Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

Dickey, Joshua; Borghetti, Brett J.; Junek, W. N.; Martin, Richard R.

doi:10.1785/0220190090

Cited by 14 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods rely on a complex combination of probabilistic models and many hyper‐parameters that control performance and accuracy. Several new approaches have been proposed to solve the earthquake phase association using: graph theory (McBrearty, Gomberg, et al., 2019), the RANSAC algorithm (L. Zhu et al., 2021; Woollam et al., 2020), and deep learning (Dickey et al., 2020; McBrearty, Delorey, & Johnson, 2019; Ross, Yue, et al., 2019). When combined with the rapid development of phase picking methods, better association methods have the potential to improve significantly the overall performance of earthquake monitoring pipelines.…”

Section: Introductionmentioning

confidence: 99%

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

et al. 2022

View full text Add to dashboard Cite

show abstract

“…An event detection is declared when a number of phases are associated to a common source event. Several deep‐learning‐based methods have been proposed to improve association by learning phase arrival time moveout patterns (Ross, Yue, et al., 2019) or waveform similarity (Dickey et al., 2020; McBrearty et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

An End‐To‐End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. In this study, we introduce a new end‐to‐end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi‐station processing of seismic waveforms recorded over a seismic network. This end‐to‐end architecture consists of three sub‐networks: a backbone network that extracts features from raw waveforms, a phase picking sub‐network that picks P‐ and S‐wave arrivals based on these features, and an event detection sub‐network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub‐networks together with a shift‐and‐stack module based on back‐projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end‐to‐end approach can effectively pick P‐ and S‐wave arrivals and achieve earthquake detection accuracy rivaling that of other state‐of‐the‐art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

show abstract

“…Deep convolutional neural networks can serve as a robust feature extractor for similarity measurement between a template‐searching image pair in the object tracking problem (Li et al., 2018). Seismologists have used this attribute to solve seismic classification problems, such as pair‐wise phase association (McBrearty et al., 2019) and source discrimination (Dicky et al., 2020). Mousavi, Zhu, Ellsworth, et al.…”

Section: Introductionmentioning

confidence: 99%