HR: 08:45h
AN: S41D-04 INVITED     [Abstracts]
TI: Evaluation Techniques for Alarm-Based Earthquake Forecasts
AU: * Zechar, J D
EM: zechar@usc.edu
AF: University of Southern California, Department of Earth Sciences 3651 Trousdale Pkwy, Los Angeles, CA 90089 United States
AU: Jordan, T H
EM: tjordan@usc.edu
AF: University of Southern California, Department of Earth Sciences 3651 Trousdale Pkwy, Los Angeles, CA 90089 United States
AB: When earthquake forecasts are stated in terms of conditional intensity, a number of well-known methods are available for hypothesis testing. For example, the Regional Earthquake Likelihood Model (RELM) group of the Southern California Earthquake Center (SCEC) will soon begin systematic, simultaneous testing of a number of algorithms that forecast earthquake intensity. The likelihood framework, however, cannot be directly applied to prediction methods that generate alarms rather than probabilistic forecasts. Because a number of investigators are using alarm-based methods in earthquake prediction experiments, it is important that they be evaluated and, when possible, compared. Methods for evaluation of deterministic binary forecasts have been developed from work related to signal detection, weather forecast verification (Jolliffe and Stephenson 2003), information theory (Jones and Jones 2003), and economic losses (Molchan 1990, Molchan and Kagan 1992). In this presentation, we provide an overview of the available techniques and highlight theoretical and practical difficulties. We illustrate the use of these methods by application to two recent pattern recognition prediction algorithms: Reverse Tracing of Precursors (Keilis-Borok et al 2004) and Pattern Informatics (Tiampo et al 2002, Rundle et al 2003).
DE: 3238 Prediction (3245, 4263)
DE: 3245 Probabilistic forecasting (3238)
DE: 7209 Earthquake dynamics (1242)
DE: 7223 Earthquake interaction, forecasting, and prediction (1217, 1242)
DE: 7230 Seismicity and tectonics (1207, 1217, 1240, 1242)
SC: Seismology [S]
MN: Fall Meeting 2005