Deep Learning Approach for Processing Fiber-Optic DAS Seismic Data

Developing automatic algorithmic tools for targets' detection and classification in a fiber-optic Distributed Acoustic Sensing (DAS) system is a challenging task. The main hurdle is the need to produce a large-scale dataset of tagged events to facilitate the training of the algorithms. This task requires considerable resources in terms of manpower, computing time and computer memory. In contrast, generating a training dataset via a computer simulation can significantly simplify the development stage and allow tremendous saving in time and costs. This approach, however, requires highly accurate modeling of the optical DAS system, the generation and propagation of the seismic/acoustic waves in the medium and the interaction between the waves to the fiber. The physical parameters and details needed for such modeling are rarely available. In this paper, a novel approach for efficient generation of training data is introduced and demonstrated. It is based on using Generative Adversarial Network (GAN) to transform simulation data to accurately mimic genuine data based on a relatively small experimental database labeled manually. The new approach is verified with experimental data taken from a 5km long DAS sensor yielding 94% classification accuracy between ambient noise and human steps at the vicinity of the buried fiber.