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Title: Pickless seismic event detection using WCEDS.


Abstract not provided.

; ; ; ;
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:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the NORSAR / SNL Technical Collaboration.
Country of Publication:
United States

Citation Formats

Arrowsmith, Stephen John, Young, Christopher J., Ballard, Sanford NMN, Slinkard, Megan Elizabeth, and Pankow, Kristine. Pickless seismic event detection using WCEDS.. United States: N. p., 2016. Web.
Arrowsmith, Stephen John, Young, Christopher J., Ballard, Sanford NMN, Slinkard, Megan Elizabeth, & Pankow, Kristine. Pickless seismic event detection using WCEDS.. United States.
Arrowsmith, Stephen John, Young, Christopher J., Ballard, Sanford NMN, Slinkard, Megan Elizabeth, and Pankow, Kristine. 2016. "Pickless seismic event detection using WCEDS.". United States. doi:.
title = {Pickless seismic event detection using WCEDS.},
author = {Arrowsmith, Stephen John and Young, Christopher J. and Ballard, Sanford NMN and Slinkard, Megan Elizabeth and Pankow, Kristine},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 7

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  • The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events thatmore » generated the phase picks.« less
  • 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
  • Adaptive digital predictors have been applied to the detection of seismic events. In this paper we review the structure of the adaptive predictor and discuss its implementation. Using seismic velocity data, we then demonstrate its ability to locate signal arrivals.
  • The Nevada Test Site Seismic Network, designed and operated by EG and G Energy Measurement, Inc. for Sandia National Laboratories, consists of five remote stations and one control point station. The system converts analog signals from five seismometers at each station to digital signals and multiplexes the data with status information for transmission over voice-grade telephone circuits to the control point at the Nevada Test Site. Intelligent modems provide automatic line equalization, transmission link diagnostics, and loopback testing. A microprocessor at the remote stations periodically refreshes the seismometer system gain settings to ensure data accuracy. The remote stations operate unattendedmore » and receive commands for setup and status check from the control point. The control point station utilizes a Digital Equipment Corporation (DEC) LSI-11/23 to perform event detection on the five data streams and automatically records up to ninety minutes of event data per station. Planned upgrades include replacing the LSI-11/23 with a DEC MicroVAX-II and implementing dialup capability to verify station status.« less
  • Abstract not provided.