The Application of Robust Principal Component Analysis for Weak Seismic Signal Enhancement

For weak seismic signal enhancement, a new application of a signal decomposition method, known as Robust Principal Component Analysis (RPCA) is introduced. The motivation of this work stems from the observation that the interested weak seismic signals are always interfered by strong ones besides noises, causing the loss of some detailed seismic information. Principal Component Analysis (PCA), based on second order statistics, however, requires the data to be white and Gaussian, which seismic data may not satisfy. Being an extension of traditional PCA, RPCA utilizes L1-norm instead as the error function and the iterative algorithm obtains the optimal projections one by one with a greedy strategy. The synthetic data experiment indicates that RPCA outperforms PCA in seismic data processing as RPCA forms less artificial horizontal events. Moreover, an example of a 3-D field data is considered on which there are two wells close to each other. The seismic events are continuous across the wells, whereas the oil and gas production of the wells is distinct. The results demonstrate that RPCA is effective for weak seismic signal enhancement and helpful to improve the reliability of oil and gas detection.