Method of Automatically Detecting the Abnormal First Arrivals Using Delay Time (December 2020)

Abnormal first-arrival times in the automatic first-arrival picking significantly affect the structural inversion and static correction. After implementing an automatic picking, the manual intervention is still required to correct or eliminate false first-arrival times, which seriously affects the efficiency of the first-arrival picking. Therefore, it is necessary to develop automatic removal methods for abnormal first-arrival times. Here, we propose a distance-based outlier detection algorithm to automatically and effectively eliminate the abnormal first-arrival times. In order to identify the abnormal first-arrival times, each first-arrival time is decomposed into a receiver delay time and a shot delay time. Then, according to offset and azimuth information, we eliminate the influences of offset and shot delay times on the first-arrival times in a single shot to get the receiver delay times. We further rearrange the delay times in polar coordinates. Finally, we apply a distance-based outlier detection algorithm to the delay times on different azimuths and delete the first-arrival times that correspond to the detected outliers. Our study indicates that the distance-based outlier detection algorithm can effectively eliminate the abnormal first-arrival times. The results from real data processing demonstrate that the proposed automatic removal approach of the abnormal first-arrival times can effectively improve the quality of the first-arrival times. In addition, we find that our proposed algorithm has a better performance than a least-squares regression method for low signal-to-noise ratio (SNR) seismic data.