The Nash-MTL-STCN Method For Prestack Three-Parameter Inversion

TL;DR

This paper introduces Nash-MTL-STCN, a semi-supervised multi-task learning approach using Nash equilibrium for prestack three-parameter inversion, improving accuracy by resolving gradient conflicts in deep learning models.

Contribution

It proposes a novel Nash equilibrium-based multi-task learning framework with hierarchical feature extraction to enhance inversion accuracy and stability in seismic data processing.

Findings

Outperforms four existing non-heuristic MTL methods in accuracy.

Alleviates gradient conflicts common in multi-task learning.

Validated with both experimental and field data.

Abstract

Deep learning (DL) techniques have been widely used in prestack three-parameter inversion to address its ill-posed problems. Among these DL techniques, Multi-task learning (MTL) methods can simultaneously train multiple tasks, thereby enhancing model generalization and predictive performance. However, existing MTL methods typically adopt heuristic or non-heuristic approaches to jointly update the gradient of each task, leading to gradient conflicts between different tasks and reducing inversion accuracy. To address this issue, we propose a semi-supervised temporal convolutional network (STCN) based on Nash equilibrium (Nash-MTL-STCN). Firstly, temporal convolutional networks (TCN) with non-causal convolution and convolutional neural networks (CNNs) are used as multi-task layers to extract the shared features from partial angle stack seismic data, with CNNs serving as the single-task layer. Subsequently, the feature mechanism is utilized to extract shared features in the multi-task layer through hierarchical processing, and the gradient combination of these shared features is treated as a Nash game for strategy optimization and joint updates. Ultimately, the overall utility of the three-parameter is maximized, and gradient conflicts are alleviated. In addition, to enhance the network's generalization and stability, we have incorporated geophysical forward modeling and low-frequency models into the network. Experimental results demonstrate that the proposed method overcomes the gradient conflict issue of the conventional MTL methods with constant weights (CW) and achieves higher precision than four widely used non-heuristic MTL methods. Further field data experiments also validate the method's effectiveness.