A non-parametric resampling method for uncertainty analysis of geophysical inverse problems

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Rahmati Shad Ashkan; Ghanati Reza; Fallah safari Mahdi

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Journal Paper

Paper Information

Journal: International Journal of Mining and Geo-Engineering Year:2025 | Volume:59 | Issue:1 Page(s): 61-67

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Information Journal Paper

Title

A non-parametric resampling method for uncertainty analysis of geophysical inverse problems

Author(s)

Rahmati Shad Ashkan | Ghanati Reza | Fallah safari Mahdi | Issue Writer Certificate

Keywords

Geophysical Inverse problem

Moving block-wise bootstrap resampling

Uncertainty analysis

Abstract

Due to non-uniqueness of Geophysical Inverse problems and measurement errors, the inversion uncertainties within the model parameters are one of the most significant necessities imposed on any modern inverse theory. Uncertainty analysis consists of finding equivalent models which sufficiently fit the observed data within the same error bound and are consistent with the prior information. In this paper, we present a non-parametric block-wise bootstrap resampling method called moving block bootstrapping (MBB) for Uncertainty analysis of geophysical inverse solutions. In contrast to conventional bootstrap in which the dependence structure of data is ignored, the block bootstrap considers the dependency and correlation among the observed data by resampling not individual observations, but blocks of observations. The application of the proposed strategy to different synthetic inverse problems as well as to synthetic and real datasets of geo-electrical sounding inversion is presented. The results demonstrated that through the block bootstrap, it is possible to effectively sample the equivalence regions for a given error bound.

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