Abstract
NASA’s InSight seismometer has been recording Martian seismicity since
early 2019, and to date, over 1300 marsquakes have been catalogued by
the Marsquake Service (MQS). Due to typically low signal-to-noise ratios
(SNR) of marsquakes, their detection and analysis remain challenging:
while event amplitudes are relatively low, the background noise has
large diurnal and seasonal variations and contains various signals
originating from the interactions of the local atmosphere with the
lander and seismometer system. Since noise can resemble marsquakes in a
number of ways, the use of conventional detection methods for catalogue
curation is limited. Instead, MQS finds events through manual data
inspection. Here, we present MarsQuakeNet (MQNet), a deep convolutional
neural network for the detection of marsquakes and the removal of noise
contamination. Based on three-component seismic data, MQNet predicts
segmentation masks that identify and separate event and noise energy in
time-frequency domain. As the number of catalogued MQS events is small,
we combine synthetic event waveforms with recorded noise to generate a
training data set. We apply MQNet to the entire continuous 20
samples-per-second waveform data set available to date, for automatic
event detection and for retrieving denoised amplitudes. The algorithm
reproduces all high quality-, as well as majority of low quality events
in the manual, carefully curated MQS catalogue. Furthermore, MQNet
detects 60% additional events that were previously unknown with mostly
low SNR, that are verified in manual review. Our analysis on the event
rate confirms seasonal trends and shows a substantial increase in the
second Martian year.