Physically Driven Self-Supervised Learning and its Applications in Geophysical Inversion

Sparse coding (SC) has been proven effective in various geological tasks, such as seismic time-frequency (TF) analysis and seismic reflection inversion. Nevertheless, it inevitably has several drawbacks, for example, low computational efficiency and difficulty in parameter selection. Recently, self-supervised learning (SSL) has emerged as a promising alternative to mitigate these issues, offering high computational effectiveness and requiring fewer labels. We suggest a generalized physically driven workflow for geophysical inversion based on SSL and SC, named the physically driven SSL network (PDSSLNet). This generalized PDSSLNet model comprises two main modules. One is the inverse model, generated by convolutional neural networks (CNNs), which can benefit from their high computational effectiveness and strong nonlinear fitting ability. The other one is the forward model based on the SC theory, ensuring the physical meaning of the geophysical applications with high accuracy. Afterward, we provide two typical geological inversion cases to demonstrate the validity and effectiveness of the suggested PDSSLNet, including sparse TF analysis and seismic reflectivity inversion. Three-dimensional field data volume applications confirm that the proposed inversion workflow may efficiently circumvent the drawbacks of the conventional SC-based approach while maintaining excellent computing efficiency.