Application of automated seismic event detection in a low seismicity region of the Baltic States

Reliable earthquake catalogues in stable continental regions are difficult to obtain due to sparse station coverage, low signal-to-noise ratios, and the predominance of low-magnitude and anthropogenic events. We evaluated the performance of three deep learning phase picking algorithms – Earthquake Transformer, PhaseNet, and Generalized Phase Detection (GPD) – combined with two phase association methods, Gaussian Mixture Model Association (GaMMA) and PyOcto, using seismic data from the Baltic States between January and October 2021. Automatic detections are benchmarked against manually compiled observations from the Latvian Environment, Geology, and Meteorology Centre. The results show that PhaseNet and Earthquake Transformer substantially outperform GPD in terms of event recall. PyOcto associator generally produces higher recall but lower precision than the GaMMA. The PyOcto event relocation using HypoInverse significantly reduces recall, highlighting the sensitivity of sparse networks to misassociated or slightly mis-timed phase picks. Detection performance strongly depends on the number of available phase observations; events recorded by fewer than five picks are rarely recovered reliably. Our analysis shows that automatic workflows are highly sensitive to the number and spatial distribution of phase observations. Ensemble combinations of multiple pickers and associators improve recovery but also amplify false detections if not carefully constrained. The results demonstrate that parameter tuning, association strategy, and network configuration together govern catalogue quality in low-seismicity intraplate environments.