Simultaneously Removing Noise and Increasing Resolution of Seismic Data Using Waveform Shaping

We propose a novel method for simultaneously removing random noise and increasing the resolution of seismic data. We formulate the problem of simultaneous denoising and increasing resolution as an inverse problem and solve it using the shaping regularization framework. The resolution of seismic data is increased by shaping the estimated seismic wavelet into a user-defined wavelet that is easier for interpretation. The random noise is removed by a simple FK thresholding step in the shaping regularization framework. There are two shaping processes here: 1) shaping the estimated wavelet to a more ideal wavelet; and 2) shaping the inverted model to a more admissible model during the iterations. We use a wedge synthetic model and land poststack seismic data to demonstrate the performance of the proposed approach. It is the first time that a simultaneous resolution enhancement and noise attenuation framework is proposed to process poststack seismic data.