Determining hypocentral parameters for local earthquakes in 1-D using a genetic algorithm

Details Determining hypocentral parameters for local earthquakes in 1-D using a genetic algorithm Determining hypocentral parameters for local earthquakes in 1-D using a genetic algorithm

Aug 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006geoji.166..590k&link_type=abstract

Geophysical Journal International, Volume 166, Issue 2, pp. 590-600.

Astronomy and Astrophysics

Astronomy

4

Genetic Algorithm (Ga), Hypocentral Parameters, Two-Point Ray Tracing

Scientific paper

This paper introduces a powerful method for determining hypocentral parameters for local earthquakes in 1-D using a genetic algorithm (GA) and two-point ray tracing. Using existing algorithms to determine hypocentral parameters is difficult, because these parameters can vary based on initial velocity models. We developed a new method to solve this problem by applying a GA to an existing algorithm, HYPO-71. The original HYPO-71 algorithm was modified by applying two-point ray tracing and a weighting factor with respect to the takeoff angle at the source to reduce errors from the ray path and hypocentre depth. Artificial data, without error, were generated by computer using two-point ray tracing in a true model, in which velocity structure and hypocentral parameters were known. The accuracy of the calculated results was easily determined by comparing calculated and actual values. We examined the accuracy of this method for several cases by changing the true and modelled layer numbers and thicknesses. The computational results show that this method determines nearly exact hypocentral parameters without depending on initial velocity models. Furthermore, accurate and nearly unique hypocentral parameters were obtained, although the number of modelled layers and thicknesses differed from those in the true model. Therefore, this method can be a useful tool for determining hypocentral parameters in regions where reliable local velocity values are unknown. This method also provides the basic a priori information for 3-D studies.

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