Deep-learning inversion: a next generation seismic velocity-model building method

By Fangshu Yang, Jianwei Ma. Geophysics, 2019

513 citations recorded by the source corpus.

Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are the key prerequisites for reverse time migration and other high-resolution seismic imaging techniques. Such velocity information has traditionally been derived by tomography or full-waveform inversion (FWI), which are time consuming and computationally expensive, and they rely heavily on human interaction and quality control. We have investigated a novel method based on the supervised deep fully convolutional neural network for velocity-model building directly from raw seismograms. Unlike the conventional inversion method based on physical models, supervised deep-learning methods are based on big-data training rather than prior-knowledge assumptions. During the training stage, the network establishes a nonlinear projection from the multishot seismic data to the corresponding velocity models. During the prediction stage, the trained network can be used to estimate the velocity models from the new input seismic data. One key characteristic of the deep-learning method is that it can automatically extract multilayer useful features without the need for human-curated activities and an initial velocity setup. The data-driven method usually requires more time during the training stage, and actual predictions take less time, with only seconds needed. Therefore, the computational time of geophysical inversions, including real-time inversions, can be dramatically reduced once a good generalized network is built. By using numerical experiments on synthetic models, the promising performance of our proposed method is shown in comparison with conventional FWI even when the input data are in more realistic scenarios. We have also evaluated deep-learning methods, the training data set, the lack of low frequencies, and the advantages and disadvantages of our method.

Subfields: Geophysics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/129080268b96ad03f3254e6cedb534ad49b88de0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geophysics

1. Siwei Yu
2. Jianwei Ma
3. Wotao Yin
4. Xiaoqun Zhang

In Ocean Engineering

1. Siwei Yu
2. Zhangyang Wang
3. Jianwei Ma

Citations in the corpus

• Learning to Optimize: A Primer and A Benchmark (2021, methodology): “Another important application of model-based L2O is seismic imaging [218; 244; 245; 254].”
• Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “To handle the situation where limited images were annotated, a CycleGAN model was used to extract key local structures from all-sky auroral images (Yang et al., 2019).”