Pairwise Association of Seismic Arrivals with Convolutional Neural Networks

McBrearty, Ian W.; Delorey, Andrew A.; Johnson, Paul A.

doi:10.1785/0220180326

Cited by 70 publications

(40 citation statements)

References 15 publications

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“…Machine learning (ML) analysis of seismic data is an expanding field, with recent studies focusing on event detection 21 , phase identification 22 , phase association 23,24 , or patterns in seismicity 25 . In the following, we investigate whether seismic signatures can be found in the period leading up to any known manifestation of major slow slip occurrence anywhere in the Cascadia region.…”

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An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

et al. 2020

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Slow slip events result from the spontaneous weakening of the subduction megathrust and bear strong resemblance to earthquakes, only slower. This resemblance allows us to study fundamental aspects of nucleation that remain elusive for classic, fast earthquakes. We rely on machine learning algorithms to infer slow slip timing from statistics of seismic waveforms. We find that patterns in seismic power follow the 14-month slow slip cycle in Cascadia, arguing in favor of the predictability of slow slip rupture. Here, we show that seismic power exponentially increases as the slowly slipping portion of the subduction zone approaches failure, a behavior that shares a striking similarity with the increase in acoustic power observed prior to laboratory slow slip events. Our results suggest that the nucleation phase of Cascadia slow slip events may last from several weeks up to several months.

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An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

et al. 2020

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“…Other places in which the method can be improved are in adaptively adjusting the RBF-kernel widths to account for known variability in travel time uncertainties (between different stations and candidate source regions), and also in incorporating additional information into the graphs prior to applying competitive assignment. For example, edge weights could be weighted by the predicted phase-likelihood of each arrival , and if pairs of arrivals are known with high certainty to come from a common source (Bergen and Beroza, 2018;McBrearty et al, 2019), a constraint could be added into the constraint matrix of (6) to force these arrivals to be assigned to a common source. An avenue of development, more generally, may be in constructing arrival-arrival indexed graphs (with edge weights proportional to the likelihood those arrivals are associated) which may contain additional, or complimentary information to be used with the current approach.…”

Section: Discussionmentioning

confidence: 99%

Earthquake Arrival Association with Backprojection and Graph Theory

McBrearty

Gomberg

Delorey

et al. 2019

Bulletin of the Seismological Society of America

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The association of seismic wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g., P-or S-waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so we use a pattern detection metric based on the principle of backprojection to reveal candidate sources, followed by graph-theory-based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. This method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile (IPOC) seismic network of northern Chile. For the synthetic tests we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of > 95%. For the real data we develop a new catalog between containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day, and a magnitudeof-completion of ∼M1.8. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the April 1, 2014 Mw 8.2 Iquique earthquake. During the highest rates of aftershock activity, > 600 earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone.

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“…Key Points: • Statistical features derived from time-windowed, filter-banked seismic data can be an effective way to characterize eruptive behavior of volcanoes • Supervised learning allows us to determine the eruptive state of the volcano given a single time window of raw seismic data from a single station • Spectral clustering can reveal different phases of eruptions and differences between various eruptions name a few (McBrearty et al, 2019;Ross et al, 2019). With regard to the applications of ML to study and characterization of volcanoes, the primary applications thus far have been in the classification of volcanoseismic signals (Hibert et al, 2017;Malfante et al, 2018;Titos et al, 2019).…”

Section: 1029/2019gl085523mentioning

confidence: 99%

“…The application of machine learning (ML) techniques to the analysis of geophysical signals has become widespread in diverse settings such as the analysis of laboratory experiments (Hulbert et al, ; Rouet‐Leduc et al, ; Rouet‐Leduc, Hulbert, Bolton, et al, ), tracking slow‐slip in real Earth (Rouet‐Leduc, Hulbert, & Johnson, ), and phase association for the development of earthquake catalogs to name a few (McBrearty et al, ; Ross et al, ). With regard to the applications of ML to study and characterization of volcanoes, the primary applications thus far have been in the classification of volcano‐seismic signals (Hibert et al, ; Malfante et al, ; Titos et al, ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

Ren

Peltier

Ferrazzini

et al. 2020

Geophysical Research Letters

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Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August–October 2015 eruption as well as the closing of the eruptive vent during the September–November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.

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Pairwise Association of Seismic Arrivals with Convolutional Neural Networks

Cited by 70 publications

References 15 publications

An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

Earthquake Arrival Association with Backprojection and Graph Theory

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

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