First-arrival picking through fuzzy c-means and robust locally weighted regression

First-arrival picking is a crucial step in seismic data processing. Because of the diverse background noises and irregular near-surface conditions, it is difcult to pick frst arrivals. In addition, existing algorithms are usually sensitive to parameter settings. Therefore, this paper proposes the frst-arrival picking through fuzzy c-means and robust locally weighted regression (FPFR) algorithm consisting of two subroutines. The pre-picking subroutine obtains initial frst arrivals through fuzzy c-means clustering and adaptive cluster-selection techniques. The smoothing subroutine handles background noises and near-ground conditions through adaptive parameter regression technique. The experiment is conducted on six feld seismic datasets and one synthetic dataset. Results show that FPFR is more accurate than three state-of-the-art methods.