Automatic P-Wave Arrival Detection and Picking with Multiscale Wavelet Analysis for Single-Component Recordings

Abstract

We have developed an automatic P-wave arrival detection and picking algorithm based on the wavelet transform and Akaike information criteria (AIC) picker. Wavelet coefficients at high resolutions show the fine structure of the time series, and those at low resolutions characterize its coarse features. Primary features such as the P-wave arrival are retained over several resolution scales, whereas secondary features such as scattered arrivals decay quickly at lower resolutions. We apply the discrete wavelet transform to single-component seismograms through a series of sliding time windows. In each window the AIC autopicker is applied to the thresholded absolute wavelet coefficients at different scales, and we compare the consistency of those picks to determine whether a P-wave arrival has been detected in the given time window. The arrival time is then determined using the AIC picker on the time window chosen by the wavelet transform. We test our method on regional earthquake data from the Dead Sea Rift region and local earthquake data from the Parkfield, California region. We find that 81% of picks are within 0.2-sec of the corresponding analyst pick for the Dead Sea dataset, and 93% of picks are within 0.1 sec of the analyst pick for the Parkfield dataset. We attribute the lower percentage of agreement for the Dead Sea dataset to the substantially lower signal-to-noise ratio of those data, and the likelihood that some percentage of the analyst picks are in error.

An improved automatic time-of-flight picker for medical ultrasound tomography

“…The wavelet-AIC TOF picker [8] is based on the AR-AIC picker which assumes that a signal can be divided into locally stationary segments and that the segments before and after the time-of-flight point are two different stationary processes ( [14]). Data points within the selected time window are divided into two segments at each data point i (i = 1; ...; k; ...; N; where N is the total number of data points in the selected time window).…”

“…(1) measures the information loss of using the current selected model to approximate reality. In the wavelet-AIC autopicker in [8], the point with minimum AIC value (that indicates the minimum information loss, therefore it is called the best model) is selected to be the TOF point.…”

“…Certain characteristics are repeatedly calculated within successive sections of the time series, producing a time dependent function. The TOF is usually identified by an obvious change in the behavior of this function ( [7,8]). The third type of auto-picker relies on using the coherence characteristic between traces.…”

“…In the AR-AIC picker, the order of the AR coefficient is determined on a trial and error basis (for details see [14]). To overcome this difficulty and inefficiency, Zhang et al [8] proposed a wavelet-AIC picker in which the AIC values are calculated directly from the seismogram using Maeda's formula [15]. In this method, a running window and a wavelet transform are used to guide the AIC picker by finding the appropriate time window that includes the TOF point of a seismogram.…”

“…In this paper, we develop an improved AIC automatic TOF picker and apply it to in vivo ultrasound breast data based on the wavelet-AIC TOF picker described in [8]. The improved method makes use of an approach termed multi-model inference (model averaging), based on the calculated AIC values, to enhance the accuracy of TOF picks.…”