Natural Resources Research最新文献

{"title":"An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences","authors":"Iason-Zois Gazis, Francois Charlet, Jens Greinert","doi":"10.1007/s11053-024-10393-7","DOIUrl":"https://doi.org/10.1007/s11053-024-10393-7","url":null,"abstract":"<p>High-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km² of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor ( &lt; 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"158 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Prediction Method for Surface Subsidence at Deep Mining Areas with Thin Bedrock and Thick Soil Layer Considering Consolidation Behavior","authors":"Jiachen Wang, Shanxi Wu, Zhaohui Wang, Shenyi Zhang, Boyuan Cheng, Huashun Xie","doi":"10.1007/s11053-024-10395-5","DOIUrl":"https://doi.org/10.1007/s11053-024-10395-5","url":null,"abstract":"<p>Among the various hazards induced by underground coal mining, surface subsidence tends to cause structural damage to the ground. Therefore, accurate prediction and evaluation of surface subsidence are significant for ensuring mining security and sustainable development. Traditional methods like the probability integral method provide effective predictions. However, these methods do not take into account the consolidation behavior of thick soil layers. In this study, based on the principle of superposition, an improved probability integral method that includes surface subsidence caused by rock layer movement and the consolidation behavior of thick soil layers is developed. The proposed method was applied in the Zhaogu No. 2 coal mine, located in the Jiaozuo mining area. Utilizing unmanned surface vehicle measurement technology, it was found that the maximum subsidence values of the two survey lines were 5.441 m and 4.842 m, with maximum subsidence rate of 62.9 mm/day at observation points. Experimental tests have shown that surface subsidence in deep mining areas with thin bedrock and thick soil layers exhibited a large subsidence coefficient and a wide range of subsidence, closely related to the consolidation behavior of thick soil layers. After verification, compared to the probability integral method, the improved probability integral method incorporating soil consolidation showed a 14.7% reduction in average error and a 22% reduction in maximum error. Therefore, the improved probability integral method proposed can be a very promising tool for forecasting and evaluating potential geohazards in coal mining areas.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"299 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonic-Induced Changes in Nanopores: Molecular Insights into Effects on CH4/CO2 Adsorption in Coal","authors":"Liang Wang, Wei Yang, Kang Yang, Chenhao Tian","doi":"10.1007/s11053-024-10392-8","DOIUrl":"https://doi.org/10.1007/s11053-024-10392-8","url":null,"abstract":"<p>The nanometer-sized pores within coal are the primary sites for CH₄ adsorption and competitive adsorption with CO₂. Reasonable modification of the nanopore structure to enhance CH₄ desorption, diffusion rates, and CO₂ competitive adsorption effects can enhance significantly coalbed methane (CBM) production. However, ultrasonic synchronous modification of multiple features of nanopores leads to complex and variable gas adsorption behaviors in coal. To reveal the effect of ultrasonic modification of coal nanopores on gas adsorption, pore measurement experiments and molecular simulation studies were conducted. The results showed that the volume ratio of diffusion pores to adsorption pores (V₂/V₁) decreased significantly after ultrasonic excitation. In the original coal sample, V₂/V₁ was 3.05, while in the coal sample after ultrasonic treatment, V₂/V₁ ranged from 0 to 2.54. With decrease in the proportion of the volume of diffusion pores, the proportion of CH₄ migration from the pore walls of the adsorption pores increased continuously. The proportion of CH₄ migration from the pore walls of the diffusion pores to the pore space of the diffusion pores decreased continuously. The results of gas–solid interaction energy calculation showed that ultrasonic treatment of coal decreases the V₂/V₁ ratio, leading to 7.1–23.3% increase in CO₂ competitive adsorption effect. It also resulted in 4–49% improvement in competitive adsorption efficiency. Additionally, based on gas–solid interaction energy data, an adsorption capacity evaluation model for coal under different gas compositions and pore volume ratios was constructed. The findings can guide ultrasonic-enhanced CBM.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"11254 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Main Controlling Factors for Quantitative Evaluation of a Favorable Carbonate Oil- and Gas-Bearing Area in the Pre-exploration Stage: Lianglitage Formation in the Central Uplift Belt of the Tarim Basin","authors":"Bin Li, Junshuai Ran, Tao Tang, Taiyu Deng, Suju Yang, Haitao Lv","doi":"10.1007/s11053-024-10382-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10382-w","url":null,"abstract":"<p>The evaluation of oil- and gas-bearing areas (OGBAs) during the pre-exploration stage has always posed challenges due to the lack of an effective geological evaluation model and validation data. This paper introduces a novel quantitative evaluation method based on the vectorization of key geological factors related to hydrocarbon accumulation. In this study, we focused on the Lianglitage Formation in the Central Uplift Belt and aimed to evaluate the application of the proposed method to the OGBA in the Tarim Basin. First, the reservoir-forming parameters were quantified based on geological analysis and expert experience. Second, the weights of the main parameters were determined using a combination of the gray correlation method and expert knowledge. Finally, the OGBA was evaluated using a multifactor fusion method. The comprehensive evaluation results indicate that the platform margin in the northeastern part of the Katake Uplift shows promise for exploration, while the southern region has a good potential for future exploration. This study emphasizes the significance of selecting key factors and vectorizing evaluation parameter mapping for accurate and quantitative evaluation of an OGBA. The results of this study provide a valuable foundation for evaluating the OGBAs in the Lianglitage Formation within the Tarim Basin and offer a valuable reference for OGBAs in similar regions during the pre-exploration stage.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"41 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at Wulonggou, Qinghai Province (Western China)","authors":"Qun Yan, Juan Zhao, Linfu Xue, Liqiong Wei, Mingjia Ji, Xiangjin Ran, Junhao Dai","doi":"10.1007/s11053-024-10386-6","DOIUrl":"https://doi.org/10.1007/s11053-024-10386-6","url":null,"abstract":"<p>Prospectivity mapping based on deep learning typically requires substantial amounts of geological feature information from known mineral deposits. Due to the limited spatial distribution of ore deposits, the training of predictive models is often hampered by insufficient positive samples. Meanwhile, data-driven mineral prospectivity mapping often overlooks domain knowledge and expert experience, leading to poor interpretability of predictive results. To address this problem, we employed the Gaussian mixture model (GMM) for spatial feature classification to expand the number of positive samples. The approach integrated the embedding of geological map knowledge graphs with geological exploration data to enhance the knowledge constraints of the prospecting model, which enabled the integration of knowledge with data. Considering the complex spatial structure of geological elements, a bi-branch utilizing the 1-dimensional convolutional neural network (CNN1D) and graph convolutional network (GCN) was used to extract geological spatial features for model training and prediction. To validate the effectiveness of the method, a gold mineralization prediction study was conducted in the Wulonggou area (Qinghai province, western China). The results indicate that, when the number of GMM spatial feature classifications was 17, the positive-to-negative sample ratio was optimal, and the embedding of the knowledge graph controlled the prediction area distribution effectively, which demonstrated strong consistency between the prospecting area and the known mineral deposits. Compared with the predictions by CNN1D, the fused prediction model of CNN1D and GCN yielded higher accuracy. Our model identified 11 classes of mineralization potential areas and provides geological interpretations for different prediction categories.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"65 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Methodology for Similarity Area Searching Using Statistical Distance Measures: Application to Geological Exploration","authors":"Felipe Navarro, Gonzalo Díaz, Marcia Ojeda, Felipe Garrido, Diana Comte, Alejandro Ehrenfeld, Álvaro F. Egaña, Gisella Palma, Mohammad Maleki, Juan Francisco Sanchez-Perez","doi":"10.1007/s11053-024-10385-7","DOIUrl":"https://doi.org/10.1007/s11053-024-10385-7","url":null,"abstract":"<p>Mineral exploration combined with prospectivity mapping has become the standard process for utilising mineral exploration data. Nowadays, most techniques integrate multiple layers of information and use machine learning for both data-driven and knowledge-driven approaches. This study introduces a novel and generalised methodology for comparing different layers of information by using superpixels instead of pixels to identify similarities. This methodology provides an enhanced statistical representation of regions, facilitating and enabling effective comparisons. Three different statistical distance measures were considered: Kullback–Leibler divergence, Wasserstein distance and total variation distance. We apply the proposed process to data from the Antofagasta region of northern Chile, a well-known area for metallogenic belts, that contain notable copper reserves. Each metric was used and compared, resulting in different similarity maps highlighting interesting mineral exploration areas. The study results lead to the conclusion that the proposed methodology can be applied at different scales and helps in the identification of areas with similar characteristics.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"36 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141754934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model","authors":"Abdallah M. Mohamed Taha, Gang Liu, Qiyu Chen, Wenyao Fan, Zhesi Cui, Xuechao Wu, Hongfeng Fang","doi":"10.1007/s11053-024-10387-5","DOIUrl":"https://doi.org/10.1007/s11053-024-10387-5","url":null,"abstract":"<p>Remote sensing data prove to be an effective resource for constructing a data-driven predictive model of mineral prospectivity. Nonetheless, existing deep learning models predominantly rely on neural networks that necessitate a substantial number of samples, posing a challenge during the early stages of exploration. In order to predict mineral prospectivity using remotely sensed data, this study introduced deep forest (DF), a non-neural network deep learning model. Mainly based on ASTER multispectral imagery supplemented by Sentinel-2 and geological data, gold ore in Hamissana area, NE Sudan was used to test the DF predictive model capability. In addition to four geological-based evidential layers, 20 remote sensing-based evidential layers were generated using remote sensing enhancing techniques, forming the predictor variables of the proposed model. The applicability of the DF was thoroughly examined including its accuracy for delineating prospective areas, sensitivity to amount of training samples, and adjustment of hyperparameters. The results demonstrate that DF model outperformed conventional machine learning models (i.e., support vector machine, artificial neural network, and random forest) with AUC of 0.964 and classification accuracy of 93.3%. Moreover, the sensitivity analysis demonstrated that the DF model can be trained with a limited number (i.e., &lt; 15) of mineral occurrences. Therefore, the DF algorithm has great potential and proves to be a viable solution for data-driven prospectivity mapping, particularly in scenarios with data availability constraints.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"142 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141754933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aeromagnetic Data Analysis of Geothermal Energy Potential of a Hot Spring Area in Western Saudi Arabia","authors":"Konstantinos Chavanidis, Ahmed Salem, Alexandros Stampolidis, Abdul Latif Ashadi, Israa S. Abu-Mahfouz, Panagiotis Kirmizakis, Pantelis Soupios","doi":"10.1007/s11053-024-10383-9","DOIUrl":"https://doi.org/10.1007/s11053-024-10383-9","url":null,"abstract":"<p>Western Saudi Arabia is a promising area for geothermal energy exploration. Its geothermal wealth is attributed to the ongoing Red Sea rift evolution and crust thinning. Several hot springs in the region indicate the presence of potential geothermal resources. The present study aimed to characterize the geothermal system of a hot spring in the region, in the area of Wadi Al Lith, where water temperature exceeds 80 °C at the surface. For this, we used aeromagnetic data from the Saudi Geological Survey. We also collected a ground magnetic gradient data profile near the hot spring. To delineate structures of interest and map the distribution of volcanic rocks and tectonic lineaments, data enhancement filters were applied to the aeromagnetic data. These data were also subjected to spectral analysis to determine the depth of the Curie isotherm, which was then used to estimate a 1D geothermal model and predict the heat flow in the study area. According to the results of the spectral analysis of aeromagnetic data, the depth of the Curie temperature isotherm was about 14.8 km. The estimated depth was validated by deep magnetotelluric soundings, which revealed a clear decrease in resistivity at the same depth level. A constrained 1D geothermal model with three different layers (upper crust, lower crust, and mantle) was constructed. The depth of the Curie isotherm and the depth to the lithosphere's base were among the constraints. Furthermore, published data were used to define the radiogenic heat production within the crust and mantle and the corresponding thermal conductivity and thickness of each layer. According to the 1D geothermal modeling results, the average heat flow of the area reaches 109.8 mW/m², indicating potential geothermal resources. The findings of this study can be used to design a drilling program that will provide detailed information on reservoir parameters and put the geothermal resources into production.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"181 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of Metallogenic Structure of Manganese Ore Using Magnetotelluric Method: A Case Study in Minle Region, Hunan Province, China","authors":"Yang Yang, Lili Ye, Fangbo Chen, Sanxi Peng, Huimei Shan","doi":"10.1007/s11053-024-10376-8","DOIUrl":"https://doi.org/10.1007/s11053-024-10376-8","url":null,"abstract":"<p>The Minle manganese (Mn) deposit is a typical Mn-bearing deposit in the Datangpo Formation in southern China. The metallogenic environment and associated changing processes directly determine the migration, enrichment, and precipitation of Mn. To have a better understanding of the metallogenic structure, magnetotelluric (MT) method was performed to explore the Minle deposit. Electrical spindle analysis of MT data was conducted based on the Swift decomposition and the phase tensor decomposition, and inversion of the transverse-electric (TE) and transverse-magnetic (TM) models was carried out using the Occam inversion method. The results revealed that the main structural strike of the MT section was approximately 37° north to east and obtained the distribution characteristics of the deep electrical properties in the study area. The “concave structure” in the resistivity model is the main geophysical marker for delineating the Mn-ore body. In the metallogenic structure of Mn ore, a “funnel-shaped structure” of the strata was found, which provided favorable space for the percolation and enrichment of Mn deposits. The results of this study will be helpful in improving geophysical prospecting techniques for sedimentary Mn deposits in southern China.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"15 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precise Evaluation of Gas Expansion Energy Within Coal Bodies in Coal-and-Gas Outbursts: Innovation in Calculation Model and Experimental Methods","authors":"Ming Cheng, Yuanping Cheng, Liang Yuan, Liang Wang, Chenghao Wang, Jilin Yin","doi":"10.1007/s11053-024-10378-6","DOIUrl":"https://doi.org/10.1007/s11053-024-10378-6","url":null,"abstract":"<p>Coal-and-gas outbursts represent a significant hazard in coal mining, with gas expansion energy (GEE) in coal seams being a primary energy source. Accurate GEE assessment is vital for outburst prediction and mitigation, thereby enhancing mining safety. Traditional calculation models have struggled with limited understanding of outburst mechanisms and experimental constraints, leading to broad GEE estimates with considerable discrepancies. Addressing this gap, this study introduces an experiment-driven, highly practical calculation model, along with innovative experimental methods to measure accurately key determinants of GEE: fracture porosity, CH₄ desorption amount, and gas pressure in coal seams. For the first time, this study employed remade and raw coal columns as media to simulate accurately the real conditions of tectonic and raw coal seams for exploring the coupling effects of stress and gas pressure on GEE. This study calculated the GEE as stress increases from 5 to 50 MPa and gas pressure decreases from 2 to 0.5 MPa. The results indicate that, for two remade coal columns, the GEE decreased from 1870 to 62 kJ/t and from 2039 to 356 kJ/t while for the raw coal column, the GEE dropped from 130 to 6 kJ/t.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"84 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}