The limits of predictability of volcanic eruptions from accelerating rates of earthquakes
The limits of predictability of volcanic eruptions from accelerating rates of earthquakes
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DOI：
10.1093/gji/ggt191
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发表时间：
2013-09-01
影响因子：
2.8
通讯作者：
Main, Ian G.
中科院分区：
文献类型：
--
作者：
Bell, Andrew F.;Naylor, Mark;Main, Ian G.
在火山喷发之前，通常会增加地震的频率。以前的研究认为，在某些情况下，这些序列遵循大森定律(IOL)，该模型可以作为预测喷发开始时间的基础。然而，喷发前序列的目录很小，人工晶状体作为预测工具的性能在很大程度上仍未得到测试。在这里，我们使用模拟来量化预测喷发时间的精度上限和偏差，在最好的情况下，基于IOL的预测喷发时间的准确性和偏差，即不确定性仅来自随机点过程的单一实现的模型参数估计。我们比较了基于IOL的不同预测方法，并证明最大似然法比目前使用的方法产生更准确和更少偏差的预测。即使在这些理想化的条件下，我们也发现巨大的预测不确定性和错误警报是人工晶体数学的固有特征。例如模型参数值和500d的喷发前序列持续时间，在喷发前25d，如果幂指数已知，10%的预报提前或延迟超过8d，如果幂指数未知，则提前或延迟超过18d。我们还对模型比较和幂指数估计的方法进行了评价。这些技术被应用于真实的喷发前地震数据集的例子。我们发现了系统偏离理想化模式的证据，表明了多个过程的作用，并导致了比合成实例更大的预测误差，特别是在接近喷发时间的地方。
Volcanic eruptions are commonly preceded by increased rates of earthquakes. Previous studies argue that in some instances these sequences follow the inverse Omori law (IOL) and that this model could be the basis for forecasting the timing of eruption onset. However, the catalogue of pre-eruptive sequences is small, and the performance of the IOL as a forecasting tool remains largely untested. Here, we use simulations to quantify upper limits to the accuracy and bias of forecast eruption times based on the IOL in the 'best-case' scenario that uncertainty only arises from model parameter estimation from single realizations of a stochastic point process. We compare different methods for forecasting based on the IOL, and demonstrate that a maximum-likelihood method yields more accurate and less-biased forecasts than methods currently employed. Even in these idealized conditions, we find that large forecast uncertainty and false alarms are inherent features of the mathematics of the IOL. For example model parameter values and 500-d pre-eruptive sequence durations, at 25 d before the eruption, 10 per cent of the forecasts are more than 8 d early or late if the power-law exponent is known a priori, and more than 18 d early or late if the power-law exponent is unknown. We also evaluate methods for model comparison and estimation of the power-law exponent. These techniques are applied to examples of real pre-eruptive earthquake data sets. We find evidence for systematic deviations from the idealized model, indicating the action of multiple processes and resulting in greater forecast error than in the synthetic examples, especially close to the eruption time.