Research on Seismic Exploration Velocity Modeling Based on Deep Learning

Abstract

As the core parameter of seismic exploration, seismic wave propagation velocity runs through the seismic exploration process. The construction of reasonable and accurate velocity models is an important basis for exploration technologies such as high-precision seismic offset imaging, data processing and interpretation. The current velocity modeling strategy is limited by the target geological body, which has a long calculation period, high calculation cost, and is seriously affected by human subjective factors, and its adaptability to complex geological formations is poor. Based on this, this paper studies the accurate speed modeling method of universities. Firstly, the principle of earthquake detection is analyzed, and then the speed modeling based on improved fully convolutional neural network is studied. A velocity modeling fully convolutional neural network (VNB-FCN) is constructed to directly extract geological features from seismic data and establish a high-precision velocity model. Compared with the traditional velocity modeling method, the VMB-FCN model effectively avoids the problem of excessive dependence of neural networks on standard seismic datasets, and improves the accuracy and efficiency of seismic exploration velocity modeling.