A Joint Inversion-Segmentation approach to Assisted Seismic Interpretation

TL;DR

This paper introduces a joint inversion-segmentation method for seismic interpretation that simultaneously estimates subsurface properties and segments the seismic data, enhancing structural understanding and horizon detection.

Contribution

It presents a novel inverse problem formulation that jointly estimates acoustic impedance and segmentation, optimized with a Primal-Dual algorithm, improving seismic interpretation accuracy.

Findings

Effective on synthetic datasets

Successful application to field data

Improves horizon detection accuracy

Abstract

Structural seismic interpretation and quantitative characterization are historically intertwined processes. The latter provides estimates of properties of the subsurface which can be used to aid structural interpretation alongside the original seismic data and a number of other seismic attributes. In this work, we redefine this process as a inverse problem which tries to jointly estimate subsurface properties (i.e., acoustic impedance) and a piece-wise segmented representation of the subsurface based on user-defined macro-classes. By inverting for the quantities simultaneously, the inversion is primed with prior knowledge about the regions of interest, whilst at the same time it constrains this belief with the actual seismic measurements. As the proposed functional is separable in the two quantities, these are optimized in an alternating fashion, where each subproblem is solved using a Primal-Dual algorithm. Subsequently, each class is used as input to a workflow which aims to extract the perimeter of the detected shapes and to produce unique horizons. The effectiveness of the proposed method is illustrated through numerical examples on synthetic and field datasets.