Application of pattern recognition method (PRM) to estimate ground motions in San Francisco Peninsula, California

Çelebi, Mehmet; Gorshkov, A. I.; Filimonov, M. B.

doi:10.3133/ofr93398

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Recorded ground motion data in the region, and azimuth and distance of each element in the test area to the rupture zone of the Loma Prieta and San Andreas fault were used for training of CORA-3 (a classifier developed by the academy of sciences of Russia). By studying classification results, Celebi et al [1993] identified the most significant parameters in amplification of earthquake strong motion records as elevation, slope, distance to the San Andreas fault, predominant topographic landform, and predominant soil type. Although the training and the testing sets in their study were relatively small but careful selection of features and training made the procedure successful.…”

Section: Classification Of Design Ground Motion Recordsmentioning

confidence: 99%

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Alimoradi¹

2005

J. Earth. Eng.

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Classification of earthquake strong ground motion (SGM) records is performed using fuzzy pattern recognition to exploit knowledge in the data that is utilised in a genetic algorithm (GA) search and scaling program. SGM records are historically treated as "fingerprints" of certain event magnitude and mechanism of faulting systems recorded at different distances on different soil types. Therefore, databases of SGM records of today present data of complex nature in high dimensions (many of the dimensionsor SGM parameters in time and frequency domain -are presently available from different archives). In this study, simple ground motion parameters were used but were combined and scaled nonlinearly such that the physical properties of the data could be preserved while reducing its dimensionality. The processed data was then analysed using fuzzy c-means (FCM) clustering method to explore the possibility of meaningfully representing earthquake SGM data in lower dimensions through finding subsets of mathematically similar vectors in a benchmark database. This representation can be used in practical applications and has a direct influence on the processes of synthesising ground motion records, identifying unknown ground motion parameters (e.g. soil type in this study), improving the quality of matching SGM records to design target spectra, and in rule generalisation for response. The results showed that the stochastic behaviour of earthquake ground motion records can be accurately simplified by having only a few of motion parameters. The very same parameters may also be utilised to derive unknown characteristics of the motion when the classification task on "training" records is performed carefully. The clusters are valid and stable in time and frequency domain and 307 J. Earth. Eng. 2005.09:307-332. Downloaded from www.worldscientific.com by UNIVERSITY OF CALIFORNIA @ SAN DIEGO on 06/03/15. For personal use only. 308 A. Alimoradi et al.are meaningful even with respect to seismological features that were not included in the classification task.

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Section: Classification Of Design Ground Motion Recordsmentioning

confidence: 99%

Untitled

Alimoradi¹

2005

J. Earth. Eng.

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Application of pattern recognition method (PRM) to estimate ground motions in San Francisco Peninsula, California

Cited by 1 publication

References 12 publications

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