Instantaneous attributes (IAs) derived from complex trace analysis using the Hilbert transform have been applied to reflection data since the late 1970s. However, the assumption of single-valued attribute is still an issue of interest. In search of an alternative solution, we use a nonlinear adaptive method, the ensemble empirical mode decomposition (EEMD), to decompose the signal into a series of intrinsic mode functions (IMFs), and then select significant components from the IMFs extracted from the original data to compute the IAs. This process overcomes the difficulties of obtaining a mono-component, zero mean signal in deriving IAs. When processing real data, we incorporate the natural logarithmic transform (NLT) into the computation to compensate the attenuation of the reflection data. The NLT EEMD algorithm yields more reliable IMFs for IAs computation than the original empirical mode decomposition (EMD); however a relevant correction is still required to limit the unexpected fluctuations occurring on IAs computation. For this reason, a local averaging technique with end effect removal is proposed to derive more interpretable IAs. Compared with other standard methods, the proposed processing scheme derives reliable IAs showing more details of the events with significant physical meaning.
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