High-dimensional time-frequency (TF) transforms are essential tools in seismic data processing. However, commonly used transforms such as Ridgelet, Curvelet, and Contourlet exhibit limitations in time-shifting invariance and basis function selection, which impacts on their effectiveness in seismic data analysis. To address these limitations, this study introduces optimal basic wavelet (OBW)-Ridgelet, a novel approach integrating the OBW with the Ridgelet transform. By combining OBW with Ridgelet, this method aims to enhance the TF localization for seismic structural analysis and time-shifting invariance property. We also present a workflow for seismic discontinuity detection, employing the C3 algorithm to the decomposed seismic data to get multiscale coherence and introduce the similarity coefficient for scale selection of the multiscale coherence. Synthetic and field data examples demonstrate the effectiveness and robustness of the proposed method, yielding promising results for seismic signal interpretation. The integration of OBW-Ridgelet enriches the toolkit for seismic signal analysis and holds the potential for refining seismic feature detection and interpretation in practical applications.

中文翻译：

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基于最优基本小波的脊波变换及其在地震不连续性检测中的应用


高维时频 (TF) 变换是地震数据处理中的重要工具。然而，常用的变换（例如 Ridgelet、Curvelet 和 Contourlet）在时移不变性和基函数选择方面表现出局限性，这影响了它们在地震数据分析中的有效性。为了解决这些局限性，本研究引入了最优基本小波 (OBW)-Ridgelet，这是一种将 OBW 与 Ridgelet 变换相结合的新方法。该方法将OBW与Ridgelet相结合，旨在增强地震结构分析的TF定位和时移不变性。我们还提出了地震不连续性检测的工作流程，对分解的地震数据采用C3算法以获得多尺度相干性，并引入相似系数来进行多尺度相干性的尺度选择。综合数据和现场数据示例证明了所提出方法的有效性和鲁棒性，为地震信号解释带来了有希望的结果。 OBW-Ridgelet 的集成丰富了地震信号分析工具包，并在实际应用中具有完善地震特征检测和解释的潜力。