Earthquake detection and seismic phase picking play a crucial role in the travel-time estimation of P and S waves, which is an important step in locating the hypocenter of an event. The phase-arrival time is usually picked manually. However, its capacity is restricted by available resources and time. Moreover, noisy seismic data present an additional challenge for fast and accurate phase picking. We propose a deep learning-based model, EPick, as a rapid and robust alternative for seismic event detection and phase picking. By incorporating the attention mechanism into UNet, EPick can address different levels of deep features, and the decoder can take full advantage of the multi-scale features learned from the encoder part to achieve precise phase picking. Experimental results demonstrate that EPick achieves 98.80% accuracy in earthquake detection over the STA/LTA with 80% accuracy, and for phase arrival time picking, EPick reduces the absolute mean errors of P- and S- phase picking from 0.072 s (AR picker) to 0.030 s and from 0.189 s (AR picker) to 0.083 s, respectively. The result of the model generalization test shows EPick’s robustness when tested on a different seismic dataset.

Many active volcanoes exhibit Strombolian activity, which is typically characterized by relatively frequent mild volcanic explosions and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of volcanoes and the underlying physical and chemical processes. Catalogs of these eruptions and, specifically, seismo-volcanic events may be generated using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative user-friendly, time-saving, automated approach labelled as: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This strategy of creating seismo-volcanic event catalogs consists of three main steps: 1) identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold. 2) catalog consolidation by comparing and verifying the initial detections based on recordings from two different seismic stations. 3) identification and exclusion of signals from regional tectonic earthquakes. The strength of the python package is the reliable detection of very small and frequent events as well as major explosions and paroxysms. Here, it is applied to publicly accessible continuous seismic recordings from two almost equidistant stations at Stromboli volcano in Italy. We tested AWESAM by comparison with a hand-picked catalog and found that around 95% of the seismo-volcanic events with a signal-to-noise ratio above three are detected. In a first application, we derive a new amplitude-frequency relationship from over 290.000 seismo-volcanic events at Stromboli during 2019–2020 which were detected by AWESAM. The module allows for a straightforward generalization and application to other volcanoes with frequent Strombolian activity worldwide. Furthermore, this module can be implemented for volcanoes with rarer explosions.

<p>This study attempts to use Deep Learning architectures to design an efficient real time magnitude classifier for seismic events. Various combinations of Convolutional Neural Networks (CNNs) and Bi- & Uni-directional Long-Short Term Memory (LSTMs) and Gated Recurrent Unit (GRUs) are tried and tested to obtain the best performing model with optimum hyperparameters. In order to extract maximum information from the seismic waveforms, this study uses not only the time series data but also its corresponding Fourier Transform (spectrogram) as input. Furthermore, the Deep Learning architecture is combined with other machine learning algorithms to generate the final magnitude classifications. This study is likely to help seismologists in improving the Earthquake Early Warning System to avoid issuing false warnings, which not only alarms people unnecessarily but can also result in huge financial losses due to stoppage of industrial machinery etc.</p>