Multispectral representation of Distributed Acoustic Sensing data: a framework for physically interpretable feature extraction and visualization

TL;DR

This paper presents a multispectral framework for visualizing and extracting interpretable features from DAS data, improving bioacoustic signal analysis and automated whale vocalization detection.

Contribution

It introduces a systematic multispectral representation for DAS data that enhances visualization, clustering, and classification of acoustic signals, with demonstrated high accuracy.

Findings

ResNet-18 classifier achieves 97.3% accuracy in whale vocalization detection.

Multispectral features effectively capture biologically meaningful spectral structures.

The framework improves visualization and automated analysis of DAS data.

Abstract

Distributed Acoustic Sensing (DAS) enables continuous monitoring of dynamic strain along tens of kilometers of optical fiber, generating massive datasets whose interpretation and automated analysis remain challenging. DAS measurements often lack a standardized visual representation, and their physical interpretation depends strongly on acquisition conditions and signal processing choices. This work introduces a systematic framework for visualization and feature extraction of DAS data based on a multispectral signal representation. The approach decomposes strain-rate measurements into predefined frequency bands and computes band-limited energy images that describe the spatial and temporal distribution of acoustic energy across distinct spectral regimes. The framework is evaluated using DAS recordings containing Fin Whale (Balaenoptera physalus) and Blue Whale (Balaenoptera musculus) vocalizations. Three experiments are conducted to assess the approach: enhanced visualization of bioacoustic signals, unsupervised clustering of acoustic patterns, and supervised event detection using a convolutional neural network. Using multispectral composites as input, a ResNet-18 classifier achieves an accuracy of 97.3% in whale vocalization detection, demonstrating that the proposed representation captures biologically meaningful spectral structure and provides an effective feature space for automated analysis of DAS data.