Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows

Abstract

The use of a computer to automatically pick the first-arrival of a seismic signal is an operation that involves picking and screening the first arrival of the wave according to the criteria established in the manual picking process. To increase the picking accuracy for data with low-to-moderate signal-to-noise ratio (SNR), we propose a new single-trace boundary detection algorithm. This algorithm includes three steps: (1) calculate the first-arrival characteristic values through multi time windows; (2) take the times corresponding to the maximum characteristic values given by different time windows as intermediate results; (3) compare the intermediate results: if the difference is too large, it is marked the time is abnormal, otherwise the average time of the intermediate results is taken as the first-arrival time. Using this energy boundary detection method, the characteristic values obtained are bi-directionally expanded to allow the use of the trace connectivity algorithm which is improved from the region growing method. Determining the connectivity between the first-arrival characteristic values is a way to simulate how the human eye discriminates true first arrivals. This method significantly improves the elimination of false or abnormal first-arrivals. Next, a small-step fitting algorithm is applied to the remaining first-arrival characteristic values to complete the calculation of the final characteristic values. Based on the retained first-arrival characteristic values, the missing values are assigned by interpolation. The characteristic values are mapped on the original record and finally the first-arrival picking is completed using a small time window. Theoretical results as well as the results obtained from real data demonstrate that the proposed automatic first-arrival picking method effectively improves the accuracy of the first-arrival picking. Finally, the new picking algorithm is presented more efficient than the energy ratio method, as well as cross-correlation method.