# Iterative Interferometric Denoising Filter for Traveltime Picking  
[PERSON]  
1 College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Geosciences, College of Petroleum Engineering and Geosciences (CPG), King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Geosciences, College of Petroleum Engineering and Geosciences (CPG), King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia; [EMAIL_ADDRESS]  
[PERSON]  
1 College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China; [EMAIL_ADDRESS]  
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However, incoherent data noises generated by the environment and instruments have unpredictable characters in space and time domains [13] and will decrease performance while picking the first-arrival traveltime [14; 15]. Numerous denoising methods, including FX filters [16; 17], KL transformations [18], singular value decomposition (SVD) methods [19], and Radon transforms [20], are used to suppress this type of noise. [PERSON] et al. [21] utilize Principal component analysis (PCA) to downweigh datasets. However, all these approaches require some parameters to adjust the noise filter threshold; inappropriate parameters will damage useful information or cause noise elimination failure. Seismic interferometry [22] has been introduced to increase the microseismic waveform signal-to-noise (S/N) ratio [23] by fixing the correct picking of a reference first break on at least one trace and then applying cross-correlation to provide picking convenience. Further, this concept has been transferred to the refraction field [24; 25; 26]. In recent years, machine learning has also been used in the seismic denoising field [27]. The pseudo-Wigner distribution filter (pseudo-WDF) was first proposed in quantum statistics [28] to focus a symmetric pattern on the center location. This type of technology has been introduced into the frequency domain to propose the interferometric imaging condition; in this imaging condition, the incoherent wavefield fluctuations caused by random velocity perturbations can be canceled using cross-correlation operations [29; 30; 31]. [PERSON] and [PERSON] [32] extended this technology into the spatial domain in Reverse Time Migration (RTM) to suppress the artifacts caused by inaccurate velocity models. [PERSON] [33] applied the pseudo-WDF from the active-source geophysics field to an acoustic Time-reversal Imaging method (acoustic TRI method) for passive source location to decrease the artifacts resulting from sparse acquisition geometry and random velocity disturbances. Artifacts on image domains caused by these two factors do not exhibit a symmetric character, so they can be eliminated. [PERSON] et al. [34] and [PERSON] et al. [35] applied the pseudo-WDF to the elastic Time-reversal Imaging method (elastic TRI method) to account for velocity inaccuracy and acquisition geometry sparsity. Then, this method was employed on separated P- and S-wavefields to capture a source image with a symmetric radiation pattern during time-reversal modeling and to demonstrate its resistance to random noises using the Monte Carlo test under surface acquisition geometry in the elastic TRI method [36]. In addition, this WDF was also used to make polarity corrections in passive source datasets [37] and to capture symmetric source radiation images in diffraction stack images of passive sources [38; 39]. In the seismic industry, seismic data generated by controlled sources are essentially the convolution of the seismic source and subsurface reflectivity series [40].