| dc.description.abstract | Since Taiwan is located at the Pacific Ring of Fire, seismic activity of varying magnitudes occurs almost every day. Among them, some of these seismic activities have in turn caused severe disasters, resulting in loss of personal property, casualties and damage to important public facilities. Therefore, investigating the long-term spatiotemporal pattern of seismic activities is a crucial task for understanding the causes of seismic activity and to predict future seismic activity, in order to carry out disaster prevention measures in advance.
Previous studies mostly focused on the causes of single seismic events on the small spatiotemporal scale. In this study, the data from 1987 to 2020 are used, including seismic events from the United States Geological Survey (USGS), the ambient environmental factors such as daily air temperature from Taiwan Central Weather Bureau (CWB) and daily sea surface temperature data from National Oceanic and Atmospheric Administration (NOAA). Then the temperature difference between the land surface temperature (LST) and the sea surface temperature (SST) to the correlation between the occurrence of seismic activities and the abnormal occurrence of temperature difference are compared. In order to explore whether the temperature difference caused by the change of the specific heat of the land and the ocean may cause stress on the land-sea junction on the surface, and whether it will trigger seismic activity . The results show that lots of seismic activities often have positive and negative anomalies of temperature difference from twenty-one days before to seven days after the seismic event. Moreover, there is a specific trend of temperature difference anomalies under different magnitude intervals. In the magnitude range of 2.5 to 4 and greater than 6, almost all of the seismic events have significant anomalous differences in the temperature difference between LST and SST compared with no seismic events.
This study uncovers anomalous frequency signatures of seismic activities and temperature differences between LST and SST. The significant difference in temperature difference between seismic events and non-seismic events was compared by using statistical analysis. Additionally, the multilayer perceptron, logistic regression and random forest of machine learning model was used to identify whether there will be a seismic event under different magnitude intervals. It is hoped that it can provide relevant information for the prediction of future seismic activity, to more accurately prevent disasters that may be caused by seismic activity. | en_US |