在光频域反射（Optical Frequency Domain Reflection， OFDR）技术的分布式光纤测量方法的基础上，提出了基于欧拉梁理论推导出光纤测量应变与土体水平位移之间的转换关系，并通过室内实验进行了验证，证实了分布式光纤测水平位移的可行性和应变⁃位移转换关系的准确性，且将传统测斜仪法与光纤测量方法分别在基坑工程中得到了应用。通过试验，光纤测量方法能够准确详细地量测深层土体在开挖过程中的水平位移变化情况。

We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber. The cell samples are illuminated by an incoherent LED light source. A deep convolutional neural network is applied to the image reconstruction process. The network training uses data generated by a setup with straight fiber at room temperature (～20 ° C) but can be utilized directly for high-fidelity reconstruction of cell images that are transported through fiber with a few degrees bend or fiber with segments heated up to 50°C. In addition, cell images located several millimeters away from the bare fiber end can be transported and recovered successfully without the assistance of distal optics. We provide evidence that the trained neural network is able to transfer its learning to recover images of cells featuring very different morphologies and classes that are never “seen” during the training process.