Detecting Thermal Anomalies of Earthquake Process Within Outgoing Longwave Radiation Using Time Series Forecasting Models

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Published: Nov 3, 2020
DOI: https://doi.org/10.4401/ag-8057
Keywords:
Time series forecasting models, Infrared outgoing long-wave radiation anomalies, Earthquake forecasting, Seismic anomalies detection

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Dulin Zhai
Xueming Zhang
Pan Xiong

Abstract

 

The catastrophic damages caused by the Jiuzhaigou earthquake in China of August 8, 2017 and the Mexico earthquake of September 20, 2017 have revealed some important weaknesses of currently operational earthquake-monitoring and forecasting systems. In this work, six time series forecasting models were applied to detect pre-earthquake anomalies within infrared outgoing longwave radiation. After comparing their prediction results using non-seismic time series data, the autoregressive integrated moving average (ARIMA) model was selected as the optimal model, and then a new prediction method based on this ARIMA model was proposed. The results show that the values observed on July 27 and August 5 before the Jiuzhaigou earthquake in China exceed the confidence interval for prediction and reaches the maximum on August 5, 2017. This indicates the infrared outgoing longwave radiation (IR-OLR) anomalies before the Jiuzhaigou earthquake in China. For the Mexico earthquake, pre-earthquake IR-OLR anomalies are detected on September 14, 18, and 19, and reaches the maximum on September 14, 2017. This demonstrates that the proposed time series forecasting model based on ARIMA could be an effective method for earthquake anomalies detection within infrared outgoing longwave radiation.

Article Details

How to Cite
Zhai, D., Zhang, X. and Xiong, P. (2020) “Detecting Thermal Anomalies of Earthquake Process Within Outgoing Longwave Radiation Using Time Series Forecasting Models”, Annals of Geophysics, 63(5), p. PA548. doi: 10.4401/ag-8057.
Issue
Vol. 63 No. 5 (2020)
Section
Advances in multi-parametric, time-Dependent Assessment of Seismic Hazard and Earthquakes Forecast

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