CREIME -- A Convolutional Recurrent model for Earthquake Identification
and Magnitude Estimation
Abstract
The detection and rapid characterisation of earthquake parameters such
as magnitude are important in real time seismological applications such
as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional
methods, aside from requiring extensive human involvement can be
sensitive to signal-to-noise ratio leading to false/missed alarms
depending on the threshold. We here propose a multi-tasking deep
learning model – the Convolutional Recurrent model for Earthquake
Identification and Magnitude Estimation (CREIME) that: (i) detects the
earthquake signal from background seismic noise, (ii) determines the
first P-wave arrival time and (iii) estimates the magnitude using the
raw 3-component waveforms from a single station as model input.
Considering, that speed is essential in EEW, we use up to two seconds of
P-wave information which, to the best of our knowledge, is a
significantly smaller data window compared to the previous studies. To
examine the robustness of CREIME we test it on two independent datasets
and find that it achieves an average accuracy of 98\%
for event-vs-noise discrimination and can estimate first P-arrival time
and local magnitude with average root mean squared errors of 0.13
seconds and 0.65 units, respectively. We compare CREIME with traditional
methods such as short-term-average/ long-term-average (STA/LTA) and show
that CREIME has superior performance, for example, the accuracy for
signal and noise discrimination is higher by 4.5\% and
11.5\% respectively for the two datasets. We also
compare the architecture of CREIME with the architectures of other
baseline models, trained on the same data, and show that CREIME
outperforms the baseline models.