Keilis-Borok V I, Shebalin P N, Zaliapin I V
Institute of Geophysics and Planetary Physics and Department of Earth and Space Sciences, University of California, Los Angeles, CA 90095-1567, USA.
Proc Natl Acad Sci U S A. 2002 Dec 24;99(26):16562-7. doi: 10.1073/pnas.202617199. Epub 2002 Dec 13.
This article explores the problem of short-term earthquake prediction based on spatio-temporal variations of seismicity. Previous approaches to this problem have used precursory seismicity patterns that precede large earthquakes with "intermediate" lead times of years. Examples include increases of earthquake correlation range and increases of seismic activity. Here, we look for a renormalization of these patterns that would reduce the predictive lead time from years to months. We demonstrate a combination of renormalized patterns that preceded within 1-7 months five large (M > or = 6.4) strike-slip earthquakes in southeastern California since 1960. An algorithm for short-term prediction is formulated. The algorithm is self-adapting to the level of seismicity: it can be transferred without readaptation from earthquake to earthquake and from area to area. Exhaustive retrospective tests show that the algorithm is stable to variations of its adjustable elements. This finding encourages further tests in other regions. The final test, as always, should be advance prediction. The suggested algorithm has a simple qualitative interpretation in terms of deformations around a soon-to-break fault: the blocks surrounding that fault began to move as a whole. A more general interpretation comes from the phenomenon of self-similarity since our premonitory patterns retain their predictive power after renormalization to smaller spatial and temporal scales. The suggested algorithm is designed to provide a short-term approximation to an intermediate-term prediction. It remains unclear whether it could be used independently. It seems worthwhile to explore similar renormalizations for other premonitory seismicity patterns.
本文探讨基于地震活动时空变化的短期地震预测问题。此前针对该问题的方法使用了在大地震之前数年的“中期”提前期出现的前兆地震活动模式。例如地震关联范围的增加和地震活动的增强。在此，我们寻求对这些模式进行重整化，以使预测提前期从数年缩短至数月。我们展示了自1960年以来在加利福尼亚东南部五次大型（M≥6.4）走滑地震之前1 - 7个月内出现的重整化模式的组合。制定了一种短期预测算法。该算法能自适应地震活动水平：它可以在不重新调整的情况下从一次地震转移到另一次地震，从一个区域转移到另一个区域。详尽的回顾性测试表明，该算法对其可调元素的变化具有稳定性。这一发现鼓励在其他地区进行进一步测试。最后的测试一如既往地应该是提前预测。所建议的算法根据即将破裂断层周围的变形具有简单的定性解释：该断层周围的地块开始整体移动。更一般的解释来自自相似性现象，因为我们的前兆模式在重整化到更小的空间和时间尺度后仍保留其预测能力。所建议的算法旨在为中期预测提供短期近似。目前尚不清楚它是否可以独立使用。探索其他前兆地震活动模式的类似重整化似乎是值得的。
