# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 3 Example
### Data Collection and Dataset Generation  
We collected four seismic vibration responses of a long-span cable-stayed bridge in China, as shown in Table 1. Each seismic event was stored in one-hour time series of the monitoring system. In this paper, we follow the vision-based anomaly detection method [7] to generate the image datasets. The continuous time series were segmented by a 30-s sliding window with a 15-s overlap, then plotted in the time and frequency domains. A dual-channel time-frequency image was made by simply stacking a pair of time and frequency responses. The dimension of a T-F image is 100 \(\times\) 100 \(\times\) 2. Note that various anomalies exist in the monitoring data due to system malfunctions. Therefore, based on the visual features and the groundtruth, we manually labeled the T-F images according to Table 2 into six classes: Normal, Seismic, Missing, Minor, Bias and Drift. Each pattern of the T-F images is shown in Figure 6. A labeled dataset of 67,200 samples was generated, as shown in Table 3.  
Figure 5: Sensor locations of the cable-stayed long-span bridge (consists of a northern-branch bridge and a southern-branch bridge). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Anomaly Patterns**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**\
\begin{tabular}{} \end{tabular}**} & \multirow{2}{*}{**
\begin{tabular}{} \end{tabular}**} \\ \hline
1 & Normal & The time series is symmetrical, the vibration amplitude is relatively steady, and the frequency response is concentrated in the mid-frequency band. \\ \hline
2 & Seismic & The time series shows sparse long period features. Additionally, the frequency response is concentrated in the low frequency band. \\ \hline
3 & Missing & Most/all of the time series is missing or meaningless values, and the frequency response is correspondingly zero or meaningless disorder. \\ \hline
4 & Minor & The time series appears sawtooth-shaped, and the vibration amplitude is very small in the time domain. \\ \hline
5 & Bias & Relative to the pattern Normal, the time history is Bias towards one side. \\ \hline
6 & Drift & The time series is nonstationary, with random drift, and the frequency response is concentrated in the low frequency band. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of each data pattern. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Data Period**} & \multirow{2}{*}{**\begin{tabular}{} \end{tabular}**} & \multirow{2}{*}{**\
\begin{tabular}{} \end{tabular}**} & \multirow{2}{*}{**
\begin{tabular}{} \end{tabular}**} \\ \hline
1 & 11:00 to 12:00, 12 May 2016 & 11:17 & 6.2 & Yilan County, Taiwan \\
2 & 21:40 to 22:40, 4 February 2018 & 21:56 & 6.4 & Hualien County, Taiwan \\
3 & 09:40 to 10:40, 3 April 2019 & 9:52 & 5.9 & Taitung County, Taiwan \\
4 & 12:50 to 13:50, 18 April 2019 & 13:01 & 6.7 & Hualien County, Taiwan \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The seismic information. Figure 6: Examples of normal, seismic, and anomalous patterns.