Scholarlink

Abstract <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年9月11日

查看全文
  • Ludmila Provost

摘要

Abstract

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.