Towards the characterization of Slow Slip deformation by means of deep learning

Detecting small Slow Slip Events (SSEs) is still an open challenge. The difficulty in revealing low magnitude events is related to their detection in the geodetic data, which must be improved either by employing more powerful equipment or by developing novel methods for the systematic discovery of small events, which can be crucial for the precise characterization of the slip spectrum. The improvement of the ability to detect small SSEs and the associated seismic response can play a decisive role in the understanding of the mechanics of active faults, remarkably subduction in which tremors cannot serve as a proxy for the slow slip or Episodic Tremor and Slip (ETS) is not regularly observed, making it necessary to provide new observations and methods to perceive potential bursts of slow slip.

Here we explore three Deep Learning–based strategies applied to GNSS data to characterize earthquakes and SSEs. Unlike seismic data, geodetic observations are crucial for dealing with SSEs, since they contain the required spatiotemporal information. Yet, since the low number of available labelled events (earthquakes or SSEs) producing significant displacement at GNSS station does not allow to adequately train Deep Learning models, we adopt synthetic geodetic data (Okada, 1985), obtained by generating events with uniformly distributed parameters. Thus, the model will not be biased towards the most numerous parameters, with a possibly stronger predictive power. The approach inspired by (van den Ende, Ampuero, 2020) was used for the characterization (i.e., estimation of epicentral location and magnitude), which associates geodetic time series with the location information of the GNSS stations. Yet, rearranging the geodetic displacement from GNSS time series into images can let Convolutional Neural Networks (CNN) to better account for the data spatial consistency, leading to more precise results. Furthermore, Transformers have also been tested with image time series of ground deformation. To assess the reliability of the tested methods, a magnitude threshold on the synthetic test set has been estimated, which depends on the depth and the hypocenter location of the event, showing a trade-off between the Signal-to-Noise (SNR) ratio and the relative position of the test events with respect to the GNSS network, revealing physical consistence. The results are also spatially consistent, as the location and magnitude errors tend to increase as the actual epicenters move offshore, with the location error showing a strong inverse proportionality on the magnitude. The employment of time series of deformation with Transformer networks lead to the best results and may allow us to better handle the noise complexity and to account for a spatio–temporal analysis of the ground deformation linked to SSE triggering. Nevertheless, the image–based model outperforms the other two on real data, showing evidence that the synthetic data does still not overlap with the real one, opening towards several perspectives. A more complex synthetic noise can be produced by allowing for synthetic data gaps and outliers (e.g., common modes), or machine learning–based denoising strategies can be envisioned to pre–process the data to improve the SNR ratio.