Application of Dynamic Time Warping in Weighted Stacking of Seismic Data

Abstract

Stacking can improve the signal-to-noise ratio (SNR) of seismic data, and therefore, it plays an important role in seismic signal processing. The commonly used stacking methods involve calculating a weighted average trace to reduce the influence of harmful samples with random noise and imprecise travel-time correction. However, abnormal misaligned traces often cause large amplitude change in the same reflection time, leading to very small or even zero weights for the purpose of improving SNR. Therefore, the existed weighted stacking methods discard too much seismic reflection information and may not be conducive to stacking. In this letter, we propose a novel weighted stacking method that uses dynamic time warping (DTW) to solve the misalignment problem of seismic reflection events. Then, the weighted stacking can be applied to suppress random noise based on the aligned samples. This proposed approach will enhance the role of the misaligned traces using larger weights without introducing additional noise, thus making the stacking procedure more robust. The application results on both synthetic and real seismic data demonstrate the effectiveness of our proposed approach.