Inferring Aftershock Sequence Properties and Tectonic Structure Using Empirical Signal Detectors

Junek, W. N.; Kværna, Tormod; Pirli, Myrto; Schweitzer, Johannes; Harris, David B.; Dodge, D. A.; Woods, M. T.

doi:10.1007/s00024-014-0938-0

“…EMFP can, in principle, be applied to single 3-compoenent sensors Seismicity on the Arctic Plate Boundary 15 with the covariance matrices evaluated between the different components, although the performance is likely to be significantly poorer at low SNR (Harris 1990). Empirical signal detectors have been used to great effect at classifying tectonic activity near Svalbard (Junek et al 2015) and EMFP has been demonstrated to provide a significantly improved detection and location capability for seismicity in a geothermal field (Wang et al 2015). However, the performance of such fully empirical classification of seismicity will improve significantly with improvement in the underlying catalog basis.…”

Section: Discussionmentioning

confidence: 99%

Locating seismicity on the Arctic plate boundary using multiple-event techniques and empirical signal processing

Gibbons

¹

,

Harris

²

,

Dahl‐Jensen

³

et al. 2017

Geophysical Journal International

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The oceanic boundary separating the Eurasian and North American plates between 70 and 84 degrees North hosts large earthquakes which are well recorded teleseismically, and many more seismic events at far lower magnitudes that are well recorded only at regional distances. Existing seismic bulletins have considerable spread and bias resulting from limited station coverage and deficiencies in the velocity models applied. This is particularly acute for the lower magnitude events which may only be constrained by a small number of Pn and Sn arrivals. Over the past two decades there has been a significant improvement in the seismic network in the Arctic: a difficult region to instrument due to the harsh climate, a sparsity of accessible sites (particularly at significant distances from the sea), and the expense and difficult logistics of deploying and maintaining stations. New deployments and upgrades to stations on Greenland, Svalbard, Jan Mayen, Hopen, and Bjørnøya have resulted in a sparse but stable regional seismic network which results in events down to magnitudes below 3 generating high quality Pn and Sn signals on multiple stations. A catalog of several hundred events in the region since 1998 has been generated using many new phase readings on stations on both sides of the spreading ridge in addition 2 S. J. Gibbons et al. to teleseismic P phases. A Bayesian multiple event relocation has resulted in a significant reduction in the spread of hypocenter estimates for both large and small events. Whereas single event location algorithms minimize vectors of time residuals on an event-by-event basis, the Bayesloc program finds a joint probability distribution of origins, hypocenters, and corrections to travel time predictions for large numbers of events. The solutions obtained favour those event hypotheses resulting in time residuals which are most consistent over a given source region. The relocations have been performed with different 1-D velocity models applicable to the Arctic region and hypocenters obtained using Bayesloc have been shown to be relatively insensitive to the specified velocity structure in the crust and upper mantle, even for events only constrained by regional phases. The patterns of time residuals resulting from the multiple-event location procedure provide well-constrained time correction surfaces for single-event location estimates and are sufficiently stable to identify a number of picking errors and instrumental timing anomalies. This allows for subsequent quality control of the input data and further improvement in the location estimates. We use the relocated events to form narrowband empirical steering vectors for wavefronts arriving at the SPITS array on Svalbard for azimuth and apparent velocity estimation. We demonstrate that empirical matched field parameter estimation determined by source region is a viable supplement to planewave f-k analysis, mitigating bias and obviating the need for Slowness and Azimuth Station Corrections (SASCs). A database of reference events and phase arrivals ...

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“…Another outstanding technique for the detection of weak teleseismic events is the phase-matched filter [22] popularized by [23,24] in the early 1990s. These pattern matching techniques are a type of Empirical Signal Detector that work by comparing incoming seismic waveforms to canonical examples in the extant seismic record [25,26]. They are particularly effective for the detection of highly correlated repeating events, even for very weak magnitudes [27].…”

Section: Related Workmentioning

confidence: 99%

Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

Dickey

¹

,

Borghetti

²

,

Junek

³

2019

Sensors

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The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

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“…Another outstanding technique for the detection of weak teleseismic events is the correlation detector first introduced by [12] and [13] in the early 1990s. Correlation Detectors are a type of Empirical Signal Detector, that work by comparing incoming seismic waveforms to canonical examples in the extant seismic record [14] [15]. This technique is particularly effective for the detection of highly correlated repeating events, even for very weak magnitudes [16].…”

Section: Related Workmentioning

confidence: 99%

A Deep Temporal Convolutional Neural Network for Regional and Teleseismic Detection

Dickey¹,

Borghetti²,

Junek³

2018

Preprint

0

View full text Add to dashboard Cite

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

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Inferring Aftershock Sequence Properties and Tectonic Structure Using Empirical Signal Detectors

Cited by 18 publications

References 15 publications

Locating seismicity on the Arctic plate boundary using multiple-event techniques and empirical signal processing

Locating seismicity on the Arctic plate boundary using multiple-event techniques and empirical signal processing

Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

A Deep Temporal Convolutional Neural Network for Regional and Teleseismic Detection

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