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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.

The devastating 2023 M7.8 Türkiye–Syria earthquake sequence once again highlighted the gap between (seismological) knowledge and action: Although the impacted region is known to be at high seismic risk (i.e., highly seismically active and densely populated), the political and societal conditions have complicated and delayed protective measures. To reduce the seismic risk and prepare local communities, experts from different disciplines must collaborate effectively in redesigning the built environment and engaging the community in risk education and management (Comfort et al., 2023). In recent years, three subjects have become increasingly relevant to build that needed bridge between scientific knowledge and societal action: open science, transdisciplinarity, and ethics (see Figure 1). They have influenced scientific discussions on how to transition from purely scientific research to practical and societally relevant applications that increase societies’ resilience to disasters (e.g., Marti et al., 2022) – just as envisioned by the European Union’s Horizon 2020 project ‘Real-time earthquake rIsk reduction for a reSilient Europe’ (RISE) concerning earthquake risk. We and other early career scientists of RISE identified these three subjects in several virtual discussions while reflecting on our needs to better understand how we can make our research efforts more societally meaningful and effective. Eventually, together with several senior scientists of RISE, we discussed and evaluated these subjects during a three-day workshop under the theme “Bringing research to practical applications that increase society’s earthquake resilience” (Supplement 1). This theme resembled RISE’s overall goal to advance the scientific and societal knowledge on dynamic seismic risk, the overarching topic we additionally wanted to explore. First, keynotes from experts gave us a background on the three subjects, and lightning talks by all participants revealed the range of our expertise. Second, we drew a rich picture for the overarching topic and each subject following the Soft Systems Methodology (Pohl, 2020): separate groups sketch and express their ideas as mental models, receive feedback from the other groups, and revise it accordingly (see Supplement S2a-d for the evolution of the rich pictures). This approach allowed us to integrate all our expertise on the topic and subjects. We provide the conceptual background of the three main subjects, stress their relevance in current research, and illustrate their link to dynamic seismic risk (Figure 1). We believe that these reflections can be transferred to any other research field since the subjects affect various disciplines.

SUMMARY The Regional Earthquake Likelihood Models (RELM) experiment, conducted within the Collaboratory for the Study of Earthquake Predictability (CSEP), showed that the smoothed seismicity (HKJ) model by Helmstetter et al. was the most informative time-independent earthquake model in California during the 2006–2010 evaluation period. The diversity of competing forecast hypotheses and geophysical data sets used in RELM was suitable for combining multiple models that could provide more informative earthquake forecasts than HKJ. Thus, Rhoades et al. created multiplicative hybrid models that involve the HKJ model as a baseline and one or more conjugate models. In retrospective evaluations, some hybrid models showed significant information gains over the HKJ forecast. Here, we prospectively assess the predictive skills of 16 hybrids and 6 original RELM forecasts at a 0.05 significance level, using a suite of traditional and new CSEP tests that rely on a Poisson and a binary likelihood function. In addition, we include consistency test results at a Bonferroni-adjusted significance level of 0.025 to address the problem of multiple tests. Furthermore, we compare the performance of each forecast to that of HKJ. The evaluation data set contains 40 target events recorded within the CSEP California testing region from 2011 January 1 to 2020 December 31, including the 2016 Hawthorne earthquake swarm in southwestern Nevada and the 2019 Ridgecrest sequence. Consistency test results show that most forecasting models overestimate the number of earthquakes and struggle to explain the spatial distribution of epicenters, especially in the case of seismicity clusters. The binary likelihood function significantly reduces the sensitivity of spatial log-likelihood scores to clustering, however; most models still fail to adequately describe spatial earthquake patterns. Contrary to retrospective analyses, our prospective test results show that none of the models are significantly more informative than the HKJ benchmark forecast, which we interpret to be due to temporal instabilities in the fit that forms hybrids. These results suggest that smoothing high-resolution, small earthquake data remains a robust method for forecasting moderate-to-large earthquakes over a period of 5–15 yr in California.

ETAS is currently the most widely accepted state-of-the-art when it comes to time-dependent earthquake forecasting. This model is based on a few simple assumptions and well-established empirical principles about earthquakes’ triggering behaviour. In ETAS, earthquakes can either be “background” events, or “triggered” events, and all of them can trigger cascades of aftershocks, following triggering laws, such as the Omori-Utsu law for the temporal distribution of aftershocks or the Gutenberg-Richter law for the frequency distribution of earthquake magnitudes. While it has been around for many years, ETAS hits a sweet spot between simplicity and accuracy, which makes it difficult for newer models to establish themselves as the new state-of-the-art.

BlenderCave is a framework for the fast creation of virtual reality applications for multi-screen display systems. It consists of a set of extensions to the open-source Blender Game Engine (BGE), leveraging the scene creation and real-time rendering capabilities of BGE, and augmenting them with easy-to-setup support for CAVE or Powerwall-type displays. The framework is implemented in a distributed manner, and it contains a virtual camera setup to control the display output, and a lightweight network communication protocol to manage events and synchronize the application state. We demonstrate that, with BlenderCAVE, 3D applications can easily be written in Blender and displayed on multi-screen displays., V Ibero-American Symposium in Computer Graphics, Displays and Computational Photography, 91, 97, Jorge Gascón, José M. Bayona, José M. Espadero, and Miguel A. Otaduy

Este trabajo está basado en un tema con una importante repercusión tanto en la opinión pública como en el ámbito jurídico como es la declaración tributaria especial. Esta es una medida tributaria llevada a cabo por el gobierno en el año 2012 que fue presentada para fomentar la regularización voluntaria de situaciones de incumplimiento de las obligaciones tributarias a la vez que se reducía el excesivo déficit público. Es por ello que empezaremos por poner de relieve el contexto económico en el que se aprobó esta controvertida medida. Veremos también como los resultados conseguidos distaron bastante de las previsiones iniciales del gobierno. A continuación, nos centraremos en el marco normativo que reguló la declaración tributaria especial. Esto nos servirá para conocer los requisitos más importantes: sujetos que podían acogerse a esta medida, así como las condiciones en que lo hacían y los plazos e importes a ingresar para normalizar sus situaciones tributarias. Tras ello, se desplegaban unos efectos que también veremos. Asimismo, nos permitirá establecer una comparación entre lo que podríamos llamar regularización ordinaria (recogida en el artículo 27 de la LGT) y regularización extraordinaria (disposición adicional 1ª del RDL 12/2012. Posteriormente, estudiaremos su naturaleza jurídica y el alcance penal, lo que nos ayudará a profundizar en su contenido y a analizar por qué se le va a considerar por la mayoría de la doctrina como una auténtica amnistía fiscal. Más adelante, incidiremos en el recurso de inconstitucionalidad interpuesto por el grupo parlamentario socialista. Dicho recurso se fundamentó en los artículos 31.1 y 86 de la constitución. Como consecuencia de su resolución por la STC de 8 de junio de 2017, haremos una especial referencia a dicha sentencia en la que se declaró la nulidad e inconstitucionalidad de la disposición estudiada. Finalmente, analizaremos la adecuación de la medida mencionada a los principios materiales de justicia tributaria. Para ello, definiremos brevemente el contenido de cada principio y, tomando como base la doctrina constitucional, concluiremos el presente trabajo razonando por qué la declaración tributaria especial vulnera dichos principios

The Interdisciplinary Journal of International Studies, Vol 9 No 1 (2018): Interdisciplinary Journal of International Studies: A Mosaic

The Global Earthquake Activity Rate (GEAR1) seismicity model uses an optimized combination of geodetic strain rates, hypotheses about converting strain rates to seismicity rates from plate tectonics, and earthquake-catalog data to estimate global mw ≥ 5.767$ shallow (≤ 70 km) seismicity rates. It comprises two parent models: a strain rate-based model, and a smoothed-seismicity based model. The GEAR1 model was retrospectively evaluated and calibrated using earthquake data from 2005-2012, resulting in a preferred log-linear, multiplicative combination of the parent forecasts. Since October 1, 2015, the GEAR1 model has undergone prospective evaluation within the Collaboratory for the Study of Earthquake Predictability (CSEP) testing center. We present initial prospective forecast test results for the GEAR1 model, its tectonic and seismicity components, and for the first iteration of the strain rate-based model, during the period October 1, 2015 to September 7, 2017. During the evaluation period, observed earthquakes are consistent with the GEAR1 forecast and comparative test results likewise support that GEAR1 is more informative than either of its components alone. Based on a combination of retrospective and prospective testing, the tectonic forecasts do not effectively anticipate observed spatial earthquake distribution, largely due to over-localization of the model with respect to observed earthquake distributions.