Published as Journal of Geophysics and Engineering, 11, no. 6, 065001, (2014)

Irregular seismic data reconstruction using a percentile-half-thresholding algorithm

Yangkang Chen[*], Keling Chen[*], Peidong Shi[*]and Yanyan Wang[*]
[*]Bureau of Economic Geology,
Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, TX 78713-8924
[*]Department of Earth and Atmospheric Sciences
University of Houston,
Houston, TX, 77004
[*]State Key Laboratory of Petroleum Resources and Prospecting
China University of Petroleum
Fuxue Road 18th
Beijing, China, 102200


In this paper, a percentile-half-thresholding approach is proposed in the transformed domain thresholding process for iterative shrinkage thresholding (IST). The percentile-thresholding strategy is more convenient for implementing than the constant-value, linear-decreasing, or exponential-decreasing thresholding because it's data-driven. The novel half-thresholding strategy is inspired from the recent advancement in the researches on optimization using non-convex regularization. We summarize a general thresholding framework for IST and show that the only difference between half thresholding and the conventional soft or hard thresholding lays in the thresholding operator. Thus it's straightforward to insert the existing percentile-thresholding strategy to the half-thresholding iterative framework. We use both synthetic and field data examples to compare the performances using soft thresholding or half thresholding with constant threshold or percentile threshold. Synthetic and field data show consistent results that apart from the threshold-setting convenience, the percentile thresholding also has the possibility for improving the recovery performance. Compared with soft thresholding, half thresholding tends to have a more precise reconstructed result.