Journal of Applied Geophysics最新文献

{"title":"Three dictionary learning algorithms and their applications for marine controlled source electromagnetic data denoising","authors":"","doi":"10.1016/j.jappgeo.2024.105475","DOIUrl":"10.1016/j.jappgeo.2024.105475","url":null,"abstract":"<div><p>Marine controlled source electromagnetic (MCSEM) is profoundly used for undersea resources exploration. The effective signal is easily contaminated by kinds of noise when the transmitter-receiver offset is large. Suppressing the noise influence is vital to improve data quality and further interpretation accuracy. Denoising becomes a research focus with the widespread application of the MCSEM technique. Many denoising approaches are proposed by different researchers. However, most of them only target a single type of noise, which severely limits the application of these approaches. The fast-developing dictionary learning technique paves a new way for MCSEM data denoising. Currently, typical dictionary learning algorithms include k-means singular value decomposition (K-SVD), data-driven tight frame (DDTF), shift-invariant sparse coding (SISC) and so on. These three algorithms are different in principles and arithmetic processes. Their applications for MCSEM data denoising are explored for the first time in this article. Besides, a comparative analysis of these three noise reduction methods is carried out. The comparison proves the effectiveness and superiority of the K-SVD, followed by the DDTF method. Besides, all these denoising methods are applied to the field data. The results further corroborates the above conclusions.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077053","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":"Pore pressure prediction based on rock physics theory and its application in seismic inversion","authors":"","doi":"10.1016/j.jappgeo.2024.105494","DOIUrl":"10.1016/j.jappgeo.2024.105494","url":null,"abstract":"<div><p>Formation overpressure seriously affects drilling and downhole operation. Accurate prediction on the formation pore pressure can not only reduce the probability of drilling accidents, but also quantitatively evaluate the original formation pressure of underground pore space, which provides an important reference for site selection of carbon sink projects using underground space resources such as CO₂ geological storage. It is therefore necessary to set up a widely applicable method that is based on rock physics theory and conforms to the characteristics of rock mechanics and fluid mechanic. This method is suitable for both logging prediction and seismic inversion of pore pressure. The traditional method of predicting pore pressure based on P-wave velocity has multiple solutions, and the prediction based on S-wave velocity which is not sensitive to fluid has new significance. Based on the Hertz-Mindlin petrophysical model that considering pressure variation and the Gassmann fluid substitution equation that addresses the change in fluid saturation, this paper firstly derived rock physical formulas for predicting pore pressure in logging, and then derived the intrinsic power function relationship between the effective pressure (<em>P</em>_<em>e</em>) and S-wave velocity (<em>V</em>_<em>s</em>) as well as S-wave impedance (<em>I</em>_<em>s</em>). Based on this, a set of geophysical methods integrating S-wave velocity prediction, pore pressure prediction in well and seismic inversion is finally established. The efficacy of this method has been well validated, with an average error of 2.35% in S-wave velocities prediction, 4.5% in single-well pore pressure prediction. The results of seismic inversion of pore pressure are consistent with the phenomenon of overpressure development in actual working area. This method can be further extended to other areas, providing invaluable reference for underground operation such as oil and gas exploration and CO₂ geological storage.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020483","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":"Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets","authors":"","doi":"10.1016/j.jappgeo.2024.105493","DOIUrl":"10.1016/j.jappgeo.2024.105493","url":null,"abstract":"<div><p>This study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997990","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":"Road underground defect detection in ground penetrating radar images based on an improved YOLOv5s model","authors":"","doi":"10.1016/j.jappgeo.2024.105491","DOIUrl":"10.1016/j.jappgeo.2024.105491","url":null,"abstract":"<div><p>Road underground defect detection plays a crucial role in assessing transportation infrastructure. Ground penetrating radar (GPR) serves as a widely used geophysical tool for this purpose. However, the traditional manual interpretation of GPR images heavily relies on the experience of the practitioner, leading to inefficiency and inaccuracies. To tackle these challenges, this paper proposes an automatic detection method for underground defects of roads based on an improved YOLOv5s model. First, the dense connection structure is integrated in the C3 module of the backbone to form the Dense-C3 module to enhance the capability of feature extraction. Subsequently, a convolutional block attention module (CBAM) is incorporated after each Dense-C3 module to refine features and enhance efficiency. Furthermore, the focal loss function is employed for the confidence loss to mitigate the impact of sample imbalance on detection performance. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 96.4% for synthetic data and 91.9% for real data, outperforming seven other models. The detection speed of the proposed model for real data reaches 51 frames per second, meeting the real-time detection requirements of road underground defects.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Importance of seismic microzonation under the threat of an earthquake of the north anatolian fault in nilüfer, bursa, turkiye","authors":"","doi":"10.1016/j.jappgeo.2024.105489","DOIUrl":"10.1016/j.jappgeo.2024.105489","url":null,"abstract":"<div><p>The Nilüfer district experienced the most recent urbanization among the central districts of Bursa in South Marmara region with the completion of rapid construction. Since 358 BCE, many destructive earthquakes were reported on the branches of the North Anatolian Fault (NAF) which caused extensive damage to buildings and loss of life near Bursa city. Besides some studies conducted to define the soil behavior in the vicinity of Bursa, a seismic hazard study in Nilüfer is still lacking. We, therefore, carried out a microzonation study including the following steps. First, an earthquake hazard analysis was conducted and the peak ground acceleration (PGA) values were determined for an expected earthquake. In the next step, MASW (Multi-Channel Analysis of Surface Wave) measurements conducted at 54 points in 28 neighbourhoods of Nilüfer district were evaluated. Soil mechanical parameters were determined at 11 boreholes to assess the liquefaction potential. It was found that almost half of the study area suffers from low damage considering only the vulnerability index (Kg) index, which depends on the site effect. Therefore, in addition to the Kg values, we created a microzonation map using the results of soil liquefaction, settlement, changes in groundwater level, and the average values of spectral acceleration. The study area is classified by four damage levels changing from low to high. Using only the Kg index could not quantify the potential damage level in the study area, thus we showed that the districts of Altınşehir, Hippodrome, Ürünlü and Alaaddinbey, Ertuğrul, 29 Ekim, 23 Nisan, Ahmetyesevi and Minareliçavuş were identified at potentially high-risk damage zones. The results of this study clearly showed that considering the Kg index, which depends only on the local site effect, may lead to inadequate damage values.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012649","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":"S-wave log construction through semi-supervised regression clustering using machine learning: A case study of North Sea fields","authors":"","doi":"10.1016/j.jappgeo.2024.105476","DOIUrl":"10.1016/j.jappgeo.2024.105476","url":null,"abstract":"<div><p>Accurate prediction of S-wave velocity from well logs is essential for understanding subsurface geological formations and hydrocarbon reservoirs. Machine learning techniques, including clustering and regression, have emerged as effective methods for indirectly estimating S-wave logs and other rock properties. In this study, we employed clustering algorithms to identify similarities among well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values using a novel semi-supervised approach. Our approach combined clustering, specifically k-means clustering, with different types of regressors, including Least Squares Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated approach compared to traditional regression methods. We validated our methodology using various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells outside the study area. We achieved a significant improvement in the <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> score metric, ranging from 2.22% to 6.51%, and a reduction in Mean Square Error (MSE) of at least 31% when compared to predictions made without clustering. This study underscores the potential of machine learning techniques for accurate prediction of S-wave velocity and other rock properties, thereby enhancing our comprehension of subsurface geology and hydrocarbon reservoirs.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047796","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 method of predicting S-wave velocity using sparse Gaussian process regression for a tight sandstone reservoir","authors":"","doi":"10.1016/j.jappgeo.2024.105480","DOIUrl":"10.1016/j.jappgeo.2024.105480","url":null,"abstract":"<div><p>The shear wave (S-wave) velocity plays a crucial role in interpreting the lithology in seismic data, identifying fluids and predicting reservoirs. However, S-wave velocity is often unavailable due to the high cost of measurement and technical constraints. Conventional methods exhibit limitations that potentially impact the accuracy or efficiency on predicting S-wave velocity. Moreover, these methods always ignore the uncertainty quantification associated with the predicted results. This paper proposes a sparse Gaussian process regression (SGPR) method to predict the S-wave velocity in tight sandstone reservoirs. SGPR is a highly efficient regression technique that is based on the Gaussian process regression (GPR) method. In the SGPR method, inducing inputs are introduced to approximate the kernel matrix to decrease the computational complexity. A sparse set of inducing inputs and kernel hyperparameters are optimized through minimizing the Kullback-Leibler (KL) divergence between the exact posterior distribution and the approximate one. In this study, we select several types of logging data, which include porosity, water saturation, shale content, lithology and P-wave velocity, as the inputs for the SGPR method to predict S-wave velocity. To validate its effectiveness, we use the SGPR method to predict S-wave velocity in tight sandstone and compare the results with those from the GPR method, the bidirectional long short-term memory (BiLSTM) method and the Xu-White model. Additionally, we conduct cross-validation to demonstrate the robustness of the SGPR method. Our findings indicate that the SGPR method presents better performance and significant advantages about the accuracy and efficiency. Moreover, the SGPR method offers uncertainty quantification for the predicted S-wave velocity.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993316","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":"DAS seismic signal recovery with non-uniform noise based on high-low level feature fusion model","authors":"","doi":"10.1016/j.jappgeo.2024.105481","DOIUrl":"10.1016/j.jappgeo.2024.105481","url":null,"abstract":"<div><p>Distributed Acoustic Sensing (DAS) is an effective exploration technology for acquiring Vertical Seismic Profile (VSP) data due to its characteristics of high-density collection and strong environmental adaptability. However, DAS-VSP is susceptible to various noises that distribute non-uniformly in both t-x and frequency domains. Existing denoising methods generally adopt single feature-extraction mechanisms (e.g. local convolutional operation or long-distance attention calculation), which are not sufficient for non-uniform feature extraction. Therefore, leveraging the advantages of Convolution (Conv) and Transformer, we propose a high-low level feature fusion model for DAS signal recovery. This model comprises three modules: low-level feature extraction (LFE), high-level feature extraction (HFE), and signal recovery (SR). First, LFE utilizes a Conv layer to extract the basic features, including energy, attributes, and fuzzy contours. The Conv utilizes small kernels to fitter the effective signal feature and introduce spatial information for the following layers. Second, HFE is the core module of the network to extract rich high-level features, such as sharper waveform features and high-dimension representation features. HFE consists of the Swin-Transformer blocks and the Conv blocks. The Swin-Transformer blocks utilize cross-window attention to extract the features between the windows and shift the window to continue recognizing the global features. Then, the Conv blocks further filter and enhance the high-attention features. The cross-use of these two blocks realizes the extract-enhance-extract-enhance process. Finally, the SR module employs a residual connection to create a direct mapping to add the low-level features to the last layer, achieving the fusion of the low-level and high-level features. Through the fusion, more complete and detailed features can be used to improve the accuracy of the recovering weak signals. The design of our model can combine long-distance and local detailed information to extract rich high-low level features, facilitating the recognition of weak signals and non-uniform noise in complex geological structures.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979873","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":"Poroelastic full-waveform inversion as training a neural network","authors":"","doi":"10.1016/j.jappgeo.2024.105479","DOIUrl":"10.1016/j.jappgeo.2024.105479","url":null,"abstract":"<div><p>In this paper, we investigate the full-waveform inversion (FWI) for recovering three media parameters of the poroelastic wave equations as training a neural network. We recast the poroelastic wave simulation in the time domain by the staggered-grid schemes into a process of recurrent neural networks (RNNs). Furthermore, the parameters of RNNs coincide with the inverted parameters in FWI. The algorithm of FWI with a stochastic gradient optimizer named Adam is proposed. The gradients of the objective function with respect to the media parameters are computed by the automatic differentiation. FWI is implemented numerically for three media parameters, i.e., solid density, Lamé parameter of of saturated matrix and shear modulus of dry porous matrix. The numerical computations with two designed models show the good imaging ability of the described method in this paper. It can be applied to invert more media parameters of the poroelastic wave equations.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of calcareous sandstone based on simultaneous broadband nonlinear inversion of Young's modulus, Poisson's ratio and S-wave modulus","authors":"","doi":"10.1016/j.jappgeo.2024.105477","DOIUrl":"10.1016/j.jappgeo.2024.105477","url":null,"abstract":"<div><p>The oilfield's further fine development is significantly impacted by the interlayer of calcareous sandstone. Projecting the lateral distribution of subterranean calcareous sandstone is crucial for determining sequence boundary division, reservoir quality, and even CO₂ storage. Research on the sensitive characteristics of calcareous sandstone is still lacking. This study computes the percentage of lithologic difference and performs an intersection analysis of rock physical properties. It is found that Young's impedance, Poisson's ratio, and S-wave modulus have pleasurable sensitivity to distinguish calcareous sandstone. On the basis of this, a new sensitive factor for calcareous sandstone was built. The traditional approximate YPD reflection coefficient equation is only applicable to the weak contrast interface, and the accuracy is limited. This difficulty is solved in this paper by deriving a new equation for the nonlinear reflection coefficient. The equation is expressed by Young's modulus, Poisson's ratio, S-wave modulus, and density. Finally, the broadband nonlinear inversion method is adopted to provide a reasonable low-frequency model for the inversion of parameters. This allows for the realization of a stable inversion of parameters. The simultaneous broadband nonlinear inversion of Young's modulus, Poisson's ratio, and S-wave modulus provides a novel approach for calcareous sandstone prediction. We tested the accuracy and rationality of the method with both synthetic and field data examples.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963314","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}