Research on Seismic Velocity Inversion Method Based on Deep Learning

Abstract

Methods for obtaining seismic velocity include offset velocity analysis, tomography velocity inversion and full waveform. inversion, but these methods all share common problems: As the amount of seismic data increases, the time required to process the data to obtain the seismic velocity increases exponentially, and the latter two methods are more dependent on the initial seismic velocity. To address the problems of the above methods, this paper improves a seismic velocity inversion method based on deep learning. At the same time, a method that can generate a large number of velocity models randomly with geological features (undulation layer, faults, anomalies, etc. ) similar to those of real subsurface structures is also proposed. The generated velocity model and fluctuation equation for forward modeling are used to perform. the forward modeling, which allows for the efficient establishment of data set. The basic principle of the improved deep learning inversion method in this paper is as follows: the characteristic information of the training data is extracted by convolution neural network, which is trained with large data to obtain a nonlinear mapping relationship between seismic record and seismic velocity. In the inversion stage, the seismic velocity can be inversed quickly by inputting the seismic records into the trained network. In order to make the network give full play to the advantages of processing seismic data, the authors obtained the dominant network structure by means of numerical simulation, and achieved satisfactory inversion results. Finally, through comparative experiments, this paper verifies the advantages and applicability of the method.