Earthquake Phase Association Using a Bayesian Gaussian Mixture Model.

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual sesimic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. We present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation‐Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid‐search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude. Plain Language Summary: Earthquakes are monitored by seismic networks consisting of several to hundreds of seismometers. An earthquake detection workflow usually has two important steps: detecting/picking seismic phases at each seismometer and associating picked phases across multiple seismometers in a network. Deep‐learning‐based phase pickers have greatly improved phase detection performance and can automatically generate many more seismic phases than conventional algorithms. These massive numbers of automatic phase picks pose a challenge for the phase association task. We have developed a new phase association method using a Bayesian Gaussian Mixture Model. We treat the phase association problem as a unsupervised clustering problem meaning that we aim to cluster detected phases into different groups based on individual earthquakes that produce these phases. The Gaussian mixture model makes it easy to consider multiple phase parameters, such as phase arrival time, phase amplitude, phase picking quality score, and phase type, to improve phase association. We test our method on both synthetic data and the 2019 Ridgecrest earthquake. The results show that our method can effectively associate phases from a temporally and spatially dense earthquake sequence and generate a more complete earthquake catalog than catalogs created using conventional methods. Key Points: We proposed an new approach to solve phase association as an unsupervised clustering problem using the Bayesian Gaussian Mixture ModelWe used the multivariate Gaussian distribution to represent both phase arrival time and amplitude to improve associationOur unsupervised method is fast without the need for conventional grid‐search or supervised training [ABSTRACT FROM AUTHOR]