  Optical fiber sensing is a technology wherein audio, vibrations, and
temperature are detected using an optical fiber; especially the
audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In
DAS, observed data, which is comprised of multichannel data, has suffered from
severe noise levels because of the optical noise or the installation methods.
In conventional methods for denoising DAS data, signal-processing- or
deep-neural-network (DNN)-based models have been studied. The
signal-processing-based methods have the interpretability, i.e., non-black box.
The DNN-based methods are good at flexibility designing network architectures
and objective functions, that is, priors. However, there is no balance between
the interpretability and the flexibility of priors in the DAS studies. The
DNN-based methods also require a large amount of training data in general. To
address the problems, we propose a DNN-structure signal-processing-based
denoising method in this paper. As the priors of DAS, we employ spatial
knowledge; low rank and channel-dependent sensitivity using the DNN-based
structure. The result of fiber-acoustic sensing shows that the proposed method
outperforms the conventional methods and the robustness to the number of the
spatial ranks. Moreover, the optimized parameters of the proposed method
indicate the relationship with the channel sensitivity; the interpretability.
