Regularization of the Solution of the Inverse VES Problem by the Contrast Stabilization Method: Testing the Algorithm on Model Data
А. А. Бобачев, А. В. Дещеревский, A. Ya. Sidorin
Abstract
А. А. Бобачев, А. В. Дещеревский, A. Ya. Sidorin
Abstract
Abstract Earlier, it was shown that conventional algorithms for solving the inverse VES problem cannot achieve the accuracy required for precision monitoring of a geoelectric section, and regularized algorithms were proposed to improve the accuracy and stability of solving the inverse VES problem. In this paper, we test the resistivity contrast stabilization algorithm on synthetic data. For modeling, a geoelectric section is used, similar to the section of the Garm test site both in the set of layers and their resistivities, and in the characteristics of seasonal variations, as well as noise. It is shown that regularization of the inverse problem greatly reduces errors. The most significant effect is achieved by suppressing the buildup of resistivity. Estimates are obtained for the accuracy in solving the inverse problem, which can be achieved when working with experimental data.
OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Abstract Earlier, it was shown that conventional algorithms for solving the inverse VES problem cannot achieve the accuracy required for precision monitoring of a geoelectric section, and regularized algorithms were proposed to improve the accuracy and stability of solving the inverse VES problem. In this paper, we test the resistivity contrast stabilization algorithm on synthetic data. For modeling, a geoelectric section is used, similar to the section of the Garm test site both in the set of layers and their resistivities, and in the characteristics of seasonal variations, as well as noise. It is shown that regularization of the inverse problem greatly reduces errors. The most significant effect is achieved by suppressing the buildup of resistivity. Estimates are obtained for the accuracy in solving the inverse problem, which can be achieved when working with experimental data.
Key concepts: Regularization (linguistics), Inverse problem, Inverse, Algorithm, Contrast (vision), Stability (learning theory), Inverse method, Set (abstract data type)