  The detection and rapid characterisation of earthquake parameters such as
magnitude are of prime importance in seismology, particularly in applications
such as Earthquake Early Warning (EEW). Traditionally, algorithms such as
STA/LTA are used for event detection, while frequency or amplitude domain
parameters calculated from 1-3 seconds of first P-arrival data are sometimes
used to provide a first estimate of (body wave) magnitude. Owing to extensive
involvement of human experts in parameter determination, these approaches are
often found to be insufficient. Moreover, these methods are sensitive to the
signal to noise ratio and may often lead to false or missed alarms depending on
the choice of parameters. We, therefore, propose a multitasking deep learning
model the Convolutional Recurrent model for Earthquake Identification and
Magnitude Estimation (CREIME) that: (i) detects the first earthquake signal,
from background seismic noise, (ii) determines first P arrival time as well as
(iii) estimates the magnitude using the raw 3-component waveform data from a
single station as model input. Considering, speed is of essence 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 (5 second window with up to of P wave data)
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 percent for event vs noise discrimination and is able to estimate first P
arrival time and local magnitude with average root mean squared errors of 0.13
seconds and 0.65 units, respectively. We also compare CREIME architecture with
architectures of other baseline models, by training them on the same data, and
also with traditional algorithms such as STA/LTA, and show that our
architecture outperforms these methods.
