Application of the recursive‐approaching signal filter (RASF) to VSP data processing

Abstract

The recursive‐approaching signal filter (RASF) is a newly developed filtering technique that combines many advantages of linear, nonlinear, and adaptive filters. It passes step functions without altering them and removes many types of noise, such as Gaussian and Laplacian distributed noise. When applied to VSP data processing, the RASF emphasizes those abrupt discontinuities that originate or terminate at discrete depth points and effectively accomplishes the separation of upgoing and downgoing wave modes. The RASF may be transformed into a desired filter by simply changing a parameter to achieve the maximum usefulness of VSP field data. In the tests with the synthetic VSP modeling data corrupted by white Gaussian noise and real VSP data, the RASF compares favorably to f-k velocity and median filtering methods in removing noise, preserving step functions, and computational simplicity.