Denoising InSight’s marsquake recordings with deep learning

Marsquake recordings by NASA’s InSight seismometer often have low signal-to-noise ratios (SNR) owing to low marsquake amplitudes - only a handful of events are over M3.5 and epicentral distances are large, due to the single station being located in a seismically quiet region, and highly fluctuating atmospheric, spacecraft and instrumental noise signals.

We have previously shown [1] how deep convolutional neural networks (CNN) can be used for 1) event detection - thereby producing an event catalogue consistent with the manually curated catalogue by the Marsquake Service (MQS) [2], and further extending it from 1297 to 2079 seismic events - as well as for 2) separating event and noise signals in time-frequency domain. Due to the low number of events readily-available for network training, we trained the CNN on synthetic event data combined with recorded InSight noise.

Here, we construct a semi-synthetic data set (with real marsquake & noise data) to assess the denoising performance of the CNN w.r.t. to various evaluation metrics such as SNR, signal-distortion-ratio, cross-correlation, and peak amplitude of the recovered event waveforms, and compare modifications of the CNN architecture and the training data set.

For a large number of identified events [1,2] no distance estimates are available (or only with high uncertainty), and for all but a small subset the back azimuth is unclear, as the relatively high background noise often obscures this information in the waveforms. We explore how the denoised waveforms can support the phase picking and polarisation analysis of marsquakes, and with that their localisation, as well as their general characterisation.

 

References:

[1] Dahmen et al. (2022), doi: 10.1029/2022JE007503

[2] Ceylan et al. (2022), doi: 10.1016/j.pepi.2022.106943