Ultra High Frequency Acoustic Coda Waves

Citation Author(s):
Venu Babu
Thati
IEMN-DOAE of Polytechnic University Hauts-de-France
Nikolay
Smagin
IEMN-DOAE of Polytechnic University Hauts-de-France
Hatem
Dahmani
IEMN-DOAE of Polytechnic University Hauts-de-France
Julien
Carlier
IEMN-DOAE of Polytechnic University Hauts-de-France
Ihsen
Alouani
IEMN-DOAE of Polytechnic University Hauts-de-France
Submitted by:
VENU BABU THATI
Last updated:
Mon, 07/19/2021 - 18:24
DOI:
10.21227/h4xq-4h55
Data Format:
License:
0
0 ratings - Please login to submit your rating.

Abstract 

Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution. While existing deterministic solutions reach their limit in handling complex data, emerging techniques such as deep learning-based algorithms have shown a promising potential to overcome these limitations. This paper proposes a deep neural network that paves the way to make the complex features of coda waves more handleable to reliably exploit coda waves in several applications even in a changing or unstable environment. Specifically, designed a Coda-Convolutional Neural Network that is able to identify coda waves with 95.65% precision in a silicon chaotic cavity including 5 emitters and 16 receivers using ultra-high frequency ultrasonic coda waves.

Instructions: 

Combine the data from different zip files into a single folder to process it on the source code