A New Method for Producing Automated Seismic Bulletins: Probabilistic Event Detection, Association, and Location

Draelos, Timothy J.; Ballard, Sanford; Young, Christopher J.; Brogan, Ronald

doi:10.1785/0120150099

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…Finally, the proposed association algorithm does not incorporate any information about the source nor the atmospheric dynamics. This procedure could be improved by assessing the consistency of arrival time differences across a network of satellites and stations using a range of possible sources, similarly to the methods used for the automated production of seismic bulletins (Draelos et al 2015). In contrast to seismic media, atmospheric velocities, i.e., winds, are time-dependent which introduces further complexity when computing theoretical source-receiver arrival times.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Near-real-time detection of co-seismic ionospheric disturbances using machine learning

Brissaud¹,

Astafyeva²

2021

Preprint

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Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Ionospheric measurements of Co-Seismic Disturbances (CIDs) offer a unique solution to characterize an earthquake's tsunami potential in Near-Real-Time (NRT) since CIDs can be detected within 15min of a seismic event. However, the detection of CIDs rely on human experts which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we train machine-learning models (random forests) over an extensive ionospheric waveform dataset to (1) classify ionospheric waveforms between CIDs and noise,(2) pick arrival times, and (3) associate arrivals across a satellite network in NRT. Our model shows excellent classification and arrival-time picking performances (∼ 95% recall, average error < 10 s). This model is the first automatic CID detector which paves the way for the NRT imaging of surface displacements from the ionosphere.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Near-real-time detection of co-seismic ionospheric disturbances using machine learning

Brissaud¹,

Astafyeva²

2021

Preprint

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“…The techniques developed for processing waveform data from low‐volume data sets, which are described above, treat each station or array separately. In general, these techniques are designed to detect signals at a given station or array; signals from separate stations and/or arrays are subsequently grouped using association techniques to build events (Draelos et al., 2015; Yeck et al., 2019). For medium volume/medium density data sets, a common approach is to process data across a network by migrating or backprojecting the waveform data back to a series of event hypotheses (Drew et al., 2013; Grigoli et al., 2014; Kao & Shan, 2004; Langet et al., 2014).…”

Section: Drivers Of Big Data Seismologymentioning

confidence: 99%

Big Data Seismology

Arrowsmith

Trugman

MacCarthy

et al. 2022

Reviews of Geophysics

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The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.

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“…We generate 8 random sequences of 5000 earthquakes each within the region. For each sequence, the time between consecutive events is randomly drawn from a uniform distribution, with a fixed minimum value of 0 seconds, and a maximum value of 10,12,16,20,24,32,64, and 128 seconds, respectively. We then define ∆t o as the average time between events over the 5000 events in the sequence.…”

Section: Stress Testing Phaselink In Japanmentioning

confidence: 99%

“…One major shortcoming of the method is that it will not only identify earthquakes when present but also any other types of impulsive transient signals that seismometers record. This led to the development of phase association algorithms, which examine combinations of triggers on different stations to see whether any set have arrival time patterns consistent with those of earthquakes (Draelos et al, ; Johnson et al, ; LeBras et al, ; Myers et al, ; Reynen & Audet, ; Stewart, ). The association process therefore evolved from one of simply grouping seismic phases together to being ultimately responsible for deciding whether an earthquake occurred.…”

Section: Introductionmentioning

confidence: 99%

PhaseLink: A Deep Learning Approach to Seismic Phase Association

et al. 2019

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Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid-free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1-D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real-time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state-of-the-art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real-time seismic monitoring, and streamline automated processing of large seismic data sets.

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A New Method for Producing Automated Seismic Bulletins: Probabilistic Event Detection, Association, and Location

Cited by 23 publications

References 9 publications

Near-real-time detection of co-seismic ionospheric disturbances using machine learning

Near-real-time detection of co-seismic ionospheric disturbances using machine learning

Big Data Seismology

PhaseLink: A Deep Learning Approach to Seismic Phase Association

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