Applying the most frequent values assisted Hilbert transform into seismic attributes




Seismic attributes, Inversion-based Fourier Transform, Robust Hilbert Transform


In the present study, we developed a new method to calculate two of the most important seismic attributes that can able to give a huge impact in the interpretation field leading to a better geological and geophysical interpretation of the data. The first attribute was the Reflection Intensity with great improvement in detection of thin layers that show low reflection coefficient and enhance the ability to follow unconformity, the other one is the Instantaneous Phase to discover the discontinuity of events. The new method depends on combining the most frequent values (MFV) with inversion to calculate the Hilbert transform and applying hybrid Seismic Denoising, using also dual inversion and calculated Steiner weight from the iterative reweighted least square method (IRLS). Scaled Hermite function in a series expansion was used to discretize the spectrum of the time trace. The procedure followed with adjusting the process using multi-windows technical on the generated synthetic seismic traces loaded with Gaussian and Cauchy noise at the same time. The result of the method was shown significant effects on outliers or normal Gaussian noise when applied to two seismic attributes better than the old way using the Discrete Fourier Transform (DFT) method.