Although these two tasks share some similarities, their objectives are not quite the same. Phase picking is the measurement of arrival times of distinct seismic phases (P-wave and S-wave phases) within an earthquake signal that are used to estimate the location of an earthquake. Detection refers to identification of earthquake signals among a wide variety of non-earthquake signals and noise recorded by a seismic sensor. Despite the differences in approaches and results, most of these studies find important advantages to deep-learning-based methods compared with traditional approaches 11, 12.Įarthquake signal detection and phase picking are challenging problems in earthquake monitoring. To date, earthquake signal detection and phase-picking form the largest portion of this relatively young sub-field 2, 3, 4, 5, 6, 7, 8, 9, 10. Earthquake monitoring has a growing need for more efficient and robust tools for processing of increasingly large data volumes, is conceptually straightforward, and has a large quantity of available labeled data, which make earthquake detection and phase picking attractive targets for the new wave of machine learning applications in seismology. Our model picks P and S phases with precision close to manual picks by human analysts however, its high efficiency and higher sensitivity can result in detecting and characterizing more and smaller events.ĭeep learning is a widely applied and effective method for a broad range of applications 1. Applying our model to 5 weeks of continuous data recorded during 2000 Tottori earthquakes in Japan, we were able to detect and locate two times more earthquakes using only a portion (less than 1/3) of seismic stations. We show that our model outperforms previous deep-learning and traditional phase-picking and detection algorithms. Performing these two related tasks in tandem improves model performance in each individual task by combining information in phases and in the full waveform of earthquake signals by using a hierarchical attention mechanism. Here we present a global deep-learning model for simultaneous earthquake detection and phase picking. Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes.
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