Estimating Magnitude Completeness in Earthquake Catalogs: A Comparative Study of Catalog-based Methods

TL;DR

This study evaluates nine methods for estimating earthquake catalog completeness, introduces a new probabilistic model, and identifies the most effective approach for both uniform and non-uniform Mc distributions, improving seismic data analysis.

Contribution

The paper provides a comprehensive evaluation framework for catalog-based Mc estimation methods and introduces BSReLU, a novel probabilistic model for improved completeness estimation.

Findings

MBS-WW method shows best overall performance.

BSReLU offers a probabilistic approach to Mc modeling.

Framework effectively tests methods on diverse datasets.

Abstract

Without rigorous attention to the completeness of earthquake catalogs, claims of new discoveries or forecasting skills cannot be deemed credible. Therefore, estimating the completeness magnitude (Mc) is a critical step. Among various approaches, catalog-based methods are the simplest, most straightforward, and most commonly used. However, current evaluation frameworks for these methods lack a unified simulation strategy for generating catalogs that are independent of specific Mc estimation methods. An effective strategy should also be capable of simulating datasets with non-uniform Mc distributions across both spatial and temporal dimensions. In this study, we assess nine catalog-based methods under a robust evaluation framework specifically tailored for this purpose. These methods are tested on datasets with homogeneous and heterogeneous Mc distributions, as well as on real-world earthquake catalogs. The method of b-value stability by Woessner and Wiemer (2005), referred to as MBS-WW in this study, demonstrates the best overall performance. The prior model generated by MBS-WW is used as the foundation for generating an updated Mc map for China with the Bayesian Magnitude of Completeness (BMC) method. We also introduce, BSReLU, an augmented Gutenberg-Richter model with a novel probabilistic framework for modeling. The BSReLU model replaces deterministic estimates of Mc with a probabilistic framework that models the smooth transition in detection likelihood from zero to one as earthquake magnitudes increase. By evaluating the limitations of these foundational catalog-based methods, this study seeks to refine our understanding of their appropriate applications, offering a clearer, unbiased perspective on seismicity through improved observational data quality.