| dc.description.abstract | GPS are essential for many geophysical applications such as crust deformation, plate tectonic, and so on. The GPS time series not only can calculate the strain rate of the area, but also can monitor the change of the surface. However, there are some abrupt changes in the GPS time series called offsets. These offsets may be caused by seismic events, equipment changes, creep events, environmental change, metadata change, or unknown reasons. Removing offsets can greatly improve GPS velocity solution and strain rate calculation. Therefore, finding these offsets can greatly improve the related research. However, manually detecting offsets is impracticable since that GPS stations are gradually increase. In this research, I use two automated methods to detect offsets, and systematically display and classify offsets’ results in statistical manner. In addition to exploring possible crustal deformation signals, I also expected that these results can benefit relevant survey network management units as records and references for station maintenance. With 479 GPS stations in Taiwan, I apply two methods JUST (Ghaderpour et al. 2018) and Hector (Bos et al. 2013) to analyze their three-component time series where the data lengths are more than 4 years. The reason I choose JUST and Hector is that JUST can resolve seasonal signals very well while Hector can analyze noises in the time series. Results show that in the offsets detected by Hector, 75% are caused by earthquakes, and 8% are due to equipment changes. For the offsets which are detected by JUST, 78% are caused by earthquakes, and 3% are due to equipment changes. | en_US |