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Revisiting the Predictability of the Haicheng and Tangshan Earthquakes
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We analyze a set of precursory data measured before but compiled in retrospect of the MS7.5 Haicheng earthquake in February 1975 and the MS7.6–7.8 Tangshan earthquake in July 1976. We propose a robust and simple coarse-graining method that aggregates and counts how all the anomalies together (levelling, geomagnetism, soil resistivity, earth currents, gravity, earth stress, well water radon, well water level) develop as a function of time. We demonstrate strong evidence for the existence of an acceleration of the number of anomalies leading up to the major Haicheng and Tangshan earthquakes. In particular for the Tangshan earthquake, the frequency of occurrence of anomalies is found to be well described by the log-periodic power law singularity (LPPLS) model, previously proposed for the prediction of engineering failures and later adapted to the prediction of financial crashes. Using a mock real-time prediction experiment and simulation study, based on this methodology of monitoring accelerated rates of physical anomalies measured at the surface, we show the potential for an early warning system with a lead time of a few days.
Title: Revisiting the Predictability of the Haicheng and Tangshan Earthquakes
Description:
We analyze a set of precursory data measured before but compiled in retrospect of the MS7.
5 Haicheng earthquake in February 1975 and the MS7.
6–7.
8 Tangshan earthquake in July 1976.
We propose a robust and simple coarse-graining method that aggregates and counts how all the anomalies together (levelling, geomagnetism, soil resistivity, earth currents, gravity, earth stress, well water radon, well water level) develop as a function of time.
We demonstrate strong evidence for the existence of an acceleration of the number of anomalies leading up to the major Haicheng and Tangshan earthquakes.
In particular for the Tangshan earthquake, the frequency of occurrence of anomalies is found to be well described by the log-periodic power law singularity (LPPLS) model, previously proposed for the prediction of engineering failures and later adapted to the prediction of financial crashes.
Using a mock real-time prediction experiment and simulation study, based on this methodology of monitoring accelerated rates of physical anomalies measured at the surface, we show the potential for an early warning system with a lead time of a few days.
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