Effects of Location Errors in Pattern Informatics

Abstract

The effect of location errors in the performance of seismicity-based forecasting methods was studied here using one particular binary forecast technique, the Pattern Informatics (PI) technique (Rundle et al., Proc Nat Acad Sci USA 99, 2514–2521, 2002; Tiampo et al., Pure Appl Geophys 159, 2429–2467, 2002). The Southern Californian dataset was used to generate a series of perturbed catalogs by adding different levels of noise to epicenter locations. The PI technique was applied to these perturbed datasets to perform retrospective forecasts that were evaluated by means of skill scores, commonly used in atmospheric sciences. These results were then compared to the effectiveness obtained from the original dataset. Isolated instances of decline of the PI performance were observed due to the nature of the skill scores themselves, but no clear trend of degradation was identified. Dependence on the total number of events in a catalog also was studied, with no systematic degradation in the performance of the PI for catalogs with events in the cases studied. These results suggest that the stability of the PI method is due to the invariance of the clustering patterns identified by the TM metric (Thirumalai and Mountain, Phys Rev A 39, 3563–3573, 1989) when applied to seismicity.