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

初至拾取是地震数据处理的关键步骤。由于背景噪音多样且近地表条件不规则，因此很难选择先到者。此外，现有算法通常对参数设置敏感。因此，本文提出了通过模糊c-means和鲁棒局部加权回归（FPFR）算法组成的两个子程序的先到拣选。预拣子程序通过模糊 c 均值聚类和自适应聚类选择技术获得初始先到值。平滑子程序通过自适应参数回归技术处理背景噪声和近地条件。实验在 6 个现场地震数据集和 1 个合成数据集上进行。结果表明，FPFR 比三种最先进的方法更准确。