Challenges for forecasting based on accelerating rates of earthquakes at volcanoes and laboratory analogues
Challenges for forecasting based on accelerating rates of earthquakes at volcanoes and laboratory analogues
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DOI：
10.1111/j.1365-246x.2011.04982.x
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发表时间：
2011-05
影响因子：
2.8
通讯作者：
A. Bell;J. Greenhough;M. Heap;I. Main
中科院分区：
文献类型：
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作者：
A. Bell;J. Greenhough;M. Heap;I. Main
摘要“平均场”模型已被提出作为实验室岩石破裂和火山爆发之前地震速率加速和其他地球物理参数的可证伪假设。重要的是，此类模型可以预测故障或喷发时间。然而，在现有的回顾性分析中，通常会找到将这些模型拟合到数据的适当技术的示例。这里，我们基于可变速率的泊松过程，使用最大似然技术和模型选择的信息标准来测试两个主要的竞争假设——指数加速和幂律加速。对于来自实验室和埃特纳火山的例子，无论是在拟合度还是由此产生的误差结构方面，幂律显然是最好的模型，这与泊松近似一致。以基拉韦厄火山和莫纳罗亚火山为例，结果不太明确，置信区间低估了异常值的数量。与模型的偏差很可能反映了平均场方法未捕获的局部相互作用和/或非平稳加载过程。此外，我们使用模拟来证明模型偏好的固有问题，即只有当故障或爆发发生在奇点附近时，幂律模型才会被首选。尽管平均场模型很可能为地震发生前兆加速的物理过程提供有价值的见解，但我们的研究结果强调了使用此类模型进行预测必须克服的主要困难。
SUMMARY ‘Mean-field’ models have been proposed as falsifiable hypotheses for the acceleration in earthquake rate and other geophysical parameters prior to laboratory rock failure and volcanic eruptions. Importantly, such models may permit forecasting failure or eruption time. However, inexistingretrospectiveanalysesitiscommontofindexamplesofinappropriatetechniquesfor fittingthesemodelstodata.Herewetestthetwomaincompetinghypotheses—exponentialand power-law acceleration—using maximum likelihood techniques and an information criterion for model choice, based on a Poisson process with variable rate. For examples from the laboratory and Mt Etna, the power law is clearly the best model, both in terms of the fit and the resulting error structure, which is consistent with the Poisson approximation. For examples from Kilauea and Mauna Loa the results are less clear-cut and the confidence interval underestimates the number of outliers. Deviations from the models most likely reflect local interactions and/or non-stationary loading processes not captured by the mean-field approach. In addition, we use simulations to demonstrate an inherent problem with model preference, in that a power-law model will only be preferred if failure or eruption occurs close to the singularity. Although mean-field models may well provide valuable insight into the physical process responsible for precursory accelerations in earthquake rate, our findings highlight major difficulties that must be overcome to use such models for forecasting.