Distributed acoustic sensing (DAS) technology is a novel technology applied in vertical seismic profile (VSP) exploration, which has many advantages, such as low cost, high precision, strong tolerance to harsh acquisition environment. However, the field DAS-VSP data are often disturbed by complex background noise and coupling noise with strong energy, affecting the quality of seismic data seriously. Therefore, we develop a time–frequency analysis method based on low-rank and sparse matrix decomposition (LSMD) and data position points distribution maps (DPM) to separate signals from noise. We adopt Multisynchrosqueezing Transform to construct the approximate ideal time–frequency representation of DAS data, which reduces the difficulty of signal to noise separation and avoids the loss of some effective information to a certain extent. The LSMD is performed to separate the signal component and noise component preliminarily. In addition, combined with the separated low-rank matrix and sparse matrix, we propose the DPM to improve the accuracy of signal component extraction and the recovery ability of the method for weak signals through the joint analysis of the maps in time domain and frequency domain. Both synthetic and field experiments show that the proposed method can suppress coupling noise and background noise and recover weak energy signals in DAS VSP data effectively.

分布式声学传感（DAS）技术是一种应用于垂直地震剖面（VSP）勘探的新技术，具有成本低、精度高、对恶劣采集环境耐受性强等优点。然而，现场DAS-VSP数据经常受到复杂背景噪声和强能量耦合噪声的干扰，严重影响地震数据质量。因此，我们开发了一种基于低秩稀疏矩阵分解（LSMD）和数据位置点分布图（DPM）的时频分析方法，将信号与噪声分离。我们采用Multisynchrosqueezing Transform来构造DAS数据的近似理想时频表示，在一定程度上降低了信噪分离的难度，避免了一些有效信息的丢失。执行 LSMD 以初步分离信号分量和噪声分量。此外，结合分离的低秩矩阵和稀疏矩阵，我们提出了DPM，通过时域和频域映射的联合分析，提高了信号分量提取的准确性和弱信号方法的恢复能力。 . 合成和现场实验均表明，该方法可以有效抑制耦合噪声和背景噪声，有效恢复DAS VSP数据中的弱能量信号。我们提出了DPM，通过对时域和频域映射的联合分析，提高了信号分量提取的准确性和弱信号方法的恢复能力。合成和现场实验均表明，该方法可以有效抑制耦合噪声和背景噪声，有效恢复DAS VSP数据中的弱能量信号。我们提出了DPM，通过对时域和频域映射的联合分析，提高了信号分量提取的准确性和弱信号方法的恢复能力。合成和现场实验均表明，该方法可以有效抑制耦合噪声和背景噪声，有效恢复DAS VSP数据中的弱能量信号。