Building Complex Seismic Velocity Models for Deep Learning Inversion

Abstract

Training a deep learning inversion network usually requires hundreds of thousands of complex velocity models, which is labor-intensive and expensive to acquire. In this work, we develop a new framework to automatically generate various velocity models with common geological structures, such as folding layers, faults and salt bodies. There are three main modules in the proposed framework. The first module generates a folded model with a given number of layers; the other two modules can add faults and salt bodies onto the folded model to form a fault or salt model, respectively. To best simulate the shape of subsurface geological structures while ensuring a good application effect in deep learning inversion, we generate the structural model based on composition of several basic functions with recommended parameter ranges. Then the generated structural model will be assigned with velocity values based on assumptions of underground seismic velocity distribution. To investigate the application effect of the generated 3D models, we conduct a deep learning inversion test. Since currently there is no 3D deep learning inversion algorithms available, the latest 2D inversion network called SeisInvNet is used to test the feasibility of the randomly generated velocity models. Through the experiments, we can see that the 2D inversion results are consistent with the true models from the aspects of velocity values and geological structure shapes, which demonstrates the rationality of the designed 3D models. In the end, we further discuss the feasibility of applying the proposed 3D model dataset to train a 3D inversion network. This work paves the way for the development of 3D deep learning inversion methods.