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Abstract

Summary

We evaluate the efficiency of the maximum likelihood estimator introduced by , using synthetic datasets exhibiting diverse but well defined properties. The deviation of the b-value estimation from its real value is quantified by Monte Carlo simulations as a function of catalogue features and data properties such as the sample size, the magnitude uncertainties distribution, the round-off interval of reported magnitude values and the magnitude range. Within the objective of this study, algorithms have been compiled for the determination of such observational-theoretical deviations and to facilitate the construction of nomograms corresponding to diverse cases of input parameters. In this way, a more accurate estimation of the uncertainty level for the b-value and MC determination can be achieved, contributing to a more robust seismic hazard assessment, especially at low activity areas and induced seismicity sites. Our results indicate that b-value analysis, especially for small datasets should be carried out together with Magnitude range analysis. Nomograms should be constructed and adjusted to each particular case study in order to achieve a more accurate estimation of the b-value and the corresponding uncertainty.

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2018-03-26
2024-04-19

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201800052
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Uncertainty Of B-Value Estimation In Connection With Magnitude Distribution Properties Of Small Data Sets
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201800052
/content/papers/10.3997/2214-4609.201800052
/content/papers/10.3997/2214-4609.201800052
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