Journal of Applied Geophysics最新文献

{"title":"Seismic data denoising using convolutional sparse coding with an efficient alternating direction multipliers minimization algorithm","authors":"Bo Yang,&nbsp;Min Bai,&nbsp;Juan Wu,&nbsp;Zixiang Zhou,&nbsp;Xilin Qin,&nbsp;Zhaoyang Ma,&nbsp;Yang Zeng","doi":"10.1016/j.jappgeo.2024.105610","DOIUrl":"10.1016/j.jappgeo.2024.105610","url":null,"abstract":"<div><div>During the acquisition of field seismic data, it is unavoidable to encounter random noise, and this will have an impact on the subsequent processing and interpretation of the seismic data. Lately, dictionary learning has demonstrated significant advancements in seismic data denoising. The most common method among patch-based dictionary learning algorithms is the K-singular value decomposition (K-SVD) method, which is a learning method based on patching schemes and processes data on overlapping patches without considering the complete data and the global features. In order to optimize these problems, we use convolutional sparse coding (CSC) for seismic data denoising, which can process the global data and capture the correlation between local neighborhoods. We propose the convolutional sparse coding based on an efficient alternating direction multipliers minimization (ADMM) for noise attenuation in seismic data. This CSC with efficient ADMM algorithm is capable of effectively addressing the subproblem of convolutional least-squares fitting, which reduces the complexity of the algorithm and converges to a valid solution. We accomplish the seismic data denoising using the learned filters and the corresponding sparse feature maps. The numerical experimental results on synthetic data and field data demonstrate that in comparison to fast and flexible convolutional sparse coding (FF-CSC) and K-SVD, the proposed method has more advantages in denoising performance and computational efficiency.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105610"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096625","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 multistage algorithm to generate a predictive porphyry intrusion evidential map with low uncertainty for mineral prospectivity mapping, case study in Pariz Area, Iran","authors":"Gholam-Reza Elyasi,&nbsp;Abbas Bahroudi,&nbsp;Maysam Abedi","doi":"10.1016/j.jappgeo.2025.105637","DOIUrl":"10.1016/j.jappgeo.2025.105637","url":null,"abstract":"<div><div>The quest for creating more reliable evidential maps with elevated prediction accuracy and minimal uncertainty remains a formidable challenge in mineral prospectivity mapping (MPM), particularly in the case of covered or concealed deposits. This study introduces a multistage algorithm to generate a predictive porphyry intrusion evidential map using magnetic data. Rooted in the formation model of porphyry copper deposits (PCDs), the algorithm encompasses several key steps: (1) initiating with a radial symmetry transformation to detect circular geological features (i.e., porphyry intrusions), (2) none-minimum suppression of circularity responses and thresholding to identify the center of these features, (3) amplitude contrast transformation to highlight the extent of the features, and finally (4) employing the active contour algorithm to determine the size and geometry of probable porphyry targets. Implemented on aeromagnetic data from the Pariz region in southeastern Iran, the results were evaluated by location of previously known PCDs and 3D Cu isoshells derived from exploratory boreholes. Remarkably, all six known deposits in the area were identified, alongside the discovery of a new porphyry copper deposit, boasting 2847 MT with a copper grade of 0.42 %. Additionally, five new prospective targets that may serve as fertile environments for porphyry mineralization were proposed for further exploration. The results demonstrated that the proposed algorithm significantly enhances MPM of PCDs by effectively narrowing the exploration search space, delineating 20 % (126 of 629 km²) of the study area as prospective targets. Furthermore, the findings indicate that the magnetic signatures of PCDs on the generated map are notably sharper than those on the original reduction to pole map, affirming the algorithm's efficacy in delineating buried porphyry copper deposits.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105637"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097103","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":"An efficient footprint-guided finite domain algorithm for common offset ground penetrating radar forward modeling","authors":"Deshan Feng ,&nbsp;Zhengyang Fang ,&nbsp;Xun Wang ,&nbsp;Siyuan Ding","doi":"10.1016/j.jappgeo.2024.105617","DOIUrl":"10.1016/j.jappgeo.2024.105617","url":null,"abstract":"<div><div>Ground penetrating radar (GPR) is a widely applied shallow geophysical exploration method. However, the huge amounts of collected data from high efficiency and sampling rate are extremely time-consuming and high-cost to interpret. As the basis of full waveform inversion (FWI) and reverse time migration (RTM), the numerical simulation of GPR directly affects the accuracy and speed of the data interpretation. Besides, calculating 3D large-scale models on the personal computer (PC) is still difficult with limited memory. Therefore, an efficient and low-cost forward algorithm is required urgently. Inspired by the footprint of the airborne electromagnetic method (AEM), we propose a GPR moving finite domain (MFD) forward algorithm based on the attenuation characteristic of GPR high frequency electromagnetic waves to avoid excessive computation by limiting the calculation to the finite domain. We explore the relation between speedup and precision, summarize the optimal range of the parameter and constrain the MFD to further ensure the acceleration according to the time window. The error source and factors affecting the algorithm's speedup are explored and discussed to demonstrate its performance fully. The extensive numerical experiments emphasize that the algorithm could improve speed efficiently with ignorable loss of accuracy. Finally, the forward modeling of a large 3D model is carried out with the memory decreased by 78 % and the speed increased by 33.88 times on the PC, which is impossible through the conventional FDTD. The reduction of costs lessens the requirements for computer equipment, which is expected to promote the practical process of FWI and RTM.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105617"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096560","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 picking of surface-wave dispersion curves with an image segmentation method","authors":"Mengyuan Hu ,&nbsp;Yudi Pan ,&nbsp;Tianxiang Wang ,&nbsp;Yiming Wang","doi":"10.1016/j.jappgeo.2024.105615","DOIUrl":"10.1016/j.jappgeo.2024.105615","url":null,"abstract":"<div><div>The surface-wave method is a widely used technique for shallow subsurface exploration, and the extraction of the dispersion curve is one of the most important steps in the surface-wave method. Traditionally, this extraction of surface-wave dispersion curves heavily relies on manual or semi-manual picking, which is both time-consuming and prone to human error, especially when dealing with large datasets. Recent developments in machine learning algorithms have provided a promising way for the automated extraction of surface-wave dispersion curves. We present a random forest (RF) algorithm designed for the automatic extraction of surface-wave dispersion curves. In this approach, the extraction task is conceptualized as an image segmentation problem, enabling a rapid and accurate extraction of dispersion curves from dispersion energy images. We generate a dataset of 1800 models and their corresponding dispersion images. The proposed method is tested on both the noise-free and noisy datasets contaminated by Gaussian noise. Synthetic results demonstrate that our proposed method achieves relatively high accuracy and efficiency in the automatic extraction of surface-wave dispersion curves. We further analyze the impact of tuning parameters, including the number and depth of random-forest trees in the proposed algorithm on its performance and choose the best parameters in our study. Finally, the trained RF model is applied to two field datasets, which confirms the validity of our proposed RF method.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105615"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096561","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":"Template-matching-based data selection for passive seismic surface wave tomography","authors":"Deng Pan ,&nbsp;Ji Gao ,&nbsp;Haijiang Zhang","doi":"10.1016/j.jappgeo.2024.105606","DOIUrl":"10.1016/j.jappgeo.2024.105606","url":null,"abstract":"<div><div>Ambient noise tomography (ANT) has been widely used to determine near surface shear-wave velocity (V_S) model. To fulfill the randomization requirement of the stationary source distribution for ANT, temporal averaging over a sufficiently long period of time is needed. However, in small-scale passive surface wave tomography, it is difficult to realize long-time observation and as a result non-stationary sources could affect the quality of the stacked dispersion measurements. In this study, we proposed a template-matching-based data selection method to obtain high-quality cross-correlation functions for dispersion analysis by only selecting time segments that are associated with similar cross-correlation functions with the template. One simple way to create the template is by stacking cross-correlations for all time segments. Two synthetic tests have demonstrated the strength of the proposed technique on recovering accurate dispersion curves. Field data analysis further proves the applicability of the proposed technique in selecting high-quality data segments with bin-stacked template. These tests show that the proposed method offers an efficient data processing method for passive surface-wave tomography.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105606"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096565","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":"New Technologies for transient electromagnetic measurement with high performance","authors":"Xin Wu ,&nbsp;Qihui Zhen ,&nbsp;Weiying Chen ,&nbsp;Fei Teng ,&nbsp;Junjie Xue ,&nbsp;Yanbo Wang","doi":"10.1016/j.jappgeo.2025.105627","DOIUrl":"10.1016/j.jappgeo.2025.105627","url":null,"abstract":"<div><div>Theoretically, grounded-wire source electromagnetic methods can be categorized into far-source methods and near-source methods. The former have been used worldwide for many years, and the key technologies used in its equipment are mainly high-power transmitter and low-noise sensors. Due to its relatively narrow working bandwidth, its resolution capability is weak. In recent years, with the breakthrough of Short-Offset Transient Electromagnetic Method (SOTEM) in detection theory, its advantages of strong signal and wide bandwidth have been increasingly valued. For the research and development of specialized detection equipment for SOTEM, we first conducted system design by studying the characteristics of signals at different offset distances, and analyzed the technical difficulties that urgently need to be overcome. On this basis, a system with a high-bandwidth, high-current broadband transmitter and low-noise broadband receiver has been designed, the key technologies of which are ultra-high stabilized clamping voltage, low-noise hybrid amplifier, and adaptive variable damping. The performance and field tests have shown that the overall operating bandwidth of the system has increased by 2.5 times compared to existing mainstream equipment, significantly improving detection accuracy. The newly developed SOTEM equipment can provide strong support for near-source, great-depth, and high-resolution surveys.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105627"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097097","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":"Obtaining an initial model for acoustic full waveform inversion using generalized regression neural networks","authors":"Doğukan Durdağ,&nbsp;Ertan Pekşen","doi":"10.1016/j.jappgeo.2025.105624","DOIUrl":"10.1016/j.jappgeo.2025.105624","url":null,"abstract":"<div><div>Acoustic full waveform inversion is a featured method extensively used to obtain subsurface velocity models. The acoustic full waveform inversion approximation based on the derivative method has the limitation of being trapped in local minima. To overcome this problem, an initial velocity model in the vicinity of the global minimum should be used as the starting point. Artificial neural networks can be used to build such initial models. In this study, a generalized regression neural network approach was applied to overcome this problem. The test results on the Marmousi and SEAM synthetic data demonstrate that the initial model estimated with the generalized regression neural network provides a better starting point for full waveform inversion. In addition, the number of iterations required to search for optimal results was reduced significantly. The reduction in the number of iterations due to determining an initial model with generalized regression neural networks also substantially reduced the computational time and reduced the probability of the model becoming stuck in local minima. The acoustic full waveform inversion yields a detailed velocity model when using the initial velocity model produced by general regression neural networks.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105624"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097093","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":"Geological disaster detection in underground mining tunnels using a new electromagnetic method: Theoretical modeling and experimental evaluation","authors":"Benyu Su ,&nbsp;Jiaqi Zhang ,&nbsp;Yu Tang ,&nbsp;Jingcun Yu ,&nbsp;Enyuan Wang ,&nbsp;Govind Vashishtha ,&nbsp;Z. Li","doi":"10.1016/j.jappgeo.2025.105641","DOIUrl":"10.1016/j.jappgeo.2025.105641","url":null,"abstract":"<div><div>Geophysical methods, especially the transient electromagnetic method (TEM), are vital tools for identifying geological hazards, especially water leaks in underground mines. However, traditional TEM methods employing small coils face challenges from metal structures common in tunnels, and their depth penetration remains limited. To address these limitations, this study presents a novel galvanic-source TEM approach. By employing electrodes as sensors, this new method eliminates metal interference and allows for long-distance detection. A mathematical model elucidates the propagation mechanism of the electromagnetic field in this technique. Numerical simulations and real-world experiments validate the effectiveness of the proposed method, showcasing its capability to accurately detect and identify disaster water within underground tunnels. This innovative technique holds significant promise for practical application in underground mining, enhancing water detection capabilities and ensuring safer working environments.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105641"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096574","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 new fast imaging method for inline electrical source transient electromagnetic data","authors":"Hai Li ,&nbsp;Pan Zhao ,&nbsp;Keying Li","doi":"10.1016/j.jappgeo.2025.105622","DOIUrl":"10.1016/j.jappgeo.2025.105622","url":null,"abstract":"<div><div>The transient electromagnetic (TEM) method is a widely used geophysical technique for obtaining electrical information about subsurface. In recent years, the electric source TEM method has gained popularity, particularly the inline configuration, which is highly sensitive to resistive targets. One of the key challenges in TEM research is the transformation of observed responses into resistivity-depth profiles. This paper introduces a new fast imaging method that addresses this challenge by directly converting the responses to resistivity-depth information. As the relationship between the TEM response and the resistivity is an implicit nonlinear function, we cannot get an explicit formula for the resistivity. Our method started from constructing a time constant using the peak-time of the impulse response and derived an explicit formula for the apparent resistivity. As a result, we can efficiently estimate the resistivity from the impulse response. Then, an apparent depth is defined based on the maximum depth of the electric field excited by a grounded wire source. Hence, the time-variant impulse response can now be imaged into the variance of resistivity with depth. To improve the accuracy of extracting the peak-time of the impulse response, an interpolation and re-extraction scheme is designed and applied. Numerical and field data examples are used to verify the effectiveness of the proposed fast imaging method. The results show that, compared with conventional apparent-resistivity imaging method, the proposed method is based on analytical derivation and can be used for at the full time range of the responses, thus with higher stability and calculation efficiency. Finally,</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105622"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136394","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":"Evaluating karst features and geological fault leakage risks based on geophysical method in Dongzhuang reservoir area, Shaanxi","authors":"Weifeng Wan ,&nbsp;Liqun Sun ,&nbsp;Haifeng Zhang ,&nbsp;Feng Zeng ,&nbsp;Jianlei Liu","doi":"10.1016/j.jappgeo.2024.105597","DOIUrl":"10.1016/j.jappgeo.2024.105597","url":null,"abstract":"<div><div>Leakage along faults, particularly in karst area faults, is a critical concern that can prevent impounding a reservoir. The Laolong Mountain fault in the Dongzhuang Reservoir Project Area in Shaanxi Province, China, is a large fault that may impede the project. To assess whether concentrated leakage can occur along this fault, we used a comprehensive approach involving footrill exploration, an electromagnetic method, optical imaging techniques, water pressure testing, and large-scale groundwater tracer tests. Footrill exploration results revealed that the karst features predominantly comprise karst cavities with some fissures and karst caves. These formations are typically clustered along fractures with a linear joint density of 0.4–1.5 joints/m. The linear karst rate was 0.39 %, and the karst cavities have small diameters of only a few centimeters and depths not exceeding 15 cm. A geophysical exploration indicated that low resistivity values (approximately 200 Ω·m) on the north side of the fault zone's hanging wall, with the lowest values being approximately 100 Ω·m. The fault's footwall was noted to have higher resistivity (approximately 2000 Ω·m). The rock properties on either side of the fault zone differ considerably. The upper plate of the fault (downstream of the river) has high-resistance carbonate rock formations with a well-preserved structure, whereas the lower plate (upstream of the river) comprises low-resistance sandstone and mudstone formations with notable fragmentation at the fault's surface compression zone. Tracer tests indicated an eastward groundwater flow from Zuantian Ridge toward the Jing River in the west. The results suggest that eastward leakage along the Laolong Mountain fault and its influence zone is unlikely upon reservoir impoundment. The geophysical exploration method in this study integrated and cross-validated the footrill exploration, hydrological test, groundwater tracing, optical imaging, and other methods; it can thus serve as a reference for future research.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105597"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096562","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}