Based on the Barnes method of discontinuity filters, I created a new fault-detection attribute and compared it with the constrained least squares spectral analysis (CLSSA) method in Barnett Shale fault detection. The fault-detection attribute is calculated using the Principal Component Analysis (PCA) of different seismic attributes such as coherency, most positive curvature, variance, smoothed seismic data, and also isofrequency phase discontinuities volume. The most positive curvature has better resolution compared with other curvature attributes, while coherency attribute is a very good way to map karst-related structures. Phase spectrum is a good way to detect lateral acoustic discontinuities, while some small discontinuities can be detected very well in the specific frequency phase map. The fault-detection attribute can thus reinforce the similar information of these attributes and reduce the dissimilar information as noise. CLSSA is a better spectral method than the short-time Fourier transform method because it reduces classical spectral smoothing. Spectral analysis can also highlight stratigraphic characterization. Based on these features, the application of fault-detection attribute and CLSSA show better resolution in Barnett Shale fault detection. As compared to coherence and curvature, the resulting PCA fault-attribute better resolves minor tectonic and karst-related fractures.Earth and Atmospheric Sciences, Department o
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