定点形变仪器观测精度高，易受仪器工作状态以及外部环境变化影响而产生噪声，这些噪声的存在不利于地震信息的提取。为了消除定点形变信号中的噪声，提出了一种结合改进的自适应噪声完备集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN）和同步压缩变换（synchrosqueezing transform, SST）的去噪模型。该模型通过对含噪声的信号进行ICEEMDAN分解获得若干固有模态函数（intrinsic mode function, IMF）分量；然后计算各分量的样本熵（sample entropy, SE），并结合方差贡献率、相关系数，划分出有效分量、含噪声分量；最后，利用SST对含噪声分量进行去噪，并与有效分量重构，获得去噪后的纯净信号。通过仿真实验以及宜昌地震台不同类型实测信号分析表明，ICEEMDAN-SST模型能有效地区分含噪声分量、有效分量，去噪后的信号还原度较高，固体潮形态清晰，去噪效果优于S-G（Savitzky-Golay）滤波、卡尔曼滤波、小波去噪等传统方法，适用于多种定点形变仪器的不同类型噪声的压制。ICEEMDAN-SST模型的提出对于定点形变仪器地震信息的提取有着重要意义，有助于这类观测仪器在地震分析预报中发挥更大的作用。

Fixed-point deformation instruments(FPDI), which observation precision is extremely high, yet are susceptible to noise generated by variations in the operational status and external environmental changes, resulting that seismic information is concealed in various interference signals. To eliminate noise from FPDI, a denoising model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the synchrosqueezing transform (SST) was proposed. According to this model, the FPDI signal containing noise was firstly decomposed into several intrinsic mode function (IMF) components through ICEEMDAN. And then, the effective IMFs and noise-containing IMFs were distinguished by considering sample entropy (SE), the variance contribution rate and correlation coefficient. Finally, SST was utilized to denoise the noise-containing IMFs, which were then reconstructed with the effective IMFs to obtain a purified signal. The simulation experiments and the analysis of different types of measured signals from the Yichang seismic station show that the ICEEMDAN-SST model can effectively distinguish the noisy components from the effective components. The signal restoration degree after denoising is relatively high, and the solid tide pattern is clear. The denoising effect is superior to traditional methods such as S-G (Savitzky-Golay) filtering, Kalman filtering, and wavelet denoising, and is suitable for suppressing different types of noise in various FPDI. The proposal of the ICEEMDAN-SST model is of great significance for the extraction of seismic information by FPDI and helps such observation instruments play a greater role in seismic analysis and prediction.