Enhancement of the Seismic Data Resolution Through Q-Compensated Denoising Based on Dictionary Learning

Abstract

Seismic wave acquisition is usually disturbed by natural noise and instrument noise. As the seismic wave propagates, the filtering effect of the Earth and its various layers will result in energy attenuation and velocity dispersion; these phenomena weaken the seismic time series amplitudes and distort the seismic phase data. In traditional processing methods, noise removal precedes the data compensation process, which attempts to retrieve information that was originally lost due to signal attenuation. If denoising is performed, weak seismic signals are often removed, resulting in the loss of useful signal data that cannot be recovered. Here, we propose a sparse regularization strategy based on dictionary learning. This inversion method performs the denoising and data compensation tasks in parallel. By applying this method to both synthetic and real datasets, we demonstrate that this technique effectively compensates and denoises the seismic data and improves both the data resolution and the signal-to-noise ratio of the seismic records of interest.