Towards Operational Earthquake Data Denoising

Earthquake catalogues are derived from continuous seismic recordings through signal detection, phase picking, association, location, and magnitude estimation, but pervasive noise still limits reliable automation, especially for small events and noisy stations, and often requires manual review. Building on the demonstration that deep-learning denoising can be applied to continuous data to improve network-wide earthquake monitoring (Dahmen et al., 2026), we advance toward operational deployment by (i) implementing denoising within the SeisComP ecosystem (Helmholtz Centre Potsdam, 2008), (ii)  evaluating on larger continuous datasets, and (iii) systematically comparing multiple denoising approaches and testing monitoring-driven methodological refinements.

We train and compare multiple denoising models using a dedicated, curated training and benchmarking dataset composed of earthquake signals and noise recordings from Switzerland and its border regions, with event waveforms pre-cleaned to enhance label quality. Denoised waveforms are then propagated through an end-to-end monitoring workflow spanning signal detection, continuous waveform denoising, phase picking with arrival-time uncertainty estimation and peak amplitude estimation, and final catalog generation. The performance is benchmarked with monitoring-relevant metrics such as signal detection capability, waveform fidelity, phase-pick quality, and the reliability of amplitude estimation, thereby quantifying, for each denoiser, the trade-offs and improvements relative to standard digital filters and relative to applying common phase pickers to raw versus denoised data.

A case study using continuous data in realistic settings shows that catalogues based on denoised data can contain significantly more detected events with more associated phase picks, improved location quality, and more reliable magnitude estimates than catalogs derived from raw data, ultimately extending catalogue depth toward smaller magnitudes while preserving reliability.

This work is carried out within TRANSFORM², funded by the European Commission under project number 101188365 within the HORIZON-INFRA-2024-DEV-01-01 call.

 

References:

Dahmen, N., J. Clinton, M.-A. Meier, and L. Scarabello, 2026, Toward Operational Earthquake Seismogram Denoising, Bull. Seismol. Soc. Am., XX, 1–23, doi: 10.1785/0120250198

Helmholtz Centre Potsdam (2008). The SeisComP seismological software package, GFZ Data Services.