{"title":"Improved spatial understanding of induced seismicity hazard from the discretization of a curved fault surface","authors":"Kevin L. McCormack, Philip J. Smith","doi":"10.1007/s10596-024-10276-z","DOIUrl":null,"url":null,"abstract":"<p>In some geomechanical treatments of induced seismicity, the fault surface is idealized to be a plane. We depart from this assumption by comparing a discretization model and a kriging model, both of which allow the incorporation of rugosity, roughness, and curvature into the fault surface and subsequent geomechanical models of hazard. We test the Hogback Flexural Faults of the San Juan Basin, which could potentially pose a problem for induced seismicity in a carbon sequestration project in the northwestern portion of the basin. The discretization model emmeshes data about the location of the fault surface in three-dimensional space into hexagonally close-packed spheres. Each sphere that contains enough data is termed a region and Bayes’ Law is used to find a distribution of strikes and dips that describe the data within the region. The kriging model uses Gaussian processes to interpolate and extrapolate a surface through all data points. The results show that the discretized regions possess, in general, lower Coulomb failure functions, but the uncertainty in the distributions, i.e., the ranges, becomes greater as the discretization increases due to overfitting. The majority of the uncertainty in both the discretization model and the kriging model is contained in the geomechanical priors. Finally, the discretization and kriging of the fault surface elucidates locations with higher Coulomb failure functions.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"10 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Geosciences","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s10596-024-10276-z","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In some geomechanical treatments of induced seismicity, the fault surface is idealized to be a plane. We depart from this assumption by comparing a discretization model and a kriging model, both of which allow the incorporation of rugosity, roughness, and curvature into the fault surface and subsequent geomechanical models of hazard. We test the Hogback Flexural Faults of the San Juan Basin, which could potentially pose a problem for induced seismicity in a carbon sequestration project in the northwestern portion of the basin. The discretization model emmeshes data about the location of the fault surface in three-dimensional space into hexagonally close-packed spheres. Each sphere that contains enough data is termed a region and Bayes’ Law is used to find a distribution of strikes and dips that describe the data within the region. The kriging model uses Gaussian processes to interpolate and extrapolate a surface through all data points. The results show that the discretized regions possess, in general, lower Coulomb failure functions, but the uncertainty in the distributions, i.e., the ranges, becomes greater as the discretization increases due to overfitting. The majority of the uncertainty in both the discretization model and the kriging model is contained in the geomechanical priors. Finally, the discretization and kriging of the fault surface elucidates locations with higher Coulomb failure functions.
期刊介绍:
Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing.
Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered.
The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.