Abstract
Machine learning algorithms have become a powerful tool in different
areas of seismology, such as phase picking/earthquake detection,
earthquake early warning and focal mechanism determination. Previously
convolutional neural networks (CNN) have been applied to continuous
seismic waveform recordings to perform efficient phase picking and event
detection with good accuracy [Zhu et al., 2018]. However, the
off-line training of current CNN requires at least a few thousands of
accurately picked seismic phases, which makes it difficult to be applied
to regions without sufficient picked phases. In this work, we will
validate the transfer learning among different geographic regions. Our
tests show that the phase picker trained on manually-labeled data
acquired from Sichuan, China following the 2008 M7.9 Wenchuan earthquake
[Zhu et al., 2018] works equally well on the continuous waveform
acquired from Oklahoma, US [Zhu et al., 2018]. Specifically, using
the CNN trained on the Wenchuan dataset, together with 895
local/regional catalog events recorded in central Oklahoma, we refine
part of the networks to pick the arrival times of the local seismicity
in Oklahoma. The refined CNN results are compatible with the matched
filter results using the same catalog events as templates. Our next step
is to extend our test to waveforms from different tectonic regions to
demonstrate the generality of CNN-based phase picker. We plan to further
use a New Zealand seismic dataset that includes more than 20 GeoNet
stations in the North Island, where the matched-filter detected results
are available to be compared with (Yao et al., 2018). Alternatively
dataset include a subset of events in the waveform relocated catalog in
Southern California. Updated results will be presented at the meeting.