# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 2 Data Introduction and Processing Methods
### Data Processing Methods  
Based on reliable observations from CSES-01, data processing methods were developed to extract abnormal information related to EQs as objectively as possible. The data preprocessing is performed as follows:  
1. In most cases, only nightside data (ascending orbit) were selected to avoid the significant impact of solar radiation on the ionosphere [33]. 2. Data under disturbed space weather conditions (i.e., \(\mid\)_Dst_\(\mid\) \(>\) 30 nT and _Kp_\(>\) 3) were excluded to eliminate interference caused by space weather [34]. 3. Any artificial interference was minimized by carefully selecting and applying appropriate filtering techniques. For example, the total geomagnetic field data from HPM can be interfered with by TBB signals with at most 30 nT; thus, we developed an algorithm to remove the overlapped disturbances induced by TBB [35]. 4. Data were further extracted within a spatial range of \(\pm\)10\({}^{\circ}\) around the epicenter (latitude/longitude), defined as \"the EQ influential zone\" according to the empirical equation put forward by [PERSON] et al. [14], and within a time window of 25 days before and 5 days after the mainshock based on previous research results [36; 37]. After the above data preprocessing steps, the data were applied for further analysis to extract possible anomaly information. Considering the different payloads with different working principles, the different parameters from different payloads have different backgrounds and anomaly features; therefore, different methods were applied and tested to extract seismic anomalies as objectively as possible from each payload for the same EQ case. Then, based on years of data processing and research results, we selected appropriate and effective processing methods for different parameters according to their characteristics and different manifestations [22]. For example, the running quartile method is mainly used for physical values provided by payloads such as LAP, PAP, GOR, and HPM; the difference map is mainly used for payloads including SCM, and EFD in this work. In addition, for the CSES HEPP payload, we mainly used the electron flux comparison method which is a common and effective processing method for energetic particles [38]. Figure 2 shows the flow of data processing. Here, we introduce the three most commonly used methods. Figure 2: The flowchart of data processing. Running quartile method  
Firstly, for each EQ event, the current orbits within 25 days before and 5 days after the main shock passing through the EQ influential zone were selected; thus, for each current orbit, we obtained six revisiting orbits within 30 days to build the background trend. Secondly, the running medians (Mb) and interquartile ranges (i.e., IQR, the difference between the third and first quartiles) were computed using the six revisiting orbits. Then, the upper and lower thresholds were computed by the Mb \(\pm\) n \(\times\) IQR values, where n is set as values from 1 to 5 depending on the regular features of different physical quantities. Thirdly, the values Mb and Mb \(\pm\) n \(\times\) IQR were rebuilt as time sequences and regarded as the background trend. Fourthly, the disturbance amplitude was extracted by comparing the current orbit (Mc) with the background trend values, using the following equation: \(\mathrm{Mc}-(\mathrm{Mb}\pm\mathrm{n}\times\mathrm{IQR})\). Lastly, we normalized the disturbance amplitude as percentages: When the current observation values fall within the upper and lower thresholds, the disturbance amplitude is then set to zero; otherwise, it is set as \(\mathrm{Mc}-(\mathrm{Mb}\pm\mathrm{n}\times\mathrm{IQR})/(\mathrm{Mb}\pm\mathrm{n} \times\mathrm{IQR})\). The long-term observations (monthly or yearly data) under quiet space weather conditions were selected to build a global background map with a resolution of \(2^{\circ}\) (lat) \(\times\)\(2^{\circ}\) (lon) grid, which helps depict large-scale ionospheric structure variations [39].