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.

Wavelet-Based Coherency Functional for Velocity Analysis of Seismic Reflection Data

Tognarelli A.
2020

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/1079798
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