震模資訊學 ( 地震資訊學， Pattern Informatics) 與地震預測之可行性

1. 震模資訊學(地震資訊學，Pattern Informatics)與地震預測之可行性 陳建志 Chien-chih Chen 中央大學地球物理研究所 Inst Geophysics, Natl Central Univ

2. Computing a PI (Pattern Informatics) Earthquake Forecast Schematic: Spatial Cross Section Of Intensity Map along a Linear Track Intensity = I Intensity Change = I Activation P = Probability = {< I >}2 Quiescence x (position)

3. Theory(Self-Organizing Spinodal Model) predicts 3 distinct temporal regimes for Gutenberg-Richter statistical laws prior to a large earthquake (“Early, Middle, Late”; Rundle et al., PAGEOPH, 2000) Early Late Middle Data from 1999 Chi-Chi, Taiwan earthquake (C.C. Chen, GJI, 2003) 1998-Sept 20,1999 1991-1993 1994-1997 An Example of Seismic Activation Early Middle Late

4. 甲仙地震震央區背景地震活動之異常

5. ★ ★ 張午龍 紅點是規模 5 以上的地震 藍點是規模 4.5 以上的

6. “Real Time” Prediction 在南台灣及花蓮地區出現兩處高「震模資訊值」叢集區，兩區於2010年3月及6月間分別發生了規模6.4的甲仙地震及規模5.5的花蓮地震。

7. Data assimilation in the Pattern Informatics of Seismicity can proceed by analysis cycles, which possibly reflect “earthquake cycles.” In each analysis cycle, observations of the past statein the data space are combined with the results from a PI modelto produce a PI hotspot map, which is considered as 'the best' estimate of the current state of the earthquake fault system. This is called the analysis step. Essentially, the analysis step tries to balance the uncertainty in the seismicity data and in the PI parameters. ‘The best' PI model/hotspot map is then advanced in time and its result may become the forecastfor thenext analysis/earthquake cycle.

8. Other Possibilities?!

11. 災害防救應用科技方案-地震預警與前兆現象整合計畫大地電磁觀測災害防救應用科技方案-地震預警與前兆現象整合計畫大地電磁觀測 陳建志 國立中央大學地球物理所

12. Thank you so much!!

14. What we have observed is some kind of spatial cooperation between main shock and aftershocks existing actually before the occurrence of main shock, since we have derived such correlation (PI) patterns from the fluctuation in seismicity before great earthquakes.

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