Forecasting the Rates of Future Aftershocks of All Generations Is Essential to Develop Better Earthquake Forecast Models

Abstract

Currently, one of the best performing earthquake forecasting models relies on the working hypothesis that the “locations of past background earthquakes reveal the probable location of future seismicity.” As an alternative, we present a class of smoothed seismicity models (SSMs) based on the principles of the epidemic‐type aftershock sequence (ETAS) model, which forecast the location, time, and magnitude of all future earthquakes using the estimates of the background seismicity rate and the rates of future aftershocks of all generations. Using the Californian earthquake catalog, we formulate six controlled pseudo‐prospective experiments with different combinations of three target magnitude thresholds: 2.95, 3.95, or 4.95 and two forecasting time horizons: 1 or 5 years. In these experiments, we compare the performance of (1) the ETAS model with spatially homogenous parameters, or GETAS; (2) the ETAS model with spatially variable parameters, or SVETAS; (3) three declustering‐based SSMs; (4) a simple SSM based on undeclustered data, and (5) a model based on strain rate data, in forecasting the location and magnitude of all (undeclustered) target earthquakes during many testing periods. In all conducted experiments, the SVETAS model comes out with consistent superiority compared to all the competing models. Consistently better performance of the SVETAS model with respect to declustering‐based SSMs highlights the importance of forecasting the future aftershocks of all generations for developing better earthquake forecasting models. Among the two ETAS models themselves, accounting for the optimal spatial variation of the parameters leads to stronger improvements in forecasting performance.

Global models for short-term earthquake forecasting and predictive skill assessment

“…However, as the earthquakes do not naturally come with labels such as "background", "aftershocks" and "foreshocks" that can be used for validation, this a posteriori identification remains highly subjective. In regards to CSEP's use of the Reasenberg's declustering algorithm, Nandan et al [2019c] pointed out that the subjective nature of declustering introduces a bias towards models that are consistent with the declustering technique, rather than the observed earthquakes as a whole. This puts into questions the value of such experiments, as their results are subject to change as a function of the declustering parameters.…”

“…4. On the regional scale, ETAS models [Nandan et al, 2019c] have been shown to be much more effective than standard smoothed seismicity models [Werner et al, 2011;Helmstetter et al, 2006], which provide forecasts of future earthquakes by smoothing the location of past background earthquakes. However, their forecasting effectiveness on the global scale remains to be assessed.…”

Uncovering predictability of individual and team success: Significant Hot Hand Effect in International Cricket

“…In this study, we present a novel methodology to better understand and predict individual and team performances. We derive our methodology from the self-excited conditional Hawkes point process (37), which has been applied in a variety of fields particularly the description of social diffusion processes (38)(39)(40), financial systems (41)(42)(43), and seismological predictions (44)(45)(46). To the best of our knowledge, this is the first use of Hawkes processes in the domain of 'science of success'.…”

“…Depending on the problem, previous researchers have used different parametric forms for , e.g. (38,45,46) use a power law kernel, whereas (51) use an exponential kernel. In the present case, as there is no reason to favor any parametric form, we decide to use a non-parametric kernel function for φ (42, 52).…”

Embracing Data Incompleteness for Better Earthquake Forecasting

“…The clustering behavior of earthquakes is relevant in a variety of contexts, and in particular it is relevant for seismicity forecasting. Epidemic-type aftershock sequence (ETAS) models (see Ogata, 1998;Veen and Schoenberg, 2008;Nandan et al, 2017) intrinsically account for the spatio-temporal clustering of earthquakes, and they have been shown to be among the best-performing earthquake forecasting models available today (Nandan et al, 2019c).…”