时频相位混合域高分辨率闭环地震反演网络

Yingtian Liu, Yong Li, Junheng Peng, Huating Li, Mingwei Wang
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摘要

薄层和储层可能隐藏在地震反射振幅较低的区域,因此难以识别。深度学习(DL)技术通过在地震数据和阻抗之间建立非线性映射,为准确预测阻抗提供了新的机会。然而,现有方法主要使用时域地震数据,这限制了对频带的捕捉,从而导致反演结果的分辨率不足。针对这些问题,我们引入了一种新的时-频-相(TFP)混合域闭环地震反演网络(TFP-CSIN),以改进薄层和储层的识别。首先,利用双向门控递归单元(Bi-GRU)和卷积神经网络(CNN)架构构建反演网络和闭环网络,实现地震数据和阻抗数据之间的双向映射。接下来,为了对整个频谱进行全面学习,我们使用傅立叶变换来捕捉频率信息并建立频域约束。同时,通过希尔伯特变换引入相域约束,提高了该方法识别弱反射区域特征的能力。在合成数据上的实验表明,TFP-CSIN 在地震反演中的表现优于传统的监督学习方法和时域半监督学习方法。野外数据进一步验证了所提出的方法提高了对弱反射区和薄层的识别能力。
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High-resolution closed-loop seismic inversion network in time-frequency phase mixed domain
Thin layers and reservoirs may be concealed in areas of low seismic reflection amplitude, making them difficult to recognize. Deep learning (DL) techniques provide new opportunities for accurate impedance prediction by establishing a nonlinear mapping between seismic data and impedance. However, existing methods primarily use time domain seismic data, which limits the capture of frequency bands, thus leading to insufficient resolution of the inversion results. To address these problems, we introduce a new time-frequency-phase (TFP) mixed-domain closed-loop seismic inversion network (TFP-CSIN) to improve the identification of thin layers and reservoirs. First, the inversion network and closed-loop network are constructed by using bidirectional gated recurrent units (Bi-GRU) and convolutional neural network (CNN) architectures, enabling bidirectional mapping between seismic data and impedance data. Next, to comprehensive learning across the entire frequency spectrum, the Fourier transform is used to capture frequency information and establish frequency domain constraints. At the same time, the phase domain constraint is introduced through Hilbert transformation, which improves the method's ability to recognize the weak reflection region features. Both experiments on the synthetic data show that TFP-CSIN outperforms the traditional supervised learning method and time domain semi-supervised learning methods in seismic inversion. The field data further verify that the proposed method improves the identification ability of weak reflection areas and thin layers.
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