Multi-dimensional Coherency Driven Denoising of Irregular Data

Many land and ocean bottom datasets suffer from high levels of noise which make the task of processing and interpretation difficult. With legacy land data, high noise levels are generally due to low CMP fold. High fold modern acquisition can also be noisy due to poor geophone coupling, ground or mud roll, or because single sensors rather than arrays are used. As these data often exhibit irregular sampling, denoising them can be difficult due to the majority of random noise attenuation algorithms requiring regularly sampled data. We introduce a semblance driven denoising algorithm in the high resolution tau-p domain that can offer strong denoising capabilities and work directly with irregularly sampled data. The algorithm can be applied in all five recording dimensions (inline, crossline, offset-x, offset-y, time) to avoid working on subsets of data, which increases the ability for weak signals to be uncovered from below high levels of noise. Application of the algorithm on irregularly sampled synthetic and real datasets demonstrate the power of the method by greatly reducing the noise content whilst accurately preserving the signal.