Automatic First-Arrival Picking Through Convolution Kernel Construction And Particle Swarm Optimization

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.