Earthquake forecasting and its verification

TL;DR

This paper introduces a pattern informatics (PI) method for probabilistic earthquake forecasting based on seismicity variations, demonstrating its effectiveness in California, Japan, and globally, and outperforming traditional intensity-based forecasts.

Contribution

The paper presents a novel PI approach for earthquake forecasting that quantifies seismicity variations and provides probabilistic hazard maps, validated through retrospective analysis.

Findings

PI method outperforms relative intensity forecasts in California

Forecasts successfully identify seismic hotspots over a 10-year span

The approach is applicable globally and improves prediction accuracy

Abstract

No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. These are primarily based on the association of small earthquakes with future large earthquakes. In this paper we discuss a new approach to earthquake forecasting. This approach is based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output is a map of areas in a seismogenic region (``hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. These forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future earthquakes will occur where earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.