大震超越概率：基于DIY本地地震集合现在预测与预测
Exceedance Probabilities for Large Earthquakes From DIY Local Earthquake Ensemble Nowcasting and Forecasting
AI总结：
本文研究如何通过统计小震数据预测本地大震概率，利用Gutenberg-Richter关系计算超前概率，方法在时间跨度增大时表现更优，经UCERF3验证并应用于洛杉矶周边区域。
摘要：
本文聚焦于预测未来大震在本地区域内的发生概率，其中本地定义为以任意半径圆圈内发生大震的概率。所有工作均基于Gutenberg-Richter (GR) 震级频率关系。本文提出一种方法，通过统计区域内小震数量来计算大震的日历时间预测。利用GR关系在周围区域的有效性，定义一个更大的周围区域地震集合用于预测。随后进行简单数据挖掘。该方法在ROC测试中表现出显著技能，随时间自上次大震后增加而提高。概率基于自上次大震后发生的中小震数量n(t)计算，直接计算为ROC曲线相关的正预测值(PPV)。该方法通过与UCERF3预测对比验证，并应用于125公里半径的洛杉矶周边区域，计算1年和5年的短期预测。
英文摘要：
This paper focuses on the problem of anticipating the local occurrence of future large earthquakes. "Local" is defined as the probability of a large earthquake occurring with a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the Gutenberg-Richter (GR) magnitude-frequency relation holds. Here we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude MT using a count small earthquakes MS < MT in the area. Using the idea that the GR relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is validated by comparison to the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco. The method is then applied to a 125-KM radius circular area around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake, and short term forecasts (1 year and 5 year ) are computed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
