## 3 Methods
### Change Point Detection
Figure 6 illustrates the implementation of the CPD algorithm in detecting the abrupt horizontal displacement occurred at the base station, as illustrated in Figure 5a,b. In a scenario of near real-time monitoring, three data streams (in each direction) are coming to the monitoring center: GPS-measured displacement (black), backward-filtered displacement (green), and forward-filtered displacement (red). On day 107, the incoming measurement in the NS direction is normal (Figure 6a), while the measurement in the EW direction shows a one-centimeter shift to the west (Figure 6b). The instant shift in the EW component can be a noisy measurement (often called an outlier) or a sudden deformation of the riverbed. The key of the near real-time monitoring is to clarify the shift: An outlier or a field event, as soon as possible. In statistics, an outlier is a data point that differs significantly from other observations. A measurement that is three times larger than the standard deviation (\(\sigma\)) of the whole time series is often called an outlier in the geodesy research community [42]. On day 108, the incoming measurements in both NS and EW directions show a shift of approximately one centimeter toward the south and the west; the backward-filtered streams indicate a slight turn toward the south (Figure 6a) and the west (Figure 6b). Meanwhile, the forward-filtered stream is just updated on day 103, which also shows the southward and westward turns. Combining these three data streams in both NS and EW directions, the monitoring person may be able to realize that a horizontal positional change has happened. On day 111, the backward-filtered stream indicates a rapid positional-change; the forward-filtered stream is updated on day 106, which also shows a positional change. So far, a positional change has been identified with a high confidence. However, the exact initial change time (point) is still not very clear. On day 114, the backward-filtered stream turns flat, the forward-filtered stream is updated on day 109, which also shows a turned corner. The date that is aligned with the corners of the backward and forward-filtered streams indicates the change point, which is between days 107 and 108. According to our field notes, the reinforcement of the base station was conducted on the afternoon of day 107. In order to detect riverbed deformation in time, DD processing can be conducted with shorter data sessions, such as every 10 min, every 30 min, and every hour. Figure 7 illustrates the three-component hourly displacement time series of GPS1 and the filtered data streams during one month from 1 May to 1 June 2018. The CPD algorithm was implemented on the incoming displacement streams. Visually, two riverbed deformational events happened at GPS1 during this period. The first displacement happened during days 136 to 137, and the second one happened during days 146 to 149. Figure 7b,e,h exhibit the first event, an abrupt horizontal displacement of 10 mm toward the southeast, which is approximately 10 days before the shield passed GPS1 (day 146, 26 May 2018). The positional change can be precisely detected by the CPD algorithm, as shown in Figure 7e. It is concluded that the abrupt displacement started at 3 PM (UTC) on day 136 (16 May 2018). Figure 7c,f,i exhibit the second event, a gradual displacement that was occurring in all three directions. This event started several hours before the shield arrived at GPS1 and continued for over two days after the shield passing the site. It took approximately 7 h (02:00-09:00, day of year 146, UTC) for the 8.2-m-long shield to pass this site. The gradual displacement continued approximately two days from days 146 to 147 with average velocities of 10, 3 and 6 mm/day in the NS, EW and vertical directions, respectively. Figure 7 indicates that the CPD algorithm works well to identify abrupt positional changes in near real-time. However, it always takes time to recognize gradual positional changes.