Enhanced b-value time-series calculation method using data-driven approach

被引:1
|
作者
Yin, Fengling [1 ]
Jiang, Changsheng [1 ]
机构
[1] China Earthquake Adm, Inst Geophys, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Time-series analysis; Earthquake hazards; Earthquake interaction; forecasting; and prediction; EARTHQUAKE SEQUENCE; MAGNITUDE; SELECTION; HETEROGENEITY; DIMENSION;
D O I
10.1093/gji/ggad419
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The temporal evolution analysis of b-value of the magnitude-frequency distribution (MFD) is essential for seismic risk analysis. However, ensuring the accuracy and rationality of these analyses depends on various factors, including data quality, data selection and the appropriate computation period partitioning. This study extends the data-driven b-value time-series calculation method, TbDD-BIC, by exploring different model selection techniques. To evaluate the method's effectiveness, we conducted assessments using both synthetic earthquake catalogues and actual seismic data. Our results indicate that selecting a proportion of optimal models (e.g. 5 per cent of the total number of models) using the Akaike information criterion (AIC) and computing the ensemble median yields accurate b0-values of the synthetic earthquake catalogue. This proposed method offers objective calculation rules and precise identification of abrupt b-value changes, enhancing seismicity simulation and seismic hazard analysis.
引用
收藏
页码:78 / 87
页数:10
相关论文
共 50 条
  • [41] Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points
    Inoue, Tomoki
    Matsuda, Yu
    Ikami, Tsubasa
    Nonomura, Taku
    Egami, Yasuhiro
    Nagai, Hiroki
    PHYSICS OF FLUIDS, 2021, 33 (07)
  • [42] A new method for calculating b-value of time sequence based on data-driven (TbDD) : A case study of the 2021 Yangbi Ms6. 4 earthquake sequence in Yunnan
    Jiang Cong
    Jiang ChangSheng
    Yin FengLing
    Zhang YanBao
    Bi JinMeng
    Long Feng
    Si ZhengYa
    Yin XinXin
    CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2021, 64 (09): : 3116 - 3124
  • [43] Active control method for the sinking of open caissons: A data-driven approach based on CNN and time series prediction
    Dong, Xuechao
    Guo, Mingwei
    Wang, Shuilin
    OCEAN ENGINEERING, 2022, 257
  • [44] A crop phenology detection method using time-series MODIS data
    Sakamoto, T
    Yokozawa, M
    Toritani, H
    Shibayama, M
    Ishitsuka, N
    Ohno, H
    REMOTE SENSING OF ENVIRONMENT, 2005, 96 (3-4) : 366 - 374
  • [45] Time-Series Identification of Fatigue Strain Data Using Decomposition Method
    Nopiah, Z. M.
    Lennie, A.
    Nuawi, M. Z.
    Abdullah, S.
    Nuryazmin, A. Z.
    Baharin, M. N.
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES, 2014, 1602 : 1209 - 1216
  • [46] Data-Driven Power Flow Calculation Method: A Lifting Dimension Linear Regression Approach
    Guo, Li
    Zhang, Yuxuan
    Li, Xialin
    Wang, Zhongguan
    Liu, Yixin
    Bai, Linquan
    Wang, Chengshan
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2022, 37 (03) : 1798 - 1808
  • [47] Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study
    Fang, Zheng-gang
    Yang, Shu-qin
    Lv, Cai-xia
    An, Shu-yi
    Wu, Wei
    BMJ OPEN, 2022, 12 (07):
  • [48] Data-driven modeling of long temperature time-series to capture the thermal behavior of bridges for SHM purposes
    Mariani, S.
    Kalantari, A.
    Kromanis, R.
    Marzani, A.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2024, 206
  • [49] Data-Driven Simulation of Complex Multidimensional Time Series
    Schruben, Lee W.
    Singham, Dashi I.
    ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION, 2014, 24 (01):
  • [50] Application of a data-driven DTSF and benchmark models for the prediction of electricity prices in Brazil: A time-series case
    Gontijo, Tiago Silveira
    de Santis, Rodrigo Barbosa
    Costa, Marcelo Azevedo
    JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2023, 15 (03)