Based on the catalog data of earthquakes with Ms ≥ 2.5 in the Longmenshan fault zone from January 2012 to September 2021, we establish an earthquake time interval series grouped by earthquake magnitude and then use the SARIMA model to predict the series in different periods. By analyzing the fitting effect of the models, the optimal model parameters of different magnitude sequences and the corresponding period values are obtained. Among them, the adjusted R2 values of each model with Ms ≥ 2.5 and Ms ≥ 3.0 sequences are more than 0.86, up to 0.911; the short-time prediction effects are good, and the values of predicted RMSE are 10.686 and 8.800. The prediction results of the models show that the overall trend of the subsequent earthquake time interval in the Longmenshan fault zone is stable, and the prediction results of the Ms ≥ 3.0 sequence have a weak fluctuating growth trend; that is, the number of earthquakes with the Ms ≥ 3.0 in this area will decrease slightly, and the seismicity will decrease in a period of time. The analysis results and method can provide a scientific basis for earthquake risk management and a feasible way to predict earthquake occurrence times.
Based on earthquake catalog data from the Longmen Mountain fault zone over the past 10 years, we constructed series of earthquake origin time intervals by grouping according to the magnitude (M) and use the ARIMA model for analysis with a 9:1 ratio of fitting-training and prediction-verification data. We found that the series of both M ≥ 2.5 and M ≥ 3.0 showed the variation of nesting with short, medium and long periods. By further predictive verification and comparative analysis, the optimal prediction models for each series were obtained: ARIMA(10,2,1)×(0,1,1)20 direct prediction model for series of M ≥ 2.5, ARIMA(8,2,1)×(0,1,1)40 rolling prediction model for M ≥ 3.0, and ARIMA(1,2,3)×(0,1,1)3 rolling prediction model for M ≥ 4.5. The predicted results suggested that the seismicity of the Longmen Mountain fault zone has a recent gradually weakening trend. This analysis process provides an effective reference and method for studying the time regularities of tectonic earthquake occurrence.
An earthquake is one of the most serious natural disasters to human beings. The damage from destructive earthquakes is enormous, and the predictions and estimations of earthquakes are urgent challenges in global science fields. In view of the shortcomings of the Markov chain model and the kriging methods in the estimation of the probabilities of the occurrence of earthquakes, the Markov chain–linear kriging coupling model has been established. The model has been applied to estimate the spatial distribution of probabilities of the occurrence of destructive earthquakes of Ms4.5 and Ms6.0 and above in the Sichuan area. According to the estimations of this model, the maximum probabilities of the occurrence of earthquakes of Ms4.5 and Ms6.0 and above in the Changning area of Yibin are 9.59% and 0.46%, respectively, which are close to the frequencies of occurrences of earthquakes of corresponding magnitude in the series of earthquakes that occurred in June 2019 in the region. The validation indicates that the average standard errors of this model for estimating the probabilities of the occurrence of earthquakes of Ms4.5 and Ms6.0 and above are 4.96% and 0.81%, respectively, which are lower than the probability kriging, and the estimation of this model highlighted the high value region of the probabilities.
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