This work presents a new coherency functional based on the Continuous Wavelet Transform for the velocity analysis of seismic reflection data. In particular, it discusses the efficacy of the wavelet-based functional when the analysis is performed on seismograms where signals are non-stationary. The new functional is defined in the first part of the abstract instead, the second part discusses the application of the method on a synthetic and a field example. Both experiments are characterized by the occurrence of weak, strongly attenuated sub-basalt reflections buried in the noise and obliterated by multiples. The velocity spectra computed by the wavelet-based functional, are compared with those obtained by the standard Semblance functional and by the unconventional high-resolution functional of Complex-Matched Semblance. Results show that the proposed functional, named Wavelet Semblance, is more efficient than standard Semblance and Complex-Matched Semblance since it is able to take into account the occurrence of non-stationary signals allowing to detect the weak attenuated reflections (i.e. sub-basalt reflections). In addition, the method produces velocity spectra with a higher resolution and it is robust against random and non-random noise.
|Titolo:||Wavelet-Based Coherency Functional for Velocity Analysis of Seismic Reflection Data|
TOGNARELLI, ANDREA (Primo)
|Anno del prodotto:||2020|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|