Min Yang, Xinqiang Xu, Wanyin Wang, Dongming Zhao, Wei Zhou
{"title":"3D Gravity Fast Inversion Based on Krylov Subspace Methods","authors":"Min Yang, Xinqiang Xu, Wanyin Wang, Dongming Zhao, Wei Zhou","doi":"10.1093/jge/gxad091","DOIUrl":null,"url":null,"abstract":"Abstract Mapping the density contrast through the 3D gravity inversion can help detect the goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. Krylov subspace method is commonly used for large linear problems due to its high computational efficiency and low storage requirement. In this study, two classical algorithms of Krylov subspace method, namely the Generalized Minimum Residual method and the Conjugate Gradient method, are applied to 3D gravity inversion. Based on the recovered models of the deep mineral and the shallow L-shaped tunnel models, it was found that the Generalized Minimum Residual method provided similar density contrast results as the Conjugate Gradient method. The obtained inversion results of density contrast corresponded well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D distribution of Fe content underground was obtained by inverting the measured gravity data from Olympic Dam in Australia. The recovered results correspond well with the distribution of Fe content in the geological profile collected. The accuracy of inversion using the Generalized Minimum Residual method was similar to that of the Conjugate Gradient method under the same conditions. However, the Generalized Minimum Residual method had a faster convergence speed and increased inversion efficiency by about 90%, greatly reducing the inversion time and improves the inversion efficiency.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":"132 45","pages":"0"},"PeriodicalIF":1.6000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysics and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jge/gxad091","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: 0
Abstract
Abstract Mapping the density contrast through the 3D gravity inversion can help detect the goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. Krylov subspace method is commonly used for large linear problems due to its high computational efficiency and low storage requirement. In this study, two classical algorithms of Krylov subspace method, namely the Generalized Minimum Residual method and the Conjugate Gradient method, are applied to 3D gravity inversion. Based on the recovered models of the deep mineral and the shallow L-shaped tunnel models, it was found that the Generalized Minimum Residual method provided similar density contrast results as the Conjugate Gradient method. The obtained inversion results of density contrast corresponded well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D distribution of Fe content underground was obtained by inverting the measured gravity data from Olympic Dam in Australia. The recovered results correspond well with the distribution of Fe content in the geological profile collected. The accuracy of inversion using the Generalized Minimum Residual method was similar to that of the Conjugate Gradient method under the same conditions. However, the Generalized Minimum Residual method had a faster convergence speed and increased inversion efficiency by about 90%, greatly reducing the inversion time and improves the inversion efficiency.
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.