Shear wave velocity prediction using Long Short-Term Memory Network with generative adversarial mechanism.

ABSTRACT: Shear wave velocity (Vs) serves as a crucial petrophysical parameter for subsurface characterization, yet its acquisition remains challenging. While long short-term memory (LSTM) networks have emerged as the predominant solution for Vs prediction by synthesizing contextual relationships among conventional logging curves, existing implementations often overlook characteristic discrepancies between training and prediction datasets, leading to suboptimal performance. This study proposes an enhanced LSTM architecture integrated with a generative adversarial mechanism (LSTM-GAM) to address this limitation. The framework employs a dual-component structure: 1) A primary LSTM backbone that captures contextual dependencies across multi-logging sequences, and 2) An adversarial module where the gener

SUBMITTER: Fu X 

PROVIDER: S-EPMC12187019 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature