Real-time classification of signals from three-component seismic sensors using neural nets

Conference · Tue May 05 00:00:00 EDT 1992

OSTI ID:7296985

Bowman, B C; Dowla, F

Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

• Research Organization: Lawrence Livermore National Lab., CA (United States)
• Sponsoring Organization: USDOE; USDOE, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 7296985
• Report Number(s): UCRL-JC-109410; CONF-921098-1; ON: DE92014886
• Resource Relation: Conference: IEEE transactions on instrumentation and measurement and 4th Institut Industriel de Transfert de Technologie (IITT) international conference on artificial intelligence and expert systems, Paris (France), 21-22 Oct 1992
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
58 GEOSCIENCES
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE
SEISMIC DETECTION
REAL TIME SYSTEMS
SEISMIC EVENTS
DATA ACQUISITION
DATA ANALYSIS
ARTIFICIAL INTELLIGENCE
COMPUTER NETWORKS
DESIGN
MONITORING
NEURAL NETWORKS
PERFORMANCE
POLARIZATION
DETECTION
990200* - Mathematics & Computers
580000 - Geosciences
450100 - Military Technology
Weaponry
& National Defense- Chemical Explosions & Explosives