The precise decomposition of P/S waves in multicomponent seismic data is critical for seismic imaging. Inaccuracies in this decomposition can result in migration images with biased amplitudes and undesirable crosstalk artifacts. Although vector decomposition (VD) methods are effective, they rely on the availability of elastic parameters at the acquisition surface. Therefore, we propose a data-driven deep-learning (DL)-P/S-VD method that eliminates the need for prior elastic parameter information. We used publicly available elastic models to generate training datasets by simulating multicomponent data and their amplitude-preserving P-/S-wave components using the decoupled elastic wave equation. Our analysis explores the impact of the loss function type, output channel quantity, and direct wave removal on the generalization ability of DL-PSVD. The critical insights from the numerical experiments include the superior generalization ability of DL-PSVD using two channels when the P-/S-wave energy distribution is highly unbalanced. Moreover, DL-PSVD exhibits improved generalization ability using four channels for observed data with removed direct waves. Finally, the L1 loss function is more effective for DL-PSVD’s generalization ability than the L2 loss function. Overall, the proposed DL-PSVD is a promising method for automatic P-/S-wave VD without requiring prior elastic parameter information.

中文翻译：

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基于深度学习的数据驱动的多分量地震数据纵波/横波矢量分解


多分量地震数据中纵横波的精确分解对于地震成像至关重要。这种分解的不准确可能会导致偏移图像具有偏差幅度和不良的串扰伪影。尽管矢量分解 (VD) 方法很有效，但它们依赖于采集表面弹性参数的可用性。因此，我们提出了一种数据驱动的深度学习（DL）-P/S-VD方法，消除了对先验弹性参数信息的需要。我们使用公开可用的弹性模型，通过使用解耦弹性波方程模拟多分量数据及其保幅纵波/横波分量来生成训练数据集。我们的分析探讨了损失函数类型、输出通道数量和直接波去除对 DL-PSVD 泛化能力的影响。数值实验的关键见解包括当纵波/横波能量分布高度不平衡时，使用两个通道的 DL-PSVD 具有卓越的泛化能力。此外，DL-PSVD 使用四个通道来获取去除了直达波的观测数据，从而表现出改进的泛化能力。最后，L1损失函数比L2损失函数对于DL-PSVD的泛化能力更有效。总体而言，所提出的 DL-PSVD 是一种有前景的自动 P/S 波 VD 方法，无需事先提供弹性参数信息。