A new picking algorithm based on the variance piecewise constant models

Abstract

In this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L’Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Although the two algorithms lead to similar results in the simulated scenarios, the proposed algorithm results in greater flexibility and automation capacity, as shown in the real data analysis. Indeed, our proposed algorithm does not require testing and optimization phases, resulting potentially very useful in earthquakes routine analysis for novel seismic networks or in regions whose earthquakes characteristics are unknown.

Statistical Picking of Multivariate Waveforms

“…The multivariate version of the picking algorithm from [17] was tested through simulations. With this aim, we simulated waveforms with different magnitudes and distances from the nearest seismic station.…”

“…This represents a further advantage of the multivariate algorithm with respect to its univariate counterpart. Indeed, the preliminary experiments in [16] showed that the univariate algorithm achieved the best performance with decreasing distances (from the nearest seismic station that recorded the event) and with increasing magnitude. NAs occurred often when the distance is large and the magnitude was small, indicating scenarios in which the P-and S-waves were basically indiscernible from the noise.…”

“…In [16], a new picking algorithm for the automatic determination of a seismic wave's time of onset was tested on a simulated waveform dataset in order to capture the variations in performance due to the characteristics of the simulated waveform. Then, in [17], the validity and robustness of the picking algorithm were tested on both synthetic seismograms and real data.…”

“…In [16], a new picking algorithm for the automatic determination of a seismic wave's time of onset was tested on a simulated waveform dataset in order to capture the variations in performance due to the characteristics of the simulated waveform. Then, in [17], the validity and robustness of the picking algorithm were tested on both synthetic seismograms and real data. In particular, seismic events of different magnitudes (2-5) recorded at different source distances (10-250 km) were simulated, and the results obtained were compared with those obtained by applying the conventional STA/LTA picking algorithm.…”

Spatiotemporal data science: theoretical advances and applications

“…At the same time, several applied case studies highlight the potential of spatiotemporal data science in giving reliable solutions to real-world problems. Papers in this issue include case studies from hydrology and hydromorphology (Budiman et al 2021;Wang et al 2021;Rolim et al 2021;Tang et al 2021;Tian et al 2021;Sottile et al 2021;Han and Morrison 2021), to geology (Giaccone et al 2021), geomechanics (Li et al 2021;Luo et al 2021;Lombardo and Tanyas 2021;Aguilera et al 2022;D'Angelo et al 2022;Bryce et al 2022;Grimm et al 2022), atmospheric phenomena and renewable energy (La Fata et al 2022;Amato et al 2022;Kajbaf et al 2022), pathogenic viruses and associated diseases (Niraula et al 2022;Temple et al 2022).…”