Seismic data usually contain various types of noise. Noise comes from signal collection environment, equipment (current noise), micro seismic, etc. Noise reduces the signal-to-noise ratio and resolution of seismic records, and affects the accuracy of seismic processing and stratigraphic interpretation. Improving the signal-to-noise ratio of seismic data is the primary task of seismic data processing. In practical research, many algorithms for suppressing random noise interference are proposed. In order to achieve better seismic data denoising technology, this paper introduces a new algorithm: improved shearlet transform algorithm. The basic principle of this algorithm is to perform shearlet transform on noisy seismic data, calculate the L2 norm of shearlet coefficients in different directions at each scale according to the distribution law of effective signals at different scales, and remove random noise according to the adaptive threshold in shearlet domain varying with scale and direction.
Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2022 in Houston, Texas.
