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| The first 20% of the full text of this article appears below. |
| INTRODUCTION |
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Earthquake magnitudes play an important role in earthquake forecasting and prediction, but their use is complicated by the fact that there are many magnitude types, measured in different ways with different uncertainties. Even a single catalog may include moment magnitude, local magnitude, surface wave magnitude, and so on. Here we adopt moment magnitude as the standard and convert other types into moment magnitude using regression relationships.
Catalog data have errors in location, magnitude, and focal mechanism that can influence the results of earthquake studies. Neglecting these errors, or estimating them poorly, could cause valid hypotheses to be rejected or invalid ones to be accepted. The uncertainties decrease with time, except that they increase temporarily after large earthquakes. Therefore we've conducted an extensive uncertainty study.
Many studies assume a distinction between mainshocks, aftershocks, and foreshocks, and some catalogs are "declustered" using somewhat arbitrary definitions (e.g., Gardner and Knopoff 1974; Reasenberg and Oppenheimer 1985). Here we take a somewhat different approach. Using a stochastic epidemic-type aftershock (ETAS) model (Zhuang et al. 2002, 2004; Zhuang et al. 2005), we assigned to each event a probability that it occurred independently. The independence probability for each earthquake can then be employed by any user to decluster the catalog to fit his or her needs. Our method is still arbitrary, but we feel it is more transparent and flexible than other methods.
Kagan, Jackson, and Rong
Department of Earth and Space Sciences
University of
California, Los Angeles
595 Charles Young Drive East
3806 Geology
Building
Box 951567
Los Angeles, California 90095-1567 U.S.A.
qiwang@ucla.edu
(Q. W.)
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