Geological Exploration Data Research Articles

Geological, Tectonic, and Lithological Evaluation Approach of the Fault Hydraulic Conductive Property.

The hydraulic characteristics of faults are of great significance for groundwater transport. Many groundwater inrush disasters are caused by hydraulically conductive faults. However, the hydraulic conductive property of a fault is complex and varies in different parts, and evaluation in coal mines has always been a hard challenge for hydrogeologists. An evaluation model proposed by Li et al. partially solves the above-mentioned problem [Li L. N.Nat. Resour. Res.2022, 31, 849-865]. However, this model has two shortcomings: (1) They focused only on small faults and did not have a specific method for large faults with a drop of more than 100 m. (2) The problem of differences between different locations of the same fault is not solved. This paper presents a method to estimate the fault hydraulic conductive property using abundant geological exploration data without hydrologic tests. First, the influence of fault rocks, water pressure, and geostress on the fault hydraulic conductive property was analyzed. Moreover, four evaluation indices, namely, the fault shaliness index (FSI), mudstone deformation index (MDI), fault rock plugging index (FRPI), and water pressure index (WPI), were proposed, and evaluation criteria were established to divide the fault hydraulic conductive property into five grades. This method was applied in Shandong Province and verified by hydrochemical experiments, surface boreholes, injection tests, and geophysical data. Furthermore, the method may be transferable to other coal mines with similar geological and hydrogeological characteristics.

Experimental study on comprehensive detection technology of shallow gas in deep soft soil layer

This study focuses on the underground shallow gas detection project in the Lingkun Island area of the northern entrance tunnel of the Wenzhou City Light Rail S2 line. Based on geological exploration data of shallow gas, we chose the technique of controlled-release gas with static pressure as the experimental foundation, integrating various technologies such as multi-functional in-situ probing, electrical methods, and seismic waves, comprehensively researching shallow gas detection technology in the Lingkun Island area. We conducted field probing experiments to accurately obtain the physical and mechanical properties of gas-rich soil layers and further studied the possibility of determining gas-rich locations. By applying parallel electrical methods, we can accurately identify and distinguish areas of anomalous resistivity in shallow geological structures. Based on abnormal changes in acoustic impedance in strata, we used seismic wave methods, including seismic CT and seismic wave scattering technology, to accurately reveal the presence and depth of shallow gas, providing reliable basis for accurate determination of shallow gas. Finally, we summarized a comprehensive plan for underground shallow gas detection technology, covering on-site data collection, data processing, and image interpretation of results, which will provide valuable references for future shallow gas exploration in relevant areas.

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)

AbstractProspectivity 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.

Discovery of Eclogite Facies Kyanite Quartzite in Central Jilin Province:Evidence of Ultra-high-pressure Metamorphism

The work area is located in the convergence domain of the Siberian plate and North China plate. The Jilin Province of the Soren-Xilamnlun river-Changchun-Heilog jiang plate collision zone. Before 2000, there was no empirical evidence of high-ultrahigh pressure transformation in the middle Jilin section. For this reason, the Changchun-Panshi-Jiaohe area was selected as the primary geological survey area. After comprehensively analysing the previous regional geological and mineral exploration data, we drew on the experience and achievements of studying the collision between the Suiu-dabie plate. Jiaohe blueschist origin was concerned. After fine field investigation, collecting fresh and representative samples, conducting microscopic identification, electron probe analysis and other work, the key identification and measurement results were obtained. Comprehensive results proved that the geological body producing kyanite undergoes high-pressure kyanite-quartzite was a metamorphic rock of gernet pgroxenite facies. This discovery is of fundamental importance for the establishment of the Jihei high pressure human ultra-high-pressure collision subduction zone. The discovery was briefly reported in 2000 and is now being published in detail. The garnet-phase blueschist quartzite is massive, laminated, ienticular, and rootless hook like bodies occur in within the luoguangou Dashihu narrow ductile shear zone. The rock being sheared is late paleozoic Early perntian Daheshan formation and late variscan Granite Porphyry, which were deformed and metamorphosed to form. blueschist dolomite quartz schist and dolomitic blueschist quartzite. The Peak mineral combination is Ky2+Qz2+Ms2 (Phn2) +Zt+Rt+Miss, without Sili and Pl, which basically meets the constraining charactenstics of eciogite facies. The peak metamorphic temperature reaches 700-750℃, indicating the Si (pfu) =3. 440-3. 535 of phengite, and indicated metamorphic pressure reaches p=2. 75-3. 30GPa, which shows that the rock has undergone ultra-high pressure metamorphic process, and its temperature and pressure conditions have been comparable to those formed by coesite. The temperature of secondary peak period of is about 550℃-600℃ and the pressure is about 1. 6-1. 7GPa. The multi-phase nature of the blueschist, muscovite and quartz indicates that the rock is subjected to prosess of subduction exhumation, and exhumation suffering from strong progressional and retrograde metamorphism. The temperature and pressure numbers of the metamorphism indicate that the paths are successively hornblende and green schist phases, with temperatures of about 500℃-450℃ and pressures of about 0. 5-0. 7GPa, and temperatures of about 300℃-350℃ and pressures of about 0. 3-0. 4GPa, respectively.

Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

At present, in the vast majority of coal mine production processes in China, the degree of hydrogeological exploration often lags behind geological exploration. The main difficulty in evaluating the water richness of coal seam top and bottom water-bearing beds is that the existing evaluation methods often rely on less hydrogeological investigation data. How to utilize the abundant geological exploration data in the mining area to appraise the water-rich distribution of sandstone aquifers is a feasible and challenging methodology. At present, some experts and scholars have tried to use multivariate factor analysis to solve the problem of water-richness evaluation, and they have achieved certain results, but there are some shortcomings: (1) The prediction results are mostly qualitative estimations of the water-richness grade, and there is a lack of quantitative analysis of the units-inflow; and (2) at present, the more advanced prediction methods, such as the back propagation (BP) neural network model, have the disadvantages of low accuracy, requiring many iterations, and slow convergence speed. Therefore, with geological exploration data of the 1503E working face of the Yili No.1 coal mine as the basis., this paper uses grey correlation analysis to screen out the factor indexes suitable for the evaluation of the water richness of a weakly cemented sandstone aquifer, and it combines principal component analysis (PCA) with a BP neural network. Based on the selected factor indexes, a prediction model of the water richness of a weakly cemented sandstone aquifer is established. The results show that compared with the existing methods, the prediction accuracy is higher and has a certain application value.

Mineral prospecting mapping with conditional generative adversarial network augmented data

Mineral Prospectivity Mapping (MPM) plays a pivotal role in identifying geo-anomalies that are indicative of potential mineralization, drawing upon various geological, geophysical, geochemical, and remote sensing data. With the increasing availability of such data in recent years, data-driven MPM methods have proven effective in discovering new mineral deposits, especially when utilizing machine learning techniques to unveil complex relationships between exploration data and mineral occurrences. Deep learning, specifically Convolutional Neural Networks (CNN), has demonstrated its superiority in this regard. However, these models encounter challenges due to the limited and imbalanced nature of geological exploration data. In this study, we address these challenges by proposing the adoption of a Conditional Generative Adversarial Network (cGAN) strategy for data augmentation. Additionally, we employ the sliding window algorithm for comparative analysis, and CNNs are utilized to assess the effectiveness of various data augmentation strategies. The results indicate that the cGAN-based data augmentation strategy exhibits higher resistance to overfitting, a critical concern in MPM applications. Furthermore, this research successfully delineated a prospectivity map for gold deposits in the Hezuo-Meiwu district, Gansu, China, providing valuable insights for future exploration efforts.

An integrated approach to the study of Achimov deposits within the South Tambey field (Western Siberia)

In order to clarify the structure of promising oil and gas objects in the South Tambey field (Western Siberia), an integrated approach to the study of Achimov deposits has been developed. The oil and gas potential level includes layers from PK1 to Yu9 at depths from 1000 to 4000 meters. Despite the fifty-year history of drilling wells in the area, the Achimov objects are currently poorly explored in the study area and may have significant potential for development. The purpose of this work was to clarify the geological structure of the Achimov deposits, which became possible thanks to the acquisition of new geological exploration data during the study of the underlying Jurassic complex. CDP 3D seismic exploration work was completed using a modern full-azimuth observation system, and active drilling of exploration wells for Jurassic deposits in the slope parts of the main structures began. The objectives of the study were the interpretation of a 3D seismic cube covering most of the area of the site, seismic geological linking of data from the Achimov interval of the field with neighboring areas, including using the sequence stratigraphy method, accounting for the results of drilling wells for the pilot development of the Jurassic complex and the transit fund of prospecting and exploration wells, as well as the inclusion of the results of a petrophysical model in a first approximation for the Achimov reservoir interval of the South Tambey field. The results of full-azimuth CDP 3D seismic surveys made it possible to obtain reliable correlations between well data and seismic data, including analysis of AVO attributes (Amplitude Variation with Offset). Based on sequence stratigraphic analysis, the stratification of the section was clarified, correlation with neighboring territories was made, and a conceptual model of the clinoform complex was created. For each layer, system tracts within the sequences were determined. There is strong relationship existing between the formation conditions, geometry of objects and reservoir properties.

Predicting zones of increased water inflows in local folded structures

Purpose. To predict zones of increased water inflows based on geological exploration and mining-geological data in the real conditions of an operating coal mine using the method of predictive evaluation of prospects of local structures for the presence of gas accumulations. Methodology. The research was carried out by constructing a map of local structures using trend analysis. The synclinal fold is allocated in the plan and its main parameters – amplitude and width – are determined. Based on these parameters of the structure, the authors performed calculations of relative linear deformations and relative volume deformation of the studied fold. Findings. According to the deviation of the hyposometry of layer (“Samarska” mine) from the approximating surface, a local synclinal structure is allocated on the map. Based on calculated data on the volumetric deformation of rocks (siltstones and sandstones) and average values of open porosity, fracture porosity and absolute permeability were calculated. The obtained data indicate the formation of a fractured zone with improved filtration and capacity properties of rocks within the studied area. According to the actual mining and geological data, within the defined area, there are increased water inflows into the mine workings, which further confirms the results of the prediction. Originality. For the first time, the algorithm of predictive evaluation of the prospects of local anticlinal structures for the presence of gas accumulations was used to predict areas of increased water inflows. Practical values. In the real conditions of an operating mine, in the area of mining operations, the method for predicting zones of increased water inflows was tested according to a predictive evaluation of the prospects of local folds for the presence of gas accumulations.

Evaluation and prediction of earth pressure balance shield performance in complex rock strata: A case study in Dalian, China

This research explores the potential for the evaluation and prediction of earth pressure balance shield performance based on a gray system model. The research focuses on a shield tunnel excavated for Metro Line 2 in Dalian, China. Due to the large error between the initial geological exploration data and real strata, the project construction is extremely difficult. In view of the current situation regarding the project, a quantitative method for evaluating the tunneling efficiency was proposed using cutterhead rotation ( R ), advance speed ( S ), total thrust ( F ) and torque ( T ). A total of 80 datasets with three input parameters and one output variable ( F or T ) were collected from this project, and a prediction framework based gray system model was established. Based on the prediction model, five prediction schemes were set up. Through error analysis, the optimal prediction scheme was obtained from the five schemes. The parametric investigation performed indicates that the relationships between F and the three input variables in the gray system model harmonize with the theoretical explanation. The case shows that the shield tunneling performance and efficiency are improved by the tunneling parameter prediction model based on the gray system model.

Engineering Characteristics and Application Analysis of Red Sandstone in the Lanzhou Metro

The water-rich red sandstone strata at the Lanzhou Metro site area have special engineering properties and vary greatly in their speed of disintegration when exposed to water. There is an urgent need for a comprehensive and systematic study of the engineering properties of red sandstone and their classification. From the disintegration speed of red sandstone encountered during the excavation of Lanzhou metro lines 1 and 2, the relationship between physical parameters such as particle size, composition, dry density, and permeability coefficient as well as mechanical parameters such as shear wave speed, dynamic penetration test (DPT), natural uniaxial compressive strength, and disintegration speed of red sandstone was analyzed through indoor and outdoor tests and geological exploration data statistics, and classification guidelines for red sandstone are given. The results show a significant correlation between dry density, permeability coefficient, natural uniaxial compressive strength, and disintegration speed. The red sandstone can be classified as I, II, and III according to the disintegration speed, dry density, permeability coefficient, and natural uniaxial compressive strength. The design of the foundations is differentiated according to the classification, and different support systems are used for the deep foundation pits of the metro stations. The category I red sandstone pit is supported by diaphragm walls with internal bracing, the category II pit by bite piles with internal bracing, and the category III pit by row piles with internal bracing. The study results can provide technical support and experience reference for the investigation, design, and construction of metro projects in similar red sandstone distribution areas.

Effects of the Excavation of a Hydraulic Tunnel on Groundwater at the Wuyue Pumped Storage Power Station

The tailwater tunnel of the Wuyue pumped storage power station is located in bedrock and extends to depths between tens and hundreds of meters. It is impossible to analyze and evaluate the whole engineering area from geological exploration data, and the hydrogeological conditions are complicated. In the early stages of the tailwater tunnel’s construction, the drinking water wells in four villages dried up. This paper reports the results from a field investigation, in situ tests, laboratory tests, and numerical simulation carried out to determine how the groundwater was affected when the tunnel was excavated. A hydrogeological model of the region was established from the inverted regional natural flow field parameters. The model was validated, and an analysis of the errors showed that there was an average error of 1.98% between the natural flow field and the hydrogeological survey flow field. The model was then used to simulate the three-dimensional transient seepage fields under normal seepage conditions and limited seepage conditions, as far as was practical. The results showed that, as the excavation of the tailwater tunnel advanced, the water inflow to the tunnel also increased. When the water inflow increased from 1000 to 5000 m3/d, the water level at a distance of 100 m from the axis of the tunnel dropped from −0.956 to −1.604 m. We then analyzed how the water level changed as the water inflow varied and proposed a formula for calculating the extent of the influence on the groundwater. We studied how the water level changed at different well points to ascertain how a groundwater well became depleted and determined the factors that influenced seepage in the regional flow field. The water level in different areas of the project area was simulated and analyzed, and the extent of the groundwater area affected by the tunnel construction was clarified. We then studied how the groundwater in different areas of, and distances from, the project area was influenced by normal seepage conditions and limited seepage conditions and proposed a formula for calculating the extent of the influence on groundwater for different water inflows. We constructed a ‘smart site’ for visualizing data, sharing information, and managing the project. Time–frequency domain analysis was applied to explore the extent of the impacts and range of the vibration effects on residential housing at different distances from the project area caused by the different methods for excavating the tailwater tunnel. The results from this analysis will provide useful insights into how the excavation of this tailwater tunnel will impact the local residents and living areas.

Development model of shallow lithologic traps and natural gas accumulation mechanisms in marine deep-water areas: A case study in the qiongdongnan basin, south China sea

Although hydrocarbon gases are widely distributed in the shallow strata of marine deep-water areas, it is generally believed that these shallow gases have little potential to form large gas accumulations due to unfavorable conditions, especially for effective trap development. Based on a comprehensive analysis of the latest geological exploration data of the deep-water areas of the Qiongdongnan Basin, this study reports a large lithologic trap that developed in the shallow Ledong Formation with burial depths of 200–600 m below the seafloor (mbsf). Systematically, this work investigates the development model of the trap and its gas accumulation mechanism. The results indicate that during the Pleistocene, sediment mainly from the southern Kunsong Uplift was transported to the Qiongdongnan Basin due to confined canyon geomorphology and developed a large channelized submarine fan in the deep-water areas. The roof of the submarine fan was cut and eroded and laterally sealed by overlying muddy channels, resulting in a dome-shaped structure, which was further covered by the Pleistocene tight mudstones forming the Ledong lithologic trap. The Yacheng coal-measure source rocks in the deep-water areas had high gas yields, which resulted in increased pressures and promoted deep gas migration into shallow reservoirs along a combined migration pathway including sand strata, structural ridges, faults, fissures, and gas chimneys. The gas accumulated in the Ledong trap as a result of the decreases in temperature and pressure. After the deep gases charged the Ledong strata, migration was blocked by the overburden consisting of Pleistocene tight mudstones, whose seal capacity was significantly enhanced by overlying gas hydrates in the deep-water areas. Meanwhile, the seawater in deep-water areas compacts the underlying cap rocks through its own weight, which enhances the seal capacity of cap rocks. In shallow reservoirs, thermogenic gases may mix with microbial gases from shallow marine source rocks, and some thermogenic gases may be reformed by microorganisms to form secondary microbial gases. The shallow Ledong gas reservoir connects the underlying gas reservoirs and the overlying gas hydrate reservoirs and vertically consists of a continuous hydrocarbon enrichment zone, which significantly expands exploration targets in the deep-water areas of the Qiongdongnan Basin. Additionally, the development model of the shallow Ledong lithologic trap and its gas accumulation mechanism may have general significance for hydrocarbon exploration in marine deep-water areas worldwide.

Improved method and practice for site selection of underground gas storage under complex geological conditions

Increasing attention has been paid to the site selection of Underground Gas Storage (UGS) due to the growing demand for natural gas peak shaving. Existing studies have made a practical contribution to this field based on the multidimensional geological exploration data and the multi-criteria decision-making method. However, previous studies mainly focused on screening or ranking technologies with different principles, which means only the attributes of UGS in the design period were considered while the performance in the operation period was ignored. The operation plan and economic performance are considerable concerns to the UGS investors especially when this type of storage facility was unbundling from other sectors in natural gas companies. To solve the UGS site selection problems more comprehensively and practically, the geological conditions and the injection & extraction plan in the operation period are considered Simultaneously. A case from the S-X area in China indicates that: 1. The improved multi-period optimization model of the natural gas (NG) supply chain can obtain the injection and production plan of UGS which are the basic parameters for the economic analysis. 2. The top potential UGS estimated by geological conditions may take inferior performance in the operation stage (less peak shaving amount, low economic profits, and high freight of NG supply chain), so the UGS investors should consider the potential profits when the finished UGS is put into operation. The proposed framework can be a new theoretical guideline for the site selection problems of UGSs and can help UGS investors judge their investment.

Fractional Differential Equations in the Exploration of Geological and Mineral Construction

Abstract As the geological exploration data is relatively sparse, unevenly distributed, and contains many geological faults, simple geological surface reconstruction has certain limitations. Based on the fractional differential equations, the paper establishes a subsidence prediction model in exploring geological and mineral resources. The dynamic system described by the reaction-diffusion equation can be mapped to a nonlinear cellular network through space and time discretization. At the same time, the original partial differential equations can be transformed into ordinary differential equations. Furthermore, we can use the difference method to simulate its evolutionary behavior quantitatively. The research results show that the error accuracy between the prediction results of the fractional gray theory established in this paper and the actual engineering results is higher.

Modeling quality of concentration factory feedstock in ferruginous quartzite mining at the Lebedinskoe deposit

Statistical modeling of daily average or mean-shift content of useful component in ore hauled from a certain production face to a rehandling point, to an intermediate (including blending) storage, or to a concentration factory enables mine planning to proceed to the next level. This also allows meeting one of the major tasks of the modern mining industry, namely, stabilization of the useful component content in processing feedstock. The geological information quality and initial interpretation are the major factors of data reliability in modeling any mineral mining, production or processing chain. Currently, almost all mining companies in Russia have introduced operational exploration procedures. The analysis of the long-term operational exploration data collectively with the geological exploration data allows determining patterns of useful component in different areas and sites of a mineral deposit. The present research made it possible to obtain magnetite content variation per levels of the test deposit and to reveal correlations between different quality characteristics. Using the statistics on magnetite distribution in the ferruginous quartzite ore body of the Lebedinskoe deposit, as well as with regard to the current capacities of production faces, the simulation model has been constructed in the framework of this research. The model enables prediction of the daily mean content of the useful component in the mineral feedstock of the concentration factory at different number of sampling points and at the varied daily average production capacity.

Method for Evaluating Fault Hydraulic Conductive Property and Its Application in Shandong, China.

Coal remains the largest contributor to the energy structure of China. However, coal production is frequently threatened by groundwater inrush accidents caused by hydraulically conductive faults. Despite the threat of such accidents, research on methods for evaluating fault hydraulic conductive property without hydraulic tests has seldom been conducted. Many faults exist in coal mines in Shandong, China. However, due to economic and technical limitations, hydrological tests are rarely performed and can be performed on only a few faults. The hydraulic conductive property of many faults is unknown, which has prevented serious groundwater inrush accidents and casualties from being avoided. Using accessible geological exploration data, we propose a method for evaluating fault hydraulic conductive property in the Jining coalfield, Shandong, China. Mudstone smearing, lithologic contact relations on the fault plane, geostress, water pressure, plastic deformation of mudstone, and the argillaceous content of the fault zone were selected as factors, and six quantitative indicators were proposed: the shale gouge ratio (SGR), lithologic juxtaposition diagram (LJD), fault closure coefficient (FCC), water pressure coefficient (WPC), mudstone deformation coefficient (MDC), and shale smear factor (SSF). The fuzzy analytic hierarchy process (FAHP) was applied to calculate the weights and establish lateral and vertical hydraulic conductive property (L and V) evaluation models for faults. The fault hydraulic conductivities were then classified as weak, medium, or strong. The hydrochemical experiments and the limited number of exposed faults were used for validation. Hence, the evaluation models were considered effective at determining the hydraulic conductive property of faults in the Jining coalfield, China.

A Novel Method for Predicting Local Site Amplification Factors Using 1-D Convolutional Neural Networks

The analysis of site seismic amplification characteristics is one of the important tasks of seismic safety evaluation. Owing to the high computational cost and complex implementation of numerical simulations, significant differences exist in the prediction of seismic ground motion amplification in engineering problems. In this paper, a novel prediction method for the amplification characteristics of local sites was proposed, using a state-of-the-art convolutional neural network (CNN) combined with real-time seismic signals. The amplification factors were computed by the standard spectral ratio method according to the observed records of seven stations in the Lower Hutt Valley, New Zealand. Based on the geological exploration data from the seven stations and the geological hazard information of the Lower Hutt Valley, eight parameters related to the seismic information were presumed to influence the amplification characteristics of the local site. The CNN method was used to establish the relationship between the amplification factors of local sites and the eight parameters, and the training samples and testing samples were generated through the observed and geological data other than the estimated values. To analyze the CNN prediction ability for amplification factors on unrecorded domains, two CNN models were established for comparison. One CNN model used about 80% of the data from 44 seismic events of the seven stations for training and the remaining data for testing. The other CNN model used the data of six stations to train and the remaining station’s data to test the CNN. The results showed that the CNN method based on the observation data can provide a powerful tool for predicting the amplification factors of local sites both for recorded positions and for unrecorded positions, while the traditional standard spectral ratio method only predicts the amplification factors for recorded positions. The comparison of the two CNN models showed that both can effectively predict the amplification factors of local ground motion without records, and the accuracy and stability of predictions can meet the requirements. With increasing seismic records, the CNN method becomes practical and effective for prediction purposes in earthquake engineering.

Identification of Preferential Recharge Zones in Karst Systems Based on the Correlation between the Spring Level and Precipitation: A Case Study from Jinan Spring Basin

The Jinan spring basin is located in the karst area of northern China, where springs serve as important sources of water supply. Several studies on spring protection and water supply have been carried out, and scholars have developed some laws on local groundwater flow dynamic and characteristics of aquifer structures. Unfortunately, there is a lack of detailed research on preferential recharge zones, which are the main recharge pathways of springs. Therefore, this research focuses on identifying preferential recharge zones based on the correlation between the spring level and precipitation. The results show that when precipitation is more intense or lasts longer, there is a stronger correlation between spring level and precipitation. It has been established that the precipitation at Donghongmiao station has the closest relationship with the dynamic of Baotu spring, which is found to be the most significant contribution to spring preservation. Two potential preferential recharge zones in the Jinan spring basin are detected through correlation analysis and geological exploration data. These findings support spring protection and water supply projects in karst regions.

High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence.

Features of the geological structure, tectonic development and oil and gas potential of the Chezhen depression (Bohai bay basin)

Background. In terms of oil and gas, the territory of the Chezhen depression has been studied insufficiently compared to the neighbouring same-range depressions. These depressions complicate the first-order Jiyang depression, geographically coinciding with the largest Shengli hydrocarbon field. In recent years, much geological and geophysical information about the oil geologyof the Chezhen depression has been accumulated, which allows its prospecting oil and gas potential to be assessed.Aim. To reveal regular features of the geological structure and location of oil deposits in the Chezhen depression in order to support the prospecting and exploration work within the Chezhen block of the Shengli field.Materials and methods. A comprehensive analysis of literature data and collected materials was conducted. A historical and geodynamic study of the evolution of the studied area according to literature data was carried out, along with an analysis of the most recent geological and geophysical information and exploration data based on the materials of the “Shengli AKOO Sinopek” oil company. The analysis was based on the data from 52 drilling wells and the results of seismic surveys performed in the central part of the Chezhen depression.Results. Specific features of the block geological structure of the area under study were established, which formed under the repeated influence of large-scale horizontal tectonic movements occurring at different periods of geological history. The role of the most recent fault system in the modern spatial distribution of oil deposits was determined.Conclusions. Our studies demonstrate a great prospecting potential of the Chezhen depression territory, where the discovery of new industrial oil deposits can be expected.