Distributed Acoustic Sensing (DAS) enables the recording of seismic wavefields with an unprecedented spatial resolution, of the order of several metres, and with a sampling frequency that is uniform in both time and space. In spite of these unique characteristics, DAS data are commonly processed trace-by-trace, essentially treating the instrument as a set of conventional seismometers. Hence, one can imagine that such processing routines do not achieve their full potential by not considering the spatial dimension of DAS. To address this, we propose detection and classification methods leveraging the spatio-temporal coherence of the data, encoded in the form of covariance matrices. Through the use of Riemannian geometry, we propose a framework for change-point detection in a stream of such covariance matrices, detecting signals based on their coherence rather than their energy. We extend this framework to facilitate clustering and classification, opening the door to unsupervised data exploration. We apply these methods to borehole and offshore DAS data, and we conclude with an outlook on how this framework can be merged with Deep Learning methods.

How to cite: van den Ende, M., Wang, X., Borsoi, R., Rivet, D., Ferrari, A., and Richard, C.: Leveraging the spatio-temporal coherence of DAS data for detection and classification, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-16, https://doi.org/10.5194/egusphere-gc12-fibreoptic-16, 2024.

Extensive monitoring initiatives driven by the urgency to address climate change have led to the rise of long-term projects, particularly in offshore environments. These projects are evolving into complex, multi-decadal operations, necessitating comprehensive monitoring. Distributed Acoustic Sensing (DAS) arrays offer unique advantages in long-distance, high-density, real-time monitoring. However, the long-term archiving of DAS data presents significant challenges, due to the need for vast storage capacities (on the order of hundreds of terabytes per year). Innovative data compression techniques are essential to make continuous high-sample-rate DAS data storage feasible.

DAS data is composed of multiple channels carrying highly correlated and coherent signals. These characteristics allow us to exploit inter-channel compression techniques, which leverage the signal from consecutive channels for prediction-based compression. Inter-channel compression methods achieve a much higher compression ratio compared to compressing each channel separately and have been little studied. In this work, we present novel inter-channel compression algorithms and demonstrate state-of-the-art lossless compression. For this purpose, a lossless coding scheme was implemented inspired by successful video coding techniques, following a pipeline composed of intra-prediction, inter-prediction, transform, and entropy coding. The implementation is divided into an encoder and a decoder. The encoder uses a bitrate optimization search and can be tuned for either speed or high-compression modes, while the decoder is optimized for quick signal reconstruction. The designed algorithms and the provided implementation facilitate the deployment of long-term DAS recording and archiving.

How to cite: Seguí, A., Ugalde, A., and Ventosa, S.: Inter-channel lossless data compression for Distributed Acoustic Sensing, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-5, https://doi.org/10.5194/egusphere-gc12-fibreoptic-5, 2024.

Machine learning models require large amounts of training samples: in most cases, thousands of unique instances are required for each class of events.

Constructing such a dataset is an extremely expensive task in terms of the time it takes to identify and label meaningful events.

Additionally, the large amount of data produced by DAS interrogator units makes storage prohibitively expensive: the mediums need to be both fast (storing one second of data should take less than one second), and of high capacity (spanning long periods of time, such that rarely-occurring events of interest can be captured). To alleviate these constraints, a typical approach is to only store "interesting" events. But how to know which events to store without having a classifier in the first place?

To solve this chicken-and-egg problem, we propose a framework for constructing datasets used to train classifiers of events detectable through the acoustic fingerprinting of DAS measurement data.

Our framework progressively builds up a dataset starting from one or more hard-coded anomaly detection rules (even as simple as energy thresholding) to solve the initial problem of managing limited storage space. This creates an intermediate dataset of unlabeled events.

The intermediate dataset is then evaluated by means of acoustic fingerprinting, which assigns a feature vector to each event. To facilitate further user input, the feature vectors are projected onto a two-dimensional space. Supervised clustering is performed on the projected representations, where users can select the granularity of the clustering process, and consequently the number of resulting intermediate classes.

By assigning labels to entire clusters instead of individual samples, the time required to annotate the dataset is significantly reduced.

We evaluate the outcomes of this framework on a dataset of more than 50000 events initially detected on an inner-city measurement line, and discuss the possibilities of developing more sophisticated models that can be bootstrapped from this approach.

How to cite: Dumitru, V., Strasser, L., and Lienhart, W.: A Framework for Bootstrapping Event Classification Datasets, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-18, https://doi.org/10.5194/egusphere-gc12-fibreoptic-18, 2024.

Due to its dense spatial sampling of the wavefield, Distributed Acoustic Sensing (DAS) holds appeal for various applications in seismology. However, it produces large and complex datasets that are practically unfeasible to analyze manually. Additionally, we often lack prior knowledge of expected signals and their source distribution. This leads to the necessity of new processing tools, particularly for early data exploration. To address this challenge, we leverage signal coherence to detect and characterize seismic sources recorded by DAS. We segment the dataset into short time windows and compute the coherence matrix for each window at all frequencies, followed by averaging across different frequency bands of interest. Subsequently, we employ non-negative matrix factorization to isolate sources and retrieve their time-dependent coefficients. The resulting features display locally well-defined regions of elevated coherence. Different frequency bands can be analyzed simultaneously, which helps discriminate between signals originating from similar locations. Additionally, there is no need to make any assumptions about the data prior to applying the processing workflow. We apply this methodology to an urban DAS dataset, demonstrating its capability to detect coherent signals in complex settings. Notably, one feature reveals stable and repetitive sources of surface waves suitable for time-lapse monitoring of the subsurface. This makes our approach particularly interesting for applications of seismic interferometry, where understanding source distribution is crucial. We further demonstrate the value of coherence-based methods by applying them to a volcano dataset. Eigendecomposition of coherence matrices enables the detection and characterization of various volcanic signals such as explosions, earthquakes, and tremor pulses.

How to cite: Grimm, J. and Poli, P.: Coherence-based methods for early data exploration of DAS data, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-61, https://doi.org/10.5194/egusphere-gc12-fibreoptic-61, 2024.

Detecting earthquakes in continuous seismic data is essential for various applications. Such applications include real-time natural or anthropogenic hazard monitoring and generating earthquake catalogues for body-wave tomography. Although numerous detection algorithms have been developed specifically for fibreoptic data, they are typically only applicable for certain fibre geometries, and generally struggle with P and S wave phase association. Furthermore, integrating fibreoptic and conventional seismometer data into current algorithms remains challenging. Here, we present an automated, faster-than-real-time earthquake back-migration method, adapted to incorporate arbitrary 3D fibre geometries and include all available seismic observations from any instrumentation. Crucially, the strength of this method lies in stacking energy based on physics-derived time-shifts, locating the event during the detection process. Unlike machine learning methods, it does not require a training dataset so is therefore readily applicable to new scenarios. We demonstrate the performance of the method to detect near-surface seismicity at an alpine glacier and crustal seismicity from the ongoing Sundhnúkur eruption near Grindavik, Iceland. We also show how subsequent automated phase-arrival refinement can provide sufficient quality picks for travel-time tomography. As we present these results, we highlight how fibreoptic sensitivity limitations can be quantified and accounted for during earthquake detection, how different data sources can be combined, and we identify areas where advances are yet to be made. Generic earthquake detection algorithms such as that presented here are essential for harnessing the dense spatial sampling that fibreoptic sensing provides, while at the same time accounting for fundamental fibreoptic sensitivity limitations.

How to cite: Hudson, T., Klaasen, S., Fontaine, O., Zunino, A., van Meulebrouck, S., Walter, F., Jonsdottir, K., and Fichtner, A.: Towards a generic fibreoptic earthquake detection and location algorithm for arbitrary fibre geometries and hybrid fibre-seismometer networks, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-26, https://doi.org/10.5194/egusphere-gc12-fibreoptic-26, 2024.

During 2018, a study was conducted to understand the response of new seismic instrumentation to the complex seismo-acoustic wavefield of Mt. Etna. The study consisted on deploying a multi-instrumental network at Pizzi Deneri (PDN) observatory, near the main craters of Mt. Etna. The multi-instrumental network comprised infrasound sensors, broad-band seismometers (BB) and a fiber optic cable buried within the local loosed scoria surface. The cable was connected to a Distributed Dynamic Strain (DDSS) interrogator. Part of the collected data reveals, what is believed to be, a case of a non-linear ground response from an air-to-ground coupling from an acoustic wave. An infrasound sensor registered the arrival of a signal from a volcanic explosion with a main frequency of ~2 Hz. Immediately, a BB and fibre optic virtual sensor (DDSS channel), co-located with the infrasound sensor, registered a signal linked to the acoustic arrival. However, the dominant frequencies captured by the BB and DDSS range between 15 and 20 Hz. To further study this phenomenon, a second experiment was conducted in 2019 in the same place (PDN) and using the same type of instrumentation, but in a different spatial arrangement. In a total of three months, we obtained more than 65000 examples of acoustic signals linked to volcanic explosions. Embedded in the examples, there are cases of non-linear ground response. However, the dataset also contains cases with no ground response triggered by acoustic signals. To understand which acoustic inputs could trigger the ground response, we performed a classification of the acoustic signals based on waveform similarity. In addition, to understand the resulted ground response, we extended the waveform similarity classification to the DDSS records to achieve spatial-temporal characterization of the phenomenon in study. The outcomes of this method allows us to understand the spatial effect of acoustic signals on the ground, monitor temporal variations, and discriminate between reliable data and DDSS signal artifacts such as saturation.

How to cite: Diaz-Meza, S., Jousset, P., Currenti, G., Costes, L., and Krawczyk, C.: Characterizing non-linear ground response using signal classification from acoustic signals and Distributed Dynamic Strain Sensing (DDSS) at Mt. Etna volcano, Sicily., Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-76, https://doi.org/10.5194/egusphere-gc12-fibreoptic-76, 2024.

An active experiment was held in Fürstenfeldbruck, Germany, to investigate and compare the performance of rotational sensors. The active sources were vibroseis truck sweeps and explosions. In this study we focused on monitoring the movement of a vibroseis truck using six-degree-of-freedom (6-DoF) measurements, which combine rotational sensors and seismometers. The investigation spanned from 20 November 2019, at 11:00 UTC, to 21 November 2019, at 14:00 UTC. Throughout this period, 480 sweep signals were emitted, each lasting 15 seconds and covering frequencies from 7 to 120 Hz. Sweeps were emitted at 160 different locations.

During the second day of measurements, SV-type of waves dominated at frequencies up to 60 Hz while SH waves were dominant between 60 Hz to 120 Hz. At lower frequencies, we estimate the direction based on a method that uses SV-type waves. The accuracy of estimates declined with increasing distance between the truck and sensors. To gain insights into this phenomenon, we scrutinized the wavefield itself. Upon separating the wavefield using a fingerprinting algorithm, we observed that only a small portion of the wavefield exhibited a strong dominance of SV waves. This is surprising as the source generated only SV waves. Thus, we were able to track the movement of the truck only after separating the wavefield and determine the portion corresponding solely on SV-type of waves.

How to cite: Izgi, G., Eibl, E. P. S., Krüger, F., and Bernauer, F.: How Does Wavefield Separation Affect Direction Estimates Using a Rotational Sensor?, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-66, https://doi.org/10.5194/egusphere-gc12-fibreoptic-66, 2024.

The analysis of signals acquired through Distributed Acoustic Sensing (DAS) technology offers an innovative method for seismic monitoring. However, owing to the high noise levels, the analysis of DAS data presents significant challenges in taking full advantage of dense temporal and spatial sampling. This is particularly true in accurately picking phase arrival times on DAS data. 

Currently, some techniques have been proposed to address the picking problem on DAS data both from classical methods and through use of machine learning approaches, including the notable model named PhaseNet-DAS. Despite this, challenges persist, especially in real-time seismic monitoring applications and in the presence of high-frequency and high-density data.

In this context, we propose a novel model that leverages visual features and is based on the fundamental principles of Transformers, a class of Artificial Intelligence models, widely recognized for their ability to model complex relationships in sequential data. Our proposed model shows its effectiveness in learning seismic wave characteristics from DAS data, enabling an efficient phase picking.

To demonstrate the effectiveness of our approach, we present preliminary results on the use of our model to DAS data acquired in the seismically active area of Campi Flegrei caldera. The experimental results show the benefits of our method in exploiting DAS technology for enhancing seismic monitoring.

How to cite: Corsaro, M., Cannavò, F., Currenti, G., Palazzo, S., Allegra, M., Jousset, P., Prestifilippo, M., and Spampinato, C.: Seismic Phases Picking with Artificial Intelligence: A Novel Approach for Distributed Acoustic Sensing Data Analysis, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-82, https://doi.org/10.5194/egusphere-gc12-fibreoptic-82, 2024.

Distributed Acoustic Sensing (DAS) can provide unprecedented spatial resolution and sensitivity to a wide frequency band. The instrument response of the interrogated optical fibre cables, however, is largely unknown and difficult to separate from source, path, and directivity effects on seismic records. This prevents us from using DAS in many staple seismological techniques that require either absolute amplitude values or a complete understanding of the full waveform (e.g., earthquake magnitude estimation, waveform tomography).

Here we present a full-waveform simulation scheme developed to model the DAS instrument response using a particle-based Elastic Lattice Model (ELM-DAS). The scheme allows us to simulate a virtual cable embedded in the medium and made of a string of connected particles. By measuring the strain along these particles, we are able to replicate the axial strain natively measured by DAS as well as the effects of irregular cable geometries. Analysing synthetic DAS data allows us to focus on the main factors that are believed to determine the instrument response: cable-ground coupling and local site effects. The particle-based numerical scheme allows us to easily simulate complex properties of the material around the cable (e.g., unconsolidated sediments, nonlinear materials) as well as different degrees of cable-ground coupling.

By simulating DAS cables in 2D, we observe that at the meter scale, realistic DAS materials, cable-ground coupling, and the presence of unconsolidated trench materials around it dramatically affect wave propagation, each change affecting the synthetic DAS record, with differences exceeding at times the magnitude of the recorded signal. By expanding the scheme to 3D, we can accurately include the effects of realistic, complex–and at times sub-wavelength–cable geometries and how they influence DAS records. Our observations show that cable coupling and local site effects have to be considered both when designing a DAS deployment and analysing its data when either true or along-cable relative amplitudes are considered.

How to cite: Celli, N. L., Bean, C. J., and O'Brien, G.: Understanding DAS records and their response with full-waveform modelling, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-2, https://doi.org/10.5194/egusphere-gc12-fibreoptic-2, 2024.

Traditional rain detection methods are often constrained by their limited spatial coverage and lack of adaptability in diverse environmental conditions. This gap highlights the need for more advanced, adaptable solutions for rain detection. Recent advances in distributed acoustic sensing (DAS) have shown potential for overcoming these limitations. DAS transforms existing optical fiber cables into distributed sensornetworks, which detects external perturbations, such as vibrations, and acoustic pulses, along the fiber by utilizing the coherent Rayleighbackscattering. Each fiber segment with a length of spatial resolution can be considered a point sensor that enables continuous, real-time measurements across the entire route. Prior studies have demonstrated the potential of DAS in detecting and classifying rain intensity under controlled laboratory conditions. Building upon this research, further work expanded the application of DAS to real-world environments, employing pre-trained supervised Convolutional Neural Networks (CNNs) for field trials in rain detection and classification. However,existing research primarily focus on environments with consistent, labeled datasets and often struggle to adapt to variable environmental conditions. Using DAS, we propose a CNN with unsupervised domain adaptation technique that utilizes already-laid optical fiber networks for rain intensity classification. Specifically, firstly we collected the field data from a live telecommunication network using DAS underdifferent field environment, which was recorded over eight different days spanning five months. After filtering and feature extraction, a Deep Reconstruction Classification Network (DRCN) is implemented to concurrently minimize both the classification loss and reconstruction lossduring the learning process, aiming to capture a domain adaptive feature representa- tion applicable to new environmental conditions. Experimental results show that our approach effectively identifies rain status and adapts well to rain intensity classification across newdomains, addressing the gap left by machine learning methods in the context of unlabeled field data. This adaptability is crucial fordeveloping more accurate and reliable rain detection systems that capable of functioning effectively across various domains.

How to cite: Ding, Y., Shi, S., Tian, Y., Jiang, Z., Ozharar, S., Wang, T., and Moore, J.: Fiber Optic Sensing in Rain Detection Using Unsupervised Domain Adaptation, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-87, https://doi.org/10.5194/egusphere-gc12-fibreoptic-87, 2024.

The vulnerability of urban assets, including soils, buildings, and infrastructure, is influenced by human activities, environmental factors, and societal vulnerabilities. Leveraging Distributed Acoustic Sensing (DAS) technology deployed on telecom fiber networks, we have developed an information model that facilitates data extraction, exchange, and networking to aid decision-making regarding civil infrastructure assets. In collaboration with the French company APRR-AREA, we focus on a 25 km stretch of telecom optic fiber along the A40 motorway concession — known as the "autoroute des Titans" — in eastern France. Ensuring the control of vehicle weight is crucial for the safety of highway transportation systems as it helps assess structural fatigue and traffic impact on roadways. Our initial goal is to predict heavy vehicle weight, speed, and lane using DAS records. For this prediction task, we designed various convolutional neural network (CNN) architectures, which take a 2-dimensional DAS panel as input. The ground truth labels are provided by a dynamic axle weighing system. The data is collected over 17 346 vehicles with weights ranging from 1,660 to 73,123 kilograms, over a 68-hour DAS acquisition period. We first train the networks over purely synthetic datasets. The forward problem, used to produce the synthetic panels, involves a Flamant-Boussinesq approximation for predicting the quasi-static road deformation caused by passing vehicles with different speeds and weights in the two lanes of circulation. We model DAS strain rate signal accounting for signal variability due to vehicles deviations in their lanes and a spatially variable path of the optic fiber. Our synthetic experiments yield promising results in predicting vehicle weight, speed and lane, allowing to disentangle the influence of vehicle weight and distance on fiber data. Moving forward, we plan to further refine our model by training, after image segmentation, the CNN on the A40 DAS dataset. We anticipate that this study will underscore the potential of DAS technology in complementing dedicated instrumentation for enforcing load limits in areas with heavy traffic.

How to cite: Santos, T., Rodet, J., Amin Panah, M., Tauzin, B., Bodin, T., and Pittet, R.: A Deep Learning Neural Network for Controlling Vehicle Weight, Speed, and Lane from Telecom Dark Fiber Distributed Acoustic Sensing, Galileo conference: Fibre Optic Sensing in Geosciences, Catania, Italy, 16–20 Jun 2024, GC12-FibreOptic-40, https://doi.org/10.5194/egusphere-gc12-fibreoptic-40, 2024.