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Methods for evaluating earthquake predictions.

机译：评估地震预测的方法。

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摘要

Earthquake prediction is one of the most important unsolved problems in the geosciences. Over the past decade, earthquake prediction research has been revitalized, and predictability experiments are currently active worldwide. In considering these experiments, a number of issues related to prediction evaluation are vital: a detailed experiment specification, the measure of success to be used, and a choice of appropriate reference model(s). Here, we address each of these, with an emphasis on testing prospective earthquake predictions.;We consider a general class of earthquake forecasts for which the forecast format allows a binary interpretation; that is, for any given interval of space and time, we can infer whether or not an earthquake of a given size is expected. This generalization allows us to test deterministic and probabilistic forecasts and compare the results; furthermore, the tests are easily understood because they are essentially the sum of many yes/no questions. As an introduction to binary performance measures and their wide applicability, we considered Reverse Tracing of Precursors (RTP), a recent earthquake prediction algorithm intended to forecast damaging earthquakes. We introduce and analyze several methods for measuring predictive performance but concede that the RTP experiment results are likely unstable due to the small number of earthquakes occurring to date.;In the context of an experiment with three 10 year seismicity forecasts---Relative Intensity, Pattern Informatics, and National Seismic Hazard Map---we introduce the area skill score, a measure of success derived from the Molchan diagram. Using this experiment and applying approaches from statistical hypothesis testing, we illustrate the importance of choosing an appropriate reference model, and show that added model complexity does not necessarily yield a significant improvement in predictive skill.;Having demonstrated the use of the area skill score as a performance metric, we explore its statistical properties and the related computational procedures in some detail. Based on this work and the previous experiment results, we used the area skill score to explore the evolution of regional seismicity and optimize simple forecast models.
机译：地震预报是地球科学中最重要的未解决问题之一。在过去的十年中，地震预测研究得到了振兴，可预测性实验目前在全球范围内正在活跃。在考虑这些实验时，与预测评估有关的许多问题至关重要：详细的实验规范，要使用的成功度量以及适当参考模型的选择。在这里，我们着眼于每种方法，重点是测试预期的地震预测。我们考虑了一般的地震预测类，其预测格式允许进行二进制解释；也就是说，对于任何给定的时间间隔，我们都可以推断出是否会发生给定规模的地震。这种概括使我们能够检验确定性和概率性预测并比较结果。此外，测试很容易理解，因为它们实质上是许多是/否问题的总和。作为对二进制性能度量及其广泛适用性的介绍，我们考虑了前体的反向跟踪（RTP），这是一种最近的地震预测算法，旨在预测破坏性地震。我们介绍并分析了几种测量预测性能的方法，但他们认为RTP实验结果由于迄今为止发生的地震数量少而可能不稳定。在以三个10年地震烈度预测的实验的背景下-相对强度，模式信息学和国家地震灾害图-我们介绍了区域技能得分，该得分是根据Molchan图得出的成功度量。使用该实验并运用统计假设检验的方法，我们说明了选择合适的参考模型的重要性，并表明增加模型的复杂性并不一定会显着提高预测技能。作为性能指标，我们将详细探讨其统计属性和相关的计算过程。基于这项工作和先前的实验结果，我们使用区域技能得分来探索区域地震活动的演变并优化简单的预测模型。

著录项

  • 作者

    Zechar, Jeremy Douglas.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Geophysics.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 148 p.
  • 总页数 148
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地球物理学;
  • 关键词

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