Automated Data Processing (ADP) research and development Page: 4 of 13
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Automated Data Processing (ADP) Research
Farid U. Dowla, Virgil N. Kohlhepp, and Richard R. Leach, Jr.
Earth Sciences Division, Lawrence Livermore National Laboratory
Sponsored by DOE CTBT R&D Program1
Monitoring a comprehensive test ban treaty (CTBT) will require
screening tens of thousands of seismic events each year. Reliable
automated data analysis will be essential in keeping up with the
continuous stream of events that a global monitoring network will
detect. We are developing automated event location and identification
algorithms by looking at the gaps and weaknesses in conventional
ADP systems and by taking advantage of modern computational
paradigms. Our research focus is on three areas: developing robust
algorithms for signal feature extraction, integrating the analysis of
critical measurements, and exploiting joint estimation techniques
such as using data from acoustic, hydroacoustic, and seismic sensors.
We identify several important problems for research and
development; e.g., event location with approximate velocity models
and event identification in the presence of outliers. We are
employing both linear and nonlinear methods and advanced signal
transform techniques to solve these event monitoring problems. Our
goal is to increase event-interpretation throughput by employing the
power and efficiency of modern computational techniques, and to
improve the reliability of automated analysis by reducing the rates of
false alarms and missed detections.
Key words: Automation, data fusion, event location, event
identification, neural networks, self-organization, seismic networks,
seismic arrays, signal processing, wavelets.
'Research performed under the auspices of the U.S. Department of Energy by the Lawrence Livermore National
Laboratory under contract w-7405-ENG-48.
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Dowla, F.U.; Kohlhepp, V.N. & Leach, R.R. Jr. Automated Data Processing (ADP) research and development, article, July 1, 1995; California. (digital.library.unt.edu/ark:/67531/metadc623588/m1/4/: accessed January 16, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.