Second round of evaluations of proposed earthquake precursors |
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| Authors: | Max Wyss |
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| Institution: | (1) Geophysical Institute, University of Alaska, 99775 Fairbanks, AK, USA |
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| Abstract: | This review summarizes the result of the second round of nominations for the IASPEI Preliminary List of Significant Precursors. Currently this List contains five cases of precursors: (1) foreshocks, (2) preshocks, (3) seismic quiescence before major aftershocks, (4) radon decrease in ground water, and (5) ground water level increase. A list of four cases that could not be accepted nor rejected by the panels reviewing them contains three on crustal deformations and one on seismic quiescence. In the second round 10 nominations were evaluated, nine new ones and one which had been considered previously. Two were accepted for the List, two were placed in the category of undecided cases. To date, a total of 40 nominations have been evaluated by IASPEI. For 37 of these the nominations, the mail reviews, the panel opinions, and, where supplied, the author's reply were published. This evaluation process remains active throughout the International Decade for Natural Hazards Reduction. Additional nominations are invited.The IASPEI Sub-commission on Earthquake Prediction does not guarantee that precursors accepted for the List can be used for earthquake prediction, nor does rejection of a nomination mean that the particular method could never become useful for prediction. However, the List, as well as the interchanges between authors and reviewers, allow us to gauge the state-of-the-art in earthquake prediction research. It is clear that we do not have an earthquake prediction capability, because the manner in which to use the few precursors on the List for predictions is not known. It also appears that many of the results thought to be conclusive by the authors, may not command the respect of the seismological research community at large. A more quantitative approach to data analysis, the use of rigorous statistical techniques, and high quality, long-term data sets are needed to make progress in earthquake prediction research. |
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