Journal of Applied Geophysics最新文献

{"title":"Lithology identification using electrical imaging logging image: A case study in Jiyang Depression, China","authors":"Juan Liu ,&nbsp;Xuanlin Min ,&nbsp;Zhongli Qi ,&nbsp;Jun Yi ,&nbsp;Wei Zhou","doi":"10.1016/j.jappgeo.2024.105536","DOIUrl":"10.1016/j.jappgeo.2024.105536","url":null,"abstract":"<div><div>Lithology identification plays a significant role in stratigraphic evaluation and geological analysis. Traditional lithology identification method is by modeling the relationship between well logging and lithology. However, well logging are not always sufficient to identify lithology since sometimes the curves are similar for different lithologies. Recently, electrical imaging logging image (EILI) with high resolution plays an increasingly important role in logging interpretation since EILI can intuitively reflect the characteristics of lithology. Unlike traditional lithology identification method by using well logging, in this paper, we propose a novel multi-dimensional automatic lithology identification method by applying deep learning to EILI. First, Filtersim algorithm is employed to fill the blank strip of the EILI. Then, an integrated convolutional neural networks (CNNs) model is designed to extract the resistivity feature, texture feature, and holistic feature of the EILI, respectively. Specifically, the integrated CNNs model can realize automatic recognition for different geological structures (massive, bedded, lamellar) and lithology (mudstone, sand-mudstone, lime-mudstone). Finally, lithology identification can be achieved by combining with multi-dimensional features. The efficacy of proposed integrated model is validated experimentally on the EILI of shale oil reservoir in the Jiyang Depression of China. Experimental results show the effectiveness and superiority of the integrated CNNs method for lithology identification.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105536"},"PeriodicalIF":2.2,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528812","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":"Study on ground-penetrating radar wave field characteristics for earth dam disease considering the medium randomness","authors":"Binghan Xue ,&nbsp;Siye Zhang ,&nbsp;Zhifeng Dong ,&nbsp;Hongyuan Fang ,&nbsp;Jianwei Lei ,&nbsp;Kejie Zhai ,&nbsp;Jianguo Chen","doi":"10.1016/j.jappgeo.2024.105535","DOIUrl":"10.1016/j.jappgeo.2024.105535","url":null,"abstract":"<div><div>Ground-Penetrating Radar (GPR) has been widely used for non-destructive testing of earth dam disease. However, the forward simulation of GPR for earth dam disease often employs layered homogeneous models, neglecting the influence of medium randomness on its wave field characteristics. Therefore, considering the randomness of the medium, a geoelectrical model for earth dam disease is established, which is based on the mixed-type autocorrelation function and the finite element time-domain method. The influence of random medium model parameters on the single-channel wave of GPR is analyzed. The electromagnetic wave propagation characteristics under different medium models are explored. The forward simulation of GPR for earth dam disease such as panel voiding, concentrated seepage, and loosening are performed. The differences in propagation characteristics for earth dam disease between uniform medium model and random medium model are compared. Compared to the calculation results of the uniform medium model, the propagation speed and amplitude of electromagnetic waves in the random medium model changes, and a number of diffraction waves are present. When performing forward simulation of GPR for earth dam disease, considering medium randomness can deepen the understanding of the GPR section view and help improve the accuracy of image interpretation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105535"},"PeriodicalIF":2.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434318","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":"Efficient self-attention based joint optimization for lithology and petrophysical parameter estimation in the Athabasca Oil Sands","authors":"M Quamer Nasim ,&nbsp;Paresh Nath Singha Roy ,&nbsp;Adway Mitra","doi":"10.1016/j.jappgeo.2024.105532","DOIUrl":"10.1016/j.jappgeo.2024.105532","url":null,"abstract":"<div><div>Accurately identifying lithology and petrophysical parameters, such as porosity and water saturation, are essential in reservoir characterization. Manual interpretation of well-log data, the conventional approach, is not only labor-intensive but also susceptible to human errors. To address these challenges of lithology identification and petrophysical parameter estimation in the Athabasca Oil Sands area, this study introduces an AutoRegressive Vision Transformer (ARViT) model for lithology and petrophysical parameter prediction. The effectiveness of ARViT lies in its self-attention mechanism and its ability to handle data sequentially, allowing the model to capture important spatial dependencies within the well-log data. This mechanism enables the model to identify subtle spatial and temporal relationships among various geophysical measurements. The model is also interpretable and can serve as an assistive tool for geoscientists, enabling faster interpretation while reducing human bias. The interpretable nature of the model should assist geoscientists in conducting faster quality checks of the predictions, ensuring that errors are not propagated to subsequent stages. This study adopts a multitask learning approach, jointly optimizing the model's performance across multiple tasks simultaneously. To evaluate the effectiveness of the ARViT model, we conducted series of experiments and comparisions, testing it against traditional artificial neural networks (ANN), Long Short-Term Memory (LSTM), and Vision Transformer (ViT) models. To showcase the versatility of ARViT, we apply Low-Rank Adaptation (LoRA) to a different smaller dataset, showing its potential to adapt to different geological contexts. LoRA not only helps in model adaptability but also helps to reduce the number of trainable parameters. Our findings demonstrate that ARViT outperforms ANN, LSTM, and ViT in estimating lithological and petrophysical parameters. While lithology prediction has been a well-explored field, ARViT's unique blend of features, including its self-attention mechanism, autoregression, and multitask approach along with efficient fine tuning using LoRA, sets it apart as a valuable tool for the complex task of lithology prediction and petrophysical parameter estimation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105532"},"PeriodicalIF":2.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434317","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":"Vehicle noise characteristics in magnetotelluric data and vehicle noise removal using waveform fitting","authors":"KyeongHo Ryu ,&nbsp;Seokhoon Oh ,&nbsp;Hyoung-Seok Kwon","doi":"10.1016/j.jappgeo.2024.105534","DOIUrl":"10.1016/j.jappgeo.2024.105534","url":null,"abstract":"<div><div>Magnetic field fluctuations due to vehicle noise were observed in magnetotelluric (MT) time-series data measured near roads. The observed vehicle noise had magnitudes ranging from tens to thousands of μA/m, whereas the observed weak natural MT signal magnitudes were approximately tens of μA/m. A small signal-to-noise ratio made it difficult to apply robust processing for removing vehicle noise. In addition, vehicle noise severely distorts the MT response in the MT deadband from 0.01 Hz to 0.3 Hz, where the MT signal is very weak, and methods to remove it are required for deep structure imaging. In this study, magnetic field fluctuations due to moving vehicles were simulated with a magnetic dipole and attempted to be removed using a waveform fitting method. A total of 378 vehicle noises were extracted from the near-road MT data and synthesized with the remote MT data without vehicle noises to investigate the effect of vehicle noise on the MT response. Removal of vehicle noise from synthesized remote MT data resulted in substantial restoration of the apparent resistivity and phase curves around the MT deadband and below 0.001 Hz. In the MT field data, the vehicle noise was simulated and removed with two moving dipoles; the magnitude of the remaining vehicle noise was reduced by approximately half compared to a single dipole, and very stable apparent resistivity and phase curves were obtained. Although electromagnetic noise distortion remains after vehicle noise removal, the waveform fitting method significantly improves the apparent resistivity and phase curve response in the 0.01–0.3 Hz frequency band<strong>.</strong></div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105534"},"PeriodicalIF":2.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438216","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":"Enhancing seismic feature orientations: A novel approach using directional derivatives and Hilbert transform of gradient structure tensor","authors":"Gagandeep Singh ,&nbsp;William K. Mohanty ,&nbsp;Aurobinda Routray ,&nbsp;Rahul Mahadik ,&nbsp;S.K. Singh","doi":"10.1016/j.jappgeo.2024.105528","DOIUrl":"10.1016/j.jappgeo.2024.105528","url":null,"abstract":"<div><div>Seismic dip calculation serves as a widely employed technique in the realms of seismic interpretation and reservoir characterization, strategically employed to highlight faults and attributes within the seismic volume. Among the various methodologies utilized for estimating structural dip and azimuth, the Gradient Structure Tensor (GST) stands out. This approach involves leveraging the dominant eigenvector of the positive definite GST matrix to ascertain the inline and crossline dip of seismic data.</div><div>In the initial phase of our innovative proposal, we employed the spectral balancing technique to enhance the fidelity of seismic data. Subsequently, leveraging this groundwork, we introduced an Analytical Directional Gradient Structure Tensor technique, a distinctive adaptation of GST. This novel approach involves the calculation of directive derivatives in both perpendicular and parallel directions to seismic features. By incorporating directive derivatives, our method excels in capturing subtle stratigraphic nuances, particularly in the dipping direction of interest. To validate the accuracy and effectiveness of our approach, we present compelling evidence through the examination of synthetic and real-field seismic volume outcomes. This underscores the robustness and reliability of our proposed method in enhancing the precision of seismic dip calculations and providing valuable insights into subsurface geological features.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105528"},"PeriodicalIF":2.2,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445615","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":"Evidence of palaeoenvironmental and climatic changes from the interpreted radar wave pictures of near surface sediments around the River Nile, Assiut, Egypt","authors":"Mohamed Osman Ebraheem ,&nbsp;Hamza Ahmed Ibrahim","doi":"10.1016/j.jappgeo.2024.105533","DOIUrl":"10.1016/j.jappgeo.2024.105533","url":null,"abstract":"<div><div>Different palaeoenvironmental features that pose natural geological, environmental, and engineering hazards to human operations occur frequently around the Nile Valley. Moreover, where these features were initially created, their relevance focuses on how the urban communities responded to the processes. So, a ground penetrating radar (GPR) field survey was carried out on different paleoenvironments of Pre-Quaternary and Quaternary sediment around Assiut. Deep and critical analyses of georadar facies were made to obtain clear images of these features with unprecedented resolution. The main objective of this study is to find some reasonable geological interpretations for these features. From this study, it is possible to identify and differentiate these features originating from different geological environments and climatological conditions in arid regions such as those around Assiut. In addition, the study serves as guidelines for environmental management and climatic changes for enhancing knowledge of urban development. Also, the study demonstrates how georadar can be used to create precise images of intricate shallow subsurface anatomy with possible palaeoenvironmental and palaeoclimatic indicators.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105533"},"PeriodicalIF":2.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445616","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":"Multichannel Analysis of Surface Waves based on Common Virtual Source Gathers of Seismic Ambient Noise Cross-Correlations: A Case Study at an Earth Dam in Brazil","authors":"Victor José Cavalcanti Bezerra Guedes ,&nbsp;Susanne Tainá Ramalho Maciel ,&nbsp;Marcelo Peres Rocha ,&nbsp;Welitom Rodrigues Borges ,&nbsp;Luciano Soares da Cunha","doi":"10.1016/j.jappgeo.2024.105529","DOIUrl":"10.1016/j.jappgeo.2024.105529","url":null,"abstract":"<div><div>The S-wave velocity (Vs) is a valuable parameter for assessing the mechanical properties of subsurface materials for geotechnical purposes. Seismic surface wave methods have become prominent for estimating near-surface Vs models. Researchers have proposed methods based on passive seismic signals as efficient alternatives to enhance depth of investigation, lateral resolution and reduce field effort. This study presents the Multichannel Analysis of Surface Waves (MASW) utilizing Common Virtual Source Gathers (CVSGs) derived from seismic ambient noise cross-correlations, based on Ambient Noise Seismic Interferometry concepts. The method is applied to passive data acquired with an array of receivers at the Paranoá earth dam in Brasília, Brazil, to construct a pseudo-2D Vs image of the massif for interpretation. Our findings showcase the adopted processing flow and combination of methods as an effective approach for near-surface Vs estimation, demonstrating its usability also for large earth dam embankments.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105529"},"PeriodicalIF":2.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422601","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":"Fast concrete crack depth detection using low frequency ultrasound array SH waves data","authors":"Jian Shen ,&nbsp;Liu Liu ,&nbsp;Zhenming Shi ,&nbsp;Shaojun Li ,&nbsp;Ming Peng ,&nbsp;Yao Wang ,&nbsp;Chunsheng Liu","doi":"10.1016/j.jappgeo.2024.105530","DOIUrl":"10.1016/j.jappgeo.2024.105530","url":null,"abstract":"<div><div>Fast detection of the depths of surface-open cracks plays an important role in evaluating the damage conditions of concrete elements. The presence of surface-open cracks and other anomalies inside concrete complicates the ultrasonic wave field and thus severely undermines the precision of traditional nondestructive testing methods. This study introduces independently developed low-frequency ultrasonic array detection equipment. The detector adopts a doubled-ray coverage strategy to enhance the imaging stability under noisy conditions. Moreover, we propose an imaging method called the crack focusing-synthetic aperture focusing technique (CF-SAFT), through which both reflected and transmitted surface waves are removed so that only diffracted SH waves converge to their origins. An extra instantaneous phase analysis is supplemented to highlight the diffraction points. We test the effectiveness of our method through a multitude of numerical examples and a model experiment. Successful depth identification was obtained regardless of different geometries of the cracks or interference from the steel reinforcements. The superiority of our method is further verified through noisy ultrasonic data and complex scenarios.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105530"},"PeriodicalIF":2.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422649","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":"MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification","authors":"Pengwei Zhang ,&nbsp;Jiadong Ren ,&nbsp;Fengda Zhao ,&nbsp;Xianshan Li ,&nbsp;Haitao He ,&nbsp;Yufeng Jia ,&nbsp;Xiaoqing Shao","doi":"10.1016/j.jappgeo.2024.105531","DOIUrl":"10.1016/j.jappgeo.2024.105531","url":null,"abstract":"<div><div>Lithology identification constitutes a crucial undertaking in formation evaluation and reservoir characterization. However, the need for improved precision arises in conventional lithology identification models due to the difficulties presented by unequal distributions of small-sample logging data. An effective combination of domain expertise and data-driven models to predict lithology is essential due to the intricate and nonlinear connection between logging parameters and lithology, combined with the distinct characteristics of the oilfield environments. In this paper, we proposed a multi-scale conditional generative adversarial network(MS-CGAN) method, which combines conditional generative adversarial networks with multi-scale spatio-temporal features to address data imbalance issues and enhance the accuracy of lithology classification. Our approach, tested on two small datasets from the Hugoton and Panoma fields, USA, and the Daqing production wells, China, stands out as the optimal choice compared to other models. Comprehensive evaluation results indicate promising practical applications and potential benefits of the new model in enhancing lithology identification using limited data.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105531"},"PeriodicalIF":2.2,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422650","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":"Electrical resistivity tomography: A reliable tool to monitor the efficiency of different irrigation systems in horticulture field","authors":"Agnese Innocenti ,&nbsp;Veronica Pazzi ,&nbsp;Marco Napoli ,&nbsp;Rossano Ciampalini ,&nbsp;Simone Orlandini ,&nbsp;Riccardo Fanti","doi":"10.1016/j.jappgeo.2024.105527","DOIUrl":"10.1016/j.jappgeo.2024.105527","url":null,"abstract":"<div><div>Water management in agricultural systems is essential for optimal crop yields without incurring excessive water costs and wastage. The choice of irrigation method is crucial for better water management and distribution. The drip system appears to be among the best methods in the field of precision agriculture. In addition to the irrigation system, mulching with ridge plastic film to drain excess water is widely used to increase crop yields in terms of plant water availability. In this study, the time-lapse Electrical Resistivity Tomography (ERT), a not-invasive geophysical technique, is proposed as a simple and reliable method to evaluate the effectiveness of the irrigation systems and to monitor the changes in water content over time and over a volume of soil. ERTs data were compared to moisture ones retrieved from sensors that record continuously over time, but punctually. The ERT investigations were conducted in melon-growing lands in southern Tuscany (Italy). Measurements were carried out on two different fields in two periods: spring and summer. The aim of the work was to evaluate, by means of volumetric measures of the soil conductivity, the effectiveness of three different drip systems and of the mulch ridge. In both the monitored fields the ridge was created in a half portion of the field itself, while the other part of the land was left plat. Geoelectrical investigations associated with humidity sensors have shown that in the summer a too high mulch ridge quickly drains the irrigation water, bringing the root zone into a water deficit. The ERTs also provided good results relating to the irrigation system, demonstrating that a three-lines drip irrigation system, compared to a two-lines one, manages to distribute the irrigation water homogeneously, guaranteeing a constant water content for the plants over time.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105527"},"PeriodicalIF":2.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}