Charles Richter’s observation that ‘only fools and charlatans predict earthquakes,’ reflects the fact that despite more than 100 years of effort, seismologists remain unable to do so with reliable and accurate results. Meaningful prediction involves specifying the location, time, and size of an earthquake before it occurs to greater precision than expected purely by chance from the known statistics of earthquakes in an area. In this context, ‘forecasting’ implies a prediction with a specification of a probability of the time, location, and magnitude. Two general approaches have been used. In one, the rate of motion accumulating across faults and the amount of slip in past earthquakes is used to infer where and when future earthquakes will occur and the shaking that would be expected. Because the intervals between earthquakes are highly variable, these long-term forecasts are accurate to no better than a hundred years. They are thus valuable for earthquake hazard mitigation, given the long lives of structures, but have clear limitations. The second approach is to identify potentially observable changes in the Earth that precede earthquakes. Various precursors have been suggested, and may have been real in certain cases, but none have yet proved to be a general feature preceding all earthquakes or to stand out convincingly from the normal variability of the Earth’s behavior. However, new types of data, models, and computational power may provide avenues for progress using machine learning that were not previously available. At present, it is unclear whether deterministic earthquake prediction is possible. The frustrations of this search have led to the observation that (echoing Yogi Berra) ‘it is difficult to predict earthquakes, especially before they happen.’ However, because success would be of enormous societal benefit, the search for methods of earthquake prediction and forecasting will likely continue. In this review, we note that the focus is on anticipating the earthquake rupture before it occurs, rather than characterizing it rapidly just after it occurs. The latter is the domain of earthquake early warning, which we do not treat in detail here, although we include a short discussion in the machine learning section at the end.

查尔斯·里希特 (Charles Richter) 的观察“只有傻瓜和江湖骗子才能预测地震”反映了这样一个事实：尽管经过 100 多年的努力，地震学家仍然无法获得可靠和准确的结果。有意义的预测涉及在地震发生之前指定地震的位置、时间和大小，其精确度高于纯粹根据某个地区已知地震统计数据所预期的偶然性。在此上下文中，“预测”意味着具有时间、位置和幅度概率规范的预测。已经使用了两种通用方法。一方面，通过断层累积的运动速率和过去地震中的滑动量来推断未来地震将在何时何地发生以及预期的震动。因为地震之间的间隔变化很大，这些长期预测的准确度不超过一百年。因此，鉴于建筑物的使用寿命长，它们对于减轻地震灾害很有价值，但也有明显的局限性。第二种方法是确定地震前地球上潜在的可观察变化。已经提出了各种前兆，并且在某些情况下可能是真实的，但还没有证明是所有地震之前的普遍特征或令人信服地从地球行为的正常变化中脱颖而出。然而，新类型的数据、模型和计算能力可能为使用机器学习取得进步提供了以前不可用的途径。目前，确定性地震预测是否可行尚不清楚。这次搜索的挫折导致观察到（与 Yogi Berra 相呼应）“很难预测地震，尤其是在它们发生之前。” 然而，由于成功将带来巨大的社会效益，因此可能会继续寻找地震预测和预报方法。在这篇综述中，我们注意到重点是在地震发生之前预测它的破裂，而不是在它发生后立即对其进行表征。后者是地震预警领域，我们在这里不详细讨论，尽管我们在最后的机器学习部分进行了简短的讨论。对地震预测和预报方法的探索可能会继续下去。在这篇综述中，我们注意到重点是在地震发生之前预测它的破裂，而不是在它发生后立即对其进行表征。后者是地震预警领域，我们在这里不详细讨论，尽管我们在最后的机器学习部分进行了简短的讨论。对地震预测和预报方法的探索可能会继续下去。在这篇综述中，我们注意到重点是在地震发生之前预测地震破裂，而不是在它发生后立即对其进行表征。后者是地震预警领域，我们在这里不详细讨论，尽管我们在最后的机器学习部分进行了简短的讨论。