An Autonomous System for Grouping Events in a Developing Aftershock Sequence

Harris, David B.; Dodge, D. A.

doi:10.1785/0120100103

Cited by 54 publications

(37 citation statements)

References 17 publications

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“…up to magnitude 6.5), correlation and subspace detectors may perform exceptionally well in characterizing events with relatively limited numbers of templates covering the entire source region effectively (e.g. Gibbons and Ringdal, 2006;Harris and Dodge, 2011;Tang et al, 2014;Junek et al, 2015;Dodge and Harris, 2016). For larger events, such as the M=7.8 Nepal earthquake, the source regions are many times larger with dimensions up to and exceeding 100 km.…”

Section: Strategies For Characterizing Repeating Sources and Aftershocksmentioning

confidence: 99%

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Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

Gibbons

Kværna

Harris

et al. 2016

Seismological Research Letters

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Aftershock sequences following very large earthquakes present enormous challenges to nearrealtime generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase association algorithms and a significant deterioration in the quality of underlying fully automatic event bulletins. Current processing pipelines were designed a generation ago and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric datastreams which are then scanned by a phase association algorithm to form event hypotheses. We consider the scenario where a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located using a separate specially targeted semi-automatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid search algorithm which may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections which could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event

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Section: Strategies For Characterizing Repeating Sources and Aftershocksmentioning

confidence: 99%

“…It is not specified in the figure which algorithm we employ for this purpose -we could be using correlators or subspace detectors (e.g. Harris and Dodge, 2011;Barrett and Beroza, 2014) or some form of grid search method (see Lomax et al, 2014).…”

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confidence: 99%

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

Gibbons

Kværna

Harris

et al. 2016

Seismological Research Letters

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“…The subspace detection framework used for this work was first described in Harris and Dodge, 2011. The system processes array data and acquires waveform templates with an STA/LTA detector operating on a beam directed at the P phases of the aftershock sequence.…”

Section: The Detection Frameworkmentioning

confidence: 99%

Application of Subspace Detectors to 2012 Sumatera Earthquake Sequence

Dodge¹

2013

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In this report the aftershock sequence of the 2012 Sumatra earthquake is used to study the performance of subspace detectors (Harris, 2006) to detect and classify the events. The ground truth used for this study is 1222 aftershock solutions from the LLNL database drawn mostly from the Reviewed Event Bulletin (REB). Subspace detectors are built using several spawning strategies with waveforms recorded by the Makanchi Array (MKAR).Because we desire the subspace detectors to have a very low false alarm rate, the subspace templates are required to be about 50 seconds long (most of the P-bundle). For the same reason, the power detections used to create the subspace templates are restricted to have high SNR. Because of these restrictions, none of the subspace detectors are able to reproduce the entire ground truth catalog. They do have a very low false alarm rate, however, and so can reliably be used as single-array detection classifiers.Because of their reliability, in a suitably designed system, these detectors could be used to segregate detections from regions of high seismicity such as aftershock sequences, and allow processing of those detections in isolation from routine seismicity. It may also be possible to use these detectors to process groups of detections simultaneously. This leads to the concept of "analyst workload reduction factor"; a measure of the reduction in effort achieved by processing N detections in M groups instead of individually. If M is much smaller than N, a significant reduction factor is achieved.We use three subspace detector spawning strategies.1. In the first strategy, correlators are spawned directly from power detections and are allowed to run to the end of the sequence. Correlation clustered detections are then used to create multirank subspace detectors which are run against the entire sequence. 2. In the second strategy, power detectors are run for the entire duration of the sequence. Nearly the entire set of detections is used to create one or more subspace detectors with a high energy capture value. 3. The final strategy attempted was to build subspace detectors as in (2) but restricted to only the first day's detections.The second strategy was the most successful. Four subspace detectors produced 781 detections for the 44-day period with at most a few false detections. Operationally, this system is not practical, but we LLNL-TR-6438632 present a method for creating the detectors incrementally which we believe would maintain a high sensitivity with few false detections, while being able to provide detections without a delay for template creation.

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“…Extensive aftershock 35 sequences provide enormous challenges for data centers compiling near-realtime bulletins and a large effort is being made to facilitate the automatic processing of such sequences, in particular using pattern detectors (e.g. Harris and Dodge, 2011;Gibbons et al, 2012;Dodge and Harris, 2016;Junek et al, 2015). We have applied the multi-stage approach described by Gibbons et al (2016) to both re-40 gional and teleseismic waveforms from the Kashmir sequence and performed an almost autonomous event detection and location procedure.…”

Section: Introductionmentioning

confidence: 99%

Illuminating the seismicity pattern of the October 8, 2005, M = 7.6 Kashmir earthquake aftershocks

Gibbons

Kværna

2017

Physics of the Earth and Planetary Interiors

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Aftershocks of the October 8, 2005, M=7.6 Kashmir earthquake continued for many weeks and covered a region extending over an aperture exceeding 100 km. Several hundred events were recorded well at teleseismic distances while many hundreds more are only observed at regional distances. Existing earthquake catalogs for this sequence are poor given an unfavorable distribution of stations, a complex tectonic setting, lack of local and near-regional data, and

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An Autonomous System for Grouping Events in a Developing Aftershock Sequence

Cited by 54 publications

References 17 publications

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

Application of Subspace Detectors to 2012 Sumatera Earthquake Sequence

Illuminating the seismicity pattern of the October 8, 2005, M = 7.6 Kashmir earthquake aftershocks

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