pyCSEP: A Python Toolkit For Earthquake Forecast Developers

Savran, William; Werner, M. J.; Schorlemmer, Danijel; Maechling, P. J.

doi:10.21105/joss.03658

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…Using the CSEP's pyCSEP toolkit (Savran, Bayona, et al, 2022;Savran, Werner, et al, 2022), we then conduct the paired T-test of Rhoades et al (2011) to comparatively evaluate the performance of the regional models with that of GEAR1. The T-test is based on the Information Gain per Earthquake (IGPE), here obtained by a (regional) model A over a (global) Figure 1.…”

Section: Model Evaluationmentioning

confidence: 99%

Are Regionally Calibrated Seismicity Models More Informative than Global Models? Insights from California, New Zealand, and Italy

et al. 2023

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Earthquake forecasting models express hypotheses about seismogenesis that underpin global and regional probabilistic seismic hazard assessments (PSHAs). An implicit assumption is that the comparatively higher spatiotemporal resolution datasets from which regional models are generated lead to more informative seismicity forecasts than global models, which are however calibrated on greater datasets of large earthquakes. Here, we prospectively assess the ability of the Global Earthquake Activity Rate (GEAR1) model and 19 time-independent regional models to forecast M 4.95+ seismicity in California, New Zealand, and Italy from 2014 through 2021, using metrics developed by the Collaboratory for the Study of Earthquake Predictability (CSEP). Our results show that regional models that adaptively smooth small earthquake locations perform best in California and Italy during the evaluation period; however, GEAR1, based on global seismicity and geodesy datasets, performs surprisingly well across all testing regions, ranking first in New Zealand, second in California, and third in Italy. Furthermore, the performance of the models is highly sensitive to spatial smoothing, and the optimal smoothing likely depends on the regional tectonic setting. Acknowledging the limited prospective test data, these results provide preliminary support for using GEAR1 as a global reference M 4.95+ seismicity model that could inform eight-year regional and global PSHAs.

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Section: Model Evaluationmentioning

confidence: 99%

Are Regionally Calibrated Seismicity Models More Informative than Global Models? Insights from California, New Zealand, and Italy

et al. 2023

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“…To visualize the output of CSEP consistency tests, the PyCSEP implementation Savran et al (2022) provides the option to display the modelled behavior of the events as a histogram created based on the set of a large number of synthetic catalogs given as the output of a model (catalog-based forecast), and compare it to the true value in the observed catalog represented by a dashed vertical line.…”

Section: Results Of Consistency Testsmentioning

confidence: 99%

Towards a Harmonized Operational Earthquake Forecasting Model for Europe

Han,

Mizrahi,

Wiemer

2024

Preprint

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Abstract. We develop a harmonized earthquake forecasting model for Europe based on the Epidemic-type Aftershock Sequence (ETAS) model to describe the spatio-temporal evolution of aftershock sequences. We propose a method modification that integrates information from the European Seismic Hazard Model (ESHM20) about the spatial variation of background seismicity during ETAS parameter inversion based on the expectation-maximization (EM) algorithm. Other modifications to the basic ETAS model are explored, namely fixing the productivity term to a higher value to balance the more productive triggering by high-magnitude events with their much rarer occurrence, and replacing the b-value estimate with one relying on the b-positive method to observe the possible effect of short-term incompleteness on model parameters. Retrospective and pseudo-prospective tests demonstrate that ETAS-based models outperform the time-independent benchmark model as well as an ETAS model calibrated on global data. The background-informed ETAS variant achieves the highest score in the pseudo-prospective experiment, but the performance difference to the second-best model is not significant. Our findings highlight promising areas for future exploration, such as avoiding the simplification of using a single b-value for the entire region or reevaluating the completeness of the used seismic catalogs.

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“…Previously, the CSEP testing suite was available as a monolithic and tightly coupled code base. Recently, it has been redesigned into an object-oriented and open-source toolkit in Python, known as pyCSEP (Savran et al 2022b, a). This toolkit provides an independent module containing all the community-endorsed statistical tests in the CSEP testing suite.…”

Section: Global Forecast Experimentsmentioning

confidence: 99%

Statistical power of spatial earthquake forecast tests

Asim

Hainzl

Schorlemmer

et al. 2023

Geophysical Journal International

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Summary The Collaboratory for the Study of Earthquake Predictability (CSEP) is an international effort to evaluate earthquake forecast models prospectively. In CSEP, one way to express earthquake forecasts is through a grid-based format: the expected number of earthquake occurrences within 0.1○ × 0.1○ spatial cells. The spatial distribution of seismicity is thereby evaluated using the Spatial test (S-test). The high-resolution grid combined with sparse and inhomogeneous earthquake distributions leads to a huge number of cells causing disparity in the number of cells and the number of earthquakes to evaluate the forecasts, thereby affecting the statistical power of the S-test. In order to explore this issue, we conducted a global earthquake forecast experiment, in which we computed the power of the S-test to reject a spatially non-informative uniform forecast model. The S-test loses its power to reject the non-informative model when the spatial resolution is so high that every earthquake of the observed catalog tends to get a separate cell. Upon analyzing the statistical power of the S-test, we found, as expected, that the statistical power of the S-test depends upon the number of earthquakes available for testing, e. g. with the conventional high-resolution grid for the global region, we would need more than 32000 earthquakes in the observed catalog for powerful testing, which would require approximately 300 years to record M ≥ 5.95. The other factor affecting the power is more interesting and new; it is related to the spatial grid representation of the forecast model. Aggregating forecasts on multi-resolution grids can significantly increase the statistical power of the S-test. Using the recently introduced Quadtree to generate data-based multi-resolution grids, we show that the S-test reaches its maximum power in this case already for as few as 8 earthquakes in the test period. Thus we recommend for future CSEP experiments the use of Quadtree-based multi-resolution grids, where available data determine the resolution.

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pyCSEP: A Python Toolkit For Earthquake Forecast Developers

Cited by 12 publications

References 17 publications

Are Regionally Calibrated Seismicity Models More Informative than Global Models? Insights from California, New Zealand, and Italy

Are Regionally Calibrated Seismicity Models More Informative than Global Models? Insights from California, New Zealand, and Italy

Towards a Harmonized Operational Earthquake Forecasting Model for Europe

Statistical power of spatial earthquake forecast tests

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