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While deterministically predicting the time and location of earthquakes remains impossible, earthquake forecasting models can provide estimates of the probabilities of earthquakes occurring within some region over time. To enable informed decision‐making of civil protection, governmental agencies, or the public, Operational Earthquake Forecasting (OEF) systems aim to provide authoritative earthquake forecasts based on current earthquake activity in near‐real time. Establishing OEF systems involves several nontrivial choices. This review captures the current state of OEF worldwide and analyzes expert recommendations on the development, testing, and communication of earthquake forecasts. An introductory summary of OEF‐related research is followed by a description of OEF systems in Italy, New Zealand, and the United States. Combined, these two parts provide an informative and transparent snapshot of today's OEF landscape. In Section 4, we analyze the results of an expert elicitation that was conducted to seek guidance for the establishment of OEF systems. The elicitation identifies consensus and dissent on OEF issues among a non‐representative group of 20 international earthquake forecasting experts. While the experts agree that communication products should be developed in collaboration with the forecast user groups, they disagree on whether forecasting models and testing methods should be user‐dependent. No recommendations of strict model requirements could be elicited, but benchmark comparisons, prospective testing, reproducibility, and transparency are encouraged. Section 5 gives an outlook on the future of OEF. Besides covering recent research on earthquake forecasting model development and testing, upcoming OEF initiatives are described in the context of the expert elicitation findings. Plain Language Summary: The exact location, time, and magnitude of future earthquakes cannot be predicted. However, based on past earthquake sequences, it is possible to assess probabilities for future earthquakes. This is called earthquake forecasting. Operational Earthquake Forecasting (OEF) systems are designed to provide near‐real‐time authoritative earthquake forecasts, based on current earthquake activity, to aid the decision‐making of various societal stakeholders. Setting up these systems is complex, involving decisions about which model to use, how to best test the model, and how to turn earthquake probability estimates into practical information. This review captures the current state of OEF worldwide and analyzes expert recommendations on the development, testing, and communication of earthquake forecasts. Section 2 provides an overview of OEF‐related research and the background knowledge required to understand the other parts. Section 3 describes existing OEF systems of Italy, New Zealand, and the United States in detail. Section 4 discusses an elicitation of expert views on modeling, testing, and communicating earthquake forecasts (Mizrahi, Dallo, & Kuratle, 2023, https://doi.org/10.3929/ethz‐b‐000637239). Data from the elicitation allow to identify consensus and dissent on OEF issues and provide guidance for future earthquake forecasting efforts. Finally, Section 5 gives an outlook on future OEF‐related research and planned OEF efforts at various institutions. Key Points: We capture the state of earthquake forecasting systems in Italy, New Zealand, and the United States, and future plans in these and other countriesExperts encourage benchmark comparison, prospective testing, reproducibility and transparency, but avoid endorsing specific models or testsExperts stress the need to co‐design forecast communication products with end‐users to ensure their societal relevance and usefulness [ABSTRACT FROM AUTHOR]

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine-learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing but do not yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine-learning forecasts shows promise, and new machine-learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts. Several countries issue real-time aftershock forecasts following significant earthquakes, providing information to aid response and recovery. Statistical models based on past aftershocks are used to compute aftershock probability as a function of space, time, and magnitude. Aftershock forecasting is advancing through better statistical models, constraints on physical triggering mechanisms, and machine learning. Large high-resolution earthquake catalogs provide an opportunity to advance physical, statistical, and machine-learning aftershock models. [ABSTRACT FROM AUTHOR]

We investigate the ground motion produced by rupture propagation through circular barriers and asperities in an otherwise homogeneous earthquake rupture. Using a three-dimensional finite difference method, we analyze the effect of asperity radius, strength, and depth in a dynamic model with fixed rupture velocity. We gradually add complexity to the model, eventually approaching the behavior of a spontaneous dynamic rupture, to determine the origin of each feature in the ground motion. A barrier initially resists rupture, which induces rupture front curvature. These effects focus energy on and off the fault, leading to a concentrated pulse from the barrier region and higher velocities at the surface. Finally, we investigate the scaling laws in a spontaneous dynamic model. We find that dynamic stress drop determines fault-parallel static offset, while the time it takes the barrier to break is a measure of fracture energy. Thus, given sufficiently strong heterogeneity, the prestress and yield stress (relative to sliding friction) of the barrier can both be determined from ground motion measurements. In addition, we find that models with constraints on rupture velocity have less ground motion than constraint-free spontaneous dynamic models with equivalent stress drops. This suggests that kinematic models with such constraints overestimate the actual stress heterogeneity of earthquakes.

To what extent can the future evolution of an ongoing earthquake rupture be predicted? This question of fundamental scientific and practical importance has recently been addressed by studies of teleseismic source time functions (STFs) but reaching contrasting conclusions. One study concludes that the initial portion of STFs is the same regardless of magnitude. Another study concludes that the rate at which earthquakes grow increases systematically and strongly with final event magnitudes. Here, we show that the latter reported trend is caused by a selection bias towards events with unusually long durations and by estimates of STF growth made when the STF is already decaying. If these invalid estimates are left out, the trend is no longer present, except during the first few seconds of the smallest events in the data set, M w 5–6.5, for which the reliability of the STF amplitudes is questionable. Simple synthetic tests show that the observations are consistent with statistically indistinguishable growth of smaller and larger earthquakes. A much weaker trend is apparent among events of comparable duration, but we argue that its significance is not resolvable by the current data. Finally, we propose a nomenclature to facilitate further discussions of earthquake rupture predictability and determinism. [ABSTRACT FROM AUTHOR]

Did the third Uniform California Earthquake Rupture Forecast (UCERF3) go overboard with multifault ruptures? Schwartz (2018) argues that there are too many long ruptures in the model. Here, I address his concern and show that the UCERF3 rupture-length distribution matches empirical data. I also present evidence that, if anything, the UCERF3 model could be improved by adding more connectivity to the fault system. Adding more connectivity would improve model misfits with data, particularly with paleoseismic data on the southern San Andreas fault; make the model less characteristic on the faults; potentially improve aftershock forecasts; and reduce model sensitivity to inadequacies and unknowns in the modeled fault system. [ABSTRACT FROM AUTHOR]

This article represents a step toward generalizing and simplifying the procedure for constructing an inversion-based seismic hazard source model for an interconnected fault system, including the specification of adjustable segmentation constraints. A very simple example is used to maximize understandability and to counter the notion that an inversion approach is only applicable when an abundance of data is available. Also exemplified is how to construct a range of models to adequately represent epistemic uncertainties (which should be a high priority in any hazard assessment). Opportunity is also taken to address common concerns and misunderstandings associated with the third Uniform California Earthquake Rupture Forecast, including the seemingly disproportionate number of large-magnitude events, and how well hazard is resolved given the overall problem is very underdetermined. However, the main aim of this article is to provide a general protocol for constructing such models. [ABSTRACT FROM AUTHOR]

In this study, we revisit the three largest historical earthquakes in California--the 1857 Fort Tejon, 1872 Owens Valley, and 1906 San Francisco earthquakes--to review their published moment magnitudes, and compare their estimated shaking distributions with predictions using modern ground-motion models (GMMs) and ground-motion intensity conversion equations. Currently accepted moment magnitude estimates for the three earthquakes are 7.9, 7.6, and 7.8, respectively. We first consider the extent to which the intensity distributions of all three earthquakes are consistent with a moment magnitude toward the upper end of the estimated range. We then apply a GMM-based method to estimate the magnitudes of large historical earthquakes. The intensity distribution of the 1857 earthquake is too sparse to provide a strong constraint on magnitude. For the 1872 earthquake, consideration of all available constraints suggests that it was a high stress-drop event, with a magnitude on the higher end of the range implied by scaling relationships, that is, higher than moment magnitude 7.6. For the 1906 earthquake, based on our analysis of regional intensities and the detailed intensity distribution in San Francisco, along with other available constraints, we estimate a preferred moment magnitude of 7.9, consistent with the published estimate based on geodetic and instrumental seismic data. These results suggest that, although there can be a tendency for historical earthquake magnitudes to be overestimated, the accepted catalog magnitudes of California's largest historical earthquakes could be too low. Given the uncertainties of the magnitude estimates, the seismic moment release rate between 1850 and 2019 could have been either higher or lower than the average over millennial time scales. It is further not possible to reject the hypothesis that California seismicity is described by an untruncated Gutenberg-Richter distribution with a b-value of 1.0 for moment magnitudes up to 8.0. [ABSTRACT FROM AUTHOR]