Artificial Intelligence in Geosciences最新文献

On the application of machine learning algorithms in predicting the permeability of oil reservoirs 机器学习算法在油藏渗透率预测中的应用研究

Artificial Intelligence in Geosciences Pub Date : 2025-06-03 DOI: 10.1016/j.aiig.2025.100126

Andrey V. Soromotin , Dmitriy A. Martyushev , João Luiz Junho Pereira

{"title":"On the application of machine learning algorithms in predicting the permeability of oil reservoirs","authors":"Andrey V. Soromotin , Dmitriy A. Martyushev , João Luiz Junho Pereira","doi":"10.1016/j.aiig.2025.100126","DOIUrl":"10.1016/j.aiig.2025.100126","url":null,"abstract":"<div><div>Permeability is one of the main oil reservoir characteristics. It affects potential oil production, well-completion technologies, the choice of enhanced oil recovery methods, and more. The methods used to determine and predict reservoir permeability have serious shortcomings. This article aims to refine and adapt machine learning techniques using historical data from hydrocarbon field development to evaluate and predict parameters such as the skin factor and permeability of the remote reservoir zone. The article analyzes data from 4045 wells tests in oil fields in the Perm Krai (Russia). An evaluation of the performance of different Machine Learning (ML) algorithms in the prediction of the well permeability is performed. Three different real datasets are used to train more than 20 machine learning regressors, whose hyperparameters are optimized using Bayesian Optimization (BO). The resulting models demonstrate significantly better predictive performance compared to traditional methods and the best ML model found is one that never was applied before to this problem. The permeability prediction model is characterized by a high R<sup>2</sup> adjusted value of 0.799. A promising approach is the integration of machine learning methods and the use of pressure recovery curves to estimate permeability in real-time. The work is unique for its approach to predicting pressure recovery curves during well operation without stopping wells, providing primary data for interpretation. These innovations are exclusive and can improve the accuracy of permeability forecasts. It also reduces well downtime associated with traditional well-testing procedures. The proposed methods pave the way for more efficient and cost-effective reservoir development, ultimately supporting better decision-making and resource optimization in oil production.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport 基于阶段深度学习的三维时空大气传输空间细化方法

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100120

M. Giselle Fernández-Godino , Wai Tong Chung , Akshay A. Gowardhan , Matthias Ihme , Qingkai Kong , Donald D. Lucas , Stephen C. Myers

{"title":"A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport","authors":"M. Giselle Fernández-Godino , Wai Tong Chung , Akshay A. Gowardhan , Matthias Ihme , Qingkai Kong , Donald D. Lucas , Stephen C. Myers","doi":"10.1016/j.aiig.2025.100120","DOIUrl":"10.1016/j.aiig.2025.100120","url":null,"abstract":"<div><div>High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion predictions. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional (3D) plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic description of rock thin sections: A web application 岩石薄片的自动描述：一个web应用程序

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100118

Stalyn Paucar, Christian Mejia-Escobar, Victor Collaguazo

{"title":"Automatic description of rock thin sections: A web application","authors":"Stalyn Paucar, Christian Mejia-Escobar, Victor Collaguazo","doi":"10.1016/j.aiig.2025.100118","DOIUrl":"10.1016/j.aiig.2025.100118","url":null,"abstract":"<div><div>The identification and characterization of rock types is a core activity in geology and related fields, including mining, petroleum, environmental science, industry, and construction. Traditionally, this task is performed by human specialists who analyze and describe the type, composition, texture, shape, and other properties of rock samples, whether collected in-situ or prepared in a laboratory. However, the process is subjective, dependent on the specialist’s experience, and time-consuming. This study proposes an artificial intelligence-based approach that combines computer vision and natural language processing to generate both textual and verbal descriptions from images of rock thin sections. A dataset of images and corresponding textual descriptions is used to train a hybrid deep learning model. Features extracted from the images using EfficientNetB7 are processed by a Transformer network to generate textual descriptions, which are then converted into verbal responses using a speech synthesis service. The experimental results show an accuracy of 0.892 and a BLEU score of 0.71. This model offers potential utility for research, professional, and academic applications and has been deployed as a web application for public use.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised multi-stage deep learning network for seismic data denoising 地震数据去噪的自监督多阶段深度学习网络

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100123

Omar M. Saad , Matteo Ravasi , Tariq Alkhalifah

{"title":"Self-supervised multi-stage deep learning network for seismic data denoising","authors":"Omar M. Saad , Matteo Ravasi , Tariq Alkhalifah","doi":"10.1016/j.aiig.2025.100123","DOIUrl":"10.1016/j.aiig.2025.100123","url":null,"abstract":"<div><div>Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow, as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses. However, finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge. In this study, we introduce a multi-stage deep learning model, trained in a self-supervised manner, designed specifically to suppress seismic noise while minimizing signal leakage. This model operates as a patch-based approach, extracting overlapping patches from the noisy data and converting them into 1D vectors for input. It consists of two identical sub-networks, each configured differently. Inspired by the transformer architecture, each sub-network features an embedded block that comprises two fully connected layers, which are utilized for feature extraction from the input patches. After reshaping, a multi-head attention module enhances the model’s focus on significant features by assigning higher attention weights to them. The key difference between the two sub-networks lies in the number of neurons within their fully connected layers. The first sub-network serves as a strong denoiser with a small number of neurons, effectively attenuating seismic noise; in contrast, the second sub-network functions as a signal-add-back model, using a larger number of neurons to retrieve some of the signal that was not preserved in the output of the first sub-network. The proposed model produces two outputs, each corresponding to one of the sub-networks, and both sub-networks are optimized simultaneously using the noisy data as the label for both outputs. Evaluations conducted on both synthetic and field data demonstrate the model’s effectiveness in suppressing seismic noise with minimal signal leakage, outperforming some benchmark methods.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thank you reviewers! 谢谢审稿人！

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100114

引用次数: 0

Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces 基于深度学习的岩石矿物识别，从未受干扰的破碎表面的未处理的数字显微图像

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100127

M.A. Dalhat, Sami A. Osman

{"title":"Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces","authors":"M.A. Dalhat, Sami A. Osman","doi":"10.1016/j.aiig.2025.100127","DOIUrl":"10.1016/j.aiig.2025.100127","url":null,"abstract":"<div><div>This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset covers 40 distinct rock mineral-types. Three CNN architectures (Simple model, SqueezeNet, and Xception) were evaluated to compare their performance and feature extraction capabilities. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize the features influencing model predictions, providing insights into how each model distinguishes between mineral classes. Key discriminative attributes included texture, grain size, pattern, and color variations. Texture and grain boundaries were identified as the most critical features, as they were strongly activated regions by the best model. Patterns such as banding and chromatic contrasts further enhanced classification accuracy. Performance analysis revealed that the Simple model had limited ability to isolate fine-grained details, producing broad and less specific activations (0.84 test accuracy). SqueezeNet demonstrated improved localization of discriminative features but occasionally missed finer textural details (0.95 test accuracy). The Xception model outperformed the others, achieving the highest classification accuracy (0.98 test accuracy) by exhibiting precise and tightly focused activations, capturing intricate textures and subtle chromatic variations. Its superior performance can be attributed to its deep architecture and efficient depth-wise separable convolutions, which enabled hierarchical and detailed feature extraction. Results underscores the importance of texture, pattern, and chromatic features in accurate mineral classification and highlights the suitability of deep, efficient architectures like Xception for such tasks. These findings demonstrate the potential of CNNs in geoscience research, offering a framework for automated mineral identification in industrial and scientific applications.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals 利用TransUNet增强微地震事件检测：一种深度学习方法，用于同时拾取p波和s波首次到达

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100129

Kun Chen , Meng Li , Xiaolian Li , Guangzhi Cui , Jia Tian , JiaLe Li , RuoYao Mu , JunJie Zhu

{"title":"Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals","authors":"Kun Chen , Meng Li , Xiaolian Li , Guangzhi Cui , Jia Tian , JiaLe Li , RuoYao Mu , JunJie Zhu","doi":"10.1016/j.aiig.2025.100129","DOIUrl":"10.1016/j.aiig.2025.100129","url":null,"abstract":"<div><div>Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance when dealing with complex and noisy data. In this study, we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network. Our model integrates the advantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously. We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam. The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data. The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the TransUNet achieves the optimal balance in its architecture and inference speed. With relatively low inference time and network complexity, it operates effectively in high-precision microseismic phase pickings. This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reservoir monitoring applications.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models 基于计算机视觉技术和一对一模型的碳酸盐岩薄片自动分类

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100117

Elisangela L. Faria , Rayan Barbosa , Juliana M. Coelho , Thais F. Matos , Bernardo C.C. Santos , J.L. Gonzalez , Clécio R. Bom , Márcio P. de Albuquerque , P.J. Russano , Marcelo P. de Albuquerque

{"title":"Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models","authors":"Elisangela L. Faria , Rayan Barbosa , Juliana M. Coelho , Thais F. Matos , Bernardo C.C. Santos , J.L. Gonzalez , Clécio R. Bom , Márcio P. de Albuquerque , P.J. Russano , Marcelo P. de Albuquerque","doi":"10.1016/j.aiig.2025.100117","DOIUrl":"10.1016/j.aiig.2025.100117","url":null,"abstract":"<div><div>Convolutional neural networks have been widely used for analyzing image data in industry, especially in the oil and gas area. Brazil has an extensive hydrocarbon reserve on its coast and has also benefited from these neural network models. Image data from petrographic thin section can be essential to provide information about reservoir quality, highlighting important features such as carbonate lithology. However, the automatic identification of lithology in reservoir rocks is still a significant challenge, mainly due to the heterogeneity that is part of the lithologies of the Brazilian pre-salt. Within this context, this work presents an approach using one-class or specialist models to identify four classes of lithology present in reservoir rocks in the Brazilian pre-salt. The proposed methodology had the challenge of dealing with a small number of images for training the neural networks, in addition to the complexity involved in the analyzed data. An auto-machine learning tool called AutoKeras was used to define the hyperparameters of the implemented models. The results found were satisfactory and presented an accuracy greater than 70% for image samples belonging to other wells not seen during the model building, which increases the applicability of the implemented model. Finally, a comparison was made between the proposed methodology and multiple-class models, demonstrating the superiority of one-class models.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale 利用环境数据和卫星图像进行作物产量预测的一种新的综合神经符号方法

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100125

Khadija Meghraoui , Teeradaj Racharak , Kenza Ait El Kadi , Saloua Bensiali , Imane Sebari

{"title":"A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale","authors":"Khadija Meghraoui , Teeradaj Racharak , Kenza Ait El Kadi , Saloua Bensiali , Imane Sebari","doi":"10.1016/j.aiig.2025.100125","DOIUrl":"10.1016/j.aiig.2025.100125","url":null,"abstract":"<div><div>Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping 变异函数建模优化使用遗传算法和机器学习线性回归：应用顺序高斯模拟映射

Artificial Intelligence in Geosciences Pub Date : 2025-05-26 DOI: 10.1016/j.aiig.2025.100124

André William Boroh , Alpha Baster Kenfack Fokem , Martin Luther Mfenjou , Firmin Dimitry Hamat , Fritz Mbounja Besseme

{"title":"Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping","authors":"André William Boroh , Alpha Baster Kenfack Fokem , Martin Luther Mfenjou , Firmin Dimitry Hamat , Fritz Mbounja Besseme","doi":"10.1016/j.aiig.2025.100124","DOIUrl":"10.1016/j.aiig.2025.100124","url":null,"abstract":"<div><div>The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy and efficiency of geostatistical analysis, particularly in mineral exploration. The study combines GA and machine learning to optimise variogram parameters, including range, sill, and nugget, by minimising the root mean square error (RMSE) and maximising the coefficient of determination (R<sup>2</sup>). The experimental variograms were computed and modelled using theoretical models, followed by optimisation via evolutionary algorithms. The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon, covering 141 data points. Sequential Gaussian Simulations (SGS) were employed for predictive mapping to validate simulated results against true values. Key findings show variograms with ranges between 24.71 km and 49.77 km, optimised RMSE and R<sup>2</sup> values of 11.21 mGal<sup>2</sup> and 0.969, respectively, after 42 generations of GA optimisation. Predictive mapping using SGS demonstrated that simulated values closely matched true values, with the simulated mean at 21.75 mGal compared to the true mean of 25.16 mGal, and variances of 465.70 mGal<sup>2</sup> and 555.28 mGal<sup>2</sup>, respectively. The results confirmed spatial variability and anisotropies in the N170-N210 directions, consistent with prior studies. This work presents a novel integration of GA and machine learning for variogram modelling, offering an automated, efficient approach to parameter estimation. The methodology significantly enhances predictive geostatistical models, contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0