Abstract
<p>This paper proposes a new method to improve the resolution of the seismic signal and to compensate the energy of weak seismic signal based on matching pursuit. With a dictionary of Morlet wavelets, matching pursuit algorithm can decompose a seismic trace into a series of wavelets. We abstract complex-trace attributes from analytical expressions to shrink the search range of amplitude, frequency and phase. In addition, considering the level of correlation between constituent wavelets and average wavelet abstracted from well-seismic calibration, we can obtain the search range of scale which is an important adaptive parameter to control the width of wavelet in time and the bandwidth of frequency. Hence, the efficiency of selection of proper wavelets is improved by making first a preliminary estimate and refining a local selecting range. After removal of noise wavelets, we integrate useful wavelets which should be firstly executed by adaptive spectral whitening technique. This approach can improve the resolutions of seismic signal and enhance the energy of weak wavelets simultaneously. The application results of real seismic data show this method has a good perspective of application.</p>
Highlights
We usually assume, in seismic exploration, that the reflectivity series is sparse in time domain and a seismic trace can be modeled by convolving the reflectivity series with a wavelet
Seismic data is nonstationary with varying frequency content in time domain, and the representation of it in frequency domain can illustrate many features which are difficult to visualize in time domain
Matching pursuit is a powerful method to decompose a seismic trace into a series of wavelets
Summary
In seismic exploration, that the reflectivity series is sparse in time domain and a seismic trace can be modeled by convolving the reflectivity series with a wavelet. In this paper, we try to develop a new method to increase the resolution of seismic data and enhance the energy of weak wavelets via decomposing the signal into a set of wavelets with different amplitude and frequency. Considering the Morlet wavelet is more suitable for energy and frequency quantification of seismic data and more appropriate for attenuation and resolution studies [Morlet et al 1982a, 1982b], they were employed as wavelets in the matching pursuit decomposition [Liu and Marfurt 2005]. In terms of improving resolution and enhancing the energy of weak signal, the differential resolution which uses a set of cascaded dipole filters to improve seismic resolution through spectral whitening [Claerbout 2004], a new algorithm [Sajid and Ghosh 2014] utilized a set of three cascaded difference operators to boost high frequencies together with a simple smoothing operator to boost low frequencies.
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