CalIPE: an open-source package for intensity prediction equation calibration

Intensity Prediction Equations (IPEs) describe macroseismic intensity as a function of parameters such as epicentral distance, magnitude, and, in some cases, depth or epicentral intensity. These equations are typically calibrated using data from recent earthquakes. This study introduces CalIPE, an o...

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Bibliographic Details
Published in:Journal of seismology Vol. 29; no. 5; pp. 1125 - 1144
Main Author: Provost, Ludmila
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.10.2025
Springer Nature B.V
Subjects:
Calibration
Datasets
Earth and Environmental Science
Earth Sciences
Earthquakes
Epistemology
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematics
Metadata
Open source software
Regions
Review
Seismic activity
Structural Geology
Synthetic data
Uncertainty
France
Open-source python package
Macroseismic intensity
Calibration
Intensity prediction equations
ISSN:1383-4649, 1573-157X
Online Access:Get full text
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Summary:Intensity Prediction Equations (IPEs) describe macroseismic intensity as a function of parameters such as epicentral distance, magnitude, and, in some cases, depth or epicentral intensity. These equations are typically calibrated using data from recent earthquakes. This study introduces CalIPE, an open-source Python package that provides a suite of tools for IPE calibration, supporting two mathematical formulations. Calibrating IPEs involves methodological decisions that introduce epistemic uncertainties. CalIPE enables users to explore some of these uncertainties, including the choice of inversion scheme, selection of calibration earthquakes, and weighting strategies. The package also offers functionalities for generating data subsets from a primary calibration dataset and for conducting post-processing analyses, such as intensity residual evaluation. This facilitates the derivation of IPE groups that account for different sources of epistemic uncertainty. To ensure robustness, CalIPE has been developed and tested using homogeneous synthetic datasets. Its application to real macroseismic data from mainland France demonstrates the package’s effectiveness in generating reliable IPEs. The results also highlight CalIPE’s potential for investigating epistemic uncertainties through residual analysis and other diagnostic tools. CalIPE is designed to support researchers and practitioners in the robust, transparent, and reproducible calibration of IPEs, offering a validated and openly accessible Python-based solution.
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ISSN:1383-4649
1573-157X
DOI:10.1007/s10950-025-10324-w