Journal of Applied Geophysics最新文献

{"title":"Progressive damage process and destabilization precursor recognition of granite under high temperature based on acoustic emission","authors":"Kui Zhao ,&nbsp;Congming Li ,&nbsp;Peng Zeng ,&nbsp;Liangfeng Xiong ,&nbsp;Cong Gong ,&nbsp;Zhen Huang","doi":"10.1016/j.jappgeo.2025.105699","DOIUrl":"10.1016/j.jappgeo.2025.105699","url":null,"abstract":"<div><div>To investigate the crack evolution and failure precursory characteristics of granite after thermal damage, uniaxial compression acoustic emission (AE) experiments were conducted on granite specimens after various temperature. AE characteristics at different stress stages were analyzed. Characteristics of the AE multiparameter informativeness of post-high-temperature granites and differences in crack precursor information are discussed. The results showed that as the temperature increases, the uniaxial compressive strength and elastic modulus of granite decrease significantly, with reductions of 46.93 % and 73.20 %, respectively, at 800 °C compared to 25 °C. Conversely, the peak strain increased by over 135 % at 800 °C, indicating a transition from brittle to ductile behavior. The characteristic stress showing a significant reduction in their thresholds with increasing temperature. The distribution of AE events during the progressive damage process was significantly influenced by thermal damage. At 25–400 °C, the low, intermediate, and high frequency bands converted to each other, with low-frequency signals being dominant. At 600–800 °C, high-frequency signals prevailed before the peak stress, while low-frequency signals increased after the peak. The RA-AF distribution revealed that tensile cracks dominated in granite during the loading process, and the higher the degree of thermal damage, the more tensile crack is. The progressive damage process of granite shifted from a mixed fracture mode to a small-scale tensile fracture mode as the treatment temperature increased. Before peak stress, the proportion of small-scale shear cracks increased, while after peak stress, the proportion of large-scale tensile cracks became more prominent. The AE entropy curve can well reflect the progressive damage characteristics of granite at different temperature. Entropy rapid increase is the precursor characteristics of granite instability. The dominant frequency entropy precursor response appears the earliest, and the amplitude entropy precursor response is the latest. The precursor response occurred earlier with increasing temperature, providing a reliable warning signal for rock failure.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105699"},"PeriodicalIF":2.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644189","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":"Automatic identification and separation of reflection patterns with the help of clustering of seismic attributes in a Rain optimization meta-heuristic algorithm","authors":"Poorandokht Soltani ,&nbsp;Amin Roshandel Kahoo ,&nbsp;Hamid Hasanpour","doi":"10.1016/j.jappgeo.2025.105690","DOIUrl":"10.1016/j.jappgeo.2025.105690","url":null,"abstract":"<div><div>Seismic exploration, a key component of geophysical methods, is crucial for analyzing subsurface structures and evaluating their potential for hydrocarbon resources. However, the interpretation of geological structures based on seismic data frequently entails ambiguity and uncertainty, making it a labor-intensive endeavor that is heavily reliant on the interpreter's expertise. Seismic attributes are essential instruments for the quantitative assessment of seismic information, facilitating the identification and delineation of structural and stratigraphic elements by revealing concealed details. This paper aims to conduct a multi-attribute analysis for the automatic and unsupervised stratigraphic interpretation of two-dimensional seismic data. The research employs optimization-based clustering utilizing the Rain meta-heuristic algorithm to enhance the detection of reflection patterns within the seismic data. To optimize computational efficiency and mitigate data redundancy, a subset of extracted seismic attributes was selected through the Laplacian scoring feature selection method. The results were validated against geological evidence to ensure both reliability and accuracy. The findings underscore the effectiveness of unsupervised clustering methodologies, particularly meta-heuristic optimization strategies, in enhancing the efficiency and precision of seismic interpretation. Notably, these methods automatically ascertain the optimal number of clusters, thus providing a degree of flexibility that traditional techniques, such as k-means, do not afford. The study further elucidates those meta-heuristic methods, especially the ROA method, yield superior clustering outcomes in comparison to genetic algorithms (GA) and particle swarm optimization (PSO).</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105690"},"PeriodicalIF":2.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637436","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":"Case study on pre-warning and protective measures against rockbursts utilizing the microseismic method in deep underground mining","authors":"Longjun Dong ,&nbsp;Xianhang Yan ,&nbsp;Jiachuang Wang ,&nbsp;Zhen Tang ,&nbsp;Hongwei Wang ,&nbsp;Wentang Wu","doi":"10.1016/j.jappgeo.2025.105687","DOIUrl":"10.1016/j.jappgeo.2025.105687","url":null,"abstract":"<div><div>Predicting rockbursts and implementing effective protection measures are of paramount importance for safe mining in deep underground mines. A more advanced approach to recognizing rockburst hazards in underground mining involves the integration of various techniques, including microseismic (MS) parameters, localization of high-magnitude MS events, seismic tomography, deep learning models, and on-site surveys. In this study, tomography results and the distribution of MS events were utilized to identify the mining activity region and velocity anomaly in the Shaanxi Zhenao mine. Subsequently, multiple MS parameters, such as microseismic moment (MSM), microseismic energy (MSE), apparent stress (AS), b-value, S value, and comprehensive microseismicity intensity (CMSI), were examined at the 842 level. Based on these analyses, a potential rockburst area was accurately determined, and appropriate protective measures were implemented in the short term at the goaf of 842 level by integrating the results from in-site surveys, deep learning predictions, and the microseismic method. It's noted that a rockburst incident occurred in the goaf four days later; however, the adjacent area remained undamaged due to the effectiveness of the protective measures in place. This case study indicates that the utilization of MS information and deep learning models can serve as a valuable pre-warning method for assessing the risk of rockburst.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105687"},"PeriodicalIF":2.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628800","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":"Identifying ultramafic rocks using artificial neural network method based on aeromagnetic data","authors":"FuXiang Liu ,&nbsp;ShengQing Xiong ,&nbsp;Hai Yang ,&nbsp;Fang Li ,&nbsp;Zhiye Jia ,&nbsp;Qiankun Liu ,&nbsp;Zhenyu Fan","doi":"10.1016/j.jappgeo.2025.105688","DOIUrl":"10.1016/j.jappgeo.2025.105688","url":null,"abstract":"<div><div>Copper‑nickel (Cu<img>Ni) resources represent strategically critical mineral commodities facing global supply shortages, making their exploration particularly valuable. Magmatic Cu<img>Ni sulfide deposits typically occur within ultramafic rock complexes that exhibit distinct magnetic signatures. High-resolution aeromagnetic surveys have proven effective in mapping the spatial distribution of these magnetic anomalies associated with ultramafic lithologies. Current interpretation methodologies predominantly rely on empirical expert analysis for lithological delineation, particularly when identifying concealed intrusions. Nevertheless, systematic approaches employing intelligent algorithms for automated detection of ultramafic rocks through aeromagnetic data analysis remain underdeveloped. This research introduces an Artificial Neural Network (ANN) method for ultramafic rock mapping based on aeromagnetic data. The processed magnetic data is normalized to a similar range and utilized as input feature vectors into machine learning models. After obtaining the final model parameters through training the known ultramafic rock data, the fully connected neural network model predicts the distribution of ultramafic rocks in the unknown region. Various theoretical models were designed to calculate magnetic datasets and test the regularity of data processing and effectiveness of model prediction. The results suggested that data normalization and the selection of feature vectors significantly influenced the prediction results. The prediction accuracy and stability of this method were tested under different spatial resolutions and noise levels. At last, the method was applied in the Northern Qilian area, China. The accuracy of predicted ultramafic rocks is up to 80 % compared with the expert interpretation results. Particularly, two predicted ultramafic rock masses were confirmed by field investigation, which proved the efficiency of this method. The prediction results presented in this paper can provide an objective basis for the delineation of ultramafic rocks, as well as further concentrate the target area for Cu<img>Ni deposit prospecting.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105688"},"PeriodicalIF":2.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609275","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":"Integrated machine learning workflow for 3D pore type modeling in the pre-salt carbonates of the Tupi Field, Santos Basin","authors":"Rafael Amaral Cataldo,&nbsp;Emilson Pereira Leite","doi":"10.1016/j.jappgeo.2025.105689","DOIUrl":"10.1016/j.jappgeo.2025.105689","url":null,"abstract":"<div><div>The discovery of pre-salt carbonates in Brazil has significantly reshaped the country's oil and gas industry, now accounting for over 78 % of national production. However, the complex heterogeneity of these reservoirs, characterized by unique mineral compositions, intricate pore systems, and diagenetic processes, poses significant challenges for reservoir characterization. This study introduces a comprehensive methodology to address these challenges, focusing on the carbonate-bearing Barra Velha Formation in the Tupi Field, Santos Basin. The workflow integrates seismic inversion, well log data, and advanced machine learning models, including the Gradient Boosting Classifier, Random Forest Regressor, and Gradient Boosting Regressor. These models are used to classify petroelastic facies, predict petrophysical properties, and generate 3D pore type volume maps for compliant, reference, and stiff pores. Key findings reveal significant heterogeneity in pore distributions, with the lower Barra Velha Formation exhibiting greater variability. Stiff pore volumes display an inverse relationship with reference pores, forming distinct “stiff corridors” in certain regions, while compliant pores are localized around transition zones. The results demonstrate strong correlations between well log data and seismic-scale predictions, highlighting the methodology's reliability. These 3D pore type volume models provide valuable insights into reservoir heterogeneity, aiding in the identification of high-quality reservoir zones and supporting improved exploration and production strategies. This study underscores the importance of incorporating geological, petrophysical, and diagenetic factors into reservoir characterization workflows and emphasizes the adaptability of the proposed methodology to different geological settings.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105689"},"PeriodicalIF":2.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631760","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":"Magnetic and magnetic gradient fields due to a finite line segment","authors":"Hyoungrea Rim ,&nbsp;Mengli Zhang ,&nbsp;Yaoguo Li","doi":"10.1016/j.jappgeo.2025.105682","DOIUrl":"10.1016/j.jappgeo.2025.105682","url":null,"abstract":"<div><div>We derive the closed-form expressions for the magnetic field and magnetic gradient tensor produced by a finite line segment with uniform magnetization. Following the classical approach, we firstly derive the gravitational potential for a line segment by a line integral and derive the gravity vector and gravity gradient tensor through differentiations with respect to Cartesian axis. The magnetic field expression is then obtained from the gravity gradient tensor using Poisson's relation. We verify the validity of the solutions numerically through comparison with the result from a different formulation as well as with a dipolar field. The results provide an efficient means to calculate the ground and drone-measured magnetic responses in environmental and engineering applications such as locating abandoned ferromagnetic pipe lines and characterizing well casings and flow lines in the legacy oil and gas fields.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105682"},"PeriodicalIF":2.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619924","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":"The joint PP- and PS-waves inversion method based on the exact reflection coefficient equation for transversely isotropic medium","authors":"Qin Li ,&nbsp;Ruoxi Xu ,&nbsp;Jiang Li","doi":"10.1016/j.jappgeo.2025.105686","DOIUrl":"10.1016/j.jappgeo.2025.105686","url":null,"abstract":"<div><div>Most inversions of the VTI medium anisotropy parameter rely on approximations of the reflection coefficient. However, it is essential to address the differences between the approximate and exact solutions for high-precision inversions. The importance of utilizing the exact solution is initially illustrated by comparing the approximate solution with the exact solution derived from the VTI medium reflection coefficient equation. Subsequently, a hybrid genetic grey wolf algorithm is developed by integrating the genetic algorithm with the grey wolf algorithm, enhancing local search capabilities while maintaining the global search efficiency of the genetic algorithm. This approach facilitates the joint inversion of PP- and PS-waves based on the exact reflection coefficient equation for VTI medium. Applying this inversion methodology to logging models and seismic data with varying noise levels indicates that the joint inversion of PP- and PS-waves using the hybrid genetic grey wolf algorithm yields reduced errors and improved noise resilience, thereby validating the method's practicality. Furthermore, field data is processed, and inversion tests are conducted after consistently compressing the time domains of the PP-wave and PS-wave profiles. The resulting profiles exhibit higher resolution and minimal errors, underscoring the method's efficacy. The findings of this research will contribute to enhancing the accuracy and reliability of seismic data inversion.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"237 ","pages":"Article 105686"},"PeriodicalIF":2.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593435","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":"Automatic 3D horizon picking using a volumetric transformer-based segmentation network","authors":"Xiaofang Liao ,&nbsp;Junxing Cao ,&nbsp;Feng Tan ,&nbsp;Jachun You","doi":"10.1016/j.jappgeo.2025.105673","DOIUrl":"10.1016/j.jappgeo.2025.105673","url":null,"abstract":"<div><div>Seismic horizon picking is a critical step in seismic interpretation and is often labor-intensive and time-consuming, particularly in three-dimensional (3D) volume interpretation. We formulated the task of automatically selecting horizon surfaces from 3D seismic data as a 3D seismic image segmentation problem and developed a method based on a volumetric transformer network. The network uses 3D seismic subvolumes as inputs and outputs the probabilities of several horizon classes. Horizon surfaces can be extracted using postprocessing segmentation probabilities. Because the full annotation of a 3D subvolume is tedious and time-consuming, we utilize a masked loss strategy that allows us to label a few two-dimensional (2D) slices per training subvolume such that the network can learn from partially labeled subvolumes and create dense volumetric segmentation. We also used data augmentation and transfer learning to improve the prediction accuracy with the limited availability of training data. For two public 3D seismic datasets, the proposed method yielded accurate results for 3D horizon picking, and the use of transfer learning improved the accuracy of the results.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"236 ","pages":"Article 105673"},"PeriodicalIF":2.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562111","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":"Prediction of coalbed methane content based on composite logging parameters and PCA-BP neural network","authors":"Hao Zhang ,&nbsp;Xulong Cai ,&nbsp;Peng Ni ,&nbsp;Bowen Qin ,&nbsp;Yuquan Ni ,&nbsp;Zhiqiang Huang ,&nbsp;Fubin Xin","doi":"10.1016/j.jappgeo.2025.105681","DOIUrl":"10.1016/j.jappgeo.2025.105681","url":null,"abstract":"<div><div>The coalbed methane content (CBM) is a key parameter for the evaluation and efficient exploration and development of coalbed methane reservoirs. The traditional gas content experiment methods are time-consuming, costly, weak in generalization ability and large in calculation error. Therefore, accurate, efficient and low-cost calculation of CBM content is of great significance in CBM development. In this paper, the coalbed methane prediction model is constructed by exploring the hidden geological information between coalbed methane content and logging parameters. Firstly, principal component analysis and person method are used to analyze the correlation between each logging parameter, and then compound parameters are constructed to improve the correlation between each parameter. Finally, BP neural network model is used to build a CBM content prediction model based on compound logging parameters. On this basis, the prediction results of BP neural network model are compared with KNN, Ridge regression, random forest, XGBoost and other machine learning models, and the determination coefficient, root-mean-square error and relative error are used to evaluate the model. The results show that BP neural network is more suitable for constructing CBM prediction model with complex logging parameters, and the prediction effect is good, the relative error is 4.5 %, and the prediction accuracy is improved by about 61 % compared with other models. This model has potential application in the field CBM reservoir development, can predict the gas content of coal seam quickly and accurately, speed up the CBM reservoir development process, and provide a new method for coal seam exploration and reservoir logging evaluation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"236 ","pages":"Article 105681"},"PeriodicalIF":2.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552613","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 multivariate time series prediction model for microseismic characteristic data in coal mines","authors":"Xingli Zhang,&nbsp;Qian Mao,&nbsp;Ruiyao Yu,&nbsp;Ruisheng Jia","doi":"10.1016/j.jappgeo.2025.105683","DOIUrl":"10.1016/j.jappgeo.2025.105683","url":null,"abstract":"<div><div>Rock burst disasters in coal mines have become a growing concern, posing significant risks to operational safety. Utilizing historical microseismic data to predict future microseismic events can provide effective prediction and early warning for rock bursts. This study proposes a multivariate microseismic sensitive features prediction network model named Deformer, which can accurately predict multiple sensitive feature values extracted from microseismic monitoring data and provide data support for the early warning and prevention of rock bursts. Deformer integrates Transformer and signal decomposition methods, considering both feature and temporal correlations. It enables a comprehensive and in-depth analysis of the relationships among multi-dimensional sensitive features and the temporal evolution of each feature. We extract three characteristic values from the microseismic monitoring data of a coal mine in Shandong Province: daily total energy, daily maximum energy, and daily average energy, and predict the daily maximum energy. By comparing with various classical time series prediction models, Deformer achieved the best results in mean square error (MSE), mean absolute error (MAE), the coefficient of determination (R²), root mean square error (RMSE), and Theil's inequality coefficient (TIC), proving Deformer's significant advantage in predicting microseismic sensitive features. Additionally, testing on various public datasets, such as those for electricity and weather, further validates the model's generalization capability.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"236 ","pages":"Article 105683"},"PeriodicalIF":2.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534215","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}