Encoder-Inverter Framework for Seismic Acoustic Impedance Inversion

TL;DR

The paper introduces an Encoder-Inverter framework that transforms seismic inversion into a linear problem using high-dimensional features, improving accuracy and robustness in geophysical exploration.

Contribution

It proposes a novel encoder-inverter architecture with auxiliary models and heterogeneous structure to enhance seismic impedance inversion performance.

Findings

Achieves superior inversion accuracy over existing methods.

Demonstrates robustness across benchmark datasets.

Enables reproducibility with open-source data and code.

Abstract

Seismic acoustic impedance inversion is a challenging problem in geophysical exploration, primarily due to the scarcity of well-logging data and the inherent nonlinearity of the task. Most existing inversion methods, including semi-supervised learning approaches, still face limitations in accuracy and robustness. In this work, we propose a novel Encoder-Inverter framework that maps continuous seismic traces into high-dimensional linear features, thereby transforming the inversion task into a linear extrapolation or interpolation problem to enhance stability and performance. To achieve this, we introduce two auxiliary models to assist in encoder training and adopt a heterogeneous model structure to prevent shortcut learning, enabling the extraction of more generalizable and effective linear features. We evaluate the proposed method on widely used benchmark datasets, and experimental results demonstrate that our approach achieves superior inversion accuracy and robustness compared to previous methods. To promote reproducibility, we will also open-source the data and code.