2022 Vol. 65, No. 6
Article Contents

SI ZhengYa, ZHUANG JianCang, JIANG ChangSheng. 2022. A Bayesian algorithm for magnitude determination by merging multiple seismic networks. Chinese Journal of Geophysics (in Chinese), 65(6): 2167-2178, doi: 10.6038/cjg2022P0138
Citation: SI ZhengYa, ZHUANG JianCang, JIANG ChangSheng. 2022. A Bayesian algorithm for magnitude determination by merging multiple seismic networks. Chinese Journal of Geophysics (in Chinese), 65(6): 2167-2178, doi: 10.6038/cjg2022P0138

A Bayesian algorithm for magnitude determination by merging multiple seismic networks

  • Fund Project:

    中国地震科学实验场专项项目(2019CSES0105, 2019CSES0106), 国家自然科学基金(41774067)联合资助

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  • The earthquake magnitude determined by a temporary seismic network usually differs from that given by a permanent seismic network due to several factors, including the undetermined site response, the determination method, and the operation mode. This problem cannot even be solved by correcting the gauge function in the magnitude determination formula for the temporary seismic network. Therefore, a large number of small earthquakes recorded only by temporary seismic networks that cannot be directly added to the standard catalogs. This paper aims to provide a novel fast algorithm for solving this problem. Assuming that an observed magnitude follows the Gaussian distribution, with a mean of its unknown true value if it is determined by the permanent seismic network, or with a mean as a linear function of its true value if it is given by a temporary seismic network, we derive the posterior probability density function of the magnitude given its observation values by different networks, based on the Bayesian formula. The parameters in the posterior probability density function can be estimated from observation data by using the maximum likelihood method, and then the posterior estimate of the earthquake magnitude, namely the revised magnitude, can be calculated. This algorithm is applied to earthquake catalogs recorded by the Xichang (XC) temporary seismic network and by the China (CN) seismic network. We find that the revised magnitude approximately follows a linear relationship against its observed value given by the CN seismic network, generally larger than its value measured by the XC seismic network. The proposed method provides a fast algorithm for correcting the unprecise magnitudes determined by temporary seismic networks, and simultaneously, improves the stability and accuracy of the magnitudes estimated by the permanent seismic network by taking account of the consistency of the magnitudes measured by the temporary seismic network.

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