The Forecasting Skill of Physics‐Based Seismicity Models during the 2010–2012 Canterbury, New Zealand, Earthquake Sequence

Abstract

The static Coulomb stress hypothesis is a widely known physical mechanism for earthquake triggering, and thus a prime candidate for physics-based Operational Earthquake Forecasting (OEF). However, the forecast skill of Coulomb-based seismicity models remains controversial, especially in comparison to empirical statistical models. A previous evaluation by the Collaboratory for the Study of Earthquake Predictability (CSEP) concluded that a suite of Coulomb-based seismicity models were less informative than empirical models during the aftershock sequence of the 1992 M w 7.3 Landers, California, earthquake. Recently, a new generation of Coulomb-based and Coulomb/statistical hybrid models were developed that account better for uncertainties and secondary stress sources. Here, we report on the performance of this new suite of models in comparison to empirical Epidemic Type Aftershock Sequences (ETAS) models during the 2010-2012 Canterbury, New Zealand, earthquake sequence. Comprising the 2010 M 7.1 Darfield earthquake and three subsequent M ≥ 5.9 shocks (including the February 2011 Christchurch earthquake), this sequence provides a wealth of data (394 M ≥ 3.95 shocks). We assessed models over multiple forecast horizons (1-day, 1-month and 1-year, updated after M ≥ 5.9 shocks). The results demonstrate substantial improvements in the Coulomb-based models. Purely physics-based models have a performance comparable to the ETAS model, and the two Coulomb/statistical hybrids perform better or as well as the corresponding statistical model. On the other hand, an ETAS model with anisotropic (fault-based) aftershock zones is just as informative. These results provide encouraging evidence for the predictive power of Coulomb-based models. To assist with model development, we identify discrepancies between forecasts and observations.

Improving Physics‐Based Aftershock Forecasts During the 2016–2017 Central Italy Earthquake Cascade

“…Although the focus of our study is on the improvements of CRS models, we acknowledge that other ETAS parametrizations may perform differently. Previous experiments show a good performance of ETAS both in retrospective (Cattania et al, ; Helmstetter et al, ; Marzocchi, Murru, et al, ; Werner et al, ; Woessner et al, ) and during unfolding sequences (e.g., Marzocchi et al, ; Marzocchi & Lombardi, ), but some weaknesses related with early catalog incompleteness (Omi et al, ; Segou & Parsons, ) and the absence of fault interaction effects (e.g., Marzocchi & Lombardi, ) have been reported.…”

“…Nowadays, the implementation of laboratory-derived friction laws describing the seismicity response to an earthquake perturbation (Dieterich, 1994) is the basis of many physics-based forecasts known as Coulomb rate-state (CRS) models (Cattania et al, 2018;Cocco et al, 2010;Parsons et al, 2012Parsons et al, , 2014Segou, 2016;Toda & Enescu, 2011;Toda et al, 2005, among others). Recent advances in CRS modeling demonstrated that addressing the uncertainties behind physics-based models leads to a predictive power comparable to empirical models (Cattania et al, 2018;Segou & Parsons, 2016). However, the limitation for an operational use of CRS models is that their parametrization goes beyond a mere earthquake catalog and requires representations of earthquake sources and nearby receiver faults that are unlikely to be available immediately after a major event.…”

Stress Transfer From Opening Hydraulic Fractures Controls the Distribution of Induced Seismicity

“…Elastic stress change models have been used for decades to determine the triggering mechanism of tectonic earthquakes (Stein, 1999;Harris, 1998;Steacy et al, 2005;Meier et al, 2014;Wedmore et al, 2017), illuminating the sometimes unexpected spatiotemporal patterns which occur during seismic sequences. These models are regularly applied in physics-based earthquake hazard forecasts, using the observed slip on faults 10.1029/2019JB018794 to model the spatial distribution of subsequent, potentially damaging, earthquakes (Cattania et al, 2018;Mancini et al, 2019). Elastostatic modeling has also been applied with tensile sources, such as the analysis by Green et al (2015) of a seismic sequence associated with dyke intrusion in Iceland.…”

“…Defining a significance threshold for the effect on a population of events (Meier et al, 2014), quantifying model uncertainties (Catalli et al, 2013;Kettlety et al, 2019), and untangling the effects of other failure mechanisms, such as dynamic triggering or poroelasticity, all provide a significant challenge. Nonetheless, elastostatic stress modeling has repeatedly provided a robust explanation for the spatial distribution of earthquake sequences (Steacy et al, 2005;Meier et al, 2014;Wedmore et al, 2017;Cattania et al, 2018), and when applied carefully, can be an effective method of studying the triggering of induced seismicity (Schoenball et al, 2012;Catalli et al, 2013;Sumy et al, 2014;Pennington and Chen, 2017;Kettlety et al, 2019).…”

Seismicity Pattern Changes Prior to the 2008 Ms7.3 Yutian Earthquake

“…The related results have provided some useful information for understanding the seismogenic structure and rupture process of the Yutian earthquake. Among the above reported changes possibly associated with earthquakes, seismicity changes may provide some useful information for intermediate-term forecasts of earthquakes [ 8 ], as even earthquake forecasting has been a controversial issue for a long time [ 8 , 9 , 10 , 11 ]. It is noteworthy that recently, analyses of seismicity based on information entropy (e.g., [ 12 ]), Tsallis entropy (e.g., [ 13 ]), and natural time entropy (e.g., [ 14 ]) have provided interesting results towards intermediate-term and short-term forecasting.…”