Journal of Applied Geophysics最新文献

{"title":"A novel nonlinear prestack inversion method based on nutcracker optimization algorithm with high convergence speed","authors":"Lin Zhou ,&nbsp;Ming Ouyang ,&nbsp;Jianping Liao ,&nbsp;Jingye Li ,&nbsp;Hanlin Xia ,&nbsp;Haiyang Ding","doi":"10.1016/j.jappgeo.2024.105580","DOIUrl":"10.1016/j.jappgeo.2024.105580","url":null,"abstract":"<div><div>Predicting reservoir parameters with high accuracy is still a crucial work of oil reservoir exploration and development. Due to the limitation of computational efficiency, deterministic methods are primarily used in practical production applications for predicting reservoir parameters. When the nonlinear forward equations are exceptionally complex and the initial model constructed deviates significantly from the true reservoir parameters, deterministic methods may have difficulty obtaining reasonable predictions of reservoir parameters. Compared to deterministic methods, intelligent optimization methods based on nature-inspired metaheuristic algorithms have unique advantages because they do not require derivative information, can achieve global optimization, and have less reliance on initial model. Therefore, they perform better in solving complex nonlinear optimization problems. In this paper, a new intelligent optimization algorithm called Nutcracker Optimization Algorithm (NOA) with a high convergence speed is introduced. By utilizing this optimization algorithm to solve the nonlinear inversion problem constructed by the highly nonlinear exact Zoeppritz equations, we analyze the potential of nonlinear reservoir parameters prediction methods based on intelligent optimization algorithms in practical production applications. The synthetic data test shows that, compared to the classical quantum particle swarm optimization (QPSO) algorithm and the highly-cited whale optimization algorithm (WOA), the prestack nonlinear inversion method based on NOA proposed in this paper ensures high convergence accuracy and exhibits high computational efficiency. It significantly reduces computation time and holds great potential for practical production applications. The field data test shows that the proposed method can rapidly and accurately estimates reservoir parameters, validating the feasibility and effectiveness of the proposed method. This has important theoretical value and practical significance for advancing the application of intelligent optimization algorithms in field reservoir exploration and development.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105580"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097301","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":"In-situ stress state and critically stressed fracture analysis from the Hassi D'Zabat field, Algeria – A study from the naturally fractured Cambro-Ordovician reservoirs","authors":"Soumya Benayad ,&nbsp;Souvik Sen ,&nbsp;Rafik Baouche ,&nbsp;Rabah Chaouchi","doi":"10.1016/j.jappgeo.2025.105630","DOIUrl":"10.1016/j.jappgeo.2025.105630","url":null,"abstract":"<div><div>This study presents a comprehensive geomechanical modeling of the naturally fractured Paleozoic reservoirs of the Hassi D'Zabat field, Algeria, to assess the in-situ stress state and critically stressed fractures. The Cambrian and Ordovician reservoirs exhibit vertical to sub-vertical open to partially open fractures, as interpreted from the cores as well as the image log. Routine core analysis indicates higher vertical permeability in the fractured reservoir samples. A cumulative of 41 m of ‘B-quality’ breakouts was interpreted from a 452 m acoustic image log indicating a mean SHMax azimuth of N118°E (standard deviation 8.95°). Shmin was calibrated with the closure pressure from the hydraulic fracture test. Based on the breakout occurrence, SHMax was constrained following the frictional faulting mechanism. The inferred in-situ stress magnitudes (SHMax &gt; Sv &gt; Shmin) indicate a strike-slip tectonic regime in the study area. The practical injection threshold has been inferred as 1850 psi to ensure caprock integrity. The onset of slip on the optimally oriented vertical fractures is estimated to occur at 1000 psi of fluid injection at the Ordovician reservoir level, while only 150 psi of injection can induce shear slippage on fractures within the Cambrian reservoirs. Within the practical injection window, the stress-based model indicates that 58 out of 215 steeply dipping fractures may become critically stressed and therefore, potentially experiencing shear slippage.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105630"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097094","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":"Generation of missing well log data with deep learning: CNN-Bi-LSTM approach","authors":"D. Haritha ,&nbsp;N. Satyavani ,&nbsp;A. Ramesh","doi":"10.1016/j.jappgeo.2025.105628","DOIUrl":"10.1016/j.jappgeo.2025.105628","url":null,"abstract":"<div><div>Well log data is generally collected by the drilling process, which is associated with huge costs and is also time taking. Furthermore, distorted data are widespread in well logs due to instrument damage, poor borehole conditions, imperfect logging, and so on, causing data loss leading to poor interpretation. The missing well log data can be retrieved using deep learning methods from the existing/ available borehole logs. In this study, we propose a Convolutional Bidirectional Long short-term memory (CNN-Bi-LSTM) with fully connected layers that could successfully predict the missing log data for two sites in the Krishna Godavari basin, namely, NGHP-01-14 and NGHP-01-06. In NGHP-01-14, the CNN-Bi-LSTM was employed to predict the S-wave log using the density and gamma logs from the same NGHP-01-14 site. Whereas, in NGHP-01-06, the sonic log is predicted using different logs from the nearby NGHP-01 sites. This method reliably extracts the important features in the logs along the depth of the borehole, which helps to predict the missing data and also the logs that are not available in the well. The accuracy of the predicted data is calculated with an error metric, and the log predicted using CNN, Bi-LSTM, and ANN network results are compared to establish the efficacy of the proposed method. The MSE value of the predicted shear wave log of NGHP-01-14 from the proposed network is 0.0025, and from CNN, Bi-LSTM and ANN are 0.003, 0.0045 and 0.0084, respectively. The error values of the predicted sonic log of NGHP-01-06 from CNN-Bi-LSTM, CNN, Bi-LSTM, and ANN are 0.0025, 0.004, 0.005, and 0.0065, respectively. The outcomes from the network establish that the proposed method predicts the missing log successfully and efficiently.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105628"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097098","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":"Adaptive DAS data refining under multiple noise levels based on wavelet inspired invertible network","authors":"Yinhui Yu,&nbsp;Yuhan Liu,&nbsp;Ning Wu,&nbsp;Yue Li,&nbsp;Yanan Tian","doi":"10.1016/j.jappgeo.2025.105640","DOIUrl":"10.1016/j.jappgeo.2025.105640","url":null,"abstract":"<div><div>Distributed Acoustic Sensing (DAS) is a rapidly developing and highly secure fiber sensing technology, which has been gradually applied to acquisition of vertical seismic profile (VSP) data. Due to the complex nature of the collecting environment, DAS data is often contaminated with various levels of noise. Traditional multi-scale refining methods are suitable for noise with a single level, but not adaptive for DAS noise with composite and uneven noise levels. To solve the issue of weak signal recovery induced by DAS noise, this paper constructs an adaptively refining wavelet inspired invertible network (ARWIN) architecture which combines the traditional wavelet method with a customized invertible Convolutional Neural Network (CNN). Within ARWIN, the Estimation Noise Network (ENNet) is first built to estimate the noise level, which provides more suitable decomposition parameters for the following K-pair Invertible Neural Network (KINN). KINN is a lightweight network where both input and output share the same set of parameters. This shared parameter approach can effectively eliminate the parameters in each scale. KINN accomplishes the further multi-scale sparse characterization so that it can provide more reasonable threshold information for the subsequent denoising network. After KINN, the Sparse-driven Denoising Network (SDN) equipped with T-layer soft-thresholding operations can effectively attenuate DAS noise. Experiments demonstrate that ARWIN can refine DAS weak signals and suppress DAS noise with multiple noise levels.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105640"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097104","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 the site effects of trapezoidal sedimentary valleys under oblique incidence of SH waves based on the IBIEM","authors":"Xiangjiang Li,&nbsp;Zhiqiang Song,&nbsp;Leichao Zhao,&nbsp;Chuang Li,&nbsp;Yunhe Liu","doi":"10.1016/j.jappgeo.2024.105609","DOIUrl":"10.1016/j.jappgeo.2024.105609","url":null,"abstract":"<div><div>Trapezoidal sedimentary valley sites exhibit significant differences in ground motion characteristics and a nonuniform distribution pattern under the oblique incidence of seismic waves. On the basis of the indirect boundary integral equation method (IBIEM), the scattered field of a sedimentary valley site is obtained by constructing and solving the Green's function of the scattering wave source from the valley sedimentary layer. This paper systematically and parametrically investigates the seismic ground motion characteristics and nonuniform distribution patterns of sedimentary valleys as they vary with changes in valley topographic parameters, oblique incidence angles of SH waves, and impedance ratios between bedrock and sedimentary layers. This study analyzed the differences between considering the scattering effects of sedimentary valleys and accounting only for the free-field seismic motion at the truncated boundary of the foundation. On the basis of this analysis, a more rational approach for ground motion input was proposed. The results indicate that as the valley height increases or the bottom width decreases, the sedimentary valley transitions from exhibiting edge effects to focusing effects. The oblique incidence of seismic waves significantly increases the amplification and nonuniformity of the peak displacements on the surface of the sedimentary valleys, with the maximum peak reaching more than five times the peak displacement of the incident wave. The change in slope has a significant effect on the seismic response at the inner and outer surfaces of the wedge. This phenomenon has been explained for the first time through ray theory. The amplification effect of sedimentary valley displacement becomes more pronounced with increasing impedance ratio. Compared with the free field on flat bedrock, the scattering effects generated by hollow and sedimentary valleys have a significant effect on the ground motion field at the truncated boundaries. The maximum errors at the bottom and right boundaries can reach 30.8 % and 54.8 %, respectively. Therefore, the total field that considers scattering effects should be used as the ground motion input.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105609"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096568","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":"Effects of a 3D basin on the near-fault ground motion by an FK-FE hybrid method","authors":"Zhenning Ba ,&nbsp;Shujuan Han ,&nbsp;Jingxuan Zhao ,&nbsp;Zhonghan Liu","doi":"10.1016/j.jappgeo.2024.105581","DOIUrl":"10.1016/j.jappgeo.2024.105581","url":null,"abstract":"<div><div>In the pursuit of accurately representing the full-process ground motion of a 3D near-fault complex site, an FK-FE hybrid method based on the idea of domain reduction is proposed. By integrating the Frequency-Wavenumber (FK) and Finite Element (FE) Methods, the hybrid method provides a new solution to seismic modeling which adaptively addresses multi-scale crustal models (from crustal to geotechnical scale) and intricate 3D site models, with consistent efficiency and precision. Then, the hybrid method is verified by comparing with results of the FK method and further validated by comparing with strong-earthquake records of the 2021 Yangbi M6.4 earthquake. Further, the proposed hybrid method is used to investigate the influence of the basin (wave velocity ratio of internal and external medium, basin's thickness) on near-fault effects due to the finite-fault source, and reveal the comprehensive mechanism of near-fault effects and basin effects. The results show that: the basin-focusing effect enhances the concentration of near-fault ground motions, and further expands the concentration range of strong-earthquake. The increases of the wave velocity ratio and basin's thickness augment the basin-focusing effect, and then the amplification effect of the basin on the fling-step effect becomes more pronounced. These results can provide a reference basis for seismic ground motion estimation and engineering seismic design in near-field complex sites.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105581"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096569","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":"Tensor ratio small subdomain filtering technique for edge detection","authors":"Zhenhua Tai,&nbsp;Yuanhao Wang,&nbsp;Guohua Zhang,&nbsp;Xiangwen Li,&nbsp;Dezhi Huang","doi":"10.1016/j.jappgeo.2025.105635","DOIUrl":"10.1016/j.jappgeo.2025.105635","url":null,"abstract":"<div><div>Edge detection is an important processing method for potential field data, used to determine the horizontal location of the edges of causative sources. We proposed an edge detection filtering based on a gradient tensor ratio and improved the small subdomain filtering technique, and merged them into a tensor ratio small subdomain filtering technique. The proposed detection filter utilizes numerical differentiation and Laplace equation to compute gradient tensors. To weaken the interference of random noise on the small subdomain filtering and the irregular bending of contour lines in its result, we replace the original data at the center with a weighted average of the data within the window, where the weighting factors are determined by the distance of each data point to the center point and the standard deviation between equidistant data points. Final filtering output is the weighted average of the data within the subdomain that has the minimum standard deviation, wherein tighten gradient belts are utilized as indicators for detecting the edges of causative sources. Test results on synthetic data show that the proposed method has higher detection accuracy and stability compared to previous methods, and can enhance local anomalies. We also apply them to a real gravity data, and the obtain results indicate that the proposed method can effectively detect fault locations and highlight the residual density characteristics of causative sources.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105635"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097100","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":"Progress of the pseudoseismic imaging technology for transient electromagnetic method","authors":"Junjie Xue ,&nbsp;Kerui Fan ,&nbsp;Xin Wu ,&nbsp;Wenhan Li ,&nbsp;Quanhui Guo","doi":"10.1016/j.jappgeo.2024.105600","DOIUrl":"10.1016/j.jappgeo.2024.105600","url":null,"abstract":"<div><div>The transient electromagnetic method (TEM) has been widely applied in metal mineral detection and engineering geology investigation. While mapping the resistivity distribution through the inversion of the TEM data, the subsurface structure of conductivity can be revealed by converting TEM data to the pseudo wavefield. The wavefield transform method is consequently an effective way to highlight the geoelectric structure. However, this field inversion belongs to a first-class Fredholm integral, which is a typical ill-posed problem. So, the key problem of wavefield transform is how to get a pseudo wavefield with proper resolution and stability. This paper first analyzes some traditional TEM imaging algorithms, including aspects such as time-frequency equivalent conversion, wavefield transform, and the Kirchhoff integral imaging and swept time transformation algorithms. Then, this paper concludes with some methods that can improve the inversion results resolution of wavefield transform. To obtain high-quality electromagnetic pseudo wavefield profiles, additional technical methods, such as Born approximation imaging, pulse spectrum inversion and full waveform inversion, are used in the inversion interpretation of electromagnetic field.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105600"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096563","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":"Semi-supervised intelligent inversion from prestack seismic attributes guided by geophysical prior knowledge","authors":"Lei Zhu ,&nbsp;Fanchang Zhang ,&nbsp;Shunan Zhang ,&nbsp;Ji-an Wu","doi":"10.1016/j.jappgeo.2025.105620","DOIUrl":"10.1016/j.jappgeo.2025.105620","url":null,"abstract":"<div><div>Supervised deep learning methods currently used for prestack parameter prediction are suffered from the problem of limited training samples. The lack of clear physical meanings for deep learning models also makes prediction results unreliable. To address these issues, we proposed a geophysical prior knowledge guided semi-supervised (GPKGS) deep learning framework for amplitude-versus-angle (AVA) inversion. Based on prior physical knowledge, the prestack seismic data are decoupled into prestack seismic attribute data of the elastic parameters. Meanwhile, according to the prestack seismic attribute data, constructing the new forward models corresponding to each elastic parameter. The intelligent inversion framework is built based on the constructed forward models. This reduces the dependence of the framework on training data. This GPKGS framework preserves the physical procedure of AVA inversion, making intelligent inversion results reliable. The framework contains three branch networks for each elastic parameter. Each branch network contains an inversion neural network (INN) and a forward neural network (FNN). The INN can invert the prestack seismic attribute data into elastic parameters, which corresponding to inversion process. The FNN convert the obtained elastic parameters into synthetic prestack seismic attribute data, which corresponding to forward process. To ensure a reliable training process, the difference between the prestack seismic attribute data and the synthetic data are used to train the framework supervised by well log data. In addition, to obtain more stable results, at prediction stage, the prior information is introduced to help the FNN update the elastic parameters output by INN. The Marmousi2 model and a deep carbonate data are used to test the proposed framework. We find that the intelligent inversion results of the proposed network perform well at the situation of few training data.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105620"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092047","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":"Suppressing short time marine ambient noise based on deep complex unet to enhance the vessel radiation signal in LOFAR spectrogram","authors":"Yuzhe Wang ,&nbsp;Shijie Qiu ,&nbsp;Guoqing Hu ,&nbsp;Bin Wu ,&nbsp;Yi Yu","doi":"10.1016/j.jappgeo.2024.105611","DOIUrl":"10.1016/j.jappgeo.2024.105611","url":null,"abstract":"<div><div>UNet-type networks have demonstrated good performance in the field of denoising. In this paper, we applied a DCUNet network specifically for denoising underwater acoustic signals, which are characterized by their nonlinear, non-smooth and non-Gaussian features. The process involves transforming noisy data into LOFAR spectrograms for input into DCUnet, redesigning the network structure based on the features of underwater acoustic signals. Subsequently, a Noise2Noise training method was employed to reconstruct the underwater background noise through the end-to-end architecture. The effectiveness of the algorithm was validated on publicly available datasets after augmentation. Extensive experimental results show that our method achieves an SNR improvement of over 10 dB and is capable of restoring signals with an initial SNR of −20 dB, demonstrating better performance compared to traditional denoising algorithms. In addition, the method is verified using the public datasets and long-distance single-frequency experiments. In conclusion, the DCUNet model exhibit effectiveness in underwater acoustic noise suppression and robustness in different data<em>.</em></div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"233 ","pages":"Article 105611"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096557","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}