To better reveal time-varying spectral components of nonstationary seismic signals, time–frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral mode separation and an adaptive wavelet bank design. The proposed adaptive mode separation-based wavelet transform (AMSWT) generates a superior time–frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

中文翻译：

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基于自适应模式分离的小波变换的地震时频分析

为了更好地揭示非平稳地震信号的时变谱分量，时频分析（TFA）已广泛应用于地震处理和分析中。在这封信中，我们提出了一种基于最优谱模式分离和自适应小波组设计的先进地震 TFA 方法。所提出的基于自适应模式分离的小波变换 (AMSWT) 产生了卓越的时频分辨率。此外，由于小波库自适应地建立在固有谱模式上，准确表征地球物理结构的能力得到了显着提高。为了证明所提出的 AMSWT 方法的有效性，我们将其应用于合成数据和现场数据。与连续小波变换（CWT）、经验模态分解（EMD）的结果相比，