Computer Science – Performance
Scientific paper
Aug 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011georl..3815304b&link_type=abstract
Geophysical Research Letters, Volume 38, Issue 15, CiteID L15304
Computer Science
Performance
Natural Hazards: Monitoring, Forecasting, Prediction (1816, 1922, 2722, 3238, 3245, 4263, 7223, 7924), Natural Hazards: Precursors, Natural Hazards: Statistical Analysis (1984, 1986), Volcanology: Volcano Monitoring (4302, 7280), Volcanology: Volcanic Hazards And Risks (4302, 4328, 4333)
Scientific paper
Power-law accelerations in the mean rate of strain, earthquakes and other precursors have been widely reported prior to material failure phenomena, including volcanic eruptions, landslides and laboratory deformation experiments, as predicted by several theoretical models. The Failure Forecast Method (FFM), which linearizes the power-law trend, has been routinely used to forecast the failure time in retrospective analyses; however, its performance has never been formally evaluated. Here we use synthetic and real data, recorded in laboratory brittle creep experiments and at volcanoes, to show that the assumptions of the FFM are inconsistent with the error structure of the data, leading to biased and imprecise forecasts. We show that a Generalized Linear Model method provides higher-quality forecasts that converge more accurately to the eventual failure time, accounting for the appropriate error distributions. This approach should be employed in place of the FFM to provide reliable quantitative forecasts and estimate their associated uncertainties.
Bell Andrew F.
Heap Michael J.
Main Ian. G.
Naylor Mark
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