Deep network reparameterized full-waveform inversion from sequential to simultaneous sources with adjoint-state method

A novel framework integrating deep learning reparameterization with the adjoint-state method is proposed for simultaneous source inversion. An innovative LSTM deep network reparameterization technique for FWI workflows is introduced, coupled with a crosstalk-free source encoding strategy to enhance accuracy in simultaneous source inversion. 1. This repository contains application results of a real field seismic dataset, derived from 20 high-quality field-acquired shot gathers along a survey profile of approximately 6 km in length. 2. The dataset includes complete survey geometry files, an initial velocity model, and detailed explanatory documentation, which facilitates understanding the full-waveform inversion (FWI) workflow. 3. Inversion results of four representative methods are provided, alongside one-click plotting supported. 4. We also release a deep learning-powered simultaneous-source inversion software package with full reproducibility. 5. The repository includes core deep learning Python scripts that implement simultaneous-source, multi-scale, and parameterized inversion, as well as an external dynamic-link library (DLL) for standalone forward modeling and gradient computation. 6. After configuring the environment using the supplied environment configuration file, users can run the entire inversion pipeline with one-click execution.