Computers & Geosciences最新文献

{"title":"Super-resolution of digital elevation models by using multiple-point statistics and training image selection","authors":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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}

{"title":"Nonlinear effect assessment for seismic ground motions of sedimentary basins based on deep neural networks","authors":"","doi":"10.1016/j.cageo.2024.105678","DOIUrl":"10.1016/j.cageo.2024.105678","url":null,"abstract":"<div><p>Rapid post-earthquake assessment of nonlinear features in geotechnical soils within sedimentary basin is crucial for quantifying site response and seismic risk zoning. However, traditional methods like the classical spectral ratio approach suffer from drawbacks such as insufficient effective data and low efficiency in calculating nonlinear degree indexes for evaluating nonlinear features. To address this issue, this study explores the use of deep neural network (DNN) algorithms as a solution. Initially, sites within sedimentary basin in Japan are identified. The results of horizontal-vertical spectral ratios (HVSR) and different proxy conditions (ground motion intensity and site conditions) are utilized to develop and train DNN models. The dependence of the nonlinear features on various combinations of ground motion intensity and site conditions is analyzed by the DNN model. Based on the differences between the values obtained under weak and strong earthquakes, evaluation indexes of nonlinear features, including the degree of nonlinearity (DNL), absolute degree of nonlinearity (ADNL), and percent nonlinear site response (PNL), are calculated. This allows a rapid assessment of the regional nonlinear features of sedimentary basins. The DNN model is used to determine the nonlinear features of several soil profiles under different ground motion intensity conditions. The results demonstrate a strong consistency between DNL, ADNL, and PNL with variations in ground motion intensity, while showing weaker consistency with site conditions. Finally, a real earthquake case study is incorporated to assess the practicality of the proposed procedure. This study provides a reference for the study of earthquake engineering problems using DNN models.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846043","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":"Stochastic Gradient Descent optimization to estimate the power-law fractal index in fracture networks","authors":"","doi":"10.1016/j.cageo.2024.105677","DOIUrl":"10.1016/j.cageo.2024.105677","url":null,"abstract":"<div><p>Fractures greatly impact hydrocarbon exploration as they modify fluid flow properties within reservoir rocks, creating an interconnected network. The hydrocarbon reservoirs are often difficult to assess, and the methods employed in acquiring information from these locations offer too sparse data or have a low spatial resolution. Otherwise, outcrops allow fracture characterization directly in the field or using 2D and 3D digital representations of outcrops. These fracture networks, usually related to fractal propagation and power-law distribution parameters, can be used as data sources providing useful information when properly adjusted to the reservoir simulation scale. In this sense, attribute estimators, like the Maximum Likelihood Estimator (MLE) and algorithms using MLE, have been widely used for their robustness when compared to linear regression estimators. However, due to the challenges in the power-law characterization, such as the large fluctuations that occur in the tail of the distribution, non-optimum values can be obtained despite the effectiveness of the MLE. Our work proposes the use of an optimization algorithm based on Stochastic Gradient Descent (SGD) with momentum to obtain best-fitting parameters for power-law distributions. The proposed method was first evaluated with synthetic data and several goodness-of-fitness metrics and later using empirical data obtained from fracture characterization in the Digital Outcrop Model (DOM) of a reservoir analogue outcrop. Stochastic DFN sampling based on empirical data was also used to simulate censoring effects. The results showed that the SGD method provided better distribution fitting than other methods based on the MLE when using empirical data while presenting reduced bias when using synthetic data. The estimation of power-law parameters in stochastic DFN data also presented the best-fitting results when applying the proposed method. In conclusion, the proposed optimization method proved a valuable alternative to estimate power-law distributions.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001602/pdfft?md5=2c78739bc321fb6c06e81fb2f158a6f8&pid=1-s2.0-S0098300424001602-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696090","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":"Dual level attention based lightweight vision transformer for streambed land use change classification using remote sensing","authors":"Kamakhya Bansal,&nbsp;Ashish Kumar Tripathi","doi":"10.1016/j.cageo.2024.105676","DOIUrl":"https://doi.org/10.1016/j.cageo.2024.105676","url":null,"abstract":"<div><p>Due to rapid urbanization, rising food demand, and changed precipitation patterns, the waterbodies are contracting their former beds. The continuous shrinking of waterbodies is deteriorating the vital cultural, supporting, provisioning, and regulating services. Thus, understanding and mitigating the impacts of streambed land cover change is crucial for maintaining healthy aquatic ecosystems and improving flood resilience of surrounding population. The existing works use high-resolution aerial imagery focusing on large waterbodies, while ignoring the most vulnerable floodplains of innumerous small water bodies due to high inter-class similarity. This limits the ability to perform a temporal analysis of land cover change along small water bodies. The present work aims to resolve this issue using open-source satellite imagery and taking patched samples along the boundary of small water bodies to identify long-term changes in land cover patterns. Sentinel-2 and Landsat 50 acquired satellite images were used to identify the land cover of this colonized stream bed. The data of Landsat 50 served as historical reference for identifying the changed land use. To capture spatial hierarchies in satellite images effectively, in this paper, a novel dual attention-based vision transformer has been developed for land-cover classification in four categories namely, water, built-up, siltation, and vegetation. The developed model is trained on the data collected from three potential sites in India. The experimental results are validated against seven state-of-the-art deep learning models. The results reveal that the proposed method outperformed all the considered methods by achieving accuracy and precision of 88.4% and 88.9%, respectively, while consuming the least number of parameters. The results reaffirm the concretization and erosion of nature’s flood buffers for economic advancement.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604925","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":"FlexLogNet: A flexible deep learning-based well-log completion method of adaptively using what you have to predict what you are missing","authors":"","doi":"10.1016/j.cageo.2024.105666","DOIUrl":"10.1016/j.cageo.2024.105666","url":null,"abstract":"<div><p>Well logs are essential tools for understanding the characteristics of subsurface formations and exploring petroleum resources. However, well logs are often missing randomly due to cost constraints, instrument failures, or other factors. Many methods have been developed for completing missing well logs, but these methods are all based on fixed types of known well-log inputs to predict specific types of missing logs. This fixed input–output mode severely limits the application of these methods in actual data, where the known and missing well-log types are often varying. To address this problem, we propose a hybrid deep learning method with two heads of heterogeneous graph neural network (HGNN) and fully connected network (FCN) to achieve mutual prediction among multiple types of well logs. It can adaptively use all known well logs to predict any missing well logs, achieving a very flexible and practical well log completion function of using what you have to complete what you are missing. Specifically, the HGNN head infers the inter-relationships among multiple well logs to predict normalized logs that contain detailed information, which achieved by using multiple independent kernels to extracting and aggregating the features of the multiple logs. The FCN head estimates the global statistics of the predicted logs, including means and standard deviations, for de-normalizing the well logs estimated by the HGNN head. Both the HGNN and FCN heads are trained simultaneously by a hybrid loss function to ensure the consistency of their predictions. Furthermore, we present an adaptive training strategy that leverages all well logs, including those with missing segments. We demonstrate the capability of our model using four well logs: gamma ray (GR), bulk density (RHOB), neutron porosity (NPHI), and compressional waves sonic (DTC). Theoretically, the model trained on other logs can also predict each other. Our approach yields high Pearson correlation coefficients and small root mean square error on a dataset obtained from an offshore North Sea field near Norway, demonstrating the efficacy of our proposed technique.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630686","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}