SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion
This addresses seismic velocity inversion for geophysics applications, but it is incremental as it builds on existing deep learning methods with architectural tweaks.
The study tackled seismic velocity inversion by proposing SVInvNet, a deep learning architecture with a densely connected encoder-decoder, which achieved superior performance on a benchmark dataset of 12,000 samples despite having fewer parameters than the baseline.
This study presents a deep learning-based approach to seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our Seismic Velocity Inversion Network (SVInvNet) introduces a novel architecture that contains a multi-connection encoder-decoder structure enhanced with dense blocks. This design is specifically tuned to effectively process time series data, which is essential for addressing the challenges of non-linear seismic velocity inversion. For training and testing, we created diverse seismic velocity models, including multi-layered, faulty, and salt dome categories. We also investigated how different kinds of ambient noise, both coherent and stochastic, and the size of the training dataset affect learning outcomes. SVInvNet is trained on datasets ranging from 750 to 6,000 samples and is tested using a large benchmark dataset of 12,000 samples. Despite its fewer parameters compared to the baseline model, SVInvNet achieves superior performance with this dataset. The performance of SVInvNet was further evaluated using the OpenFWI dataset and Marmousi-derived velocity models. The comparative analysis clearly reveals the effectiveness of the proposed model.