Automatic first-arrival picking through convolution kernel construction and particle swarm optimization. (October 2021) - Gao, Lei; Jiang, Haokun; Min, Fan
Abstract: First-arrival picking is a necessary step in seismic data processing. Existing algorithms are either inaccurate or inefficient when the number of geophone groups is large and signal-to-noise ratio is low. In this paper, we propose the automatic first-arrival picking through convolution kernel construction and particle swarm optimization (FPCO) algorithm. First, abrupt energy fluctuations are detected using convolution kernels through convolution. Second, the locations of abrupt fluctuation are calculated in each convolution result to produce the index vectors. Third, the index vectors are integrated to one vector called the centerline by the Gaussian kernel. Finally, adaptive threshold is applied to obtain first arrivals around the centerline. The particle swarm optimization is applied to train the parameters. Experimental results on field datasets show that FPCO is more accurate and stable than popular algorithms. Highlights: We propose a construction method of convolution kernels. Convolution kernels and gaussian kernel are used to detect first arrivals. We designed an adaptive method to fine-tune the picking result of first arrivals. Particle swarm optimization is applied to obtain proper parameters of our algorithm. Results show that our algorithm outperforms three state-of-the-art ones.