Short-time homomorphic wavelet estimation

TL;DR

This paper introduces a short-time homomorphic wavelet estimation method combining classical homomorphic analysis with log-spectral averaging, improving wavelet estimation accuracy in seismic data processing.

Contribution

It presents a novel approach that enhances wavelet estimation by increasing sample points through short-term Fourier transform and log-spectral averaging.

Findings

Method performs well on synthetic data

Method demonstrates good results on real data

Reduces estimation variance compared to traditional methods

Abstract

Successful wavelet estimation is an essential step for seismic methods like impedance inversion, analysis of amplitude variations with offset and full waveform inversion. Homomorphic deconvolution has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. Associated disadvantages like phase unwrapping and restrictions of sparsity in the reflectivity function limit its application. We explore short-time homomorphic wavelet estimation as a combination of the classical homomorphic analysis and log-spectral averaging. The introduced method of log-spectral averaging using a short-term Fourier transform increases the number of sample points, thus reducing estimation variances. We apply the developed method on synthetic and real data examples and demonstrate good performance.