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Title: Waveform Correlation Detection: Putting Theory into Practice.

Abstract

Abstract not provided.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
OSTI Identifier:
1373163
Report Number(s):
SAND2016-7121C
646036
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the NorSAR visit, AWE visit.
Country of Publication:
United States
Language:
English

Citation Formats

Slinkard, Megan Elizabeth, Sundermier, Amy, Young, Christopher J., Heck, Stephen, Schaff, David, and Richards, Paul. Waveform Correlation Detection: Putting Theory into Practice.. United States: N. p., 2016. Web.
Slinkard, Megan Elizabeth, Sundermier, Amy, Young, Christopher J., Heck, Stephen, Schaff, David, & Richards, Paul. Waveform Correlation Detection: Putting Theory into Practice.. United States.
Slinkard, Megan Elizabeth, Sundermier, Amy, Young, Christopher J., Heck, Stephen, Schaff, David, and Richards, Paul. 2016. "Waveform Correlation Detection: Putting Theory into Practice.". United States. doi:. https://www.osti.gov/servlets/purl/1373163.
@article{osti_1373163,
title = {Waveform Correlation Detection: Putting Theory into Practice.},
author = {Slinkard, Megan Elizabeth and Sundermier, Amy and Young, Christopher J. and Heck, Stephen and Schaff, David and Richards, Paul},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 7
}

Conference:
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  • In order to demonstrate certain air-entrainment principles established earlier, tests were conducted on atmospheric industrial burners, relating burner performance to these principles. The study confirmed that (1) the performance of a burner can be predicted by examining its design in terms of air entrainment and (2) the performance limits of air induction burners depend upon air-entrainment laws rather than combustion-stability principles.
  • We have developed a working prototype of a grid-based global event detection system based on waveform correlation. The algorithm comes from a long-period detector but we have recast it in a full matrix formulation which can reduce the number of multiplications needed by better than two orders of magnitude for realistic monitoring scenarios. The reduction is made possible by eliminating redundant multiplications in the original formulation. All unique correlations for a given origin time are stored in a correlation matrix (C) which is formed by a full matrix product of a Master Image matrix (M) and a data matrix (D).more » The detector value at each grid point is calculated by following a different summation path through the correlation matrix. Master Images can be derived either empirically or synthetically. Our testing has used synthetic Master Images because their influence on the detector is easier to understand. We tested the system using the matrix formulation with continuous data from the IRIS (Incorporate Research Institutes for Seismology) broadband global network to monitor a 2 degree evenly spaced surface grid with a time discretization of 1 sps; we successfully detected the largest event in a two hour segment from October 1993. The output at the correct gridpoint was at least 33% larger than at adjacent grid points, and the output at the correct gridpoint at the correct origin time was more than 500% larger than the output at the same gridpoint immediately before or after. Analysis of the C matrix for the origin time of the event demonstrates that there are many significant ``false`` correlations of observed phases with incorrect predicted phases. These false correlations dull the sensitivity of the detector and so must be dealt with if our system is to attain detection thresholds consistent with a Comprehensive Test Ban Treaty (CTBT).« less
  • The goal of the Waveform Correlation Event Detection System (WCEDS) Project at Sandia Labs has been to develop a prototype of a full-waveform correlation based seismic event detection system which could be used to assess potential usefulness for CTBT monitoring. The current seismic event detection system in use at the IDC is very sophisticated and provides good results but there is still significant room for improvement, particularly in reducing the number of false events (currently being nearly equal to the number of real events). Our first prototype was developed last year and since then we have used it for extensivemore » testing from which we have gained considerable insight. The original prototype was based on a long-period detector designed by Shearer (1994), but it has been heavily modified to address problems encountered in application to a data set from the Incorporated Research Institutes for Seismology (IRIS) broadband global network. Important modifications include capabilities for event masking and iterative event detection, continuous near-real time execution, improved Master Image creation, and individualized station pre-processing. All have been shown to improve bulletin quality. In some cases the system has detected marginal events which may not be detectable by traditional detection systems, but definitive conclusions cannot be made without direct comparisons. For this reason future work will focus on using the system to process GSETT3 data for comparison with current event detection systems at the IDC.« less
  • Waveform Correlation Event Detection System (WCEDS) prototypes have now been developed for both global and regional networks and the authors have extensively tested them to assess the potential usefulness of this technology for CTBT (Comprehensive Test Ban Treaty) monitoring. In this paper they present the results of tests on data sets from the IDC (International Data Center) Primary Network and the New Mexico Tech Seismic Network. The data sets span a variety of event types and noise conditions. The results are encouraging at both scales but show particular promise for regional networks. The global system was developed at Sandia Labsmore » and has been tested on data from the IDC Primary Network. The authors have found that for this network the system does not perform at acceptable levels for either detection or location unless directional information (azimuth and slowness) is used. By incorporating directional information, however, both areas can be improved substantially suggesting that WCEDS may be able to offer a global detection capability which could complement that provided by the GA (Global Association) system in use at the IDC and USNDC (United States National Data Center). The local version of WCEDS (LWCEDS) has been developed and tested at New Mexico Tech using data from the New Mexico Tech Seismic Network (NMTSN). Results indicate that the WCEDS technology works well at this scale, despite the fact that the present implementation of LWCEDS does not use directional information. The NMTSN data set is a good test bed for the development of LWCEDS because of a typically large number of observed local phases and near network-wide recording of most local and regional events. Detection levels approach those of trained analysts, and locations are within 3 km of manually determined locations for local events.« less
  • Swarms of earthquakes and/or aftershock sequences can dramatically increase the level of seismicity in a region for a period of time lasting from days to months, depending on the swarm or sequence. Such occurrences can provide a large amount of useful information to seismologists. For those who monitor seismic events for possible nuclear explosions, however, these swarms/sequences are a nuisance. In an explosion monitoring system, each event must be treated as a possible nuclear test until it can be proven, to a high degree of confidence, not to be. Seismic events recorded by the same station with highly correlated waveformsmore » almost certainly have a similar location and source type, so clusters of events within a swarm can quickly be identified as earthquakes. We have developed a number of tools that can be used to exploit the high degree of waveform similarity expected to be associated with swarms/sequences. Dendro Tool measures correlations between known events. The Waveform Correlation Detector is intended to act as a detector, finding events in raw data which correlate with known events. The Self Scanner is used to find all correlated segments within a raw data steam and does not require an event library. All three techniques together provide an opportunity to study the similarities of events in an aftershock sequence in different ways. To comprehensively characterize the benefits and limits of waveform correlation techniques, we studied 3 aftershock sequences, using our 3 tools, at multiple stations. We explored the effects of station distance and event magnitudes on correlation results. Lastly, we show the reduction in detection threshold and analyst workload offered by waveform correlation techniques compared to STA/LTA based detection. We analyzed 4 days of data from each aftershock sequence using all three methods. Most known events clustered in a similar manner across the toolsets. Up to 25% of catalogued events were found to be a member of a cluster. In addition, the Waveform Correlation Detector and Self Scanner identified significant numbers of new events that were not in either the EDR or regional catalogs, showing a lowering of the detection threshold. We extended our analysis to study the effect of distance on correlation results by applying the analysis tools to multiple stations along a transect of nearly constant azimuth when possible. We expected the number of events found via correlation would drop off as roughly 1/r2, where r is the distance from mainshock to station. However, we found that regional geological conditions influenced the performance of a given station more than distance. For example, for one sequence we clustered 25% of events at the nearest station to the mainshock (34 km), while our performance dropped to 2% at a station 550 km distant. However, we matched our best performance (25% clustering) at a station 198 km distant.« less