Data merging methods for S-wave velocity and azimuthal anisotropy from different regions

When inverting the S-wave velocity and azimuthal anisotropy from ambient noise data, it is always to obtain the partial overlapped inversion results in contiguous different regions. Merging different data to achieve a consistent model becomes an essential requirement. Based on the S-wave velocity and azimuthal anisotropy obtained from different contiguous regions, this paper introduces three kinds of methods for merging data. For data from different regions with partial overlapping areas, the merged results could be calculated by direct average weighting (DAW), linear dynamic weighting (LDW), and Gaussian function weighting (GFW), respectively. Data tests demonstrate that the LDW and GFW methods can effectively merge data by reasonably allocating data weights to capitalize on the data quality advantages in each zone. In particular, they can resolve the data smoothness at the boundaries of data areas, resulting in a consistent data model in larger regions. This paper presents the effective methods and valuable experiences that can be referred to as advancing data merging technology.