In this work, we validated the automatic inversion of the U.S. Geolog-ical Survey (USGS) intensity data set of the Whittier NarrowsML5.9, 1987 earth-quake. This validation of our new technique was obtained by comparing the sourceinversion results with the principal source parameters coming from instrumental mea-surements independent of our study. To perform the inversion, first, we used a geneticalgorithm (GA) with a population of 20,000 individuals (i.e., sources). Second, be-cause the problem is bimodal, we also used a niching genetic algorithm, (NGA) withfour demes of 2,000 individuals. This gave us almost the same results. The siteintensities were calculated by our kinematicKFfunction. Twelve source parameterswere involved in the inversions, the most sensitive of which are the epicentral co-ordinates and the fault-plane solution. Two minimum variance models were deter-mined by both theGAand theNGAinversions: (1) one east–west trending dip-slipsource, which is in agreement with that already known from instrumental measure-ments, and (2) one almost coinciding with its auxiliary plane in the fault-plane so-lution. These findings almost coincide with those produced by the grid-search in-version method, but theGA-NGAinversion is much faster and does not need strongconstraints. This confirms that it is possible to get an approximate idea of the sourceof the studied earthquake also by automatically inverting the regional pattern of theUSGSintensities. This result encourages us to validate our inversion technique withmore well documented earthquakes and to treat intensities of preinstrumental earth-quakes, which are the principal target of our work.

Validation of the Automatic Nonlinear Source Inversion of the U.S. Geological Survey Intensities of the Whittier Narrows 1987 Earthquake

PETTENATI F;
2004

Abstract

In this work, we validated the automatic inversion of the U.S. Geolog-ical Survey (USGS) intensity data set of the Whittier NarrowsML5.9, 1987 earth-quake. This validation of our new technique was obtained by comparing the sourceinversion results with the principal source parameters coming from instrumental mea-surements independent of our study. To perform the inversion, first, we used a geneticalgorithm (GA) with a population of 20,000 individuals (i.e., sources). Second, be-cause the problem is bimodal, we also used a niching genetic algorithm, (NGA) withfour demes of 2,000 individuals. This gave us almost the same results. The siteintensities were calculated by our kinematicKFfunction. Twelve source parameterswere involved in the inversions, the most sensitive of which are the epicentral co-ordinates and the fault-plane solution. Two minimum variance models were deter-mined by both theGAand theNGAinversions: (1) one east–west trending dip-slipsource, which is in agreement with that already known from instrumental measure-ments, and (2) one almost coinciding with its auxiliary plane in the fault-plane so-lution. These findings almost coincide with those produced by the grid-search in-version method, but theGA-NGAinversion is much faster and does not need strongconstraints. This confirms that it is possible to get an approximate idea of the sourceof the studied earthquake also by automatically inverting the regional pattern of theUSGSintensities. This result encourages us to validate our inversion technique withmore well documented earthquakes and to treat intensities of preinstrumental earth-quakes, which are the principal target of our work.
Inverse theory; Genetic algorithm; Macroseismic intensity; Tessellation
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.14083/2713
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