Encoder-Inverter framework for seismic acoustic impedance inversion

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.