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Fault image enhancement using a forward and backward diffusion method

Yan, Zhe, Liu, Shaoyong, Gu, Hanming
Computers & geosciences 2019
algorithms, computers, geophysics, probability
Fault interpretation is a key step for seismic structural interpretation and reservoir characterization. In conventional approach, faults are identified as reflection discontinuity or abruption in post-stack seismic data. In order to highlight fault, several fault attributes by measuring waveform similarity or diversity have been proposed. However, noise and stratigraphic features can also be highlighted in a fault attribute image. We propose a forward and backward diffusion method to enhance the fault image so that the noisy features which unrelated to faults are suppressed and the fault features are sharpened. We first estimate the planarity (linearity in 2D) and fault orientation (strike and dip) of every sample in fault image by using a PCA-based algorithm. Combining the planarity and fault orientation, we then construct fault probability map based on the assumption that faults can be seemed as relatively steep planes in a small spatial window. Thanks to the fault probability map, we can adaptively switch the diffusion process from a forward mode to a backward mode. Forward diffusion force is used to suppress the non-fault features within the low fault probability area and backward diffusion force is used to enhance the fault features within the high fault probability area. A real 3D example is included to demonstrate that this method can effectively suppress the noise and sharpen the faults. Furthermore, automatic fault tracking on an enhanced fault image can get more accurate result than on an original fault image.