Open AccessTime-Frequency Domain Deconvolution based on Synchrosqueezing Generalized S Transform
Author(s) -
Shulin Zheng,
Zhiping Shen
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrset218233
Subject(s) - deconvolution , convolution (computer science) , wavelet , algorithm , computer science , time–frequency analysis , frequency domain , time domain , resolution (logic) , wavelet transform , signal (programming language) , amplitude , temporal resolution , artificial intelligence , computer vision , optics , physics , filter (signal processing) , artificial neural network , programming language
Complex geological characteristics and deepening of the mining depth are the difficulties of oil and gas exploration at this stage, so high-resolution processing of seismic data is needed to obtain more effective information. Starting from the time-frequency analysis method, we propose a time-frequency domain dynamic deconvolution based on the Synchrosqueezing generalized S transform (SSGST). Combined with spectrum simulation to estimate the wavelet amplitude spectrum, the dynamic convolution model is used to eliminate the influence of dynamic wavelet on seismic records, and the seismic signal with higher time-frequency resolution can be obtained. Through the verification of synthetic signals and actual signals, it is concluded that the time-frequency domain dynamic deconvolution based on the SSGST algorithm has a good effect in improving the resolution and vertical resolution of the thin layer of seismic data.