Pertinent Multigate Mixture-of-Experts-Based Prestack Three-Parameter Seismic Inversion

Abstract

Seismic inversion is a method used to identify spatial structure and obtain physical properties of underground strata by processing seismic data. As a data-driven approach, deep learning (DL) is widely used in pre-stack three-parameter inversion to solve its non-linearity and ill-posed problems. However, traditional DL-based methods involve the construction of a separate network for each task and thus ignore the correlations between different tasks. Multi-task learning (MTL) aims to promote the effectiveness of each task with implicit information amplification by training multiple tasks simultaneously. However, information sharing in conventional MTL may cause negative effects on parallel tasks. To solve this problem, a novel multi-task learning method, called pertinent multi-gate mixture-of-experts (PMMOE), was proposed for pre-stack three-parameter inversion. PMMOE introduces mixture-of-experts (MOE) structure for multi-task learning and creatively divides experts into three special experts and a shared expert. In PMMOE, the input data of different experts are discrepant, enabling the retrieval of different features for different tasks. Experiments revealed that our proposed method has higher accuracy than other methods, and the inversion results of synthetic data and field data further demonstrate the effectiveness of our proposed method.