Estimating Observation Error Covariance Matrix of Seismic Data from a Perspective of Image Processing

Estimating observation error covariance matrix properly is a key towards successful seismic history matching. Observation errors of seismic data are usually correlated, therefore the observation error covariance matrix is non-diagonal. Estimating such a non-diagonal covariance matrix is the focus of the current study. We decompose the estimation into two steps: (1) estimate observation errors; and (2) construct covariance matrix based on the estimated observation errors. Our focus is on step (1), whereas at step (2) we use a procedure similar to that in Aanonsen et al., 2003. In Aanonsen et al., 2003, step (1) is carried out using a local moving average algorithm. By treating seismic data as an image, this algorithm can be interpreted as a discrete convolution between an image and a rectangular window function. Following the perspective of image processing, we consider three types of image denoising methods, namely, local moving average with different window functions (as an extension of the method in Aanonsen et al., 2003), non-local means denoising and wavelet denoising. The performance of these three algorithms is compared using both synthetic and field seismic data, and it is found that the wavelet denoising method leads to the best performance in our investigated cases.