Computers & Geosciences最新文献

{"title":"Nonlinear effect assessment for seismic ground motions of sedimentary basins based on deep neural networks","authors":"Jia-wei Zhao ,&nbsp;Si-bo Meng ,&nbsp;Zhong-xian Liu ,&nbsp;Cheng-cheng Li ,&nbsp;Kang Tang","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":"191 ","pages":"Article 105678"},"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":"Graciela Racolte ,&nbsp;Ademir Marques Jr ,&nbsp;Eniuce Menezes ,&nbsp;Leonardo Scalco ,&nbsp;Delano Menecucci Ibanez ,&nbsp;Mauricio Roberto Veronez ,&nbsp;Luiz Gonzaga Jr","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":"191 ","pages":"Article 105677"},"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":"191 ","pages":"Article 105676"},"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":"Chuanli Dai,&nbsp;Xu Si,&nbsp;Xinming Wu","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":"191 ","pages":"Article 105666"},"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}

{"title":"pySimFrac: A Python library for synthetic fracture generation and analysis","authors":"Eric Guiltinan,&nbsp;Javier E. Santos,&nbsp;Prakash Purswani,&nbsp;Jeffrey D. Hyman","doi":"10.1016/j.cageo.2024.105665","DOIUrl":"10.1016/j.cageo.2024.105665","url":null,"abstract":"<div><p>In this paper, we introduce <span>pySimFrac</span> , an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. <span>pySimFrac</span> allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, <span>pySimFrac</span> includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present <span>pySimFrac</span> along with integration examples and discuss the ability to extend <span>pySimFrac</span> from a single complex fracture to complex fracture networks.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105665"},"PeriodicalIF":4.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001481/pdfft?md5=5d27e62672e4e49e6eb5ec852f09ad80&pid=1-s2.0-S0098300424001481-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639204","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":"Chroma: A MATLAB package and open-source platform for biomarker data processing and automatic index calculations","authors":"Julian Traphagan,&nbsp;Guangsheng Zhuang","doi":"10.1016/j.cageo.2024.105675","DOIUrl":"https://doi.org/10.1016/j.cageo.2024.105675","url":null,"abstract":"<div><p>The molecular ratio indices of biological markers (biomarkers), such as the Carbon Preference Index (CPI) or <em>P</em>_aq, are frequently used as proxies for paleoclimatic and palaeoecological conditions. These indices are regularly extracted from the relative abundances of target molecules detected by a Gas Chromatography analyzer with a Flame Ionization Detector (GC-FID). Despite their use in biogeochemical studies for over a half-century, it remains common procedure to quantify the abundance of individual compounds by manual integration of chromatogram peaks (i.e., interpret baselines visually and characterize peaks by hand), which is time consuming and can lead to inconsistent results. Here, we introduce a new MATLAB package (Chroma) for the automatic detection and integration of standard-referenced biomarker abundances and the calculation of a variety of established hydrocarbon indices commonly reported in the published literature. The algorithm identifies the detector response timing of specific target peaks in a sample chromatogram by cross-referencing to a standard (e.g., Mix-A6, Schimmelmann, Indiana University Bloomington), then calculates the peak areas for an approximation of molecular abundance. This new toolkit for automatic and rapid integration of GC-acquired data provides a consistent and reproducible approach for the calculation of hydrocarbon indices and offers a standardized inter-laboratory platform for data comparisons and exchange. We validate the utility of the Chroma package with the chromatograms of plant wax <em>n</em>-alkanes, a widely used proxy for ecology and hydrology, from six stratigraphic sections in the Tibetan Plateau. Chroma is an effective tool for efficient data processing and will continuously evolve to accommodate extended uses in related areas of biomarker research beyond <em>n</em>-alkanes.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105675"},"PeriodicalIF":4.2,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541909","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":"Ensemble hindcasting of winds and waves for the coastal and oceanic region of Southern Brazil","authors":"Gustavo Souza Correia ,&nbsp;Leandro Farina ,&nbsp;Claudia Klose Parise ,&nbsp;Gabriel Bonow Münchow ,&nbsp;Rita de Cássia M. Alves","doi":"10.1016/j.cageo.2024.105658","DOIUrl":"https://doi.org/10.1016/j.cageo.2024.105658","url":null,"abstract":"<div><p>When hindcasting wave fields of storm events with wave models, the quality of the results strongly depends on several factors such as the computational grid resolution and the accuracy of the atmospheric forcing. In an effort to minimize the uncertainties involved in this process, three ocean wave and surface wind ensemble hindcast systems were established using the Simulating WAves Nearshore (SWAN) model and Weather Research and Forecasting Model (WRF) with atmospheric data from ERA5 Ensemble of Data Assimilation (EDA) as well as deterministic high-resolution systems. We established three ensemble systems to tackle this: SWN-ERA5EDA using ERA5-EDA global reanalyses winds, SWN-WRFERA5 employing WRF downscaling of ERA5-EDA, and SWN-WRFPPar incorporating WRF multi-physics runs for dynamical downscaling. This study focuses on extreme events in southern Brazil during an austral winter, highlighting the importance of increasing the resolution of ocean wave and surface wind data to provide more accurate and reliable forecasts for coastal and marine activities. Our analyses revealed that atmospheric downscaling performed with WRF not only increased the ensemble spread by significant amounts but also enhanced the sharpness of the wave ensemble hindcast compared with those based solely on the ERA5 EDA. Specifically, for the Rio Grande buoy location, the significant wave height (<span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>) from the SWN-WRFERA5 system showed an increase of 0.5 over SWN-ERA5, and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> from the SWN-WRFPPar system increased by 0.6. Additionally, the wave peak period (<span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>) for both SWN-WRFERA5 and SWN-WRFPPar systems experienced an increase of 1.2 compared to SWN-ERA5. Additionally, the ensemble produced with the WRF multi-physics approach captured peaks in the significant wave height registered by the buoy that were not reproduced by other ensemble systems, demonstrating an improvement in predictive accuracy, despite presenting a smaller correlation between spread and strong localized wave variations. Besides quantifying the hindcast error, the methodology presented in this work also offers a way to generate alternative and improved representations of past extreme events. This approach significantly contributes to our ability to sample recent climatic conditions and expand the dataset for statistical analyses, which is especially valuable for ocean and coastal engineering projects. This study underscores the critical role of enhancing computational and methodological approaches in wave modeling for better understanding and mitigating the impacts of extreme weather events on coastal and oceanic regions.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105658"},"PeriodicalIF":4.2,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593892","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":"Prediction and mapping of soil thickness in alpine canyon regions based on whale optimization algorithm optimized random forest: A case study of Baihetan Reservoir area in China","authors":"Zhenghai Xue ,&nbsp;Xiaoyu Yi ,&nbsp;Wenkai Feng ,&nbsp;Linghao Kong ,&nbsp;Mingtang Wu","doi":"10.1016/j.cageo.2024.105667","DOIUrl":"https://doi.org/10.1016/j.cageo.2024.105667","url":null,"abstract":"<div><p>Accurate measurements of soil thickness are crucial for assessing landslide susceptibility, slope stability, and soil conservation. However, there is a relative scarcity of research on the spatial distribution of soil thickness in areas with complex terrains, such as alpine canyon regions. Given this research gap, the aim of this study is to develop a reliable method for predicting soil thickness in these regions. In this study, the Baihetan Reservoir area (China), characterized by typical alpine canyon regions, was selected as the research site. The slope index (SI) and slope (S) factor, in addition to other factors, were used to predict soil thickness. Subsequently, the random forest (RF) model and its version based on the whale optimization algorithm (WOA) were used to model soil thickness. The results showed that compared to the other models, the WOA-RF model, which considers the slope index factor, performed best in 100 tests, achieving the highest coefficient of determination (R² = 0.93) and the lowest root mean square error (RMSE = 5.6 m). Furthermore, the soil thickness data from the WOA-RF (SI) model displayed the highest congruence with the soil thickness data obtained from environmental noise measurements. Therefore, predicting soil thickness in alpine canyon regions by comprehensively considering environmental variables and using the WOA-RF model is feasible. The resulting soil thickness maps can serve as key fundamental inputs for further analysis.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105667"},"PeriodicalIF":4.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485229","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":"Efficient modeling of fractional Laplacian viscoacoustic wave equation with fractional finite-difference method","authors":"Bingluo Gu ,&nbsp;Shanshan Zhang ,&nbsp;Xingnong Liu ,&nbsp;Jianguang Han","doi":"10.1016/j.cageo.2024.105660","DOIUrl":"https://doi.org/10.1016/j.cageo.2024.105660","url":null,"abstract":"<div><p>The fractional viscoacoustic/viscoelastic wave equation, which accurately quantifies the frequency-independent anelastic effects, has been the focus of seismic industry in recent years. The pseudo-spectral (PS) method stands as one of the most widely used numerical methods for solving the fractional wave equation. However, the PS method often suffers from low accuracy and efficiency, particularly when modeling wave propagation in heterogeneous media. To address these issues, we propose a novel and efficient fractional finite-difference (FD) method for solving the wave equation with fractional Laplacian operators. This method develops an arbitrary high-order FD operator via the generating function of our fractional FD (F-FD) scheme, enhancing accuracy with L2-optimal FD coefficients. Similar to classic FD methods, our F-FD method is characterized by straightforward programming and excellent 3D extensibility. It surpasses the PS method by eliminating the need for Fast Fourier Transform (FFT) and inverse-FFT (IFFT) operations at each time step, offering significant benefits for 3D applications. Consequently, the F-FD method proves more adept for wave-equation-based seismic data processes like imaging and inversion. Compared with existing F-FD methods, our approach uniquely approximates the entire fractional Laplacian operator and stands as a local numerical algorithm, with an adjustable F-FD operator order based on model parameters for enhanced practicality. Accuracy analyses confirm that our method matches the precision of the PS method with a correctly ordered F-FD operator. Numerical examples show that the proposed method has good applicability for complex models. Finally, we have carried out reverse time migration on the Marmousi-2 model, and the imaging profiles indicate that the proposed method can be effectively applied to seismic imaging, demonstrating good practicability.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105660"},"PeriodicalIF":4.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541910","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":"FaultQuake: An open-source Python tool for estimating Seismic Activity Rates in faults","authors":"Nasrin Tavakolizadeh ,&nbsp;Hamzeh Mohammadigheymasi ,&nbsp;Francesco Visini ,&nbsp;Nuno Pombo","doi":"10.1016/j.cageo.2024.105659","DOIUrl":"https://doi.org/10.1016/j.cageo.2024.105659","url":null,"abstract":"<div><p>In regions experiencing ongoing aseismic deformation, fault’s Activity Rate (AR) calculations often lead to an overestimation of hazard potential. This study proposes a novel methodology that integrates the Seismic Coupling Coefficient (SCC) into the fault Seismic Activity Rate (SAR) calculation process to discriminate seismic moment rates. We introduce FaultQuake, an open-source Python tool equipped with a Graphical User Interface (GUI), designed to implement this methodology and accurately estimate SAR for faults. These activity rates can be included in Probabilistic Seismic Hazard Assessment (PSHA) frameworks and assist in differentiating the seismic and aseismic deformation. FaultQuake also presents an innovative embedded workflow, the Optimal Value Computation Workflow (OVCW), based on Conflation of Probabilities (CoP), for calculating the Maximum Magnitude (<span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></math></span>) from the empirical relationships and the observed magnitudes (<span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>o</mi><mi>b</mi><mi>s</mi></mrow></msub></math></span>) assigned to a single fault. This enhancement improves the estimation of seismic moment rates and the SAR calculation process. FaultQuake outputs are provided in the format of OpenQuake engine input files to facilitate the PSHA process. We present a sample case study focusing on the PSHA of a region in southern Iran characterized by a substantial aseismic deformation to illustrate the practical application of FaultQuake in seismic hazard analysis. Peak Ground Acceleration (PGA) maps for 10% and 2% Probabilities of Exceedance (PoE) are plotted to compare PGAs with and without applying the FaultQuake algorithm. The results provide an enhanced view of the area’s hazard with mitigation of the overestimation, resulting in more representative hazard maps. The source codes of FaultQuake are available at the <span>FaultQuake</span><svg><path></path></svg> GitHub repository, contributing to the computer and geoscience community.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"191 ","pages":"Article 105659"},"PeriodicalIF":4.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541899","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}