A physically-informed deep-learning model using time-reversal for locating a source from sparse and highly noisy sensors data

Adar Kahana, Eli Turkel, Shai Dekel, Dan Givoli

Research output: Contribution to journalArticlepeer-review

Abstract

We approximate the underwater acoustic wave problem for locating sources in that medium. We create a time dependent synthetic data-set of sensor recorded pressures, based on a small set of sensors placed in the domain, and perturb this data with high random multiplicative noise. We show that reference time-reversal based method struggles with high noise, and a naive deep-learning method also fails. We propose a method, based on physically-informed neural-networks and time-reversal, for approximating the source location even in the presence of high sensors noise.

Original languageEnglish
Article number111592
JournalJournal of Computational Physics
Volume470
DOIs
StatePublished - 1 Dec 2022

Keywords

  • Inverse problems
  • Learning
  • Physically-informed
  • Sensors
  • Time-reversal

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