High resolution deconvolution and acoustic impedance inversion of seismic data

High resolution deconvolution and acoustic impedance inversion of seismic reflection data is extremely important in order to describe the geometry and the physical properties of subsurface structures. High resolution deconvolution results are taken as the reflectivity coefficient series of the earth. However, when the content of the seismic data noise is increased, traditional inversion methods such as the least squares and quadratic regularization methods mostly lead to unreliable and low quality results. In this thesis, in order to produce high resolution deconvolution and reliable acoustic impedance results Cauchy norm regularization inversion method was used. This method, has high anti-noise ability and provides high impedance accuracy. Based on Cauchy probability density function, a prior information was included into the Cauchy norm regularization to enforce sparseness. An iterative re-weighted least squares (IRLS) algorithm was used and the quality of the solution was improved by adding Cauchy norm regularization prior information (diagonal matrix) at the start of the inversion matrix. On every iteration result, the solution was weighted. The respective results of the high resolution deconvolution were transformed into high resolution, reliable and accurate acoustic impedance traces using inversion algorithms. Key Words: Cauchy norm regularization, High resolution deconvolution, Acoustic impedance inversion, Seismic reflection data, Reflectivity coefficient series