Robust time-frequency analysis of seismic data using general linear chirplet transform

Huang, Yu‐Cheng; Zheng, Xiaodong; Duan, Yonghong; Luan, Yi

doi:10.1190/geo2017-0145.1

Cited by 9 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…; Wu and Castagna ; Huang et al . ). In our investigation, we used the continuous wavelet transform (CWT; e.g.…”

Section: Methodsmentioning

confidence: 97%

“…Examples of common spectral decomposition approaches include short-time Fourier transform, wavelet transform, S-transform and combinations of these approaches (e.g. Chakraborty and Okaya 1995;Castagna, Sun and Siegfried 2003;Sinha et al 2005;Kazemeini et al 2009;von Hartmann et al 2012;Wu and Castagna 2017;Huang et al 2018). In our investigation, we used the continuous wavelet transform (CWT; e.g.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Bauer

Norden

Ivanova

et al. 2019

Geophysical Prospecting

View full text Add to dashboard Cite

At the geothermal test site near Groß Schönebeck (NE German Basin), a new 3D seismic reflection survey was conducted to study geothermal target layers at around 4 km depth and 150°C. We present a workflow for seismic facies classification and modelling which is applied to a prospective sandstone horizon within the Rotliegend formation. Signal attributes are calculated along the horizon using the continuous Morlet wavelet transform. We use a short mother wavelet to allow for the temporal resolution of the relatively short reflection signals to be analysed. Time‐frequency domain data patterns form the input of a neural network clustering using self‐organizing maps. Neural model patterns are adopted during iterative learning to simulate the information inherent in the input data. After training we determine a gradient function across the self‐organizing maps and apply an image processing technique called watershed segmentation. The result is a pattern clustering based on similarities in wavelet transform characteristics. Three different types of wavelet transform patterns were found for the sandstone horizon. We apply seismic waveform modelling to improve the understanding of the classification results. The modelling tests indicate that thickness variations have a much stronger influence on the wavelet transform response of the sandstone horizon compared with reasonable variations of seismic attenuation. In our interpretation, the assumed thickness variations could be a result of variable paleo‐topography during deposition of predominantly fluvial sediments. A distinct seismic facies distribution is interpreted as a system of thicker paleo‐channels deposited within a deepened landscape. The results provide constraints for the ongoing development of the geothermal test site.

show abstract

“…; Wu and Castagna ; Huang et al . ). In our investigation, we used the continuous wavelet transform (CWT; e.g.…”

Section: Methodsmentioning

confidence: 97%

Section: Continuous Wavelet Transformmentioning

confidence: 99%

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Bauer

Norden

Ivanova

et al. 2019

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…With the development of information technology, more and more signal analysis and processing technologies have been applied to the study of time–frequency analysis, including the short‐time Fourier transform (STFT), continuous wavelet transform (CWT) and S transform. The methods described above are mainly used to depict seismic thin‐layer (Huang et al., 2018; Wang et al., 2014). In order to take advantage of various methods, Stockwell et al.…”

Section: Introductionmentioning

confidence: 99%

A seismic thin‐layer detection factor calculated by integrated S transform with non‐negative matrix factorization

Zhao,

Cao,

Yang

et al. 2024

Geophysical Prospecting

View full text Add to dashboard Cite

Time–frequency analysis is one of the effective methods for seismic thin‐layer detection. Conventional time–frequency analysis technology for seismic thin‐layer detection is interfered by the energy of adjacent frequency signals, and there is information redundancy in the frequency‐domain analysis. Therefore, an S transform with improved window factor, which is based on the constrained non‐negative matrix factorization, is constructed to realize seismic thin‐layer detection. First, the seismic data is processed by the S transform of the improved window factor, and then we can obtain the frequency‐domain information with strong time–frequency focus by changing the adjustment factor and attenuation factor in the window function. Furthermore, the key frequency of the seismic data spectrum, which can also be called the key frequency characteristic factor, can be calculated by the non‐negative matrix factorization algorithm. Fortunately, the overthrust model shows a good correspondence between the key frequency characteristic factor and the thin‐layer interface. The field data example shows that this approach provides a new approach for thin‐layer detection.

show abstract

Synchroextracting chirplet transform-based epileptic seizures detection using EEG

Jiang

Chen

et al. 2021

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Robust time-frequency analysis of seismic data using general linear chirplet transform

Cited by 9 publications

References 54 publications

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

A seismic thin‐layer detection factor calculated by integrated S transform with non‐negative matrix factorization

Synchroextracting chirplet transform-based epileptic seizures detection using EEG

Contact Info

Product

Resources

About