Auto-Linear Phenomenon in Subsurface Imaging

• URL: http://arxiv.org/abs/2305.13314v3
• Date: Tue, 21 May 2024 17:59:10 GMT
• Title: Auto-Linear Phenomenon in Subsurface Imaging
• Authors: Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin,
• Abstract summary: Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. The usual approach is to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. This paper extends this direction by demonstrating that only linear mapping necessitates paired data, while both the encoder and decoder can be learned from their respective domains through self-supervised learning.
• Score: 31.36376355719394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping between the latent spaces of the two domains, and the decoder requires paired data for training. This paper extends this direction by demonstrating that only linear mapping necessitates paired data, while both the encoder and decoder can be learned from their respective domains through self-supervised learning. This unveils an intriguing phenomenon (named Auto-Linear) where the self-learned features of two separate domains are automatically linearly correlated. Compared with existing methods, our Auto-Linear has four advantages: (a) solving both forward and inverse modeling simultaneously, (b) applicable to different subsurface imaging tasks and achieving markedly better results than previous methods, (c)enhanced performance, especially in scenarios with limited paired data and in the presence of noisy data, and (d) strong generalization ability of the trained encoder and decoder.

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