Seismology allows us to explore the interior of the Earth by studying the numerous earthquakes that occur each year and are recorded by a worldwide network of thousands of seismometers. In contrast, extraterrestrial seismology has been relying on the limited data sets collected during the Apollo (Latham et al., 1970), Viking (Anderson et al., 1977) or Venera (Ksanfomaliti et al., 1982) missions to the Moon, Mars, and Venus. While numerous moonquakes were found in the Apollo data set, no quake was unambiguously identified on Mars or Venus due to the high seismic noise level or the short mission duration. On Mars, this has changed with the arrival of National Aeronautics and Space Administration's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (NASA's InSight) mission in 2018 (Banerdt et al., 2020). With the lessons learned during the Viking mission, the InSight lander installed a seismometer package directly on the ground and covered it with a Abstract NASA's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) seismometer has been recording Martian seismicity since early 2019, and to date, over 1,300 marsquakes have been cataloged 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 catalog 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 cataloged 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 (>1,000 Martian days), 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 catalog. 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.
Plain Language Summary Interior Exploration using Seismic Investigations, Geodesy and HeatTransport's seismometer on Mars has recorded over 1,300 marsquakes since its full deployment in early 2019. Marsquakes are often weak compared to the seismic background noise, which m...