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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.