Space-Varying Iterative Restoration of 2-D Inversion Models Computed from Marine CSEM Data

TL;DR

This paper introduces a space-varying deconvolution method using iterative algorithms to enhance the resolution of marine CSEM inversion models, demonstrated on synthetic and real data from the Barents Sea.

Contribution

It proposes a novel space-varying deconvolution approach with iterative algorithms to improve marine CSEM inversion resolution, including a blind deconvolution variant.

Findings

Improved resolution of inversion results in synthetic data

Enhanced agreement with known targets in field data

Demonstrated effectiveness of iterative deconvolution methods

Abstract

Marine Controlled Source Electromagnetic (CSEM) is employed both in large-scale geophysical applications as well as within exploration of hydrocarbons and gas hydrates. Due to the diffusive character of the EM field only very low frequencies are used leading to inversion results with rather low resolution. In this paper, we calculate the resolution matrix associated with the inversion and derive the corresponding point spread functions (PSFs). The PSFs give information about how much the actual inversion has been blurred, and use of space-varying deconvolution can therefore further improve the inversion result. The actual deblurring is carried out by use of the nonnegative flexible conjugate gradient algorithm for least squares problem (NN-FCGLS), which is a fast iterative restoration technique. For completeness, we also introduce results obtained by use of a blind deconvolution algorithm based on maximum likelihood estimation (MLE) with unknown PSFs. The potential of the proposed approaches have been demonstrated using both complex synthetic data as well as field data acquired at the Wisting oil field in the Barents Sea. In both cases, the resolution of the final inversion result has improved and shows better agreement with the known target area.