关键词: 中国海域及邻区, 地震目录, 完整性分析, 自适应空间平滑地震活动模型, 概率增益函数

Abstract:

Ocean earthquakes pose a serious threat to the security of the economic construction in coastal areas and the marine resource exploitation of China, so building proper seismicity models for China seas and adjacent regions is one of the focuses of the next generation seismic zoning map of China. Different from mainland China, there is lack of multidisciplinary data for the sea areas, such as seismic geology and geophysical exploration, thus the earthquake catalogue is the most important basic data for analyzing its seismic activity characteristics. Recently, a unified catalogue was compiled by researchers for China seas and neighboring regions, which provides a basis for further work.
The Smoothed Seismicity Model (SSM) is a classic model based on earthquake catalogue and has become a basic model for the United States National Seismic Hazard Map. The Adaptively Smoothed Seismicity Model (ASSM) is an improved version of SSM. Compared with SSM, ASSM has optimized the value algorithm of the kernel function, thereby improving the model’s capability on mid- and long-term earthquake forecast. Due to the outstanding performance of ASSM in the CSEP project, it has become a research hotspot for seismologists. Based on a further improved ASSM algorithm and the newly compiled catalogue, this study established for the first time an adaptively smoothed seismicity model for China seas and adjacent areas.
First, similar to most studies on mid-to-long-term seismicity models, we removed the foreshocks and aftershocks from the catalogue. Then we used seismic zones as the unit to estimate the start and end time of completely recorded earthquakes in different magnitude intervals. Furthermore, the maximum likelihood method was used to estimate the seismic activity parameters such as a-value and b-value for every seismic zone. On this basis, an improved adaptively smoothed algorithm was used to build the model. The function of probability gain per earthquake was applied to evaluate the performance of models under different parameter settings. Finally, our model was compared with the traditional SSM, and the advantages and limitations of our model were analyzed.
Results show that: Compared with SSM, our model has a better performance on the probability gain function, and this advantage is not affected by the minimum magnitude(M_min)of the input earthquakes. When M_min is set as M4.0, our model achieves its best performance. The performance of the model does not necessarily improve as M_min decreases or the number of earthquakes increases. This indicates that we need to comprehensively consider the distribution of earthquakes in the study area and the integrity of the earthquake catalogue when selecting parameters. The algorithm used in this study can make full use of seismic data with varying record level over time and space, and has a certain application value in seismic hazard analysis and mid- and long-term earthquake forecast. Meanwhile, our model can be used as one of the basic models to analyze the seismic hazard for China’s maritime areas, and provide technical support for the compilation of seismic zoning maps in China’s sea areas. In addition, the completeness analysis results and seismic activity parameters obtained by this study can also provide references for other peer research.
Since ASSM is only based on historical and instrumental earthquake catalogues, it has certain limitations. For example, it cannot calculate the upper limit of the magnitude and reflect the distribution of faults, nor can it consider the time-dependence of the recurrence of large earthquakes. Therefore, this model can be used alone to describe the occurrence probability of small to moderately strong earthquakes, and it can also be used as one of the important factors to determine the spatial distribution functions for potential seismic source zones. However, when conducting seismic hazard analysis, it is recommended to combine the results of other disciplines such as geology and geodesy to form a hybrid model, so as to further improve the applicability and effectiveness of the model.

Key words: China seas and adjacent regions, earthquake catalogue, completeness analysis, adaptively smoothed seismicity model, the function of probability gain per earthquake