Computers & Geosciences最新文献

{"title":"Bayesian learning of gas transport in three-dimensional fracture networks","authors":"Yingqi Shi ,&nbsp;Donald J. Berry ,&nbsp;John Kath ,&nbsp;Shams Lodhy ,&nbsp;An Ly ,&nbsp;Allon G. Percus ,&nbsp;Jeffrey D. Hyman ,&nbsp;Kelly Moran ,&nbsp;Justin Strait ,&nbsp;Matthew R. Sweeney ,&nbsp;Hari S. Viswanathan ,&nbsp;Philip H. Stauffer","doi":"10.1016/j.cageo.2024.105700","DOIUrl":"10.1016/j.cageo.2024.105700","url":null,"abstract":"<div><p>Modeling gas flow through fractures of subsurface rock is a particularly challenging problem because of the heterogeneous nature of the material. High-fidelity simulations using discrete fracture network (DFN) models are one methodology for predicting gas particle breakthrough times at the surface but are computationally demanding. We propose a Bayesian machine learning method that serves as an efficient surrogate model, or emulator, for these three-dimensional DFN simulations. Our model trains on a small quantity of simulation data with given statistical properties and, using a graph/path-based decomposition of the fracture network, rapidly predicts quantiles of the breakthrough time distribution on DFNs with those statistical properties. The approach, based on Gaussian Process Regression (GPR), outputs predictions that are within 20%–30% of high-fidelity DFN simulation results. Unlike previously proposed methods, it also provides uncertainty quantification, outputting confidence intervals that are essential given the uncertainty inherent in subsurface modeling. Our trained model runs within a fraction of a second, considerably faster than reduced-order models yielding comparable accuracy (Hyman et al., 2017; Karra et al., 2018) and multiple orders of magnitude faster than high-fidelity simulations.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105700"},"PeriodicalIF":4.2,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001833/pdfft?md5=f8b3ab68ca2f9563aa76f642b21453d3&pid=1-s2.0-S0098300424001833-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Desurveying drillholes: Methods for calculating drillhole orientation and position, and the effects of drillhole length and rock anisotropy on deviation","authors":"Benjamin J. Williams,&nbsp;Thomas G. Blenkinsop","doi":"10.1016/j.cageo.2024.105684","DOIUrl":"10.1016/j.cageo.2024.105684","url":null,"abstract":"<div><p>Directional drilling of longer drillholes is becoming increasingly important as resources are exploited at greater depths. As drillholes lengthen, the choice of desurveying method becomes more crucial as the assumptions that are inherent to all methods are compounded. The aim of this study is to first discuss the assumptions involved in each desurveying method and their potential implications for plotting drillhole pathways, and secondly to compare the established desurveying methods to find the most precise one for plotting the drillhole pathway, using examples from Mount Isa, Australia.</p><p>The orientations (azimuth and plunge) of drillholes are required to orient drill core (also known as rock or well core), which can be used to measure the orientations of geological structures at any point. Knowledge of the 3D positions for points of interest along the drill core are required to locate drillhole intersections with geological boundaries, faults or underground mine workings. New computer code has been developed to estimate the orientations and positions of drillholes at any point along their length using the existing desurveying methods. Such orientation and location estimates from the computer codes allow the original orientations of geological structures observed in drill core to be calculated. The codes are available in both R and Python languages in an easy access repository. Results from the codes show that the Basic Tangent method is consistently the least precise, whilst the industry standard Minimum Curvature method has a high precision compared to the other desurveying methods. The impact of rock anisotropy and drillhole length on the precision of the desurveying methods was investigated. Distances between end-of-hole points for each desurveying method increase with increasing drillhole length and angle between the drillhole and anisotropy.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105684"},"PeriodicalIF":4.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperparameter determination for GAN-based seismic interpolator with variable neighborhood search","authors":"Daniel N. Pinheiro ,&nbsp;Jaime C. Gonzalez ,&nbsp;Gilberto Corso ,&nbsp;Mesay Geletu Gebre ,&nbsp;Carlos A.N. da Costa ,&nbsp;Samuel Xavier-de-Souza ,&nbsp;Tiago Barros","doi":"10.1016/j.cageo.2024.105689","DOIUrl":"10.1016/j.cageo.2024.105689","url":null,"abstract":"<div><p>We propose an automatic global search algorithm based on the Variable Neighborhood Search (VNS) metaheuristic for tuning the hyperparameters of a generative adversarial network (GAN) seismic interpolator. We perform an exhaustive search to study the influence of each hyperparameter in the training process, and compare the proposed method with Random search and Bayesian Search. The seismic data set used for this study was synthetically modeled from a typical velocity model, estimated from a pre-salt field of the Brazilian cost. We also employ the proposed method with a real field data to show the importance and applicability of the search for optimum hyperparameters of GAN. The training data was constructed with decimated seismic data and the results were tested by comparing the reconstructed data with the original one. We performed two hyperparameter impact analyses: the first consists of an exhaustive grid exploration and the second consists of our proposed automatic exploration method using the VNS algorithm, comparing it with the other two algorithms. We concluded that the proposed method, which has a user-friendly usage, as it is almost parameter-free, can reach solutions with very good quality quickly, in any range of hyperparameter values. When compared with other methods of hyperparameter tuning, the one we propose proves to be better in the ease of configuration, while being efficient in the search process.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105689"},"PeriodicalIF":4.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyLGRIM: Modelling 3D-ERI with infinite elements in complex topography context","authors":"Antoine Tonnoir ,&nbsp;Cyrille Fauchard ,&nbsp;Yannick Fargier ,&nbsp;Vincent Guilbert ,&nbsp;Raphael Antoine","doi":"10.1016/j.cageo.2024.105685","DOIUrl":"10.1016/j.cageo.2024.105685","url":null,"abstract":"<div><p>Electrical Resistivity Imaging (ERI) is one of the most used techniques in geophysics. As for many imaging methods, Digital Elevation Models (DEMs) are required to consider complex topography conditions. In this paper, we present some developments implemented into a new 3D-ERI software optimized in this context. The article focuses on the forward problem and discusses (i) the meshing methodology that directly consider DEMs in the processing and several profiles where electrodes are not necessarily aligned and (ii) new aspects for taking into account the unbounded domain. Indeed, defining boundary conditions of a numerical modelling problem arises as one of the most important issues into solving Partial Differential Equations (PDE). In order to solve the 3D-ERI forward problem, we propose an original implementation of the infinite elements, together with conventional finite elements. This methodology is first validated on synthetic case reproducing cliffs and, then, on a real case study presenting Badlands-like cliffs. Our results show that both the meshing procedure as well as the use of infinite elements enhance the efficiency of the forward problem as well as the accuracy of the inverse problem. In particular, this allows to reproduce more closely the local geology in complex environments than with a conventional 2D approach.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105685"},"PeriodicalIF":4.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001687/pdfft?md5=b366dd50e958d23bea4d586df230069d&pid=1-s2.0-S0098300424001687-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Super-resolution of digital elevation models by using multiple-point statistics and training image selection","authors":"Guanghui Hu ,&nbsp;Yinghui Jiang ,&nbsp;Sijin Li ,&nbsp;Liyang Xiong ,&nbsp;Guoan Tang ,&nbsp;Gregoire Mariethoz","doi":"10.1016/j.cageo.2024.105688","DOIUrl":"10.1016/j.cageo.2024.105688","url":null,"abstract":"<div><p>Super-resolution (SR), also called downscaling, has been widely explored in hydrology, climate, and vegetation distribution models, among others. Digital elevation model (DEM) SR aims to reconstruct terrain at a finer resolution than available measurements. The raw terrain data are often non-stationary and characterized by trends, while terrain residuals are generally stationary in geomorphologically heterogeneous areas. Here, we develop a multiple-point statistics approach that decomposes the target low-resolution DEM into a deterministic low-frequency trend component and a stochastic high-frequency residual component. Our simulation is focusing on the residual component. A training image selection process is applied to determine locally appropriate high-resolution residual training images. The high-resolution residual of the target DEM is simulated with an open-source multiple-point statistics (MPS) framework named QuickSampling. The residual of the low-resolution target DEM is used as conditioning data to ensure local accuracy. The deterministic trend component is then added to obtain the final downscaled DEM. The proposed algorithm is compared with the bicubic interpolation, a convolutional neural network(CNN), a generative adversarial network (GAN), a modified super-resolution residual network (MSRResNet), and geostatistical area-to-point-kriging. The results show that the proposed approach maintains the statistical properties of the fine-scale DEM with its spatial details, and can be easily extended to other fields such as the super-resolution/downscaling of precipitation, temperature, land use/cover, or satellite imagery.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105688"},"PeriodicalIF":4.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mutual-guided scale-aggregation denoising network for seismic noise attenuation","authors":"Tie Zhong ,&nbsp;Zheng Cong ,&nbsp;Xunqian Tong ,&nbsp;Shiqi Dong ,&nbsp;Shaoping Lu ,&nbsp;Xintong Dong","doi":"10.1016/j.cageo.2024.105682","DOIUrl":"10.1016/j.cageo.2024.105682","url":null,"abstract":"<div><p>The background noise contained in seismic records contaminate the effective reflection waves and impact the subsequent processes, such as inversion and migration. The properties of seismic noises, such as non-Gaussianity and non-linearity, will be even more complex in challenging exploration environments. Deep-learning techniques are effective in suppressing complex seismic noises and outperform conventional denoising algorithms. Nonetheless, most deep learning networks are designed to extract the features of input data in single-scale only, which leads to inadequate performance when dealing with complicated seismic data. To enhance the denoising capability for seismic noises of deep learning, a novel mutual-guided scale-aggregation denoising network (MSD-Net) is designed to suppress seismic noises by utilizing the multi-scale features of input data. Specifically, the MSD-Net achieves functions including multi-scale feature extraction, fusion, and guidance through information interaction between different scales. Spatial aggregation attention is used in MSD-Net to enhance relevant features, which improves the separation of effective reflection waves and noises further. Additionally, a model-based training data generation strategy is devised to ensure the efficiency of learning and the denoising capability of MSD-Net. Compared to conventional denoising algorithms and typical deep learning networks, MSD-Net shows powerful result in suppressing complex seismic noises and generalization.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105682"},"PeriodicalIF":4.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CSIDRS – stable isotope data reduction software for the CAMECA LG SIMS","authors":"Ruby C. Marsden ,&nbsp;Laure Martin ,&nbsp;Matvei Aleshin ,&nbsp;Paul Guagliardo","doi":"10.1016/j.cageo.2024.105683","DOIUrl":"10.1016/j.cageo.2024.105683","url":null,"abstract":"<div><p>Reduction of stable isotope data from the CAMECA LG SIMS is a vital stage in stable isotope analysis. Currently, both visual basic programs and excel spreadsheets, and other in-house programs are used for this data reduction from raw data to final δ values; uncertainty propagations have previously been carried out using the Taylor expansion method. In this paper an open-source program, CSIDRS, which uses Monte Carlo uncertainty propagation, is presented for community use and development. Two example datasets are provided and compared to previous data reduction strategies. Additionally, CSIDRS can be used for quality checking of stable isotope SIMS data.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105683"},"PeriodicalIF":4.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001663/pdfft?md5=4399948c25056847e4a24f879eb7d1c9&pid=1-s2.0-S0098300424001663-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An offline data-driven dual-surrogate framework considering prediction error for history matching","authors":"Jinding Zhang ,&nbsp;Kai Zhang ,&nbsp;Liming Zhang ,&nbsp;Wensheng Zhou ,&nbsp;Chen Liu ,&nbsp;Piyang Liu ,&nbsp;Wenhao Fu ,&nbsp;Xu Chen ,&nbsp;Ziwei Bian ,&nbsp;Yongfei Yang ,&nbsp;Jun Yao","doi":"10.1016/j.cageo.2024.105680","DOIUrl":"10.1016/j.cageo.2024.105680","url":null,"abstract":"<div><p>High computer power has long been a critical ingredient that affects the effectiveness and efficiency of history matching. Data-driven surrogate modeling as an efficient strategy can accelerate the history-matching process by constructing machine learning-based models with high computing speed but reduced accuracy. However, the applicability of surrogate models for different history-matching problems is uncertain due to the influence of data quality and quantity, model architectures, and hyperparameters. To overcome this issue, an offline data-driven dual-surrogate framework (ODDF) that considers the prediction error of surrogate models for history matching is proposed, where one surrogate model predicts the production data of reservoirs and the other one learns the prediction error of the former surrogate. The first surrogate model considers the time-series characteristics of production data using a recurrent neural network, while the second surrogate model regards the two-dimensional spatial correlation characteristics of multivariate prediction error using a fully convolutional neural network. Furthermore, an enhanced error model is applied to incorporate the prediction error into the objective function to reduce the influence of the prediction error on inversion results. Based on this hybrid framework, one can improve the prediction accuracy of surrogate models in history matching when the architectures or hyperparameters of surrogate models are not optimal. Additionally, one can obtain satisfactory results for history matching and uncertainty quantification based on surrogate modeling. The proposed framework is validated on the history matching of two- and three-dimensional reservoir models. The results show that the proposed method is robust in constructing the surrogate models and predicting the production data of reservoirs, which improves the efficiency and reliability of history matching.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105680"},"PeriodicalIF":4.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding 3D seismic data visualization with C++, OpenGL and GLSL","authors":"Farhan Naseer,&nbsp;Vladimir Kazei,&nbsp;Weichang Li","doi":"10.1016/j.cageo.2024.105681","DOIUrl":"10.1016/j.cageo.2024.105681","url":null,"abstract":"<div><p>Seismic data visualization in 3D space is a valuable interpretation tool. Several open-source visualization tools are available. However, little explanation is provided about the inner working of visualization process. The current work discusses a “hello world” equivalent source code for 3D seismic data visualization using Graphical Processing Units (GPUs) with OpenGL and the OpenGL Shading Language (GLSL) programming languages. Rendering is the core process generating 2D image that we see on the screen from the 3D data structures being visualized. Texture mapping-based rendering commonly applied to seismic data starts with creating an OpenGL object called texture. The texture is then mapped over a rectangular object to display a seismic line. The work presented here is performed using OpenGL, GLSL, C++ and Qt toolkit. Here Qt provides the application GUI framework, C++ is used for data I/O, filtering, and sorting, and OpenGL and GLSL are used for 3D rendering. This paper describes the data flow through the application, and two implementations of vertex and fragment GLSL shaders. Visualization is critical for seismic data processing and interpretation. The low-level details of the process presented here will hopefully help readers in obtaining better understanding of the visualization concept and inner working principle and facilitate further development.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105681"},"PeriodicalIF":4.2,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local phase-constrained convolutional autoencoder network for identifying multivariate geochemical anomalies","authors":"Qiliang Liu ,&nbsp;Gusheng Wu ,&nbsp;Zhankun Liu ,&nbsp;Xiancheng Mao ,&nbsp;Jie Yang ,&nbsp;Min Deng","doi":"10.1016/j.cageo.2024.105679","DOIUrl":"10.1016/j.cageo.2024.105679","url":null,"abstract":"<div><p>Autoencoder is a powerful tool for identifying multivariate geochemical anomalies. However, existing autoencoder-based geochemical anomaly detection methods primarily rely on a global reconstruction error (e.g., mean square error) to define the lower limit of geochemical anomalies, neglecting the common, local structure information of geochemical data. This limitation inevitably results in the decreased accuracy of geochemical anomaly identification. This study proposed a local Phase-Constrained Convolutional AutoEncoder network (PC-CAE) for the identification of multivariate geochemical anomalies. Initially, we employed a local Fourier transform to extract phase information from both the original and the reconstructed data. Subsequently, a convolutional autoencoder network was utilized to learn the latent representation of geochemical background, using the local phase difference between the original and reconstructed data to preserve the local data structure related to geology setting. Additionally, an adaptive weighting strategy was employed to mitigate the overfitting issue. The training samples with high reconstruction errors were finally identified as anomalies. We tested the validity of PC-CAE using the stream sediment geochemical dataset collected in the Jiaodong gold province, Eastern China. The results demonstrated that PC-CAE outperforms existing convolutional autoencoder network and spectrum–area multifractal model in identifying multivariate geochemical anomalies associated with Au mineralization.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105679"},"PeriodicalIF":4.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}