The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data

Rhoades, David A.; Rastin, Sepideh J.; Christophersen, Annemarie

doi:10.3390/e22111264

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…We applied the EEPAS earthquake forecasting model to Italy, similarly to previous application in other seismic regions of the world (e.g. Evison & Rhoades 2005;Rhoades 2007Rhoades , 2011Rhoades et al 2020), using a suite of computing codes completely rewritten in Matlab and implementing both EEPAS formulations with the input earthquakes not weighted (EEPAS-NW) and weighted (EEPAS-W). We calibrated and fitted the model parameters using earthquakes of the HORUS seismic catalogue of Italy (Lolli et al 2020) for the learning period 1990-2011.…”

Section: O N C L U S I O N Smentioning

confidence: 99%

“…The details of the EEPAS method are described in a number of papers (e.g. Evison & Rhoades 2005;Rhoades 2007Rhoades , 2011Rhoades et al 2020), some of which contain typos that make the formulation not perfectly identical in all of them. For such reason in Appendix A we describe again the method as well as some assumptions made without explicit mentions in previous papers.…”

Section: Introductionmentioning

confidence: 99%

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Application of the EEPAS earthquake forecasting model to Italy

Biondini

Rhoades

Gasperini

2023

Geophysical Journal International

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Summary The EEPAS (Every Earthquake a Precursor According to Scale) forecasting model is a space–time point-process model based on the precursory scale increase ($\psi $) phenomenon and associated predictive scaling relations. It has been previously applied to New Zealand, California and Japan earthquakes with target magnitude thresholds varying from about 5 to 7. In all previous application, computations were done using the computer code implemented in Fortran language by the model authors. In this work we applied it to Italy using a suite of computing codes completely rewritten in Matlab. We first compared the two software codes to ensure the convergence and adequate coincidence between the estimated model parameters for a simple region capable of being analysed by both software codes. Then, using the rewritten codes, we optimised the parameters for a different and more complex polygon of analysis using the Homogenized instRUmental Seismic catalogue (HORUS) data from 1990 to 2011. We then perform a pseudo-prospective forecasting experiment of Italian earthquakes from 2012 to 2021 with Mw≥5.0 and compare the forecasting skill of EEPAS with those obtained by other time independent (SUP, SVP, and PPE) and time dependent (ETAS) forecasting models using the information gain per active cell (IGPA). The preference goes to the ETAS model for short time intervals (3 months) and to the EEPAS model for longer time intervals (6 months to 10 years).

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Section: O N C L U S I O N Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of the EEPAS earthquake forecasting model to Italy

Biondini

Rhoades

Gasperini

2023

Geophysical Journal International

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“…Contemporary research about forecasting earthquakes follows numerous approaches, including those based purely on a statistical analysis of the earthquake catalogs and physics-based methods (e.g. Helmstetter et al 2007;Morales-Esteban et al 2010;Martínez-Álvarez et al 2013;Asim et al 2018;Maleki Asayesh et al 2019;Mancini et al 2019;Ahmad et al 2019;Tareen et al 2019;Tariq et al 2019;Asayesh et al 2020;Mignan & Broccardo 2020;Rhoades et al 2020;Sharma et al 2020;Asayesh et al 2022;Ebrahimian et al 2022, etc. ).…”

Section: Introductionmentioning

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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Developing, Testing, and Communicating Earthquake Forecasts: Current Practices and Future Directions

Mizrahi,

Dallo,

van der Elst

et al. 2024

Reviews of Geophysics

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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.

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The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data

Cited by 6 publications

References 40 publications

Application of the EEPAS earthquake forecasting model to Italy

Application of the EEPAS earthquake forecasting model to Italy

Statistical power of spatial earthquake forecast tests

Developing, Testing, and Communicating Earthquake Forecasts: Current Practices and Future Directions

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