Journal of Applied Geophysics最新文献

{"title":"Automatic assessing and masking algorithms for electromagnetic transfer functions based on machine learning methods","authors":"Kun Ning ,&nbsp;Hao Dong ,&nbsp;Cheng Guo","doi":"10.1016/j.jappgeo.2025.105989","DOIUrl":"10.1016/j.jappgeo.2025.105989","url":null,"abstract":"<div><div>The magnetotelluric (MT) method is an electromagnetic geophysical technique to investigate the Earth's electrical conductivity structure. It has been widely applied from deep structural studies to near-surface resource explorations. The raw MT time series data are usually transformed and processed into frequency-domain transfer functions (TFs), before being applied in geophysical inversion and interpretations. However, the assessment and elimination of low-quality TF data points still rely heavily on manual operations, which are time-consuming and requiring substantial expertise for operators, thereby reducing the overall efficiency of the data processing workflow. The rise of machine learning (ML) algorithms nowadays has opened up possibilities for rapid and automated data classification, leading to extensive success in fields like finance and image processing. This study explores two popular ML classification algorithms, namely Support Vector Machine (SVM) and Deep Neural Network (DNN), to automatically assess the TF quality. Various data features, such as the difference between a given data point and its neighboring points, were extracted to form a reduced subspace for classification. The classification accuracy of the two algorithms was compared against their manual counterpart, which indicates that the SVM algorithm achieved an accuracy of 93 %, while the DNN algorithm achieved 86 % with the real-world TF data. Consequently, an automated electromagnetic TF masking program based on the SVM algorithm was developed, enabling the accurate and rapid identification and removal of low-quality data points. For instance, manual masking of TF data from a single site may typically require approximately five minutes, whereas the new algorithm accomplishes the task in just a few seconds, significantly enhancing the efficiency of data masking.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"244 ","pages":"Article 105989"},"PeriodicalIF":2.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 deep learning for porosity prediction in tight sandstone reservoirs: A case study of blocks Su14 and Su36","authors":"Yumeng Tian,&nbsp;Zhongjie Xu","doi":"10.1016/j.jappgeo.2025.105987","DOIUrl":"10.1016/j.jappgeo.2025.105987","url":null,"abstract":"<div><div>Porosity is a fundamental parameter for assessing reservoir characteristics, significantly impacting the storage capacity of hydrocarbons, groundwater, and other subsurface resources. Traditional methods for measuring porosity, such as core analysis and well logging, are limited by high costs, low efficiency, and inadequate applicability in heterogeneous reservoirs. To address these limitations, this study proposes a novel hybrid deep learning model, CNN-BiLSTM-Attention, for predicting porosity using well log data from Blocks Su14 and Su36 in the Sulige Gas Field. The model combines the feature extraction capabilities of Convolutional Neural Networks (CNN), the temporal dependency modeling of Bidirectional Long Short-Term Memory (BiLSTM), and the dynamic weighting provided by the Attention mechanism. Leveraging ten key well log parameters and advanced preprocessing techniques, the model achieved an R² of 0.86112 and RMSE of 0.036274 on the training set, and an R² of 0.8591 and RMSE of 0.037009 on the test set. Validation using independent datasets from Blocks Su14 and Su36 yielded an R² of 0.8533, RMSE of 0.015465, and RPD of 2.4641, highlighting the model's robustness and practical applicability. Comparative analysis demonstrated that the CNN-BiLSTM-Attention hybrid model outperforms traditional methods, including BP, CNN, ELM, RF, and SVM. This study offers a reliable, efficient, and cost-effective approach for porosity prediction in complex reservoirs, effectively addressing challenges associated with heterogeneity and data nonlinearity.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105987"},"PeriodicalIF":2.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of changes in the Frequency Content of the Ultrasonic Pulse Waves Propagating through Stabilized Soils","authors":"Deniz Bas ,&nbsp;Eray Yildirim","doi":"10.1016/j.jappgeo.2025.105988","DOIUrl":"10.1016/j.jappgeo.2025.105988","url":null,"abstract":"<div><div>This study investigated the changes in the frequency content of ultrasonic waves propagating through stabilized soils under different curing conditions and durations. Additionally, the relationship between the observed frequency changes and the results of soil stabilization was explored. Şile, Ukrainian, and Pazaryeri clays and organic soil were used, with calcium aluminate cement (CAC) used as a binder. Unconfined compressive strength (UCS) and UPV tests were performed on untreated and stabilized soils under immediately, air-cured, and wet-cured conditions. During the UPV tests, numerical data were obtained by recording the measurement waveforms in the time domain using an oscilloscope. The relationships among the UCS, UPV, arrival times, and spectral amplitudes were examined through regression analysis, yielding significantly high coefficients of determination. The amplitude spectra were obtained by performing a Fourier transform of the waveforms. Significant changes were observed in the amplitude spectra of the untreated and stabilized samples. Untreated soils exhibited heightened attenuation at higher frequencies compared with stabilized soils. All samples exhibited distinct amplitude spectra. The samples with higher UCS exhibited higher amplitudes and predominant frequencies. In organic soils with the lowest UCS, higher frequencies were attenuated, and lower frequencies predominated. In addition, the amplitudes decreased in the frequency domain. These findings highlight the effect of soil stabilization and performance on frequency content.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105988"},"PeriodicalIF":2.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geophysical characterization of a sand deposit in a small-scale mining in the central east region of São Paulo State, Brazil","authors":"Jorge Luís Porsani ,&nbsp;Victor Hugo Hott Costa ,&nbsp;Nathália de Souza Penna ,&nbsp;Rodrigo Corrêa Rangel ,&nbsp;Marcelo Cesar Stangari ,&nbsp;Gustavo Isnard Jarussi ,&nbsp;Gabriel Alencar Silva Almeida Dantas ,&nbsp;Rafael Josimar Araos Huaman","doi":"10.1016/j.jappgeo.2025.105986","DOIUrl":"10.1016/j.jappgeo.2025.105986","url":null,"abstract":"<div><div>This research employs Electrical Resistivity Tomography (ERT), Ground-Penetrating Radar (GPR), and Transient Electromagnetic (TEM) to characterize a sand deposit located in a small-scale mining (SSM) in Leme, São Paulo State, Brazil. The region has a high demand for sand, particularly in the construction industry. The applied geophysical methods provide complementary information. ERT and GPR data enable the investigation of shallow subsurface layers (up to ∼30 m), whereas TEM is employed to investigate deeper structures, reaching depths of up to ∼350 m. The integrated geophysical results allowed the identification of a geoelectrical stratigraphy down to a few hundred meters, characterizing both unsaturated and saturated sand layers. A 3D model of the sand deposit was built based on the geophysical results and lithological information from boreholes in the study region. This model includes the deposit's geometry, mass, and volume, which are crucial information for an economic assessment of the SSM. Moreover, our results demonstrate how near-surface geophysical methods can be employed to help with a sustainable exploration of a sand deposit in an SSM.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"244 ","pages":"Article 105986"},"PeriodicalIF":2.1,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic facies identification using a U-KAN network with multi-scale spatial pyramid attention mechanism","authors":"Binpeng Yan,&nbsp;Mutian Li,&nbsp;Rui Pan,&nbsp;Jiaqi Zhao","doi":"10.1016/j.jappgeo.2025.105985","DOIUrl":"10.1016/j.jappgeo.2025.105985","url":null,"abstract":"<div><div>Accurate identification of seismic facies plays a critical role in characterizing subsurface structures, locating hydrocarbon reservoirs, and guiding resource exploration and development. Traditional manual interpretation methods are highly subjective and notoriously inefficient. In recent years, deep learning-based techniques have emerged as powerful alternatives to address these shortcomings. The introduction of Kolmogorov–Arnold Networks (KAN) has provided new insights into interpreting conventional network architectures, facilitating the development of hybrid models such as U-KAN, which integrates convolutional operators with KAN. In this study, we apply U-KAN to seismic facies identification and further augment its performance by incorporating a Multi-Scale Spatial Pyramid Attention (MSPA) mechanism. The proposed MSPA-UKAN architecture leverages the superior nonlinear representation and interpretability of KAN, along with the efficient multi-scale feature extraction capabilities of MSPA. This combination allows the model to capture multi-scale seismic features more effectively and represent complex facies transitions accurately. To mitigate limited generalization caused by geological variability, we introduce a model-based transfer learning strategy in which a pre-trained model is adapted to datasets from new regions, thereby enhancing recognition accuracy. The MSPA-UKAN was first trained on a public dataset from the F3 block in the North Sea, Netherlands, and subsequently transferred to and evaluated on the Parihaka block in New Zealand, where it demonstrated excellent seismic facies recognition performance.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105985"},"PeriodicalIF":2.1,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretability analysis of rockburst risk prediction in coal mines based on machine learning-SHAP","authors":"Mei Hongjia ,&nbsp;Wang Yanbing ,&nbsp;Zhang Xiangliang ,&nbsp;Wang Jianlong ,&nbsp;Qi Gaowei ,&nbsp;Han Yingying ,&nbsp;Zheng Wenjing","doi":"10.1016/j.jappgeo.2025.105982","DOIUrl":"10.1016/j.jappgeo.2025.105982","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The traditional prediction model has significant limitations in the risk assessment of rockburst in deep coal mines, which is mainly reflected in its insufficient ability to analyze complex geological conditions and mining disturbance factors. These models are usually difficult to accurately capture the impact of various environmental factors on rockburst risk, lack the dynamic assessment ability of the importance of characteristics, and the traditional models are difficult to formulate targeted prevention measures, which can not meet the refined needs of deep coal mine safety production. This limitation not only reduces the reliability of prediction results, but also limits the effectiveness of control measures. Therefore, in view of the problems that the traditional prediction model lacks the corresponding interpretation and analysis of rockburst in deep coal mines, cannot distinguish the characteristic importance of various environmental factors, and is difficult to carry out targeted prevention and control measures, In view of the problems that the traditional prediction model lacks the corresponding interpretation and analysis of the rockburst in deep coal mines, cannot distinguish the characteristic importance of various environmental factors, and is difficult to carry out targeted prevention and control measures, this paper uses three algorithms in machine learning, namely random forest (Random Forest), support vector regression (Support Vector Regression), and extreme gradient lifting (Extreme Gradient Boosting), and takes the daily footage, the number of coal seam pressure relief holes, the number of drill cuttings detection holes, the number of bottom coal pressure relief holes, the number of bottom coal blasting holes, the total energy of microseismic, the frequency of microseismic, the maximum microseismic energy, the maximum stress, the amount of drill cuttings, and the depth of drill cuttings as 12 eigenvalues. After data processing, through cross validation and superparameter adjustment,the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and The algorithm with the best regression performance is comprehensively screened out under the conditions of R&lt;sup&gt;2&lt;/sup&gt; and STD deviation. The corresponding interpreter is used to analyze the interpretability of the algorithm and SHAP. Finally, the model is applied in the field to verify its feasibility. The research results indicate that the XGBoost algorithm performs the best on this dataset, with a sample MSE of 0.0001, RMSE of 0.0024, MAE of 0.0011, MAPE of 1.3164 %, R&lt;sup&gt;2&lt;/sup&gt; of 0.9930, and Std Deviation of 0.0021 the XGBoost-SHAP model, the feature importance sequence was obtained using global interpretation, and the feature value with the highest weight in this environment was selected; By combining local explanations to make the inference and prediction processes within the model transparent, the bla","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105982"},"PeriodicalIF":2.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deterioration mechanism of sandstone under coupling compression-shear stress and local borehole water pressure","authors":"Yixin Liu ,&nbsp;Jiaxin Cheng ,&nbsp;Gang Wang ,&nbsp;Yan Gao ,&nbsp;Chenrui Jiang ,&nbsp;Jiang Xu","doi":"10.1016/j.jappgeo.2025.105984","DOIUrl":"10.1016/j.jappgeo.2025.105984","url":null,"abstract":"<div><div>This study focuses on the shear mechanical characteristics and crack propagation process under the influence of borehole water injection pressure. A self-developed rock shear-flow coupling experimental device was employed to investigate the progressive damage process and mechanical deterioration characteristics of a rock mass under local borehole water injection pressure. Acoustic emission (AE) technology analyzed the damage that occurred during the loading process, with particular emphasis on the AE rate, cumulative AE count, and <em>b</em>-value. In addition, the morphology of the fracture surface was reconstructed and analyzed using 3D scanning. The shear failure process under borehole water injection pressure can be classified into four main stages, with the most notable features being the variations in shear stress and AE rate. The experimental results indicated that the borehole water injection pressure significantly promoted the initiation and extension of tensile cracks and reduced the proportion of shear cracks. The effect of the water injection pressure is significant on the main fracture surface and is more pronounced in reducing the overall internal damage of the sandstone sample. Furthermore, we combined the Dugdale–Barenblatt model to calculate the main crack connectivity before shear fracture under borehole water injection pressure and achieved a quantitative evaluation of the weakening of the shear strength under local borehole water injection pressure.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105984"},"PeriodicalIF":2.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 label optimization method for VSP wavefield separation with deep learning","authors":"Qiaomu Qi ,&nbsp;Xiaobin Li ,&nbsp;Jianlong Su ,&nbsp;Yuyong Yang ,&nbsp;Linxin Li ,&nbsp;Yulin Wu","doi":"10.1016/j.jappgeo.2025.105978","DOIUrl":"10.1016/j.jappgeo.2025.105978","url":null,"abstract":"<div><div>Vertical seismic profiling (VSP) has a wide range of applications in the field of earth sciences. It is utilized not only for seismic imaging in oil and gas exploration but also for the geophysical monitoring of CO2 reservoirs. Acquiring high-precision upgoing and downgoing waves from Vertical Seismic Profile (VSP) data is crucial since the majority of VSP applications use separated upgoing or downgoing waves, such as seismic imaging with upgoing waves or Q-attenuation estimation with downgoing waves. Traditional methods for wavefield separation typically depend on transform-domain techniques like <em>f-k</em> filtering or Radon transform, as well as time-domain methods such as median filtering and Singular Value Decomposition (SVD). However, transform-domain approaches face challenges like spatial aliasing; median filtering and SVD rely on manual selection of wave events, and are less effective for far-offset data. A critical aspect of using deep learning for wavefield separation is the preparation of the training dataset. Numerous studies utilize field labels derived from traditional methods or synthetic labels created using convolution operators. The significant difference between synthetic labels and actual field data, along with the inherent defects in labels generated by traditional methods, limits the effectiveness of the wavefield separation. To overcome these challenges in label creation, we introduce the label-optimized autoencoder (LOAE). The labels containing artifacts, which are produced by traditional methods, are trained through the LOAE network using unsupervised learning. After appropriate training, the LOAE can remove noise from the labels and output relatively pure upgoing or downgoing waves with consistent waveforms. The refined upgoing and downgoing waves are then merged to create a dataset for supervised learning in wavefield separation tasks. Both the synthetic and field data tests demonstrate that this label optimization method substantially improves the accuracy of wavefield separation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105978"},"PeriodicalIF":2.1,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic first-arrival traveltime simulation based on reciprocity-constrained PINN","authors":"Hang Geng ,&nbsp;Chao Song ,&nbsp;Umair bin Waheed ,&nbsp;Cai Liu","doi":"10.1016/j.jappgeo.2025.105967","DOIUrl":"10.1016/j.jappgeo.2025.105967","url":null,"abstract":"<div><div>Simulating seismic first-arrival traveltime plays a crucial role in seismic tomography. First-arrival traveltime simulation usually relies on solving the eikonal equation. The accuracy of conventional numerical solvers is limited to a finite-difference approximation. In recent years, physics-informed neural networks (PINNs) have been applied to achieve this task. However, traditional PINNs encounter challenges in accurately solving the eikonal equation, especially in cases where the model exhibits directional scaling differences. These challenges result in substantial traveltime prediction errors when the traveling distance is long. To improve the accuracy of PINN in traveltime prediction, we incorporate the reciprocity principle as a constraint into the PINN training framework. Based on the reciprocity principle, which states that the traveltime between two points remains invariant when their roles as source and receiver are exchanged, we propose to apply this principle to multiple source–receiver pairs in PINN-based traveltime prediction. Furthermore, a dynamic weighting mechanism is proposed to balance the contributions of the eikonal equation loss and the reciprocity-constrained loss during the training process. This adaptive weighting evolves dynamically with the training epochs, enhancing the convergence of the training process. Experiments conducted on a simple lens velocity model, the Overthrust velocity model, and a 3D velocity model demonstrate that the introduction of the reciprocity-constrained PINN significantly improves the accuracy of traveltime predictions.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105967"},"PeriodicalIF":2.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural features and evolution of the Southern Junggar Basin: Insights from discrete numerical simulations","authors":"Wei Qiao ,&nbsp;Chong Shi ,&nbsp;Aifeima Ahetamuxi ,&nbsp;Xuan Tang ,&nbsp;Li-kun Hao","doi":"10.1016/j.jappgeo.2025.105983","DOIUrl":"10.1016/j.jappgeo.2025.105983","url":null,"abstract":"<div><div>The geotectonic movement mechanism and trend prediction are important components of energy exploration and geological structure analysis. Based on the survey results of the Mesozoic-Cenozoic intracontinental extrusion movement in the southern Junggar Basin in China, this study used the particle flow numerical simulation method to study the influence of strata extrusion, denudation, and sedimentation on the formation of topography. The results show that the topography and geomorphology obtained were in line with the survey results when the particle flow method was used to simulate the large-scale geological tectonic movement and the soft bond model and linear contact model were used for the competent and the detachment layers, respectively. The surface topography generated by discrete element method simulations of the deposition and erosion of the surface can be used to identify the average terrain line. The upper and lower parts of the average terrain were set as the erosion and deposition areas, respectively. The denudation-deposition and horizontal extrusion rates were linked to control the reduction and increase of particles, and the extrusion results of the numerical model for the southern Junggar Basin showed that multi-slip delamination extrusion and differential subsidence of the basement were the main reasons for the formation of the geology. When the geological plate was relatively large, anticlines and folds were more difficult to develop during the tectonic process. The direction and rate of extrusion significantly influence the structural morphology. These research results can provide a reference for the mechanistic analysis of tectonic movement.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105983"},"PeriodicalIF":2.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}