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. 2020 Aug 7;11:3952. doi: 10.1038/s41467-020-17591-w

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — Four representative waveforms (a–d) from the test set and four waveforms from applying the model to continuous data in Tottori, Japan (e) and Redgcrest, California (f–h) presenting performance of the model on different types of data. Each waveform is 60 seconds long with 100 samples per second band-bass filtered from 1-45 Hz. Each panel shows three channel waveforms on top and output predictions of the model for earthquake signal detection, P-arrival, and S-arrival at the bottom. In a to d, the vertical color-coded lines on first two channels are manual arrival time picks from catalogs. a is an event with the local magnitude of 2.2 recorded in 55 km distance from the epicenter, b is an event with the local magnitude of 4.3 recorded 173 km away from the epicenter, c is an event with the local magnitude of 0.1 recorded 38 km away from the epicenter, and d is an event with the local magnitude of 2.0 recorded 110 km away from the epicenter. The output probabilities are presented as distributions that can represent variations or model uncertainties. e to h are detected events after applying the model to continuous data, representing the performance of the model when more than one event exists in a 1-minute window (e and f), when data contains gaps filled by zeros (e), when an event occurs near the edge (e), when two channels are broken or noisy (g), or when only one component data exists (h).