# Shallow Magmatic System of Arxan Volcano Revealed by Ambient Noise Tomography with Dense Array
## 2 Data and Methods  
Coupled with the ongoing use of short-period seismometers in seismology, research into the shallow crustal structure is becoming increasingly sophisticated. In September 2020, researchers from Jilin University set up a linear dense seismic array in the study region, consisting of 227 stations that comprised 197 EPS-2-M6Q and 30 Smartsolo seismographs (Figure 1b). In order to detect the magmatic system beneath the AVF, most of the craters in the AVF were covered with seismometers. The TCV and GD1213 craters were situated close to the center of the array. The station interval increased to approximately 500 m as the distance from the TCV volcano cone increases, providing enough tomographic resolution below the TCV crater. This research focused exclusively on vertical component data. According to the modified method of data processing and analysis put forward by researchers [35], the raw data fragments were first merged, cut, band-pass filtered, de-instrumented, and downsampled, and then the daily SAC [45] format files were created for every station. The daily waveform data from all stations underwent preprocessingsteps, containing demeaning, detrending, normalization in the time domain, and spectral whitening in the frequency domain. After preprocessing, cross-correlation calculations were conducted on each station pair to derive daily noise cross-correlation (NCC) functions. Subsequently, all daily NCC functions were linearly overlaid and compute the mean across negative and positive lags. This process aimed to enhance the signal-to-noise ratio to create the last CC function. Notably, Rayleigh waves became evident in the NCC functions following the application of band-pass filters in the 0.08-2 Hz and 0.2-1 Hz ranges (Figure 2). The SNR demonstrates variability dependent on both the station interval and the frequency band of the applied band-pass filter, as depicted in Figure S1. Rayleigh waves exhibit a notably great value of the signal-to-noise ratio in the low-frequency band, particularly evident with greater distances between stations. To enhance the detection and analysis of Rayleigh waves, multiple filtering techniques were executed to the ambient noise CC function, as outlined by [PERSON] [46], ultimately facilitating the calculation of the Rayleigh wave group velocity. Figure 3a presents the Rayleigh wave group velocity dispersion data obtained from the NCC functions using the frequency-time analysis method [35; 47; 48; 49; 50], focusing on a time range of 1-6 s. Data were filtered using band-pass filter ranges of 1-20 s and 4-10 s, respectively, to isolate Rayleigh wave signals, and the group velocity was calculated for each frequency band. The resulting velocity data were integrated and concatenated to generate the final dispersion curves. Quality control measures were implemented by selecting dispersion data from NCC functions with an SNR exceeding 3. After screening out dispersion curves with significant velocity fluctuations, we obtained 1894 group velocity curves with a great SNR value from 18,749 station pairs (Figure 3a). Statistical analysis reveals notable peaks in ray path numbers, with one located in periods of approximately 2-5 s (Figure 3a). Additionally, Figure 3b illustrates a relatively even distribution of ambient noise power across the research region. Figure 2: Ambient noise seismic waveforms after cross-correlation (CC). The reference velocities of 2.0 km/s and 4.0 km/s are represented by blue and red lines, respectively. (**a**) The filter range is a period between 0.5 and 20 s. (**b**) The band-pass filter range is 1–5 s periods. As a result of intense scattering and attenuation, extracting high-frequency Rayleigh waves (period 0.25-2 s) from station pairs separated by hundreds of kilometers presents considerable challenges [34,36,37]. However, dense station arrays enable the detection of high-frequency Rayleigh wave signals (0.25-2 s) even with small station spacing separated by a few kilometers or even hundreds of meters.