Coherence is widely used for detecting faults in reservoir characterization. However, faults detected from coherence may be contaminated by some other discontinuities (e.g., noise and stratigraphic features) that are unrelated to faults. To further improve the accuracy and efficiency of coherence, preprocessing or postprocessing techniques are required. We developed an enhanced fault-detection method with adaptive scale highlighting and high resolution, by combining adaptive spectral decomposition and super-resolution (SR) deep learning into coherence calculation. As a preprocessing technique, adaptive spectral decomposition is first proposed and applied on seismic data to get a dominant-frequency-optimized amplitude spectrum, which has features of scale focus and multiple resolution. Eigenstructure-based coherence with dip correction is then calculated to delineate fault discontinuities. Following the remarkable success of SR deep learning in image reconstruction, a convolutional neural network (CNN) model is built and it then takes fault-detection images as the input to achieve enhanced results. The effectiveness of our proposed method is validated on a seismic survey acquired from Eastern China. Examples demonstrate that coherence from adaptive amplitude spectrum without dip correction is comparable to the dip-corrected one from seismic amplitude data at a certain degree, and they even highlight the specific scale of fault targets. Comparing fault detections from adaptive spectrum and some specific-frequency components, it can be concluded that adaptive spectral-based coherence highlights the primary scale of faults at various depths with only one single volume of data, thus improving the interpretation efficiency and reducing storage cost. Furthermore, with the trained CNN model, the resolution and signal-to-noise ratio of coherence images are effectively improved and the continuity of detected fault is promisingly enhanced.
A Cloud Computing Fault Detection Method Based on Deep Learning
