The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global cyberinfrastructure for prospective evaluations of earthquake forecast models and prediction algorithms. CSEP’s goals are to improve our understanding of earthquake predictability, advance forecasting model development, test key scientific hypotheses and their predictive power, and improve seismic hazard assessments. Since its inception in California in 2007, the global CSEP collaboration has been conducting forecast experiments in a variety of tectonic settings and at a global scale and now operates four testing centers on four continents to automatically and objectively evaluate models against prospective data. These experiments have provided a multitude of results that are informing operational earthquake forecasting systems and seismic hazard models, and they have provided new and, sometimes, surprising insights into the predictability of earthquakes and spurned model improvements. CSEP has also conducted pilot studies to evaluate ground‐motion and hazard models. Here, we report on selected achievements from a decade of CSEP, and we present our priorities for future activities.

A seismicity rate model (SRM) has been developed as part of the 2022 Aotearoa New Zealand National Seismic Hazard Model revision. The SRM consists of many component models, each of which falls into one of two classes: (1) inversion fault model (IFM); or (2) distributed seismicity model (DSM). Here we provide an overview of the SRM and a brief description of each of the component models. The upper plate IFM forecasts the occurrence rate for hundreds of thousands of potential ruptures derived from the New Zealand Community Fault Model version 1.0 and utilizing either geologic‐ or geodetic‐based fault‐slip rates. These ruptures are typically less than a couple of hundred kilometers long, but can exceed 1500 km and extend along most of the length of the country (albeit with very low probabilities of exceedance [PoE]). We have also applied the IFM method to the two subduction zones of New Zealand and forecast earthquake magnitudes of up to ∼ $Mw$ 9.4, again with very low PoE. The DSM combines a hybrid model developed using multiple datasets with a non‐Poisson uniform rate zone model for lower seismicity regions of New Zealand. Forecasts for 100 yr are derived that account for overdispersion of the rate variability when compared with Poisson. Finally, the epistemic uncertainty has been modeled via the range of models and parameters implemented in an SRM logic tree. Results are presented, which indicate the sensitivity of hazard results to the logic tree branches and that were used to reduce the overall complexity of the logic tree.

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an open and global community whose mission is to accelerate earthquake predictability research through rigorous testing of probabilistic earthquake forecast models and prediction algorithms. pyCSEP supports this mission by providing open-source implementations of useful tools for evaluating earthquake forecasts. pyCSEP is a Python package that contains the following modules: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines and various other utilities. Most significantly, pyCSEP contains several statistical tests needed to evaluate earthquake forecasts, which can be forecasts expressed as expected earthquake rates in space–magnitude bins or specified as large sets of simulated catalogs (which includes candidate models for governmental operational earthquake forecasting). To showcase how pyCSEP can be used to evaluate earthquake forecasts, we have provided a reproducibility package that contains all the components required to re-create the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this article. By providing useful tools to earthquake forecast modelers and facilitating an open-source software community, we hope to broaden the impact of the CSEP and further promote earthquake forecasting research.

We explore nonlinear site effects in the new Japanese ground‐motion dataset compiled by Bahrampouri et al. (2020). Following the approach of Seyhan and Stewart (2014), we evaluate the decrease of soil amplification according to the increasing and corresponding ground motion on surface rock (⁠ $VS30=760m/s$⁠). To better predict the rock ground motion associated with each record, we take into account the between‐event variability of the ground motion, and to better evaluate the impact of nonlinearity, we correct observed ground motion on soil by the site‐specific linear amplification. Instead of grouping the stations by site‐response proxy, we focus on individual stations with several strong‐motion records. We develop a framework to test recently published nonlinear site amplification models against a linear site amplification model and compare the results with recent building codes that include nonlinearity. The results show that the site response varies greatly from site to site, indicating that conventional site proxies, such as $VS30$⁠, are not sufficient to characterize nonlinear site response. Out of all of the Kiban–Kyoshin network stations, 20 stations are selected as having recorded sufficient data to be used in the test. Out of these 20 stations, five stations show signs of nonlinearity, that is, the nonlinear models performed better than the linear‐amplification model for all periods $T$⁠. For most sites, however, the linear site amplification models get the best score. This suggest that, for the range of predicted rock motion considered in this study (peak ground acceleration $<0.2g$⁠), nonlinearity may not have a sufficiently large impact on soil ground motion to justify the use of nonlinear site terms in ground‐motion functional forms and seismic building codes for such moderate‐level shaking.

We present a complete history of earthquake detection probabilities and detection completeness levels for the Japanese seismic network from 1923 to, and including, 2014 in high resolution over space and time. In our study, we employ the probability‐based magnitude of completeness method developed by Schorlemmer and Woessner (2008). Using this method, for each station we derive detection capabilities over time from empirical data only, namely earthquake information, phase data, station information, and network‐specific attenuation relations. From the entire phase‐data history, we estimate the operational times of each station and then synthesize detection‐probability maps for specific magnitudes or completeness maps. We compute these maps for every month in the period 1923–1987 and for approximately every 10 days in the period 1988–2014. We document the improvement of the Japanese seismic network from its early stage in 1923, when the network reaches completeness levels for shallow seismicity of magnitude 4 only in the wider Tokyo area, to the recent years exhibiting completeness levels of approximately magnitude 1 in all of Japan. We provide detailed completeness estimates for the eight major periods of the Japanese seismic network, defined by the types of seismometers used and the location technique employed, and show the development of completeness levels for selected regions. Finally, we discuss the effects of the 2011 Tohoku‐Oki earthquake on completeness levels in Japan.

Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.