Time-varying wavelet estimation and its applications in deconvolution and seismic inversion

Abstract

Wavelet holds an essential role in seismic data processing and characterization, for examples deconvolution and seismic inversion. Unfortunately, wavelet is an unknown data. Several existing methods attempt to estimate and extract the wavelet from seismic data. However, the methods give only a single wavelet from one seismic trace. When seismic data are non-stationer, single wavelet usage will cause a problem, that is raising the error. This paper proposes a time-varying wavelet estimation method to accommodate this problem. It uses matrix diagonalization to estimate a set of wavelets. Next, the time-varying wavelet is applied to deconvolution and seismic inversion. The experiment shows that time-varying wavelet improves the results in both deconvolution and seismic inversion. The errors decreased and spectrum bandwidth broadened.

The Direct-Inversion Deconvolution and Its Application in Seismic Data

“…12 is used implicitly in seismic inversion. For example, model-based inversion uses Eq.12 in its equation (Hampson-Russel Software, 1999;Pranowo, 2019).…”

“…Second, Gabor deconvolution combines the essential ideas of stationary deconvolution and inverse Q filtering (Margrave et al, 2011), or assuming seismic traces are split into segments which are called the molecular-Gabor transform (Wang et al, 2013). Finally, in recent developments, time-varying deconvolution uses the lessassumption technique in its processes, such as using S-transform (Jia et al, 2017;Winardhi & Pranowo, 2019) or other mathematical approaches (Pranowo, 2019;van der Baan, 2008).…”