Abstract and subjects
Seismic full-waveform inversion has become a promising tool for velocity estimation in complex geological structures. The traditional seismic full-waveform inversion problems are usually posed as nonlinear optimization problems. Solving full-waveform inversion can be computationally challenging for two major reasons. One is the expensive computational cost and the other is the issue of local minima. In this work, we develop an end-to-end data-driven inversion technique, called "InversionNet", to learn a regression relationship from seismic waveform datasets to subsurface models. Specifically, we build a novel deep convolutional neural network with an encoder-decoder structure, where the encoder learns an abstract representation of the seismic data, which is then used by the decoder to produce a subsurface model. We further incorporate atrous convolutions in our network structure to account for contextural information from the subsurface model. We evaluate the performance of our InversionNet with synthetic seismic waveform data. The experiment results demonstrate that our InversionNet not only yields accurate inversion results but also produces almost real-time inversion.