Thread: Ph.D on Passive imaging of the subsurface along transportation infrastructures using DAS and ML

Started: 2023-05-11 17:42:43
Last activity: 2023-05-11 17:42:43
*Abstract of the Ph.D project*
A key aspect in the development of sustainable smart cities is to leverage
the existing information and communication infrastructure for developing
new sensing strategies. In that framework, Telecom-Distributed Acoustic
Sensing (DAS) is a recent breakthrough that has shown it is possible to use
existing telecommunication fiber optic already deployed in cities for
recording the passive seismic vibrations at high frequencies in space and
time, over large distances and over long-time span. The objective of the
thesis is to evaluate how we can use DAS data and Machine Learning (ML)
techniques to image and monitor the subsurface along transportation
infrastructures.
Indeed, DAS comes with new challenges in handling the various types of
recorded seismic signals that impede the application of usual data
processing workflows. DAS produces data in the order of 1Tb daily and
requires the use of effective processing methods capable of recognizing
multiple acoustic signatures simultaneously, under low signal-to-noise
ratio conditions. Over the last decade, machine learning methods have been
successfully applied to tackle geophysical problems and assist in laborious
tasks such as earthquake detection, and phase arrival picking. Recent
studies have explored leveraging ML approaches for the analysis and
processing
of DAS ambient seismic noise recordings. In the majority of these studies,
the objective is event detection and classification, and the feature of
interest is the seismic signal generated by vehicles or persons traveling
past the array. In (van den Ende et al., 2022), the authors propose a
self-supervised deep learning approach that deconvolves the characteristic
car impulse response from the DAS data. In (Liu et al., 2020), the authors
introduce a ML approach for vehicle detection, classification and
estimation. In (Jakkampudi et al., 2020), they develop a convolutional
neural network to detect footstep signals in ambient seismic recordings
from urban DAS arrays. In a similar approach, (Huot and Biondi, 2018) use a
convolutional neural network to detect car-generated seismic signals. The
study by (Dumont et al., 2020) focuses on the identification and
quantification of the useful seismic waves embedded within the seismic
signal generated by the vehicles. In (van den Ende et al., 2021), the
authors explore a deep learning blind denoising method that optimally
leverages the spatiotemporal density of DAS recordings to separate
earthquake signals from the spatially incoherent background noise.
All these works anticipate the development of ML as a central tool for
processing and monitoring seismic DAS signals. Indeed, ML techniques can
handle very large volumes of data, and enable fast and efficient processing
and interpretation of DAS data in comparison with classic approaches. The
goal of the thesis is to develop a ML framework for processing DAS data and
performing subsequent seismic noise analysis that will serve as input for
passive imaging and monitoring the near-surface geomechanical properties
beneath transportation infrastructures.

*Project partners*
SNCF company, BRGM, TelecomParis, Université Grenoble Alpes, Université
Côte d'Azur

*Location*
Université Côte d'Azur
Downtown Nice, France

*Contact*
Prof. Cédric Richard
cedric.richard<at>unice.fr
www.cedric-richard.fr

00:30:03 v.01697673