Synthetic data for fiber sensing tasks with controllable generation and differentiable inference

A system, comprising:

• a hardware processor;
• and
• a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
• collect real-world distributed-optic fiber sensing (DFOS) sensing data from a target environment as a reference dataset;
• construct a synthetic sketch dataset as a parameterized program;
• generate a synthetic waterfall from a deep neural network as an image translator from the synthetic sketch dataset with nonlinear distortions and background noises added;
• and
• optimize parameters for generating the synthetic waterfall under a loss function where the loss function encodes a generalization performance on the reference dataset and encodes granularities from a sensing process and uncontrollable factors.

A computer program product, the computer program product comprising a computer readable storage medium storing program instructions embodied therewith, the program instructions executable by a hardware processor to cause the hardware processor to:

• collect real-world distributed-optic fiber sensing (DFOS) sensing data from a target environment as a reference dataset;
• construct a synthetic sketch dataset as a parameterized program;
• generate a synthetic waterfall from a deep neural network as an image translator from the synthetic sketch dataset with nonlinear distortions and background noises added;
• and
• optimize parameters for generating the synthetic waterfall under a loss function where the loss function encodes a generalization performance on the reference dataset and encodes granularities from a sensing process and uncontrollable factors.