Multiresolution Chirp-Modulated W-Transform

Abstract For seismic characterization of reservoirs, it is necessary to extract the time-frequency characteristics hidden in time-domain data for effective use in processing and interpretation. In the time-frequency analysis algorithm of the chirp-modulated W-transform (CW-transform), the spread of the analysis kernel is the primary factor controlling the resolution of the power spectral density. We have proposed a multi-resolution CW-transform that improves the localization of the power spectral density and extends the capability of time-frequency algorithms in delineating reservoirs and thin layers. We have also proposed multiplying CW-transforms with different resolutions, to produce a highly localized and well-resolved power spectral density. The resultant spectrum can be regarded as the geometric mean of the spectra estimated with different scaling factors. Compared with the arithmetic mean, this approach is more robust and noise resistant, resulting in higher resolution and greater energy concentration. We have investigated the application of statistical properties of the time-varying spectrum to indicate the presence of gas-bearing reservoirs. We have conducted experiments demonstrating that the multi-resolution CW-transform can be successfully used to estimate a time-frequency spectrum with high energy concentration and has great potential for application in the seismic characterization of complex reservoirs.

Authors

Institutions

Publication Details

Journal
Geophysics
Published
2026-08-17
DOI
https://doi.org/10.1190/geo-2025-0320
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multiresolution Chirp-Modulated W-Transform

Yanghua Wang, Ying Rao, Zhencong Zhao, Sheng Ding
Geophysics
Seismic Imaging and Inversion Techniques
article

Multiresolution Chirp-Modulated W-Transform

Yanghua Wang, Ying Rao, Zhencong Zhao, Sheng Ding
article en

Abstract

Abstract For seismic characterization of reservoirs, it is necessary to extract the time-frequency characteristics hidden in time-domain data for effective use in processing and interpretation. In the time-frequency analysis algorithm of the chirp-modulated W-transform (CW-transform), the spread of the analysis kernel is the primary factor controlling the resolution of the power spectral density. We have proposed a multi-resolution CW-transform that improves the localization of the power spectral density and extends the capability of time-frequency algorithms in delineating reservoirs and thin layers. We have also proposed multiplying CW-transforms with different resolutions, to produce a highly localized and well-resolved power spectral density. The resultant spectrum can be regarded as the geometric mean of the spectra estimated with different scaling factors. Compared with the arithmetic mean, this approach is more robust and noise resistant, resulting in higher resolution and greater energy concentration. We have investigated the application of statistical properties of the time-varying spectrum to indicate the presence of gas-bearing reservoirs. We have conducted experiments demonstrating that the multi-resolution CW-transform can be successfully used to estimate a time-frequency spectrum with high energy concentration and has great potential for application in the seismic characterization of complex reservoirs.

Geophysics
China University of Petroleum, Beijing (CN), Institute of Geophysics (RU), Institute of Geology and Geophysics (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 12%
Seismic Imaging and Inversion Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.