Denoised ambient noise cross-correlation for a linear array across Chenghai fault

In January 2018, a linear dense array was deployed and operated for approximately one month, crossing the central-to-southern portion of the Chenghai Fault (CHF) (Yang et al., 2020). The array comprised 158 short-period three-component sensors with an inter-station spacing of about 40–50 m, covering a distance of approximately 8 km. Situated beneath the center of the array is the Binchuan Basin. However, due to the low signal-to-noise ratio of surface waves in the ambient noise cross-correlation (ANC) data, the tomographic model based on the raw ANC data was overly smoothed, limiting its ability to resolve detailed lateral variations within the basin or features associated with the underlying CHF.
To improve imaging resolution, we apply the three-station-interferometry-based denoising method (Qiu et al., 2021). This denoising process iteratively enhances the SNR of surface wave signals, particularly at high frequencies, and achieves clear convergence after three iterations.
The denoised ANC data, along with a Python script demonstrating how to read it, is provided here.

References
Yang, H., Duan, Y., Song, J., Jiang, X., Tian, X., Yang, W., et al. (2020). Fine Structure of the Chenghai Fault Zone, Yunnan, China, Constrained From Teleseismic Travel Time and Ambient Noise Tomography. Journal of Geophysical Research: Solid Earth, 125(7), 1–14. https://doi.org/10.1029/2020JB019565
Qiu, H., Niu, F., & Qin, L. (2021). Denoising Surface Waves Extracted From Ambient Noise Recorded by 1-D Linear Array Using Three-Station Interferometry of Direct Waves. Journal of Geophysical Research: Solid Earth, 126(8), 1–18. https://doi.org/10.1029/2021JB021712