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As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through…
We simulate the response of acoustic seismic waves in horizontally layered media using a deep neural network. In contrast to traditional finite-difference modelling techniques our network is able to directly approximate the recorded seismic…
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary…
For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and normally consist of multiple processing steps. Besides, the…
Distributed Acoustic Sensing (DAS) converts existing fibre-optic cables into dense seismic arrays at near-zero deployment cost, but measures strain rate rather than particle velocity -- the quantity required by virtually all seismological…
We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging…
Recently, Convolutional Neural Networks (CNNs) have shown unprecedented success in the field of computer vision, especially on challenging image classification tasks by relying on a universal approach, i.e., training a deep model on a…
Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In DAS, observed data, which…
In this paper, we study data-aided sensing (DAS) for distributed detection in wireless sensor networks (WSNs) when sensors' measurements are correlated. In particular, we derive a node selection criterion based on the J-divergence in DAS…
In this article, we present a novel Deep Learning model based on Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells, designed as a real-time Volcano-seismic Signal Recognition (VSR) system for Distributed Acoustic…
Distributed Acoustic Sensing (DAS) has become a popular method of observing seismic wavefields: backscattered pulses of light reveal strains or strain-rates at any location along a fiber-optic cable. In contrast, a few newer systems…
Distributed fiber-optic acoustic sensing (DAS) has emerged as a transformative approach for distributed vibration measurement with high spatial resolution and long measurement range while maintaining cost-efficiency. However, the…
Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp…
Controlling systems governed by partial differential equations is an inherently hard problem. Specifically, control of wave dynamics is challenging due to additional physical constraints and intrinsic properties of wave phenomena such as…
Automatic event detection from time series signals has wide applications, such as abnormal event detection in video surveillance and event detection in geophysical data. Traditional detection methods detect events primarily by the use of…
This paper presents a novel deep learning approach for analyzing massive underwater acoustic data by leveraging a model trained on a broad spectrum of non-underwater (aerial) sounds. Recognizing the challenge in labeling vast amounts of…
Deep learning applications are drastically progressing in seismic processing and interpretation tasks. However, the majority of approaches subsample data volumes and restrict model sizes to minimise computational requirements. Subsampling…
Seismic data preconditioning is essential for subsurface interpretation. It enhances signal quality while attenuating noise, improving the accuracy of geophysical tasks that would otherwise be biased by noise. Although classical poststack…
Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have…
Newtonian noise limits the low-frequency sensitivity of ground-based gravitational wave detectors. While seismometers and geophones are commonly employed to monitor ground motion for Newtonian noise cancellation, their limited spatial…