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Optical fiber sensing is a technology wherein audio, vibrations, andtemperature are detected using an optical fiber; especially theaudio/vibrations-aware sensing is called distributed acoustic sensing (DAS). InDAS, observed data, which is comprised of multichannel data, has suffered fromsevere noise levels because of the optical noise or the installation methods.In conventional methods for denoising DAS data, signal-processing- ordeep-neural-network (DNN)-based models have been studied. Thesignal-processing-based methods have the interpretability, i.e., non-black box.The DNN-based methods are good at flexibility designing network architecturesand objective functions, that is, priors. However, there is no balance betweenthe interpretability and the flexibility of priors in the DAS studies. TheDNN-based methods also require a large amount of training data in general. Toaddress the problems, we propose a DNN-structure signal-processing-baseddenoising method in this paper. As the priors of DAS, we employ spatialknowledge; low rank and channel-dependent sensitivity using the DNN-basedstructure. The result of fiber-acoustic sensing shows that the proposed methodoutperforms the conventional methods and the robustness to the number of thespatial ranks. Moreover, the optimized parameters of the proposed methodindicate the relationship with the channel sensitivity; the interpretability.

Low-rank constrained multichannel signal denoising considering  channel-dependent sensitivity inspired by self-supervised learning for  optical fiber sensing