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.
Comment: 26 pages, 4 figures