Seismic exploration desert noise suppression based on complete ensemble empirical mode decomposition with adaptive noise

Abstract

A foundation for the imaging and interpretation of stratigraphic structures is the high quality seismic data. The desert seismic events are contaminated by strong random noise. The random noise in desert seismic records has complex features, including nonlinear, non-Gaussian and low frequency, which is different from the random noise in people's previous cognition. In addition, the effective signal of desert seismic record and noise has the same frequency band. These situations have brought great difficulties to the denoising of conventional methods. While the conventional denoising algorithm for dealing with seismic signals is mainly for the suppression of random Gaussian noise. So the suppression result of random desert noise is not ideal. Based on these problems, we introduce the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm into the noise reduction process of desert seismic records. Compared with other time-frequency analysis methods, CEEMDAN algorithm has significant feature recognition effect, and remains the characteristics of adaptability. At the same time, it overcomes the problems of modal aliasing and false components. The experimental results show that denoising effect is obviously better than those of conventional denoising methods, and the suppression of the surface wave in the real seismic record is relatively thorough.