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The Sparse Adaptive Generalized S Transform
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DOI：10.1109/LGRS.2025.3634759.png)
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Abstract
En 中文
High-resolution time–frequency analysis is crucial for seismic interpretation. Conventional sparse time–frequency transforms, such as the sparse generalized S transform (SGST), are not adaptive to the intrinsic characteristics of the signal. To address this limitation, we propose a sparse adaptive generalized S transform (SAGST). This method incorporates the signal amplitude spectrum into the Gaussian window function, allowing the window to adapt dynamically to the signal characteristics. This adaptive mechanism enables the construction of wavelet bases that are better matched to the signal. We apply the SAGST to the time–frequency analysis of both synthetic signal and field seismic data. The synthetic signal test shows that the SAGST achieves higher energy concentration, superior computational efficiency, and enhanced weak signal extraction compared with the sparse adaptive S transform (SAST) and SGST. A field example demonstrates that the SAGST can be used to indicate low-frequency shadow associated with hydrocarbon reservoirs.
Keywords:
Low-frequency shadow
sparse adaptive generalized S transform (SAGST)
time–frequency transform
weak signals extracting
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