# Improved NB Model Analysis of Earthquake Recurrence Interval Coefficient of Variation for Major Active Faults in the Hetao Graben and Northern Marginal Region

## TL;DR

This paper improves an earthquake recurrence model to better estimate variability in fault activity, which is crucial for accurate seismic hazard assessments.

## Contribution

The novel contribution is an improved NB model with a linear transformation and iterative optimization to correct statistical biases in coefficient of variation estimates.

## Key findings

- Traditional NB methods underestimate coefficient of variation by 30-40% for small datasets.
- The improved model increases the coefficient of variation from 0.307 to 0.381 in the Hetao graben region.
- The correction eliminates sampling bias and provides more reliable forecasting parameters.

## Abstract

This study presents an improved Nishenko–Buland (NB) model to address systematic biases in estimating the coefficient of variation for earthquake recurrence intervals based on a normalizing function TTave. Through Monte Carlo simulations, we demonstrate that traditional NB methods significantly underestimate the coefficient of variation when applied to limited paleoseismic datasets, with deviations reaching between 30 and 40% for small sample sizes. We developed a linear transformation and iterative optimization approach that corrects these statistical biases by standardizing recurrence interval data from different sample sizes to conform to a common standardized distribution. Application to 26 fault segments across 15 major active faults in the Hetao graben system yields a corrected coefficient of variation of α = 0.381, representing a 24% increase over the traditional method (α0 = 0.307). This correction demonstrates that conventional approaches systematically underestimate earthquake recurrence variability, potentially compromising seismic hazard assessments. The improved model successfully eliminates sampling bias through iterative convergence, providing more reliable parameters for probability distributions in renewal-based earthquake forecasting.