Magnitude Uncertainties Impact Seismic Rate Estimates, Forecasts and Predictability Experiments

TL;DR

This paper investigates how magnitude uncertainties in earthquake catalogs affect seismic rate estimates, forecasts, and their evaluation, revealing that uncertainties can significantly bias results and suggesting improvements for forecast testing methods.

Contribution

It quantifies magnitude uncertainties, analyzes their impact on seismic forecasts, and highlights the inadequacy of current evaluation methods under uncertainty.

Findings

Magnitude uncertainties are heavy-tailed, often modeled as double-sided exponential.

Forecast deviations due to noise follow a power law distribution with a specific tail exponent.

Current Poisson-based evaluation tests are inadequate for noisy seismic forecasts.

Abstract

The Collaboratory for the Study of Earthquake Predictability (CSEP) aims to prospectively test time-dependent earthquake probability forecasts on their consistency with observations. To compete, time-dependent seismicity models are calibrated on earthquake catalog data. But catalogs contain much observational uncertainty. We study the impact of magnitude uncertainties on rate estimates in clustering models, on their forecasts and on their evaluation by CSEP's consistency tests. First, we quantify magnitude uncertainties. We find that magnitude uncertainty is more heavy-tailed than a Gaussian, such as a double-sided exponential distribution, with scale parameter nu_c=0.1 - 0.3. Second, we study the impact of such noise on the forecasts of a simple clustering model which captures the main ingredients of popular short term models. We prove that the deviations of noisy forecasts from an exact forecast are power law distributed in the tail with exponent alpha=1/(a*nu_c), where a is the exponent of the productivity law of aftershocks. We further prove that the typical scale of the fluctuations remains sensitively dependent on the specific catalog. Third, we study how noisy forecasts are evaluated in CSEP consistency tests. Noisy forecasts are rejected more frequently than expected for a given confidence limit. The Poisson assumption of the consistency tests is inadequate for short-term forecast evaluations. To capture the idiosyncrasies of each model together with any propagating uncertainties, the forecasts need to specify the entire likelihood distribution of seismic rates.