Reduction of seismic random noise in mountainous metallic mines based on adaptive threshold RCSST

The suppression of random noise in seismic data is an essential step in processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals gets weaker and the Signal to Noise Ratio (SNR) of seismic data is much lower which brings great difficulty to seismic data processing and interpretation. In order to solve this problem, a Shearlet transform denoising algorithm based on adaptive threshold recursive cycle spinning is proposed in view of the exploration environment of metal mines in Yunnan mountainous regions. In this algorithm, the Shearlet transform is combined with recursive cycle spinning. By virtue of multiscale and multidirection features of Shearlet transform, the seismic signals are transformed into different scales and directions. Then, we propose a new adaptive threshold to prevent the coefficients being killed excessively and protect the amplitude of the effective signals. Experiments show that this adaptive threshold RCSST algorithm can overcome the disadvantages of conventional Shearlet transform denoising algorithm and protect the amplitude of signals effectively. Application to of simulative and real seismic data in Yunnan mountainous regions with low SNR demonstrates that this algorithm can suppress the random noises effectively and protect the amplitude of valid signals.