Open Pit Mining Area Research Articles

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

Understanding land use and damage in open-pit coal mining areas is crucial for effective scientific oversight and management. Current recognition methods exhibit limitations: traditional approaches depend on manually designed features, which offer limited expressiveness, whereas deep learning techniques are heavily reliant on sample data. In order to overcome the aforementioned limitations, a three-branch feature extraction framework was proposed in the present study. The proposed framework effectively fuses deep features (DF) and shallow features (SF), and can accomplish scene recognition tasks with high accuracy and fewer samples. Deep features are enhanced through a neighbouring feature attention module and a Graph Convolutional Network (GCN) module, which capture both neighbouring features and the correlation between local scene information. Shallow features are extracted using the Gray-Level Co-occurrence Matrix (GLCM) and Gabor filters, which respectively capture local and overall texture variations. Evaluation results on the AID and RSSCN7 datasets demonstrate that the proposed deep feature extraction model achieved classification accuracies of 97.53% and 96.73%, respectively, indicating superior performance in deep feature extraction tasks. Finally, the two kinds of features were fused and input into the particle swarm algorithm optimised support vector machine (PSO-SVM) to classify the scenes of remote sensing images, and the classification accuracy reached 92.78%, outperforming four other classification methods.

Object-oriented U-GCN for open-pit mining extraction from high spatial resolution remote-sensing images of complex scenes

ABSTRACT Precise extraction of open-pit mines is crucial for resource management and ecological and environmental dynamic monitoring. Current methods for extracting open-pit mines encounter challenges such as low accuracy and difficulty detecting complex scenes of open-pit mining. To address these issues, this paper proposes an object-oriented intelligent extraction method for complex mining scenes using Gaofen-2(GF-2) high-resolution remote-sensing images, which expresses the pixel-level features at the object level and utilizes the unique feature propagation and aggregation capabilities of the graph structure for the extraction of the mines. First, an object-oriented feature expression strategy is introduced to express multi-level pixel-level features as object-level features by constructing objects with appropriate multi-resolution segmentation parameters. This approach effectively reduces the impact of isolated pixel noise and outliers on classification results. Second, this paper proposes a U-GCN-based open-pit mining extraction method that combines the powerful multi-level feature extraction capabilities of U-GCN to propagate and aggregate information in a graph structure, effectively modelling spatial relationships between different objects. This method achieves high-precision extraction of open-pit mining areas. In experiments conducted on two study areas of varying scales, the F1 scores for open-pit mine extraction reached 93.32% and 83.06%. Comparative experiments demonstrate that the proposed object-oriented U-GCN method performs superiorly in terms of accuracy, stability and robustness across mining scenes with different levels of complexity. The proposed open-pit mine extraction method offers new insights and methodologies for current extraction practices.

Hydrogeochemical evolution patterns of diverse water bodies in mining area driven by large-scale open-pit combined underground mining-taking Pingshuo Mining Area as an example

Large-scale open-pit combined underground mining activities (OUM) not only reshape the original topography, geomorphology, and hydrogeochemical environment of the mining area, but also alter the regional water cycle conditions. However, due to the complexity arising from the coexistence of two coal mining technologies (open-pit and underground mining), the hydrological environmental effects remain unclear. Here, we selected the Pingshuo Mining Area in China, one of the most modernized open-pit combined underground mining regions, as the focus of our research. We comprehensively employed mathematical statistics, Piper diagram, Gibbs model, ion combination ratio, principal component analysis and other methods to compare the hydrochemistry and isotope data of different water bodies before (2006) and after (2021) large-scale mining. The changing patterns of hydrochemical characteristics of different water bodies and their main controlling factors in mining area driven by OUM were analyzed and identified, revealing the water circulation mechanism under the background of long-term coal mining. The results showed that: (1) The chemical composition of water has changed greatly due to large-scale coal mining. The hydrochemical types of Quaternary and Permian-Carboniferous aquifers shifted from predominantly HCO3-Ca·Mg before intensive mining to primarily HCO3·SO4-Ca·Mg, HCO3-Na, HCO3·SO4-Na·Mg, and HCO3·SO4-Ca·Mg, HCO3-Ca·Na, HCO3·SO4-Mg·Ca post-mining. Variations in the hydrochemical types of surface water were found to be complex and diverse. (2) Coal mining activities promote the dissolution of silicate rock and sodium-bearing evaporites, enhancing the strength and scale of positive alternating adsorption of cations. The oxidation of pyrite, dissolution of silicate weathering, and the leaching of coal gangue were identified as the main reasons for the significant increase of SO42−, while decarbonation in confined aquifers led to a decrease in HCO3−. (3) Results from the principal component analysis and stable isotopes demonstrated the hydraulic connection among surface water, Quaternary aquifers, and Permian-Carboniferous aquifers induced by long-term OUM. The research findings provide a reference basis for the coordinated development of coal and water in the Pingshuo Mining Area and other open-pit combined underground mining areas.

Quantitative identification of landslide hazard in mountainous open-pit mining areas combined with ascending and descending orbit InSAR technology

Quantitative identification of landslide hazard in mountainous open-pit mining areas combined with ascending and descending orbit InSAR technology

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models.

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east-west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Open-Pit Mining Area Extraction Using Multispectral Remote Sensing Images: A Deep Learning Extraction Method Based on Transformer

In the era of remote sensing big data, the intelligent interpretation of remote sensing images is a key technology for mining the value of remote sensing big data and promoting a number of major applications, mainly including land cover classification and extraction. Among these, the rapid extraction of open-pit mining areas plays a vital role in current practices for refined mineral resources development and management and ecological–environmental protection in China. However, existing methods are not accurate enough for classification, not fine enough for boundary extraction, and poor in terms of multi-scale adaptation. To address these issues, we propose a new semantic segmentation model based on Transformer, which is called Segmentation for Mine—SegMine—and consists of a Vision Transformer-based encoder and a lightweight attention mask decoder. The experimental results show that SegMine enhances the network’s ability to obtain local spatial detail information and improves the problem of disappearing small-scale object features and insufficient information expression. It also better preserves the boundary details of open-pit mining areas. Using the metrics of mIoU, precision, recall, and dice, experimental areas were selected for comparative analysis, and the results show that the new method is significantly better than six other existing major Transformer variants.

Study on the sliding mechanism of the external dump slope in the open-pit mine with wind-blown sand base

Wind-blow sand (WBS) is widely distributed in the “Desert Gobi” region. This study is aimed at exploring the mechanism of how different thicknesses of the WBS layer influence the slope movement of external dumps in open-pit mines. To achieve this aim, the slope of the external dump in the open-pit mining area of Panel 3 in Daliuta Coal Mine was taken as the research object. First, similar simulation experiments were performed for investigating the failure modes and deformation characteristics of the external dump slopes under three geo-morphological conditions: loess base, 10-m-thick WBS base, and 20-m-thick WBS base, respectively. The following results were obtained from the experiments. For the slope with a loess base, its failure is mainly caused by circular sliding from the dump to the interior of the loess layer. For the slope with a 10-m-thick WBS base, the sliding mode involves circular sliding from the dump area to the interior of the WBS layer, linear sliding along the WBS base, and shearing along the foot of the dump area. For the slope with a 20-m-thick WBS base, the sliding mode is circular sliding from the dump area to the interior of the WBS layer. Besides, the sliding area of the dump slope expands as the WBS layer thickens. Furthermore, the results of similar simulation experiments were verified by the finite difference software FLAC3D based on the strength reduction method, and an equation of relationship between the safety factor of the dump slope with a WBS base and the thickness of the WBS layer was derived.

A Novel Hybrid Fuzzy Multiple-Criteria Decision-Making Model for the Selection of the Most Suitable Land Reclamation Variant at Open-Pit Coal Mines

The expansion of the open-pit exploitation of mineral raw materials, and especially the energy resources of fossil fuels, makes open-pit coal mines spatially dominant objects of large mining basins. Exploitation activities are accompanied by negative ecological impacts on the environment, which requires the integral planning, revitalization, reclamation, and rehabilitation of the disturbed area for human use in the post-exploitation period. The post-exploitation remediation and rehabilitation of open-pit mining areas and disposal sites, i.e., space disturbed by mining activities and accompanying facilities, are complex synthetic multidisciplinary multiphase engineering project tasks. In this paper, a hybrid fuzzy MCDM model (Multiple-Criteria Decision-Making) was developed for the selection of a reclamation solution for the Tamnava-West Field open-pit mine. IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define the weights of 12 criteria of different structures used in the evaluation of reclamation solutions. The Fuzzy ROV (Range of Value) method was applied to select the reclamation solution from a total of 11 solutions previously obtained using a process approach. The results of the hybrid IMF SWARA—Fuzzy ROV model show that forestry is the best solution for the Tamnava-West Field open-pit mine. After the results had been obtained, verification analyses of the proposed model were performed and the best stable proposed reclamation solution was determined.

Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data

Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.

Effects of Different Reclamation Years and Modes on Soil Moisture Transport Pathways and Permeability Characteristics in an Open-Pit Mining Area in Guangxi

The microstructural characteristics of reclaimed soil in Guangxi’s bauxite mining area play a pivotal role in determining soil reclamation quality, yet they remain poorly understood. To explore the impact of varying reclamation years and modes on pore structure characteristics in this region, we selected four reclamation regions: grassland reclamation with 2 years (RG2a), grassland reclamation with 10 years (RG10a), bare ground reclamation with 2 years (RW2a) and bare ground reclamation with 10 years (RW10a). Utilizing X-ray CT technology, we scanned soil columns within a 30 cm depth to analyze pore distributions using Avizo 2020 software combined with ImageJ 1.53c and its plugins. The findings revealed a significant increase in the number of 2D and 3D macropores by 1.09% and 88.89% in RG10a compared to RG2a, as well as 39.01% and 13.33% in RG10a compared to RW10a, respectively. Furthermore, RG10a was observed to be more effective in enhancing the rounding rate of macropores and mesopores, as well as average branch length and density. Additionally, RG10a demonstrated a greater capacity to increase porosity and connectivity while reducing curvature and specific surface area among the three-dimensional parameters. The 3D reconstruction illustrated that RG modes exhibited a more abundant distribution of macropores compared to RW modes and 10a modes showed a higher presence of macropores than 2a modes, which displayed a tilted laminar pattern. Soil moisture movement simulations conducted through Avizo 2020 under various modes indicated that RG modes presented a more extensive capacity for vertical flow, while 10a modes displayed more abundant flow lines and a higher infiltration rate (K) compared to 2a modes. In conclusion, prolonged reclamation years favoring grassland reclamation modes could enhance water and nutrient transport pathways toward traditional agricultural soil.

Effects of different reclamation measures on soil quality restoration in open-pit mines: A meta-analysis based on the Chinese Loess Plateau

In the Loess Plateau region, ecological restoration projects have been implemented to restore the ecological environment of open-pit mines. However, no consistent conclusion has been reached regarding the response of soil remediation efforts to different ecological measures and restoration years. A meta-analysis was performed on the results of studies published over the past 30 years for open-pit mining areas on the Loess Plateau in China. The findings reveal that land rehabilitation considerably improved soil quality in open-pit mining areas on the Loess Plateau. Different ecological measures revealed a significant variability in soil quality restoration. Specifically, forestland reclamation affected soil organic matter and microbial carbon the most. Grassland reclamation substantially influenced soil total potassium, organic carbon, enzyme activity, and microbial abundance. Plowland caused the most effective improvement in soil total nitrogen and physical properties. However, total phosphorus and pH were unaffected by restoration measures. In addition, the period of land reclamation and soil nutrients showed a high correlation in the early stage of reclamation. After long-term reclamation, a decreased rate of soil quality improvement by remediation measures occurred after the soil quality returned to its premining level. A relatively lag was observed in the response of soil microbial indicators to environmental changes, and the premining level was reached after longer-term reclamation.

Study on timeliness external DEM for open-pit mining area deformation monitoring using LuTan-1 SAR data

Study on timeliness external DEM for open-pit mining area deformation monitoring using LuTan-1 SAR data

2020–2022年环渤海地区8 m分辨率露天采矿区空间分布数据集

With the increase of mining activities in China, the ecological environment around mines is facing unprecedented pressure, leading to a series of resource, ecological and environmental problems, such as air pollution, soil erosion, solid waste pollution, landslides and debris flows. To precisely obtain the spatial distribution data of open-pit mining, scholars and regulatory bodies are actively seeking timely and efficient methods to monitor open-pit mining areas by using the remote sensing, an innovative technology, for this purpose. In this research, we adopted high-resolution optical remote sensing image GF-6 with a resolution of 8m to obtain the open-pit mining dataset in the Bohai Rim through rule-based feature extraction, time-series image discrimination and visual interpretation methods, covering over 8000 open-pit mining areas. The dataset exhibits a high level of accuracy, with an overall accuracy of 95.0%, a producer accuracy of 98.0% and a user accuracy of 92.4%. It effectively reflects the spatial dynamics in the distribution of open-pit mining areas within the Bohai Rim region and reveals the influence of economic growth and policy-making on these changes. Thus, the dataset can offer valuable data support for managing open-pit mining operations and the development of related policies.

Mapping open-pit mining area in complex mining and mixed land cover zone using Landsat imagery

The monitoring of open-pit mining and reclamation activities is an important part of ecological protection across various countries. Among the many methods of monitoring open-pit mining areas, satellite remote sensing is the most widely used and effective. However, few works have been conducted to map open-pit mines over a larger geographical scale, or have considered complex land cover types, or a wide variety of mine properties. This work proposes a framework for mapping open-pit mines for areas over complex land cover and mining properties, including preprocessing, classification, and postprocessing steps. First, the pixels that did not contain mines were masked, second the mines were divided into two categories according to their spectral characteristics, then the selected samples and classified to obtain the classification results of mines, and third mis-classified pixels were removed. This work used Landsat images and Google Earth Engine to map the open-pit mines over Shanxi in 5-year steps from 2000 to 2020. The average producer and user accuracy of the results was found to be 79.4% and 83.2% respectively. The experimental results showed that open-pit mining areas in Shanxi first increased and subsequently decreased over the past twenty years. Additionally, the new preprocessing, classification, and postprocessing workflow was found to be effective, especially in environments with more vegetative cover.

Tracking deformation velocity via PSI and SBAS as a sign of landslide failure: an open-pit mine-induced landslide in Himmetoğlu (Bolu, NW Turkey)

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year−1 and between − 24 and 6 mm year−1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year−1 and between − 18 and 20 mm year−1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.

Microscopic characteristics and sources of atmospheric dustfall in open-pit mining coal resource-based city in the arid desert area of Northwest China

Atmospheric dustfall is solid air pollutant, has a major impact on the environment and human health. The objective of this study was to investigate the microscopic characteristics and sources of atmospheric dustfall in open-pit mining coal resource-based city in the arid desert area of Northwest China. The characteristics of size and shape factors, variation of shape factors with size distribution, types of individual particles, and sources of atmospheric dustfall, which were collected in the open-pit mining area and surrounding areas, were analyzed by X-ray diffraction (XRD) and scanning electron microscopy coupled with an energy dispersive spectrometer (SEM–EDS) combined with graphical method and shape factors. The results showed that the atmospheric dustfall in all functional areas was dominated by coarse-grained particles. The shape of the atmospheric dustfall deviated from spherical shape, and with decreasing particle size, the difference in shape factors increased in each functional area. The EDS and XRD analyses indicated the presence of 13 types of particles. The sources were mainly local and included soil dust from each functional area; industrial dust, construction dust, biogenic impurities, fossil fuel combustion, wear products of motor vehicle parts, motor vehicle exhaust emissions, and emission and excreta from biological activities in each functional area except the desert area; emissions from a steel plant in the industrial area; coal-associated ore, coal dust, coal gangue emissions, and emissions from the spontaneous combustion of coal gangue in the open-pit mining area; secondary chemical crystallization products in the industrial area and the open-pit mining area; dust generated by vehicles abrading the surface of the off-mine coal road and in the open-pit mining area.

Extracting unstructured roads for smart Open-Pit mines based on computer vision: Implications for intelligent mining

The mining industry is rapidly advancing towards automation and intelligence, with smart mines emerging as a future trend. Open-pit mining areas are semi-enclosed, and roads are essential for unmanned trucks to perceive the mining environment and execute various production tasks. The dynamic nature of open-pit mines, driven by production progress, leads to frequent alterations in roadways. As a consequence, roads become unstructured, with indistinct edges that easily blend into the surrounding mine environment. This poses a challenging operational environment for unmanned vehicles. To address this challenge in the realm of intelligent mining, this study establishes a dataset of mining roads based on different rock types and proposes an unstructured road segmentation method for mines by integrating residual networks, Contrast Limited Adaptive Histogram Equalization (CLAHE), and the Efficient Channel Attention (ECA) mechanism. This method is applied to four semantic segmentation networks: FCN, UNet, PSPNet, and DeepLab v3 +. The dataset and network model undergo validation using a specific hybrid loss function and relevant evaluation metrics. The results show that the established road dataset has good applicability, with an ablation experiment confirming the effectiveness of the added modules. This study introduces a new perspective for advancing unmanned driving in smart mines.

Open-Pit Granite Mining Area Extraction Using UAV Aerial Images and the Novel GIPNet

The ability to rapidly and accurately delineate open-pit granite mining areas is pivotal for effective production planning and environmental impact assessment. Over the years, advancements in remote sensing techniques, including the utilization of satellite imagery, LiDAR technology and unmanned aerial vehicles, have revolutionized the way mining areas are monitored and managed. Simultaneously, in the context of the open-pit mining area extraction task, deep learning-based automatic recognition is gradually replacing manual visual interpretation. Leveraging the potential of unmanned aerial vehicles (UAVs) for real-time, low-risk remote sensing, this study employs UAV-derived orthophotos for mining area extraction. Central to the proposed approach is the novel Gather–Injection–Perception (GIP) module, designed to overcome the information loss typically associated with conventional feature pyramid modules during feature fusion. The GIP module effectively enriches semantic features, addressing a crucial information limitation in existing methodologies. Furthermore, the network introduces the Boundary Perception (BP) module, uniquely tailored to tackle the challenges of blurred boundaries and imprecise localization in mining areas. This module capitalizes on attention mechanisms to accentuate critical high-frequency boundary details in the feature map and synergistically utilizes both high- and low-dimensional feature map data for deep supervised learning. The suggested method demonstrates its superiority in a series of comparative experiments on a specially assembled dataset of research area images. The results are compelling, with the proposed approach achieving 90.67% precision, 92.00% recall, 91.33% F1-score, and 84.04% IoU. These figures not only underscore the effectiveness of suggested model in enhancing the extraction of open-pit granite mining areas but also provides a new idea for the subsequent application of UAV data in the mining scene.

Stability analysis of pumped storage hydropower plant in abandoned open-pit mine affected by dynamic surface subsidence of combined mining.

The construction of a pumped storage hydropower plant (PSHP) in an abandoned open-pit mine is a potential alternative to green mining and energy storage, which can increase the utilization rate of renewable energy and develop residual resources of abandoned mines. Dynamic surface subsidence affected by combined underground and open-pit mining (CUOPM) seriously affects the construction and operation of the PSHP and is one of the critical scientific issues that needs to be solved immediately. The stability of the PSHP was analyzed and treatment scheme of the goafs was proposed based on on-site measurement, theoretical analysis, and numerical simulation. First, the distribution of goafs in the Haizhou open-pit mining area was investigated and surface subsidence value was obtained using InSAR technology and ground monitoring. Secondly, the surface subsidence mechanism affected by CUOPM is analyzed and indicates the subsidence maximum values and scope of influence are greater than those of single underground mining. A dynamic surface subsidence prediction model for combined mining is established based on the Knothe time function model. Thirdly, based on the CVISC model, the numerical calculation models were established by using FLAC3D, and the characteristics and laws of surface subsidence in different periods of CUOPM were studied. The comparative analysis of the observation results shows that the proposed model and numerical simulation calculation method have excellent applicability and accuracy. Finally, a stability evaluation method of PSHP was established, and the results of the evaluation show that the affected areas are the semi-ground powerhouse (SGPH) and the west side of the lower reservoir. The method of grouting filling was used to treat the goafs, and the results showed that it effectively alleviates the dynamic surface subsidence affected by CUOPM, and provides a safety guarantee for PSHP.

Analysis of soil fertility and toxic metal characteristics in open-pit mining areas in northern Shaanxi

The study specifically focused on the Hongliulin mining area, where a total of 40 soil samples were meticulously collected and analyzed from within a 1000 m radius extending from the tailings dam. The findings revealed that soil pH within the 0–1000 m range generally leaned towards the alkaline side. In terms of soil nutrient content, encompassing factors such as soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali nitrogen (AK), available phosphorus (AP), and quick-acting potassium (AK), the variations fell within the following ranges: 2.23–13.58 g/kg, 0.12–0.73 g/kg, 0.18–1.15 g/kg, 9.54–35.82 g/kg, 2.89–6.76 mg/kg, 3.45–11.25 mg/kg, and 5.86–130.9 mg/kg. Collectively, these values indicate relatively low levels of soil nutrients. Within the 0–500 m range of soil samples, the average concentrations of Cd, Hg, Pb, and As were 0.778, 0.198, 24.87, and 17.92 mg/kg, respectively. These concentrations exceeded the established soil background values of Shaanxi Province and emerged as the primary pollutants in the study area. Within this same range, the mean values of eight toxic metals (Pi) were ranked in the following descending order: 1.726 (Hg), 1.400 (As), 1.129 (Cr), 1.109 (Pb), 0.623 (Zn), 0.536 (Cd), 0.309 (Cu), and 0.289 (Ni). With the exception of Hg, As, Cr, and Pb, which exhibited slight pollution, the other toxic metals were found to be within acceptable pollution limits for this sampling range, in line with the results obtained using the geo-accumulation index method. The average potential ecological risk index for the eight toxic metals in the study area stood at 185.0, indicating a moderate overall pollution level. When assessing individual elements, the proportions of ecological risk attributed to Hg, As, Pb, and Cd were 34.57%, 27.44%, 25.11%, and 23.11%, respectively. This suggests that the primary potential ecological risk elements in the study area are Hg and As, followed by Cd and Pb. Notably, toxic metals Hg and Pb, as well as As and Pb, exhibited significant positive correlations within the sampling area, suggesting a common source. An analysis of the relationship between soil physicochemical properties and toxic metals indicated that soil pH, SOM, TN, and TP were closely linked to toxic metal concentrations. The toxic metal elements in the research area's soil exhibit moderate variability (0.16 < CV < 0.36) to high variability (CV > 0.36). Within the range of 0–200 m, the CV values for Cd and Hg exceed 1, indicating a high level of variability. The coefficient of variation for SOM, TP, AP, AK and TK is relatively high with the of 2.93, 2.36, 2.36, 21.01, 7.54. The soil in the sampling area has undergone significant disturbances due to human activities, resulting in toxic metal pollution and nutrient deficiencies.