Journal
GEOPHYSICAL JOURNAL INTERNATIONAL
Volume 234, Issue 2, Pages 1326-1331Publisher
OXFORD UNIV PRESS
DOI: 10.1093/gji/ggad134
Keywords
b-value; Probabilistic forecasting; Statistical methods; Statistical seismology
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In this study, we estimate the b-value parameter of the Gutenberg-Richter law for earthquake magnitudes in the early stage of the Costa Marchigiana seismic sequence. We obtained the completeness magnitude M-c and the b-value within the first 4 to 7 days after the initial strong event in the sequence. Our research provides a practical example of real-time b-value estimation and its potential interpretation in short-term forecasting. We emphasize the critical issues to consider in estimating/interpreting the b-value and evaluating the real-time estimation of M-c.
We estimate the b-value parameter of the Gutenberg-Richter law for earthquake magnitudes in the early stage of the Costa Marchigiana (Italy) seismic sequence, starting on 2022 November 9, with an M-L 5.7 event in the Adriatic sea. In particular, we estimate both the completeness magnitude M-c and the b-value within the first 4 and 7 d after the initial strong event in the sequence. Our work represents a practical example of b-value estimation in 'true' real time, that is, during the seismic sequence, and its possible interpretation in terms of short-term forecasting. We highlight some critical issues to consider both in estimating/intepreting the b-value, and in evaluating the real time estimation of M-c. These issues are mainly due to the fact that preliminary catalogues available in real time are quite different from the revised ones, which are usually delivered after a few months. The criticalities are linked to the raw data recorded at an early-stage, an unreliable evaluation of the M-c with statistical approaches, the Short Term Aftershock Incompleteness entailed after the initial strong event, and the magnitude binning. Our results show that real time estimation of the b-value can give insights into the evolution of an ongoing seismic sequence, when attention is paid to data quality and quantity.
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