Modeling Land Seismic Exploration Random Noise in a Weakly Heterogeneous Medium and the Application to the Training Set

Abstract

In seismic exploration, random noise is an obstacle to the extraction of the effective signals, so the investigation aimed at random noise is the basis of signal processing. It is of great significance to analyze the noise properties and establish accurate noise models. Since the complex changes of the actual medium seriously affect propagation characteristics, it is necessary to establish a noise model in a more realistic medium. In this letter, we suppose a weakly heterogeneous medium whose properties vary with the position. And the link between the Lam constants of the medium and noise properties is established. Therefore, a wave equation is deduced in that medium to describe the propagation law of desert seismic exploration random noise. Based on the Greens function, the random noise field is obtained by superimposing all wave fields excited by each pointlike source. Afterward, quantitative comparisons between the actual random noise and the proposed random noise model are given. The results manifest that there are significant similarities in mathematical characteristics between them. Moreover, compared with the noise model in the homogeneous medium, the proposed noise model is more reliable. In order to prove the application value of the random noise model, it is first applied to construct a complete training set for denoising convolutional neural networks, which is valuable for attenuating the desert seismic exploration random noise. This is an effective way to extend noise data. Consequently, this feasible application will strongly promote the application of neural networks in seismic exploration.