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WITHDRAWN Auto-linear: A Self-supervised Framework for Robust Subsurface Imaging Through Latent Space Correlations

Description: 

WITHDRAWN Subsurface imaging, central to geophysics, involves solving Full-Waveform Inversion (FWI) to predict geophysical properties from measurements. Conventional methods rely on encoder-decoder networks trained with paired data from two domains: geophysical properties and measurements. Building on recent work (InvLINT), we demonstrate that only the linear mapping between latent spaces requires paired data, while the encoder and decoder can be trained independently through self-supervised learning.

This discovery, termed Auto-Linear, reveals that self-learned features from two domains are inherently linearly correlated. The Auto-Linear framework offers key advantages: (a) simultaneous forward and inverse modeling, (b) applicability across diverse subsurface imaging tasks with superior performance, (c) robustness with limited paired data and noisy inputs, and (d) strong generalization across datasets. These findings advance data-driven subsurface imaging and open new avenues for geophysical modeling.

Session: Scientific Machine Learning for Forward and Inverse Wave Equation Problems [Poster]

Type: Poster

Date: 4/15/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Youzuo

Student Presenter: No

Invited Presentation: 

Poster Number: 137

Authors