Journal of Applied Geophysics最新文献

{"title":"A pixel-based finite element implementation to estimate effective wave velocity in heterogeneous media","authors":"","doi":"10.1016/j.jappgeo.2024.105447","DOIUrl":"10.1016/j.jappgeo.2024.105447","url":null,"abstract":"<div><p>In the present work, we present a 2D pixel-based Finite Element strategy to simulate the elastic wave propagation in heterogeneous media. An assembly-free approach is employed for the stiffness matrix, leveraging a pixel-based structured mesh to reduce the memory required to store computations. Additionally, a diagonal Lumped-Mass matrix technique is utilized to address challenges associated with the inversion and storage of the mass matrix. The Leap-frog integration method, known for its amalgamation of stability, precision, and efficiency, is adopted. The combination of these features is aimed at facilitating massive parallel computations for very large systems with 10⁸ to 10⁹ degrees of freedom. In that sense, the present work can be understood as a first step toward a very efficient massive parallel GPU-based voxel-based Finite Element implementation to treat very large digital images with personal computers. The implementation presented here has been validated against theoretical predictions and analytical results derived from classical wave propagation theory. Finally, the transmission test is simulated in two digital models, one representing a layered medium and another representing a medium with complex microstructue obtained via micro-tomography. For the first model, the results are compared with the so called Bakus average, while, for the second model, the results are compared with the corresponding outcomes acquired through an in-house developed static finite element homogenization implementation.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846176","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 opposing coils transient electromagnetic method in urban area with metal interference","authors":"","doi":"10.1016/j.jappgeo.2024.105467","DOIUrl":"10.1016/j.jappgeo.2024.105467","url":null,"abstract":"<div><p>Opposing coils transient electromagnetic method (OCTEM) adopts small and weak-coupling transmitting-receiving coils configuration, which helps to reduce side effect and improve the detection resolution. With such advantages, it has been widely used for shallow sub-surface target detection. However, when the method is used in urban area, measured data may be distorted by electromagnetic (EM) interference from nearby metal objects. In practical application, it is necessary to perform modelling to provide guidance for measuring data analysis. Two OCTEM application cases in detecting shallow sub-surface karst caves in urban area with metal objects nearby are presented in this paper. Corresponding modelling are carried out to study the interference effect of the nearby metal objects. The first application case is about the EM interference of a vertical steel tower, of which the influence distance reaches up to 9 m by modelling. The second application case is about the EM interference of a thin aluminum fence, of which the influence distance reaches up to 6 m by modelling. Only when the observation is outside the influence zone, the metal influence can be ignored. When the measurement is inside the influence zone, the metal influence cannot be ignored. However, as the nearby metal objects mainly affects the early data, the subsurface target may also be detected in condition that the target response is stronger than the metal interference, or the target response time window is wider than that of the metal interference.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851401","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":"Compact source inversion of self-potential data generated by geomicrobes","authors":"","doi":"10.1016/j.jappgeo.2024.105463","DOIUrl":"10.1016/j.jappgeo.2024.105463","url":null,"abstract":"<div><p>The self-potential (SP) method is a classical geophysical exploration method which has a wide application prospect in underground pollutant monitoring and other fields. However, due to the complexity of the formation mechanism and the lack of prior information, there are still quite a few difficulties in the precise quantitative inversion of the SP sources, and qualitative interpretation is frequently adopted in practical applications. In this work, we carry out inversion research on the SP data of geomicrobes to accurately invert and locate the spatial distribution of the SP sources which is closely relevant to microbial activities. The resistivity-based depth weighting matrix is added to the inversion algorithm to promote the migration of the SP sources from the earth surface to their original depth. And to conform to the actual distribution of the SP sources, the minimum support stabilizing function is introduced to impose additional compact constraint. Two synthetic models are firstly designed to verify the effectiveness and accuracy of the proposed algorithm. On this basis, the sandbox experiment that continuously observes and records the SP signals generated by the typical organism: Shiwanella Oneida MR-1 breaking down the organic matter is carried out. Then the observed data is inverted to locate the SP sources. The inversion results demonstrate that with the addition of Shiwanella Oneida MR-1 into the humus, the negative SP source immediately appear on the top of the humus, which increase sharply, then remain stable and then slowly decay over time. The negative SP sources are concentrated on the top of the humus, which is consistent with the theoretical analysis of the biogeobattery model.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851519","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":"Porosity prediction using a deep learning method based on bidirectional spatio-temporal neural network","authors":"","doi":"10.1016/j.jappgeo.2024.105465","DOIUrl":"10.1016/j.jappgeo.2024.105465","url":null,"abstract":"<div><p>Deep learning is one of the best machine learning algorithms for modeling complex mapping relationships between independent and dependent variables, and thus it can be viewed as an ideal approach to predict porosity. In this study, to overcome the deficiencies in current porosity prediction based on deep learning and improve the prediction accuracy, we proposed a deep learning model based on bidirectional temporal convolutional network (BTCN) and bidirectional long short-term memory (BLSTM) network, called bidirectional spatio-temporal neural network (BSTNN), to establish a porosity prediction model. First, the maximum information coefficient is used to analyze the correlation between well logs and porosity, which provides a basis for determining the inputs of the prediction model. Then, a hybrid network structure is constructed by using BTCN and BLSTM, in which BTCN goes to learn the bidirectional long sequence features and BLSTM goes to learn the variation trend and context information with depth, so the hybrid network structure can learn richer logging signal features. Finally, the extracted features are passed through the fully connected layer to output the porosity prediction results. Porosity prediction experiment are conducted by using the actual field data set. The results show that the proposed method has the lower prediction errors for the porosity modeling (RMSE = 0.368 and MAE = 0.260) compared to the benchmark models convolutional neural network (RMSE = 0.404 and MAE = 0.292) and long short-term memory network (RMSE = 0.418 and MAE = 0.298), which verifies the effectiveness of this prediction method.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844823","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":"Multiple noise reduction for distributed acoustic sensing data processing through densely connected residual convolutional networks","authors":"","doi":"10.1016/j.jappgeo.2024.105464","DOIUrl":"10.1016/j.jappgeo.2024.105464","url":null,"abstract":"<div><p>Distributed acoustic sensor (DAS), which utilizes the entire optical fiber as the sensing medium, provides distinct advantages of high resolution, dynamic monitoring, and resistance to high temperatures. This technology finds diverse applications in the seismic exploration, oil survey, and submarine cable monitoring industries. However, DAS signals are susceptible to various kinds of noise, such as horizontal noise, erratic noise, random noise, and so on, which significantly degrade the SNR. This low SNR is likely to affect some subsequent analyses, such as inversion and interpretation. The mixed noises feature of the DAS data poses a serious challenge for SNR enhancement. To address this issue, we develop a supervised learning-based densely connected residual convolutional denoising network (DCRCDNet), which leverages both encoding and decoding processes to extract features and reconstruct DAS data. The design of dense connectivity and residual blocks allow the network to extract both shallow and deep features. The network is trained using both synthetic and field data to obtain the optimal network parameters. Testing on synthetic data demonstrates that DCRCDNet improves the signal-to-noise ratio (SNR) from −10.21 dB to 15.61 dB. The test results from both synthetic and field data indicate that, compared to traditional filtering methods and other deep learning approaches, this network effectively suppresses noise in DAS signals. Consequently, DCRCDNet shows great potential in reconstructing DAS signals from hidden noise, suppressing strong and mixed noise, and extracting hidden signals.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729248","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":"Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study","authors":"","doi":"10.1016/j.jappgeo.2024.105453","DOIUrl":"10.1016/j.jappgeo.2024.105453","url":null,"abstract":"<div><p>A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic–viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams–Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water volume. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water volume estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0926985124001691/pdfft?md5=601434ea8f6386330f635ccd4f7550ef&pid=1-s2.0-S0926985124001691-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963313","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}

{"title":"Study on the influencing factors of combined processing of active and passive surface-wave data on dispersion imaging","authors":"Ling Ning ,&nbsp;Tianyu Dai ,&nbsp;Hao Zhang ,&nbsp;Ya Liu ,&nbsp;Liduan Zheng ,&nbsp;Chaoqiang Xi","doi":"10.1016/j.jappgeo.2024.105462","DOIUrl":"https://doi.org/10.1016/j.jappgeo.2024.105462","url":null,"abstract":"<div><p>Active and passive surface-wave methods have garnered significant attention in the near-surface geophysical community for their non-destructive, non-invasive, low-cost, and accurate advantages in delineating subsurface shear (<em>S</em>)-wave velocity structures. They are increasingly being utilized to address numerous engineering and environmental problems. Surface waves obtained from actively excited sources such as a hammer and a harmonic shaker, however, lack low-frequency components, resulting in limited investigation depth. Conversely, passive surface waves such as microseisms (&lt; 0.4 Hz, associated with natural ocean wave activity) and microtremor (&gt;1 Hz, generated by cultural and wind sources) retrieved from ambient seismic noise typically lack high-frequency components, which is not conductive to characterizing fine near-surface structures. To overcome these frequency limitations, we employ a “mixed-source data” strategy, imposing active shot gathers into ambient noise data, to widen the frequency range of dispersion images and depth of investigative capabilities. We simulate both active and passive surface-wave data based on a two-layer model, noting that their dispersion images suffer from a mode kissing phenomenon at lower frequencies. By analyzing influencing factors such as the amplitude intensity, the signal-to-noise ratio and the excitation locations of active shot gathers, as well as the length of passive surface-wave data, we better understand their impacts on dispersion images from mixed-source surface-wave data. Simulation tests demonstrate that processing mixed-source data can effectively distinguish the mode kissing phenomenon. Moreover, the effectiveness of this strategy in enhancing the quality of dispersion image is verified, especially when surface-wave dispersion images perform poorly in either the low- or high-frequency bands. Additionally, a real-world example further demonstrates that processing mixed-source data offers significant advantages in improving the quality of dispersion images. This way provides a convenient and efficient measurement strategy for delineating shear-wave velocity profiles in finer shallow layers and deeper penetration depths.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605518","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":"3D closed-loop surface-related multiple elimination based on GPU acceleration","authors":"","doi":"10.1016/j.jappgeo.2024.105454","DOIUrl":"10.1016/j.jappgeo.2024.105454","url":null,"abstract":"<div><p>The Closed-Loop surface-related multiple elimination (CL-SRME) shares the common theoretical foundation with traditional surface-related multiple elimination (SRME). Nevertheless, it introduces an inversion-based approach to avoid the adaptive subtraction process in SRME, aiming to prevent the energy damage to the primaries that may occur when they interfere with multiples during multiple suppression. With the advancements of computing power, the seismic data for processing has evolved from 2D to 3D. However, traditional 2D algorithms are no longer sufficient to effectively suppress surface-related multiples in 3D data. Consequently, based on the theories of 3D SRME and 2D CL-SRME, the 3D CL-SRME algorithm is proposed in this study. Moreover, the implementation of the CL-SRME necessitates numerous matrix operations and frequent data conversions between the time domain and frequency domain, resulting in colossal computational costs. Therefore, a GPU acceleration strategy is introduced to address this challenge. Numerical examples of 3D seismic data demonstrate that 3D CL-SRME can provide higher accuracy of multiple suppression and wider adaptability to complex 3D cases. Simultaneously, the graphics processing unit (GPU) parallel computing can substantially enhance the computational efficiency. This study employs a novel approach that achieves significant improvements in performance and accuracy for surface-related multiple elimination tasks in 3D applications. The combination of its closed-loop approach and GPU acceleration renders it a valuable tool for 3D multiple suppression, enabling high-precision multiple suppression with less computational cost.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690697","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 mixed domain deblending approach for simultaneous source data based on deep learning","authors":"","doi":"10.1016/j.jappgeo.2024.105451","DOIUrl":"10.1016/j.jappgeo.2024.105451","url":null,"abstract":"<div><p>The simultaneous source acquisition technology breaks the limitations of traditional seismic survey, which allows more than one source to fire almost at the same time. When the survey time is fixed, simultaneous source acquisition can increase the number of sources, while when the number of sources is fixed, this technique can greatly reduce the survey time. At present, the great advantages of this high-efficiency acquisition technology have received wide attention from academia and industry, and researchers have proposed a series of deblending methods and obtained good results. In recent years, the rapid development of deep learning provides a new solution for deblending, and it has obvious advantages in computational time compared to traditional methods when processing large-scale seismic data. We proposed a novel iterative deblending method based on deep learning, which integrates the advantages of seismic data processing in different domains. In the proposed method, by selecting the appropriate combination of domains, the separation quality is significantly improved compared to the deblended results in a single domain. The effectiveness of the proposed method is verified by deblending the synthetic and field data, and the better performance of the proposed method are demonstrated by comparing it with the multilevel median filter method and conventional deep learning-based methods.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637609","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":"Numerical simulation of borehole compressional wave and shear wave in 3D vug formation","authors":"","doi":"10.1016/j.jappgeo.2024.105446","DOIUrl":"10.1016/j.jappgeo.2024.105446","url":null,"abstract":"<div><p>The staggered grid finite difference method (SGFDM) of a monopole source is used to simulate a three-dimensional vug reservoir model and study the effect of acoustic logging responses of different vug models on radial probing depth. The results show that the first arrival of the head wave peak of the compressional wave (P-wave) and the shear wave (S-wave) is unrelated to the radius of the vug, and the amplitude of the head wave peak of the P-wave and S-wave decreases as the vug volume increases. Compared with the volume change of the vug, the radial distance from the vug wall has little influence, while the vertical source distance has large influence on the P-wave and S-wave. When there are multiple vugs in the model, the amplitudes of the P-wave and S-wave head wave peaks change sinusoidally with the angle between the vugs. The ellipsoidal vug model with the same volume has a greater influence on the P-wave and S-wave than the spherical vug model. In the ellipsoidal vug model, the axial vug size has a greater impact on the first arrival of the head wave peak, while the radial vug size significantly influences the amplitude of the head wave peak. Finally, we validate the numerical simulation conclusions by comparing them with actual logging data responses in complex formations, demonstrating the practical value of the elastic wave response simulations for vugs.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637610","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}