{"title":"Evaluating imaging uncertainty associated with the near surface and added value of vertical arrays using Bayesian seismic refraction tomography","authors":"I. Silvestrov, A. Egorov, A. Bakulin","doi":"10.1093/jge/gxad044","DOIUrl":null,"url":null,"abstract":"\n Traditional tomographic methods do not consider the uncertainties associated with near-surface velocities and static corrections and provide a deterministic solution to the estimation problem. However, these uncertainties significantly impact structural mapping and interpretation of seismic imaging results. On the other hand, Bayesian first-arrival tomography provides multiple near-surface models that fit observed traveltimes equally well and enable the study of potential solution distributions. We demonstrate this approach on a complex synthetic near-surface model, representative of arid environments, to quantify associated velocity and statics uncertainties. We evaluate two different parameterizations for subsurface velocities in the context of near-surface Bayesian tomography: Voronoi tessellation with natural neighbor interpolation and the more conventional Delaunay triangulation with linear interpolation. Our analysis shows that the Voronoi cell parameterization with natural neighbor interpolation is more appropriate for this problem. Finally, the new approach is applied to compare two alternative acquisition geometries comprising conventional surface receivers and surface receivers augmented with vertical receiver arrays. The results demonstrate that adding vertical receiver arrays to conventional surface receivers can significantly reduce the near-surface velocity uncertainty and thus increases the accuracy of the seismic imaging results. Furthermore, the study shows that Bayesian tomography can be used as a tool for evaluating different source and receiver geometries during the acquisition design stage.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysics and Engineering","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1093/jge/gxad044","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 1
Abstract
Traditional tomographic methods do not consider the uncertainties associated with near-surface velocities and static corrections and provide a deterministic solution to the estimation problem. However, these uncertainties significantly impact structural mapping and interpretation of seismic imaging results. On the other hand, Bayesian first-arrival tomography provides multiple near-surface models that fit observed traveltimes equally well and enable the study of potential solution distributions. We demonstrate this approach on a complex synthetic near-surface model, representative of arid environments, to quantify associated velocity and statics uncertainties. We evaluate two different parameterizations for subsurface velocities in the context of near-surface Bayesian tomography: Voronoi tessellation with natural neighbor interpolation and the more conventional Delaunay triangulation with linear interpolation. Our analysis shows that the Voronoi cell parameterization with natural neighbor interpolation is more appropriate for this problem. Finally, the new approach is applied to compare two alternative acquisition geometries comprising conventional surface receivers and surface receivers augmented with vertical receiver arrays. The results demonstrate that adding vertical receiver arrays to conventional surface receivers can significantly reduce the near-surface velocity uncertainty and thus increases the accuracy of the seismic imaging results. Furthermore, the study shows that Bayesian tomography can be used as a tool for evaluating different source and receiver geometries during the acquisition design stage.
期刊介绍:
Journal of Geophysics and Engineering aims to promote research and developments in geophysics and related areas of engineering. It has a predominantly applied science and engineering focus, but solicits and accepts high-quality contributions in all earth-physics disciplines, including geodynamics, natural and controlled-source seismology, oil, gas and mineral exploration, petrophysics and reservoir geophysics. The journal covers those aspects of engineering that are closely related to geophysics, or on the targets and problems that geophysics addresses. Typically, this is engineering focused on the subsurface, particularly petroleum engineering, rock mechanics, geophysical software engineering, drilling technology, remote sensing, instrumentation and sensor design.