Recommendation system algorithms, having recently garnered significant attention in the field of digital Earth science, are expected to be widely applied in metallogenic prediction. Traditional metallogenic prediction studies fail to fully mine the various types of semantic information in massive geoscience data. The deep interest evolution network (DIEN), as a recommendation system algorithm, can fully mine semantic information to predict user preferences. Therefore, this study employed the DIEN model as the prediction model and the semantic information extracted from bedrock interpretation as the ore-controlling elements according to the database provided by the Western Australian government. The model was trained to perform metallogenic prediction for the study area. The prediction results indicate that 92.95% of uranium ore occurrences fell within the medium-high probability zone in the prediction map, with some unknown zones also showing high prediction probabilities. After removing known uranium ore occurrences in some zones, the retrained model still yielded medium-high prediction probabilities in these zones. The results suggest that the DIEN can effectively mine semantic information in metallogenic prediction studies, and the DIEN model exhibits strong predictive capacity for the study area, providing a novel approach for metallogenic prediction studies.

The Mianning-Dechang area in western Sichuan serves as the most important metallogenic belt of light rare earth elements (LREEs) in China. To make breakthroughs in the exploration of rare earth resources in the Dechang area, a 1:50 000 stream sediment survey was conducted in this study. The analysis of test data characteristics and the extraction of geochemical anomalies reveal that the total rare-earth oxides (REOs) in the area exhibit a pronounced enrichment pattern and that element anomalies largely overlap the spatial distribution of related geological bodies. Through comprehensive analysis using the cumulative frequency method and iterative method, this study determined the lower limits of anomalies and, accordingly, plotted element anomaly maps, with five main anomaly areas being delineated. The comprehensive evaluation of anomalies revealed four prospecting target areas: Huangjiaba, Jiaobacun, Yibasan, and Huajiaoyuan. The analysis of metallogenic geological conditions and the anomaly verification based on drilling in the Gannan area led to the discovery of three light rare earth deposits in the Huangjiaba prospecting target area, two heavy rare earth deposits in the Jiaobacun prospecting target area, two heavy rare earth deposits in the Yibasan prospecting target area, and two heavy rare earth deposits in the Huajiaoyuan prospecting target area. This study posits that the Triassic biotite moyite and biotite monzogranite in the study area are the metallogenic parent rocks of ion adsorption-type rare earth resources, with REEs primarily undergoing enrichment and mineralization in completely weathered layers. Therefore, a simple and effective prospecting pattern for "endogenous and exogenous" ion adsorption-type rare earth deposits in the Dechang area consists of analyzing the geochemical characteristics of the total REOs based on a 1:50 000 stream sediment survey, delineating metallogenic prospect areas that indicate parent rocks for REE enrichment, and selecting sections favorable for the formation and preservation of weathered crusts based on the hypergenic conditions of the enrichment areas, thus achieving the quick delineation of the prospecting target areas.

To evaluate the resource potential of the Jinchanshan mining area, this study conducted analysis and tests of metal element content in soil samples from two geochemical profiles using the electro-adsorption method. By combining the geological conditions of the mining area, this study revealed pronounced positive geochemical anomalies of Au, Ag, Cu, Pb, and Zn above the known gold deposits. The anomalous areas corresponded well with deposits, confirming the effectiveness and feasibility of the electro-adsorption method in the study area. The study identified geochemical anomalies Ⅰ and Ⅱ to the west of the Guaibanggou-Yangpo-Xiaoxigou mineralization zone in the first geochemical profile. Along the second profile, located between the Guaibanggou-Yangpo-Xiaoxigou and Nandawa-Limazigou-Loufengmao mineralization zones, anomalies Ⅲ and Ⅳ were observed at the westernmost end. The comprehensive geological analysis of the mining area indicates that the Ⅰ and Ⅲ anomaly zones exhibit favorable geological conditions for mineralization, serving as play fairways. The play fairway prediction conducted in this study provides targets for future exploration in the mining area.

The exploration and exploitation practices in the Sichuan Basin in recent years indicate that breakthroughs have been achieved in the Jurassic continental tight sandstones.Nevertheless,due to the low porosity and permeability of tight sandstone,conventional post-stack inversion frequently exhibits limited resolution,failing to meet the accuracy requirements for the prediction of actual exploration reservoirs.This necessitates pre-stack inversion for detailed characterization of tight sandstones,while S-wave velocity is crucial to pre-stack inversion.Based on continental exploration wells drilled in the southeastern Sichuan Basin in recent years,this study developed a petrophysical modeling technique for dense sandstones in this region.Specifically,given the low permeability of tight sandstones and the uneven mixing of fluids in the pore space,the Domenico model was preferentially employed to calculate the pore fluid modulus.Although fluid modulus and density are inevitably variable under the actual subsurface conditions,previous studies typically use constant values to conduct petrophysical modeling for tight sandstones.In this study,depth-dependent values were applied.Tight sandstones in the southeastern Sichuan Basin generally exhibit a porosity of less than 10%.Therefore,calculations using the Nur and the Krief models will yield high errors.Given this,this study preferred using the Lee-Pride model to calculate the skeleton modulus and controlled the relationship between the rock matrix and the skeleton by introducing the value of the cementation parameter.The application of the established petrophysical model of tight sandstone to an actual survey area indicates high agreement with data from actual wells.Additionally,based on log statistics,Poisson's ratio,the most sensitive parameter is used for high-precision pre-stack inversion in the proposed technique,enabling detailed characterization and prediction of the internal structure of channel sandstones.

Small- and micro-scale faults fractures (fractures and faults with fault throw less than 10 m) that originally developed in shale strata have a significant impact on the probability of penetration, stimulation volume, and production capacity of high-quality reservoirs in horizontal well sections. Therefore, it is critical to conduct fine-scale fault and fracture prediction. However, any single method struggles to accurately identify and predict these faults and fractures. Based on the developmental conditions of small-and micro-scale faults and fractures in the shale gas reservoirs of the Longmaxi Formation in the southern Sichuan Basin, this study conducted forward modeling, response mechanism analysis, and characterization of fracture responses, developing a prediction method integrating predicting and modeling. Furthermore, this study preferentially investigated techniques including seismic data processing, small-scale fault and fracture prediction, multi-scale fracture modeling, and fusion characterization. The results of the proposed method were highly consistent with the geological anomalies including small and micro-scale faults, lost circulation, and inter-well pressure channeling observed during the drilling of horizontal wells in the shale gas reservoirs of the Longmaxi Formation. Furthermore, these results exhibit a strong positive correlation with the single-well production capacity. All these corroborate that it is feasible to use this method to predict small- and micro-scale faults and fractures. This study can serve as a reference for predicting small-scale faults and fractures in other strata of the same type.

To improve the inversion accuracy of reservoirs in the Paleogene strata with limited wells and sedimentary and structural complexity, two key technologies were used in seismic data processing: sparse pulse inversion for primary wave estimation and anisotropic Q-pre-stack depth migration (PSDM). This contributed to improved quality of seismic gathers and imaging. Then, the pre-stack simultaneous inversion method was applied as follows: (1) Stacking velocity and layer-constrained Dix inversion were employed to obtain a low-frequency model of P-wave impedance; (2) Elastic impedance inversion was performed using angle-stacked data and well-calibrated wavelets, yielding far, medium, and near elastic impedance; (3) Initial P- and S-wave impedance, as well as initial density, were obtained through Fatti inversion; (4) Pre-stack simultaneous inversion was performed to obtain the final P- and S-wave impedance and density; (5) Lithology and physical property inversion results were used to predict the reservoir distribution range. This method, driven by three-dimensional seismic data and exhibiting low dependence on logs, can serve as a reference for reservoir prediction under similar geological settings.

The Triassic strata in the Lunnan area of the Tarim Basin represent a continental deltaic sedimentary system,with sedimentary subfacies including deltaic plain,deltaic front,and prodelta.The rapid phase transition of reservoirs and the high-frequency sand-mudstone alternating deposition create thin,highly heterogeneous reservoirs,rendering high-precision reservoir prediction extremely challenging.Under the guidance of sedimentary petrology and seismic sequence stratigraphy,this study conducted comprehensive seismic and geological research.Then,based on isochronous sequence interpretation,as well as seismic facies analytical results,this study established a lithofacies model.By constraining the pre-stack geostatistical inversion process,this study determined the spatial distribution of sand bodies.The practical application demonstrates that the proposed method enhances the vertical resolution of heterogeneous,thin reservoirs while significantly reducing lateral uncertainty.The predicted results align with actual drilling and production performance data and thus can provide valuable support for the efficient exploitation of hydrocarbon reservoirs.

Due to numerous thin interbeds in weakly elastic media,seismic excitation typically yields rapidly attenuated seismic wave energy and a narrow dominant frequency band,resulting in low-resolution seismic data.Therefore,selecting a favorable lithology plays a crucial role in improving the seismic excitation effect.This study explored the dominant factors influencing the quality of seismic data obtained from the northern Jiangsu exploration area,a region with a dense river system.Specifically,this study determined the top boundary of the high-velocity layer based on microlog surveys and the dominant lithologic member using the cone penetration test and lithologic coring.It quantitatively analyzed seismic wavelet attributes,including octave band,resolution,main-to-side lobe energy ratio,and wavelet clarity,establishing their matching relationship with the lithology for seismic excitation.By selecting a lithologic surface featuring a high seismic wave propagation velocity,a favorable elastic property,and a wide frequency band in the study area,it plotted a surface lithology map for pointwise well depth design,ensuring wide-frequency excitation.The above techniques were applied to well depth design for seismic excitation in the YA and SDX areas,achieving well-normalized single-shot frequencies and widening the dominant frequency band of the target layer in the seismic profile by over 10 Hz,with an increase of 1.5 octave bands.The results show that the excitation strategy of "selecting the dominant lithology from weakly elastic media" in regions with dense river systems can effectively enhance the seismic excitation effect in weakly elastic media,thereby improving the imaging accuracy and resolution of seismic data.

Conventional splicing methods aim to serve structural interpretation and align the reflection energy and frequency of large strata based on seismic data themselves.They adopt seismic data only from one study area in processing data of overlap zones,failing to fully utilize the effective information in seismic data of all study areas.Consequently,the resulting low-fidelity seismic data in overlap zones severely affect subsequent discrimination of reservoir connectivity and hydrocarbon-bearing properties.This study proposed a weighted fusion-based data spicing method for overlap zones,where seismic data from different study areas are fused with corresponding weights to generate a trace of seismic data.The application of the proposed method to actual seismic data shows that the processing results based on the fusion and splicing of data in overlap zones can effectively improve the quality of seismic data,with high continuity of seismic events on the profile, contributing significantly to the discrimination of the connectivity of shallow sand bodies.

The construction and presentation of a geological modelprove to be ahot topic and challenge in research on 3D geological modelling. Given the large scale, involvement of complex surfaces, and insufficient geological constraints of geological body data, this study achieved the rapid construction of a large-scale geological surface model using the domain decomposition-based implicit generation method. Initially, implicit functionswere constructed by taking radial basis functions as the kernel functions.Then, the distribution functions of various domainswere solved in parallel usinganoverlapping domain decompositionmethod, reducing the spatiotemporalcost and accelerating the solving process.Subsequently, normal vectors were extracted to generate control pointsand formconstraints on surface fluctuation, thereby effectively controlling the model boundaries. The experimental results indicate that the method proposed in this study can significantly improve the efficiency associated with the solving of distribution functionswhile ensuring the high quality of the model. This study effectively solves the problem of balance between efficiency and precision in geological modeling and provides methodological support for the refinement of geological surfaces.

In actual exploration, the surface-to-borehole transient electromagnetic (TEM) method is prone to be affected by irregular transmitter loops and inclined boreholes, resulting in increased measurement errors of the three-component induced magnetic fields and decreased interpretation accuracy. By establishing surface-to-borehole TEM method-based three-component measurement models under the excitement of transmitter loops of various shapes, this study derived the calculation formulas for surface-to-borehole TEM responses under inclined boreholes through coordinate transformation. Then, it achieved one-dimensional forward modeling of the full-space TEM field using a numerical filtering algorithm. The calculation results of multiple typical models indicate that the three components of the transient magnetic fields were significantly influenced by the shapes of the transmitter loops, with the impacts on horizontal components x and y far more significant than those on vertical component z. The transmitter loops of regular polygons with even edges as the sources exhibited uniform and symmetric distribution of the TEM fields of the three components. Under the condition of the same perimeter, a greater number of edges of the transmitter loops associated with greater primary field energy excited by the loops. Therefore, rectangular transmitter loops as the sources prove the most cost-effective. The inclination and offset primarily affected the amplitude of the three-component responses. In contrast, the borehole azimuth mainly influenced the sign of the horizontal components, bearing rich information on location. Therefore, in the exploration using the surface-to-borehole TEM method, it is necessary to accurately determine source paths and the geometric morphologies of boreholes to make essential corrections, thus improving the accuracy and reliability of interpretations.

As an important part of gravity investigation, the performance tests of gravimeters are required throughout field surveys. In these tests, mean squared error (MSE), accuracy, and root mean squared error (RMSE) are commonly employedto quantitatively describe the test results. The comparison of the theoretical equations for dynamic and consistency tests in specifications on geological surveys, petroleum, and measurement reveals a pronounced confusion in the usage of MSE, accuracy, and RMSE. This issue is observed in the consistent equations forconsistency testsin these specifications. Through investigations into the equations used in the dynamic and consistency tests inthe threespecifications, this study analyzed the differences between mean MSE and RMSE, elucidated the normativity of RMSE relative to MSE, and determined the applicablerange of RMSE. To avoid confusion, it is recommended that accuracy be used for qualitative expression and RMSE for quantitative expression in these specifications.

Noise detection and suppression of oil well pumping units pose challenges in data processing for mature exploration areas.The conventional method in the industry is to identify pumping unit noise through manual interactions and then suppress it as high-amplitude interference.However,manual identification wastes manpower and yields low detection accuracy,often resulting in missed detections.Hence,based on the noise characteristics of pumping units,this study conducted noise detection on seismic data containing pumping unit noise using deep learning methods.It then estimated the bandwidth of the detected noise using mathematical morphology techniques to determine the final position and distribution pattern of the noise.This allows for adaptive parameter support for the anomalous amplitude attenuation(AAA) method to achieve automatic detection and efficient suppression of pumping unit noise.The processing results of actual seismic data reveal that the methodology used in this study enables intelligent detection of pumping unit noise,significantly reducing the manual effort required for noise identification,improving the detection accuracy,and enhancing the fidelity and robustness of the AAA method.

Slope tomography is a method to estimate subsurface velocity macromodels from the slopes and traveltimes of local coherent reflection events. In geologically complex areas, the macromodels obtained from slope tomography tend to yield larger errors. To address this issue, this study proposed a method for error correction of the models using deep learning. Specifically, with macromodels determined using slope tomography-based inversion serving as input and corresponding theoretical models as labels, a neural network was trained, yielding a nonlinear mapping from the slope tomography-derived macromodel to the corresponding theoretical model. To ensure that the trained neural network was applicable to measured seismic data, the training samples were generated from the inversion model and migration profiles of measured seismic data. Tests based on the data synthesized using the theoratical model validated the accuracy and effectiveness of the proposed method. The proposed method was then applied to the 2D measured seismic data from beaches and shallow seas, yielding velocity models with elevated precision and depth migration imaging profiles with high quality.

Seismic data collected in the field frequently suffer from missing values due to constraints of acquisition cost or terrain. Data reconstruction is a critical step in seismic data preprocessing. Based on the compressed sensing theoretical framework, this study subsampled synthesized data using the 2D random undersampling technique. Then, the 3D seismic data were divided into a series of time slices. By introducing the sparse Shearlet transform and using the convex set projection (POCS) algorithm, this study conducted sequential data reconstruction for various time slices. As a result, a Shearlet transform-based time-domain 3D seismic data reconstruction method was developed. Numerical experiments and measured results demonstrate that the proposed reconstruction method exhibits a higher signal-to-noise ratio, a higher computational speed, and better effects than a Curvelet transform-based approach.

In practical detection operations using ground-penetrating radar (GPR), factors such as environmental noise and instrument errors frequently cause signals to be mixed with substantial noise, seriously reducing signal quality and the reliability of analytical results. To address this issue, this study proposed a time-frequency peak filtering method combined with minimum mean cross-entropy (TFPF-MMCE) for denoising GPR signals. This method combined time-frequency peak filtering with the cross-entropy function, enabling effective noise suppression and precise preservation of valid signals through precise optimization of the time-frequency representation, thereby significantly improving the quality of GPR signals. Numerical simulation and field GPR experiments validated that the TFPF-MMCE method exhibited a high noise removal capability and, thus, can effectively eliminate random noise while significantly improving signal clarity and reliability. Compared to traditional denoising methods, TFPF-MMCE shows significant advantages in denoising effectiveness and noise resistance stability, suggesting promising application potential and practical value in the field of GPR signal processing.

This study investigated data from a 1:50,000 stream sediment survey in the Juxian-Wulian area, Shandong Province using factor analysis. By extracting eight representative factors, this study identified element assemblage types. Then, this study performed geochemical zoning based on the factor scores and discussed the relationships between various sub-zones and their geological background. Based on the geochemical zoning results, as well as geological conditions for mineralization, this study determined favorable geological bodies and prospecting targets and discovered new prospecting clues.The most significant mineralization combination in this area is Au-Cu-Ag-Mo-Bi, followed by Pb-Zn; The V-Ti-Co-Mn combination and Ni-Cr combination can serve as exploration elements, indicating that there may be favorable mineralization mechanisms in the center and deep parts of the combination anomaly zone. Finding volcanic craters and hidden explosive breccia tubes in the distribution areas of volcanic rocks in this region or area is an important direction for mineral exploration; In the area of Shichang Fangzi Village in the eastern part of the research area, there is a comprehensive zoning with a F₂-F₄-F₁ three factor combination model. The Mesozoic Buliu sequence and Weideshan sequence rock bodies are favorable geological bodies for mineralization, and the NE trending structural belt is a favorable place for mineralization, indicating the direction of prospecting.

Traditional gridding methods struggle to balance computational accuracy and efficiency when processing irregularly distributed magnetic anomaly data. To address this issue, this study applied the classic least-squares collocation method from geodesy to the gridding of ground-based magnetic anomaly data. This application was verified through the test and analysis of the simulation data and the actual coalfield data. The results indicate that the computational accuracy of gridding based on least-squares collocation is dictated by the error estimation of discrete observational data and the selection and fitting of the covariance function. More accurate error estimation contributes to higher-accuracy interpolation. A polynomial function is a simple and effective empirical covariance function for processing magnetic anomaly data. The least-squares collocation method demonstrates more effective noise suppression compared to the Kriging, minimum curvature, and radial basis function methods. Overall, applying the least-squares collocation to the gridding of magnetic anomaly data can enhance the accuracy and efficiency of data processing.

The spectral parameters of the Cole-Cole model can improve the resolution of comprehensive interpretation of time-domain induced polarization (IP) data, contributing somewhat to the exploration of metal deposits. Applying the backpropagation neural network (BPNN) model to the prediction and inversion of spectral parameters can avoid high computational complexity to improve the inversion speed. Moreover, the BPNN model can fully explore the utilization efficiency of time-domain IP data to enrich the characteristic information of subsurface ore bodies. Based on this, this study derived the mathematical expression of the time-domain apparent polarizability attenuation curve using the digital filtering algorithm. With the mathematical expression as the forward/inverse model, this study comparatively analyzed the impacts of four factors-the sample size of the training set, the number of neurons in the input layer, the node number of hidden layers, and the number of hidden layers-on the training and inversion effects of the BPNN model, determining the optimal model. Furthermore, this study trained the BPNN model using time-domain IP data from eight time windows. Finally, this study applied the trained BPNN model for prediction and inversion based on the measured time-domain IP data. The results indicate that the BPNN model is feasible in inverting spectral parameters based on both theoretical and measured datasets, manifesting high inversion accuracy and minor errors. Overall, the results of this study can assist in distinguishing paragenetic and associated minerals and reducing misinterpretation.

Ray tracing-based first-arrival traveltime tomography is widely used to construct near-surface velocity models to achieve the static correction of seismic data from complex near surface.However,this method necessitates the calculation of ray paths for first-arrival traveltimes and the iterative updating of initial velocity models.As a result,significant computational time is required when applying this method to measured 3D high-density seismic data.To address this issue,this study proposed a method for quickly building 3D near-surface velocity models utilizing diving wave traveltimes under rugged surface.Specifically,based on the ray and traveltime equations of diving waves corresponding to velocities subjected to lateral and vertical changes under rugged surface,the velocity distribution from the observation surface downward was determined using common offset gathers.The proposed method eliminates the need for ray tracing and iterative updates of initial velocity models,offering high modeling efficiency.Tests based on data from theoretical models verified the effectiveness of the proposed method.When applied to measured 3D seismic data,the proposed method yielded static correction results comparable to those obtained using the Fresnel-volume first-arrival traveltime tomography while significantly improving computational efficiency.

With the application of high-precision,high-density 3D seismic technology and significant improvements in acquisition efficiency,the volume of data from nodal seismic data acquisition has expanded sharply.This leads to growing demands for the quality monitoring of nodal seismic acquisition.Especially,the lag in the synthesis of common shot gather data in node data has affected the quality monitoring and processing efficiency of seismic data.Therefore,the demand for quality control technology has become increasingly prominent in nodal seismic data acquisition.By delving into technologies including the visual monitoring of pre-acquisition node states,node data synthesis,and seismic data quality monitoring,this study independently developed a quality monitoring platform for nodal seismic data acquisition,enabling quality control for the whole nodal seismic data acquisition,involving equipment status,data synthesis,and data quality control.This platform has achieved satisfactory application results in a practical seismic data acquisition project.

In the era of rapid digitalization development, artificial intelligence (AI) technology has brought revolutionary changes to traditional work patterns. Based on Baidu Comate, this study proposed an automatic numbering method for sampling points in irregular geochemical networks. Automatic numbering tests, conducted on 12 000 geochemical sampling points, demonstrate that the method improved the efficiency by 99.8% and achieved 100% accuracy compared to the traditional manual method. This indicates that the proposed method is more efficient and accurate than traditional approaches, effectively avoiding human errors and improving work efficiency. This study also discussed the challenges AI faces in processing complex instructions, the importance of instruction clarity, the identification of complex logic, and the necessity of developing knowledge reserves. Although AI technology has significantly improved the efficiency and accuracy of the automatic numbering of sampling points in irregular geochemical networks, the early development of packaging tools requires personnel who can read codes to modify and verify the codes. Additionally, AI-assisted demand processing should be in phases, and ultimately, it is necessary to encapsulate verified codes into a tool for reuse.

Despite bearing valuable recyclable elements, mine tailings pose environmental risks. However, there is a lack of studies on the geochemical characteristics of tailings in China and abroad, hindering their appropriate treatment and reuse. This study collected tailing samples from the Han-Xing Iron deposit in Hebei Province, China and conducted the analysis and tests of these samples. This study determined the concentrations and chemical speciation (i.e., exchangeable, carbonate-bound, Fe-Mn oxide-bound, organic-bound, and residual forms) of elements including Fe, Co, S, Cu, and Zn in tailing particles with varying grain sizes. Accordingly, it explored the implications for the exploitation and utilization of tailing resources, along with the assessment of environmental risks. This study provides deeper insights into the geochemical characteristics of tailings, producing positive impacts on the exploitation and utilization of tailing resources, as well as the prevention of environmental risks.

Based on high-precision, high-quality data on SiO₂, Al₂O₃, Fe₂O₃, and organic matter in topsoils obtained from the 1:250,000-scale multi-purpose regional geochemical survey in Fujian Province, this study assessed topsoils and, for the first time, established a geochemical method-based geochemical assessment system for desertified land in the province. Specifically, this system involves methods for calculating the silicon-aluminum-iron ratio (Saf), carbon-silicon ratio (K_Si), and their comprehensive index value (Szh_ f), which serves as the indicator for assessing the degree of land desertification. Specifically, a higher Szh_ f value indicates a higher degree of desertification, and vice versa. Furthermore, this study determined the statistics of geochemical classification parameters for desertified land in Fujian Province and delineated the distribution ranges of geochemical grades. This aims to assess the current status of desertified land in the province from the microscopic perspective of soil elements and to further ascertain the distribution characteristics of the land. The results indicate that the land with strong, moderate, slight, and very slight desertification in Fujian Province exhibits areas of 39 531 hectares (0.326%), 65,790 hectares (0.542%), 103 601 hectares (0.853%), and 360 329 hectares (2.968%), respectively, primarily distributed along the coastal zone to the south of the Minjiang River and in Changting County of Longyan City. Field verification demonstrates that this evaluation method is scientific and reliable and yields accurate classification results of desertification grades, thus objectively reflecting the distribution status of desertified land in Fujian Province.

Understanding the distribution patterns of selenium in soils and crops is critical to developing selenium-rich industries. Enshi City in Hubei Province is known for its extensive selenium-rich soils, establishing this city as a promising area for selenium-rich agriculture. This study investigated Xintang Township in Enshi. Based on the organization and analysis of the geochemical data of 2 469 soil samples and 237 crop samples of maize, potatoes, rice, radish, cabbage, and tea, this study offered a systematic summary of the selenium distribution in soils and factors influencing selenium content in crops in the study area. The results indicate that the topsoils exhibit selenium content ranging from 0.14×10^-6 to 25.74×10^-6, with a background value of 0.81×10^-6, which is 3.7 times the national background of selenium content in soils. Selenium-rich soils cover 86.23% of the total area of the study area, and two NEE-directed selenium-rich belts are found. The spatial distribution of selenium in soils is closely related to soil-forming parent materials. Soils with Permian black rock series as parent materials exhibit notably higher selenium content, with an enrichment coefficient of 3.74. In high-selenium-content areas, rice, radish, and cabbage exhibit selenium enrichment rates exceeding 65%. Except for potatoes, crops display positive correlations between their selenium content and the selenium content in their root soils, with tea showing the highest correlation (P<0.01, R=0.84). This suggests a close relationship between the selenium content in crops and their root soils. The crops in cultivated areas with Permian black rock series and Triassic carbonate rock series as soil-forming parent materials exhibit high bioconcentration factors of selenium, with soils and crops with Permian black rock series as soil-forming parent materials presenting the highest average selenium content. This highlights the significant impacts of soil-forming parent materials on crop selenium content.

To understand the characteristics and sources of heavy metal contamination in the sediments of the Jinsha River basin, this study investigated the Qingling River basin-a primary tributary of Longchuan River on the south bank of the Jinsha River. Samples were collected from the sediments of 22 representative sections, and the concentrations of eight heavy metal elements, i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, were tested and analyzed. Statistical analysis was conducted on the distribution characteristics of these heavy metal elements in the sediments along the basin. The sources of these heavy metals were investigated using correlation analysis and principal component analysis, and the degree of heavy metal contamination in the sediments was assessed using the geo-accumulation index and the Nemero index. The results indicate that As, Cd, Cu, Hg, Ni, Pb, and Zn are primarily influenced by mining, agricultural, and industrial activities. Cr and Ni originate primarily from soil-forming parent materials. Besides natural sources, Ni is also affected by anthropogenic sources. The assessment results derived using the geo-accumulation and Nemero indices reveal that the eight heavy metal elements exhibit moderate or low contamination on average. However, partial elements, represented by Cd, Hg, Pb, and Zn, exhibit localized enrichment within the basin, primarily concentrated in the Laojiezi Au-Pb-Ag polymetallic mining area and the urban-rural junction in the lower reaches of the county.

Land emerges as the most fundamental resource for agricultural production, and addressing land quality-related issues is the key to ensuring high food quality. A comprehensive understanding of land quality is crucial to the scientific and sustainable utilization and development of land resources. This study conducted a 1:50,000 geochemical survey of land quality in the contiguous farmland in Dongchuan Town, Yao'an County, Yunnan Province. Accordingly, this study provided a comprehensive assessment of soil nutrients, environment, dry and wet atmospheric deposits, and agricultural irrigation water. Based on the assessment results, this study determined the production areas of green food and pollution-free agricultural products, aiming to provide a reference for the development and utilization of sustainable, green cultivated land. The results indicate that soils in the study area are nutrient-rich and enjoy a favorable environment, high-quality irrigation water, and an excellent atmospheric environment. The comprehensive grade assessment results of soil quality align with those of land quality, revealing that the first and second-grade high-quality soils account for 68.98% of the total area. A batch of production areas of pollution-free and green food were determined, and those that can be directly used to develop green, zinc- and copper-rich crops were delineated. These findings will provide geochemical data support for land use planning, food security, building agricultural products with plateau characteristics, and serving rural revitalization efforts.

Due to dense buildings and structures and insufficient drilling surveys, the construction of shield tunnels passing through urban karst formations that host dense buildings faces significant risks of surface fracturing and subsidence caused by karst development. Hence, this study employed the microtremor survey technology with strong anti-interference capability in complex environments to address this challenge. Based on the technology, it analyzed the structural characteristics of wave velocities in underground rock formations through the inversion of the apparent shear-wave velocity profile. Combined with geological drilling data, it inferred the bedrock interface, highly weathered unconsolidated formations, and karst cave anomaly zones. Key findings are as follows: (1) The apparent shear-wave velocities in the study area gradually increased from the shallow to deep formations. Formations with wave velocities above and below 300 m/s were inferred to be limestone and Quaternary formations, respectively, with the rock-soil interface at depths approximately between 10~15 m; (2) Seven low-value anomaly zones of apparent shear-wave velocities ranging from 150~240 m/s were interpreted. They were presumed to be unconsolidated formations or karst caves at depths ranging from 8~30 m. Relying on strong anti-interference and high accuracy, the microtremor survey technology can accurately identify the shear-wave velocity structures of underground profiles, lithologic interfaces of formations, unconsolidated formations, and karst cave anomalies. Therefore, the technology is effective in the geological exploration of urban dense building areas with karst development.