For the traditional denoising method, it is necessary to accurately model the signal and noise and optimize and adjust the manual input parameters, which causes the problem that it is difficult to remove the noise of the seismic data. Especially random noise in remote areas has the characteristics of non-stationary, high energy, and serious aliasing of effective signals and random noise in the frequency domain, which brings great difficulty to the recovery of seismic data by conventional denoising methods, but the existence of random noise seriously affects subsequent earthquakes. The processing and interpretation of data makes it difficult to determine the exact location of oil and gas. It is necessary to develop an efficient denoising algorithm, which can retain the complex edge information and rich texture information of the signal as much as possible while removing random noise, so as to restore the seismic data and improve the utilization rate of the seismic data. In this paper, the convolutional neural network is applied to seismic data processing. The algorithm has a good denoising effect in seismic data. While eliminating a large amount of random noise, it retains the texture features in the data, realizes denoising of seismic data, and enhances vision quality.

Wang Jinhuan, Ang Ling Weay, Sellappan Palaniappan. Research on Application of Deep Learning Algorithm in Earthquake Noise Reduction. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 10: 44-48. https://doi.org/10.25236/AJETS.2022.051008.

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