Two-dimensional Seismic Velocity Inversion via Enhanced Multi-view Convolutional Neural Networks for Regression

Seismic exploration is a mainstream technique to find subsurface raw oil and gas in energy industry, which stimulates seismic wave on the Earth’s surface, receives seismic waves from the subsurface layers, processes these seismic data and lastly infers oil and gas existence. All these steps are associated with a physical parameter: velocity. Nowadays, this parameter is estimated via a so-called seismic waveform inversion method, which is to derive a subsurface velocity model of the Earth by inverting seismic data observed at the surface. Besides this traditional physics-guided full waveform inversion, recently, more attention has paid on data-driven inversion techniques, mainly deep learning based pre-stack inversion, which builds a nonlinear mapping from a multi-shot record to a velocity profile for two-dimensional situation. Usually, a multi-shot record is widely simulated as a multiple channel color image. However, the multiple shots are located at the distinct surface positions, which is different from the fixed camera position of images, and whose shot gathers include less complementary and much redundant information. In order to characterize this situation, we regard each shot gather as a view in machine learning and propose an enhanced multi-view convolutional neural network for regression (MVCNNR) to velocity inversion, in this paper. Our MVCNNR generalizes multi-view convolutional neural network for 3D shape recognition into a pixel-level regression network that further is added both addition connections in residual network and skipping concatenation connections in U-net, to improve its performance. Experiments on four types of velocity datasets (Layered, Faulted, SaltBody, and SaltDome) show that our MVCNNR is superior to two representative data-driven techniques (FCNVMB and VelocityGAN), via quantitative evaluation and visualization analysis.