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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

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Published on: August 7, 2017

A syntactic approach to seismic pattern recognition.

H H Liu¹, K S Fu

¹School of Electrical Engineering, Purdue University, West Lafayette, IN 47907.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study compares two seismic pattern classification methods. The nearest-neighbor rule achieved similar accuracy to finite-state grammars but with significantly faster computation for earthquake and explosion data.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Area of Science:

Seismology
Computer Science
Pattern Recognition

Background:

Seismic pattern classification is crucial for distinguishing between natural earthquakes and man-made explosions.
Traditional methods often require complex computational resources.

Purpose of the Study:

To evaluate the effectiveness of the nearest-neighbor decision rule for syntactic seismic pattern classification.
To compare its performance against a method utilizing finite-state grammars and error-correcting parsers.

Main Methods:

Representing seismic patterns as strings and employing string-to-string distance as a similarity measure.
Implementing a secondary method based on finite-state grammars inferred from training data.
Utilizing error-correcting parsers for the grammar-based approach.

Main Results:

Both the nearest-neighbor rule and the finite-state grammar method demonstrated comparable recognition accuracy.
The nearest-neighbor approach exhibited substantially higher computational speed compared to the grammar-based method.

Conclusions:

The nearest-neighbor decision rule is an efficient and accurate method for seismic pattern classification.
This technique offers a computationally faster alternative for analyzing real earthquake/explosion data.