Suitability Analysis of Ground Motion Prediction Equations for Western and Central Himalayas and Indo-Gangetic Plains

ABSTRACT

Ground motion prediction equations (GMPEs) play a key role in seismic hazard assessment (SHA). Considering the seismo-tectonic, geophysical, and geotectonic characteristics of a target region, all the GMPEs may not be suitable in predicting the observed ground motion effectively. With a fairly large number of published GMPEs, the selection and ranking of suitable GMPEs for the design of logic trees in SHA for a particular target region have become a necessity of late. This paper presents a detailed quantitative evaluation of performance of 16 GMPEs against recorded ground motion data in two target regions, characterized by distinct seismo-tectonic, geophysical, and geotectonical nature. The dataset comprises 465 three-component spectral accelerograms corresponding to 122 earthquake events. The suitability of a GMPE is tested by two widely accepted data-driven statistical methods, namely, likelihood (LH) and log-likelihood (LLH) method. Different suites of GMPEs are shown suitable for different periods of interest. The results will be useful to scientists and engineers for microzonation and estimation of seismic design parameters for the design of earthquake-resistant structures in these regions.

KEYWORDS:

• Ground motion prediction equations
• likelihood
• log-likelihood
• seismic hazard assessment
• data-driven methods

Acknowledgments

Fig. 1 was prepared by using the Generic Mapping Tools (GMT) software package (Wessel et al. 2019), available at https://www.generic-mapping-tools.org/. We sincerely acknowledge the GLOBE Task Team (http://www.ngdc.noaa.gov/mgg/topo/globe.html)) for providing the DEM data for preparation of Fig. 2.

The authors are thankful to Dr. R. S. Kankara, Director, Shri Rizwan Ali, Scientist - E and Shri Sachin Khupat, Scientist - C for their continuous support and encouragement in the present research. The authors thankfully acknowledge two anonymous referees for their valuable comments which helped in improving the manuscript.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/13632469.2023.2297294.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

All the data used in this research are available in public domain and the links for accessing the data are given inside the text.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.