Rapid Earthquake Association and Location

Abstract

Rapid association of seismic phases and event location are crucial for real‐time seismic monitoring. We propose a new method, named rapid earthquake association and location (REAL), for associating seismic phases and locating seismic events rapidly, simultaneously, and automatically. REAL combines the advantages of both pick‐based and waveform‐based detection and location methods. It associates arrivals of different seismic phases and locates seismic events primarily through counting the number of P and S picks and secondarily from travel‐time residuals. A group of picks are associated with a particular earthquake if there are enough picks within the theoretical travel‐time windows. The location is determined to be at the grid point with the most picks, and if multiple locations have the same maximum number of picks, the grid point among them with smallest travel‐time residuals. We refine seismic locations using a least‐squares location method (VELEST) and a high‐precision relative location method (hypoDD). REAL can be used for rapid seismic characterization due to its computational efficiency. As an example application, we apply REAL to earthquakes in the 2016 central Apennines, Italy, earthquake sequence occurring during a five‐day period in October 2016, midway in time between the two largest earthquakes. We associate and locate more than three times as many events (3341) as are in Italy's National Institute of Geophysics and Volcanology routine catalog (862). The spatial distribution of these relocated earthquakes shows a similar but more concentrated pattern relative to the cataloged events. Our study demonstrates that it is possible to characterize seismicity automatically and quickly using REAL and seismic picks.

Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine‐Learning Phase Picker

“…Our workflow generally follows the one used by Zhang et al (2019) who used a conventional short-term average/long-term average picker. In this study, a machine-learning phase picker is used to build sequential earthquake catalogs for the 2019 Ridgecrest earthquake sequence.…”

“…The M W 6.4 and 7.1 mainshocks are indicated by dark green stars, along with their focal mechanisms. sequence steps: (1) phase detection and picking using the deep-neural-network-based arrival-time picker-PhaseNet (Zhu & Beroza, 2019); (2) phase association and initial event location using our newly developed rapid earthquake association and location method (REAL; Zhang et al, 2019); and (3) earthquake sequential relocation using absolute and relative location algorithms (Kissling et al, 1994;Waldhauser & Ellsworth, 2000;Zhang et al, 2019). PhaseNet was trained on the prodigious dataset of analyst-labeled P-and S-arrival times from over 30 years of earthquake recordings from the Northern California Seismic Network (Zhu & Beroza, 2019).…”

“…Here, we empirically set the probability threshold to 0.5 (see an example in Figure S2). REAL associates and initially locates earthquakes primarily through counting the number of P and S picks and secondarily from travel time residuals by grid searching (Zhang et al, 2019). In this study, the searched area is 0.4°in horizontal dimension with a grid size of 0.02°(about 1.7 km) and from the surface to 20-km depth with a grid size of 2 km.…”

“…Earthquake catalog building strongly depends on earthquake detection and location techniques. Two main classes of methods are widely used in earthquake detection and location: pick-based methods and waveform-based methods (Grigoli et al, 2018;Pesicek et al, 2014;Stewart, 1977;Zhang et al, 2019). Independent of seismic phase picking, waveform-based methods detect, associate, and locate earthquakes simultaneously in a single step based on the delay-and-sum concept (Kao & Shan, 2004;Peng & Zhao, 2009;Shelly et al, 2007;Zhang & Wen, 2015b).…”

Revisiting the Timpson Induced Earthquake Sequence: A System of Two Parallel Faults

“…In this study, we enriched the catalog by reanalyzing continuous recordings from 26 May 2012 when the local stations were deployed through the end of 2013. We ran PhaseNet (Zhu & Beroza, 2019), a machine‐learning‐based phase picker to generate P and S phase picks and associated them using REAL (Zhang et al, 2019), a grid search associator. The associated picks were used as input for VELEST (Kissling et al, 1994) with an initial velocity model determined by Frohlich et al (2014) and subsequently HypoDD (Waldhauser, 2001; Waldhauser & Ellsworth, 2000).…”