Low-frequency noise suppression of desert seismic data based on signal oscillation component decomposition

Abstract

Exploration of oil and gas resources in complex areas has always been a key issue in seismic exploration. The Tarim Basin, covered by a large area of desert, contains abundant oil-gas reservoir resources. However, due to the complex exploration environment, the signal-to-noise ratio of seismic data collected in desert area is low and the resolution is poor. The frequency band overlaps between effective reflected signal and low-frequency desert noise. Aiming at the specific characteristics, a desert noise suppression method based on signal oscillation component decomposition had been proposed for desert seismic data. According to the oscillation characteristics of desert seismic data, tunable-Q wavelet transform was introduced to achieve oscillation component decomposition. We put forward the multi-Q (multi-quality factors) model to determine basis function, so as to achieve the better matching of signals and noise. It can better adapt to the decomposition of desert seismic data, and complete the separation of reflected events and low-frequency desert noise with overlapping frequency band under the condition of low signal-to-noise ratio. It can be seen from the processing results of synthetic records and real data that the proposed method has better advantages in desert noise suppression compared with the Shearlet transform, empirical mode decomposition and F-x deconvolution methods.