Adaptive Seismic Random Noise Attenuation Using Curvelet Transform
J. Cao and A. Shao
Event name: 79th EAGE Conference and Exhibition 2017
Session: Attenuation of Random and Other Noises
Publication date: 12 June 2017
Info: Extended abstract, PDF ( 735.75Kb )
Price: € 20
The sparse inversion based random noise elimination methods utilize the soft threshold operation to realize denoising on the basis that seismic signals have sparsity expression in a transformed domain. The threshold values are very crucial for the final inversion results, they should match the energy of noise. However, the energy of noise is hard to achieve, the popular method to get proper threshold values is try many times manually which will cost much computation resource and labor. This paper proposed an adaptive random noise elimination method without any a priori information using the curvelet transform as the sparse transform, the inner relation between the sparsity of solution and residual with the iteration can be used to decide a proper threshold value, thus suitable denoising results can be provide based on this idea. Numerical experiment demonstrate that the proposed method can protect valid signal, eliminate random noise, render the events clear and improve signal-noise-ratio and resolution of seismic data.