Automatic First-Arrival Detection and Picking With Multiscale Wavelet Analysis

Quickly detecting and accurately picking the P wave first-arrival is of great importance in locating earthquakes and characterizing velocity structure, especially in the era of large volumes of digital and real-time seismic data. The detector should be capable of finding the onset of the P-wave arrival against the background of microseismic and cultural noise. Normally, P-wave onset is characterized by a rapid change in amplitude and/or the arrival of high-frequency energy. The wavelet transform decomposes the signal at different scales, thus adaptively characterizing its components at different resolutions. Wavelet coefficients at high resolutions show the fine structure of the signal, and those at low resolution characterize its coarse features. The main features in the signal will be retained over several resolution scales and irrelevant ones will decay quickly at larger scales. We move a 30 s time window from the first sample of the earthquake data and decompose the signal in the window into 3 different resolutions with the fast wavelet transform. The border effect of the wavelet transform is compensated for by overlapping neighboring time windows by 5 s at both ends. At different resolutions, the Akaike Information Criteria (AIC) picker is used on the corresponding wavelet coefficients. If no two time picks in different resolution bands are within 0.6 s, then it is concluded that there is no P first-arrival in this window. The window is then moved forward in time until a P first-arrival is found. We test our method on regional earthquake data from Dead Sea Rift region and find that it can detect about 95% of P first-arrivals correctly. It will detect the wrong P-wave onset when the time window only includes an isolated glitch. When the detector finds the P first-arrival, the picker will determine the onset time and its uncertainty based on the features of the time picks corresponding to the different resolutions. Compared with manual picks, our picker provides onset times and uncertainties with high confidence. 92% of the autopicks are within 0.15 seconds of analyst picks for our data set.