Syntactic approach and vlsi architectures for seismic signal classification

Technical Report · Sat Jan 01 04:00:00 EST 1983

OSTI ID:6755488

Liu, H; Fu, K S

Syntactic pattern recognition has been applied to seismic classification in this study. Its performance is better than many existing statistical approaches. VLSI architectures for syntactic seismic recognition are also proposed which take advantage of parallel processing and pipelining so that a constant time complexity is attainable when processing large amount of data. Application of syntactic pattern recognition to damage assessment is also proposed and demonstrated on a set of experimental data. Seismic waveforms are represented by strings of primitives, i.e., sentences, in this study. String-to-string similarity measures based on both distance and likelihood concepts are discussed along with the symmetric property and the hierarchy. A fixed-length segmentation is used in the experiment. Encouraging results comparable to those of the best statistical approaches are obtained with only two very simple features, namely zero-crossing count and log energy. Primitives are automatically selected using a hierarchical clustering procedure and two decision criteria. Nearest-neighbor decision rule and finite-state error-correcting parsers are used for classification. For error-correcting parsing, finite-state grammars are first inferred from the training samples. These two approaches have same performance in the experiment, whereas the nearest-neighbor rule is faster in speed.

• Research Organization: Purdue Univ., Lafayette, IN (USA). School of Electrical Engineering
• OSTI ID: 6755488
• Report Number(s): AD-A-124398/9
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
990200* -- Mathematics & Computers
DATA PROCESSING
DETECTION
EARTHQUAKES
EXPLOSIONS
PARALLEL PROCESSING
PATTERN RECOGNITION
PROCESSING
PROGRAMMING
SEISMIC DETECTION
SEISMIC EVENTS
SEISMIC WAVES
UNDERGROUND EXPLOSIONS
USES