dry-stone retaining walls. In 2015, he was appointed as an Associate Professor with the Civil Engineering Department, INSA Strasbourg, France, a position he continues to hold. His teaching portfolio encompasses topics in structural engineering, geotechnics, and numerical methods. He is an active researcher affiliated with the ICube Laboratory, where his work centers on numerical modeling using discrete element method (DEM) and finite element method (FEM) in geomechanics, with applications to geomaterials.

<figure><img src="image_27.png" /></figure>

**PIERRE BREUL** was born in France, in 1972. He received the bachelor's and Ph.D. degrees in civil engineering from Université Blaise-Pascal, Clermont-Ferrand, France, in 1995 and 1999, respectively. He was an Engineer at Geotechnical Engineering Company for five years. Then, he is currently a Professor with the National Polytechnic Institute, Clermont Auvergne University, and the Head of the Engineering School, Polytech Clermont. His research interests include geotechnics, soils and granular materials mechanics, images and data analysis for soil recognition and identification, infrastructure diagnosis, and risk analysis.

<figure><img src="image_28.png" /></figure>

**VINICIUS MINATOGAWA** was born in Americana, São Paulo, Brazil, in 1990. He received the M.Sc. and Ph.D. degrees in mechanical engineering from the University of Campinas, Brazil. Since 2021, he has been an Associate Professor with the Construction and Transportation Engineering School, Pontificia Universidad Católica de Valparaíso. In addition, he is a Project Management Professional (PMP®) certified by the Project Management Institute (PMP® Number:

3778535), with a particular focus on agile projects. His research interests include digital transformation projects, aiming to integrate emerging technologies, data-driven decision-making, and organizational innovation in engineering and related sectors. His work has been featured in high-impact journals, such as *Automation in Construction, Review of Managerial Science*, and *Journal of Manufacturing Technology Management*.

<figure><img src="image_29.png" /></figure>

**JAIME MORALES** was born in 1973. He is professionally trained in teaching and research, with communication and leadership skills. His experience in teaching in the area of thermodynamics and unit operations, applying evaluation concepts through competencies, and strategies of new evaluation alternatives. His development of research in the study of thermodynamic, volumetric and transport properties applying electrochemistry, thermodynamic modeling in systems

containing electrolytes and electrochemistry of lithium batteries, chemical stability of tailings, and slag valorization based on circular economy.

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