Median filtering in Kirchhoff migration for noisy data

Abstract

Although non-Gaussian noise may be suppressed by signal processing prior to seismic migration, in many cases its remnant still presents difficulties by spreading its energy into the migration aperture. Non-Gaussian noise reduction can be performed in seismic migration. Median filtering is often effective in removing non-Gaussian noise in a series of values, and it is often applied in the spatial direction to reduce spatial inconsistency. It is very easy to incorporate median filter noise reduction with standard Kirchhoff time migration, in which data is weighted and summed along a T-X curve, as defined by the local RMS velocity. We tested a simple median filter and a n-point median filter before Kirchhoff summation and the results are compared with standard Kirchhoff migration. This method showed an advantage over standard Kirchhoff migration when noise bursts are present It also shows some potential in reducing spatial aliasing effect in Kirchhoff time migration.

Comparative Study of Different Denoising Filters for Speckle Noise Reduction in Ultrasonic B-Mode Images

“…The median of the pixel values in the window is computed, and the center pixel of the window is replaced with the computed median. Median filtering is done by, first sorting all the pixel values from the surrounding neighborhood into numerical order and then replacing the pixel being considered with the middle pixel value [9,10].…”

Nonlinear structure‐enhancing filtering using plane‐wave prediction*

“…Median filters find important applications in seismic data analysis. Mi and Margrave (2000) incorporated noise reduction using median filter into standard Kirchhoff time migration. Zhang and Ulrych (2003) used a hyperbolic median filter to suppress multiples.…”

Swell Noise Attenuation Using a Cascade of F-X Filter and Median Filter

“…파랑잡음과 같은 돌출값 형태로 존재하는 잡음을 제거하기 위해 주로 사용하는 방법으로 중앙값 필터(median filter)가 있 다. 중앙값 필터는 간단하지만 매우 효과적이며 (Bednar, 1983;Duncan and Beresford, 1995), 잡음의 특성에 따라 보다 복잡 한 형태의 중앙값 필터도 제시되고 있다 (Mi and Margrave, 2000;Liu et al, 2006). 불규칙 잡음을 제거하기 위한 다른 방 법으로는 비인과(noncausal) 예측필터를 이용한 잡음제거법 (Gulunay, 2000), 적응필터를 통한 잡음제거법 (Ristau and Moon, 2001), 복소 트레이스 분석(complex-trace analysis)을 통한 잡음제거법 (Karsli et al, 2006) Ma 경에 이르러 섭입이 중단된 것으로 알려져 있다 (Kim et al, 1995;Larter et al, 1997;Livermore et al, 2000).…”