arXiv subjects

Dídac Diego-Tortosa

Publications and source records attributed to Dídac Diego-Tortosa.

4 recordsLinked to original sources

arXiv2026

An End-to-End Workflow for Fin Whale Song Detection, Note Characterization, and Localization with Distributed Acoustic Sensing

Submarine fiber-optic cables instrumented with distributed acoustic sensing (DAS) provide an effective approach for large-scale monitoring of fin whales. We present an end-to-end workflow for detecting, characterizing, and localizing fin whale notes, tested on two submarine telecom cables in the Strait of Gibraltar and western Alboran Sea. The workflow applies a kurtosis-value picker adapted to narrow-band fin whale notes. Channel-wise detections are grouped into individual notes using density-based spatio-temporal clustering, cluster agglomeration, and hyperbolic fitting to reject incoherent picks. The retained clusters are characterized through temporal, spectral, and energy-related descriptors that support note-type discrimination and estimation of inter-note intervals. Relative arrival times across DAS channels are then used in a grid-search procedure to estimate candidate source locations. Evaluation against manually annotated detections from six fin whale songs yielded median pick-level precision of 0.990 and recall of 0.744, and median cluster-level precision of 0.880 and recall of 0.806. Representative applications demonstrate separation of overlapping vocalizations, characterization of type-A and type-B notes, and the inference of apparent source movement. By transforming dense DAS recordings into compact note-level bioacoustic information, the workflow provides an integrated framework for fin whale monitoring and a basis for adaptation to other synchronized acoustic receiver arrays.

cs.SD↗

arXiv2026

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

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.

physics.ins-det↗

arXiv2020

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.

astro-ph.IM↗

arXiv2020

gSeaGen: the KM3NeT GENIE-based code for neutrino telescopes

The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able to generate events induced by all neutrino flavours, considering topological differences between track-type and shower-like events. Neutrino interactions are simulated taking into account the density and the composition of the media surrounding the detector. The main features of gSeaGen are presented together with some examples of its application within the KM3NeT project.

astro-ph.IM↗