What does this research mean for the field?

A semi-automated computational workflow, CyberShake NZ, successfully enables nationwide probabilistic seismic hazard analysis using physics-based ground-motion simulations as a proof-of-concept. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate a semi-automated computational workflow for conducting nationwide probabilistic seismic hazard analysis using physics-based ground-motion simulations.

September 10, 2026Earthquake SpectraOpen Access

CyberShake NZ: Probabilistic Seismic Hazard Analysis in New Zealand Using Physics‐Based Ground‐Motion Simulations

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Authors

BBBrendon Bradley MAMorteza Abbasnejadfard KTKarim Tarbali

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Overview

Computational modeling study demonstrates nationwide physics-based ground-motion simulation workflows, indicating the feasibility of simulation-based probabilistic seismic hazard assessment.

Key Points

To develop and evaluate a semi-automated computational workflow for conducting nationwide probabilistic seismic hazard analysis using physics-based ground-motion simulations.
Implemented an automated workflow on high-performance computing facilities, executing 16,043 finite-fault simulations across a nationwide grid of 25,948 non-uniform surface receivers.
Utilized the Graves and Pitarka hybrid broadband ground-motion simulation technique with a 0.2 km computational grid, a 0.5 Hz transition frequency, and an empirically calibrated local site response model.
Employed Monte Carlo sampling across hypocenter locations, rupture magnitudes, and kinematic rupture realizations, while integrating empirical models for distributed seismicity.
Generated nationwide uniform hazard ground-motion maps, site-specific seismic hazard curves, and hazard disaggregation profiles across 18 workflow executions over an 8-year period.
Demonstrated proof-of-concept feasibility for simulation-based hazard analysis, establishing baseline requirements for historical validation and modeling uncertainty treatment prior to practical implementation.

Cite This Study

Bradley et al. (2026) studied this question.

synapsesocial.com/papers/6aa27aca58559d80afc7399a https://doi.org/10.1002/esp4.70120

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September 10, 2026Earthquake SpectraOpen Access

CyberShake NZ: Probabilistic Seismic Hazard Analysis in New Zealand Using Physics‐Based Ground‐Motion Simulations

View Full Paper

Ask AI

Bookmark

Authors

BBBrendon Bradley MAMorteza Abbasnejadfard KTKarim Tarbali

Discussion

Member takes

Overview

Computational modeling study demonstrates nationwide physics-based ground-motion simulation workflows, indicating the feasibility of simulation-based probabilistic seismic hazard assessment.

Key Points

To develop and evaluate a semi-automated computational workflow for conducting nationwide probabilistic seismic hazard analysis using physics-based ground-motion simulations.
Implemented an automated workflow on high-performance computing facilities, executing 16,043 finite-fault simulations across a nationwide grid of 25,948 non-uniform surface receivers.
Utilized the Graves and Pitarka hybrid broadband ground-motion simulation technique with a 0.2 km computational grid, a 0.5 Hz transition frequency, and an empirically calibrated local site response model.
Employed Monte Carlo sampling across hypocenter locations, rupture magnitudes, and kinematic rupture realizations, while integrating empirical models for distributed seismicity.
Generated nationwide uniform hazard ground-motion maps, site-specific seismic hazard curves, and hazard disaggregation profiles across 18 workflow executions over an 8-year period.
Demonstrated proof-of-concept feasibility for simulation-based hazard analysis, establishing baseline requirements for historical validation and modeling uncertainty treatment prior to practical implementation.

Cite This Study

Bradley et al. (2026) studied this question.

synapsesocial.com/papers/6aa27aca58559d80afc7399a https://doi.org/10.1002/esp4.70120

View Full Paper

Ask AI

Bookmark