This study presents passive downhole Distributed Acoustic Sensing (DAS) measurements conducted in the Groningen gas field, Netherlands, for subsurface and induced seismicity monitoring. The optical fiber installation, completed in Sept 2015, was partially cemented behind the inner casing along a deviated well (~3800 m), extending into the sandstone reservoir at temperatures ranging from 100-110 degrees Celsius. In Nov 2022, we interrogated the optical fiber utilizing a 10 m gauge length and 1 m sampling spacing. Within this setup, most DAS traces exhibit the lowest self-noise floor in the frequency range of 0.1 to 30 Hz. Noteworthy is the absence of visible differences between cemented and uncemented sections. Strong ambient seismic noise is observed in the near-surface unconsolidated sediment at approximately 800 m depth. Noise cross-correlation (CC) analysis is performed for DAS channels and DAS seismometer pairs. Surface wave signals in the 0.1 to 1 Hz range are identified in DAS-seismometer CCs, displaying amplitude and polarization changes with depth, following Surface wave theory. Induced seismicity is also recorded, with wavefields of events exhibiting clear amplitude variations along the fiber, strongly correlating with sonic logging. Our findings suggest that downhole DAS has the potential to characterize the subsurface with high resolution.

We analysed Distributed Acoustic Sensing (DAS) data from a fibre optic sensing system deployed on an existing submarine cable located offshore Oregon to characterize fin whale calls. A sequence of over 300 calls in a 2-hour period was identified using the conventional earthquake detection technique of template matching. With these initial detections we then used a robust correlation, and stacking process to estimate the call signatures and timings. Calls were found to be of two distinct types that are typical for fin whales and referred to as doublets. The calls typically alternate between the two types with an inter-call interval of approximately 15 seconds. These sequences pause approximately every 12 minutes for a couple of minutes before recommencing. These breaks are interpreted to be the whale resurfacing to breath. We track the whale's location over two hours using conventional location methods from time picks derived from a correlation process. This shows that the whale moved eastwards, towards the Pacific coastline, before turning to the south. Coincidentally, during this time frame a large container vessel also traverses the submarine fibre optic cable. The distance between the vessel and the whale ranges between 16 km and 2km at the closest point of approach. The whale initially appears to turn north as the vessel approaches to within 10km of the vessel and then follows an erratic localized track before proceeding in a southward direction away from the vessel. This behaviour may be indicative of an avoidance behaviour. This observation suggests fibre optic acoustic measurement systems could routinely monitor underwater radiated noise from marine traffic and marine mammals using existing seafloor cables to establish typical behavioural patterns.

Distributed Acoustic Sensing (DAS) enables sampling seismic wavefields along optical fibers at a spatial resolution of less than one meter, over distances beyond several tens of kilometers. This makes DAS a powerful tool to record seismic events densely along 2D directions, whether horizontally along the Earth’s surface or vertically in boreholes. Compared to traditional seismic sensors measuring ground motion units, DAS provides uniaxial strain measurements along the fiber with often imperfectly known transfer functions between the measurements and true ground motion. This can generate uncertainties in the derivation of seismic source parameters, such as the magnitude, that require an absolute measurement of the ground motion and a known instrument response. In this study, we examine the DAS transfer function, mapping DAS data to reference velocity records obtained from multiple co-located accelerometers. Our investigation makes use of downhole recordings from the FORGE (Frontier Observatory for Research in Geothermal Energy) field site situated in Utah, USA. Overall, we find that the DAS response estimated at different depth positions follows a consistent trend and deviates significantly from a flat response only below 80 Hz. An average site-specific DAS system response is then used to convert microseismic event recordings into calibrated velocity records with improved amplitude accuracies. Subsequently moment magnitudes Mw are derived from the P-wave records with results matching the independent accelerometer-based estimations with high fidelity for events with Mw > -1.0.

Summary Distributed acoustic sensing (DAS) provides axial strain measurements along optical fibers whereas ground motion quantities can be required for interpretation or inversion processes. Using co-located DAS and reference accelerometer records from the downhole monitoring setup of the FORGE (Frontier Observatory for Research on Geothermal Energy) site, we apply a DAS transfer function workflow to obtain calibrated velocity records of microseismic events from initial DAS (strain rate) measurements. The calibration workflow is based on the conversion of physical units using frequency-wavenumber domain transformations and on the correction of the remaining misfit to the reference. This misfit, which corresponds to a factor of approximately two for the P-wave arrivals, is quantified in terms of a frequency response in the range 50–200 Hz. We analyze such DAS system responses as a function of the co-located depth positions and we show that, after corrections, we can determine the moment magnitude (Mw) reliably from DAS for microseismic events with Mw > −0.9.

CO2 capture and underground storage, combined with geothermal resource exploitation, are vital for future sustainable and renewable energy. The SUCCEED project explores the feasibility of re-injecting CO2 into geothermal fields to enhance production and store CO2 for climate change mitigation. This integration requires novel time-lapse monitoring approaches. At the Hellisheiði geothermal power plant in Iceland, seismic surveys utilizing conventional geophones and a permanent fiber-optic helically wound cable (HWC) for Distributed Acoustic Sensing (DAS) were designed to provide subsurface information and CO2 monitoring. This work details the feasibility study and active seismic acquisition of the baseline survey, focusing on optical fiber sensitivity, seismic modeling, acquisition parameters, source configurations, and quality control. Post-acquisition signal analysis using a novel electromagnetic vibrating source is discussed. The integrated analysis of datasets from co-located sensors improved quality-control performance and geophysical interpretation. The study demonstrates the advantages of using densely sampled DAS data in space by multichannel processing. This experimental work highlights the feasibility of using HWC DAS cables in active surface seismic surveys with an environmentally friendly electromagnetic source, providing also a unique case of joint signal analysis from different types of sensors in high-temperature geothermal areas for energy and CO2 storage monitoring in a time-lapse perspective.

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Summary Geothermal energy projects vary significantly in the temperature and target depths of the thermal energy source. The industry seeks for cost-effective, continuous monitoring solutions to maximise operational efficiency and safety. Distributed fibre optic sensing (DFOS) solutions provide flexible, multi-parameter measurements for the exploration and exploitation of the full range of geothermal resources, from shallow borehole, ground source heat to hydrothermal geothermal projects and Enhanced Geothermal Systems (EGS). Permanently installed integrated fibre optic sensing-based solution offers reliable, long-term geothermal reservoir monitoring and helps operators comply with legal requirements. The sensing element, a single fibre optic cable, either installed in shallow heat exchanger wells or cemented behind casing to the reservoir depth, can provide simultaneous and continuous measurements including temperature, seismic, microseismic, flow distribution, and strain. Here we present an overview of case studies of DFOS monitoring for a range of geothermal applications.

Geothermal power production may result in significant CO2 emissions as part of the produced steam. CO2 capture, utilisation, subsurface storage (CCUS) and developments to exploit geothermal resources are focal points for future clean and renewable energy strategies. The Synergetic Utilisation of CO2 Storage Coupled with Geothermal Energy Deployment (SUCCEED) project aims to demonstrate the feasibility of using produced CO2 for re-injection in the geothermal field to improve geothermal performance, while also storing the CO2 as an action for climate change mitigation. Our study has the aim to develop innovative reservoir-monitoring technologies via active-source seismic data acquisition using a novel electric seismic vibrator source and permanently installed helically wound cable (HWC) fibre-optic distributed acoustic sensing (DAS) system. Implemented together with auxiliary multi-component (3C and 2C) geophone receiver arrays, this approach gave us the opportunity to compare and cross-validate the results using wavefields from different acquisition systems. We present the results of the baseline survey of a time-lapse monitoring project at the Hellisheiði geothermal field in Iceland. We perform tomographic inversion and multichannel seismic processing to investigate both the shallower and the deeper basaltic rocks targets. The wavefield analysis is supported by seismic modelling. The HWC DAS and the geophone-stacked sections show good consistency, highlighting the same reflection zones. The comparison of the new DAS technology with the well-known standard geophone acquisition proves the effectiveness and reliability of using broadside sensitivity HWC DAS in surface monitoring applications.

Summary This presentation will outline a monitoring platform for Distributed Fibre Optic Sensing (DFOS) and current applications in CO2 sequestration monitoring. Case studies from active CCS projects and analogous monitoring operations from other industries will be discussed.

Summary We present the approach and initial QC results of the dual-signal processing of VSP data acquired using semipermanent DAS technology during a baseline survey of a CO2 injection- monitoring project in the Kizildere (Turkey) geothermal-production reservoir. The data were recorded in the framework of the SUCCEED project in two wells using a high-sensitivity engineered fibre with the cable suspended in the vertical cased wells. The source was a new electric seismic vibrator operated at the surface with a 3D configuration, supported with measurements on two bi-axial geophone lines. Good-quality VSP results were obtained during the initial QC performed by in-field and remote control and from the prompt data processing after the survey acquisition. The VSP-data processing takes advantage of the dual-field separation method effectively applied with the DAS well data densely sampled every 1 m. This approach enabled us to quickly separate up- going and down-going VSP wavefields. This technique does not require first-arrival picking, which is advantageous for processing extensive 3D-VSP datasets. The results from sample VSP revealed the reflection information contained in the data, relevant for target characterization. This analysis demonstrates the potential of the dataset for carbonate- reservoir monitoring purposes, to be compared in the future with time-lapse measurements.

This article presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e., pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully-denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a dataset from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g., Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 seconds of data recorded at a sampling frequency of 1000 Hz over 985 channels (approx. 1 km of fiber) in $<$1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring.

Carbon Capture and Storage (CCS) technology offers an opportunity to reduce the concentration of CO2 in the atmosphere. Typically, CO2 is injected underground into depleted oil and gas fields, coalbeds or deep saline geological formations, where it is securely stored. For a formation to be suitable for CO2 storage three main requirements need to be fulfilled; capacity, injectivity and containment.

&lt;p&gt;Distributed Dynamic Strain Sensing (DDSS), also known as Distributed Acoustic Sensing (DAS), is becoming a popular tool for volcano monitoring. The sensing method relies on sending coherent light pulses into an optical fibre and measuring the phase-shift of Rayleigh back-scattered light due to strain on the fibre. This provides distributed strain rate measurements at high temporal and spatial sampling rates. Standard telecom fibres have been conventionally used for this purpose, however engineered fibres are being developed to enhance the back-scattered light, providing up to 100 times improved sensitivity in contrast to the conventional standard fibre. Despite the technical advantages of engineered fibres, standard fibres already have extensive coverage around the Earth surface, and so there is an interest in using the existing telecommunication infrastructure. In this study we compare stack DDSS data from a fibre loops made of several fibres within the same optical fibre cable, with DDSS data measured on an engineered fibre. We analyse how stacking can improve the signal quality of the recorded DDSS data. In an area located 2.5 km NE from the craters of Mt. Etna, we spliced 9 standard fibres together from a 1.5 km long cable to create a single optical path and interrogated using an iDAS unit. At the same time, we interrogated with a Carina unit a 0.5 km engineered fibre installed parallel to the standard multi-fibre cable. Both fibres were interrogated in a common period of 5 days. We use a spatial cross-correlation function to find the channel equivalences between each fibre and then stack them to evaluate the changes in the DDSS data and compare with the engineered fibre data. Our results show that, despite engineered fibres have lower noise, a stack of 5 fibres can achieve a maximum noise reduction of 20% outside of the optical noise band, in comparison to the engineered fibre. We achieved this noise reduction for our specific configuration, and so we show how the stack improvement is dependent on the type of configuration in terms of fibres stacked and length of the fibres. Our findings motivate the exploitation of multi-fibre cables in existing infrastructures, so-called dark fibres, for monitoring volcano and applications to other environments.&lt;/p&gt;

Earthen dams and embankments are prone to internal erosion, their most significant source of failure. Standard monitoring techniques often measure erosion effects when they appear at the surface, reducing the potential response time to address the problem before failure. Through their integrative sensitivity along their propagation, seismic signals are well suited to assess mechanical changes in the bulk of a dam. Moreover, seismic velocities are strongly sensitive to porosity, pore pressure, and water saturation, physical properties that vary the most for internal erosion. Here, we used fiber optics and a Distributed Acoustic Sensing (DAS) array installed on an experimental dam with built-in defects to record the ambient seismic wavefield for one month while the dam reservoir is gradually filled up. The position and nature of the dam defects are unknown to us, to allow an actual blind-detection experiment. We computed cross-correlations between equidistant channels along the dam every 15 minutes and monitored the relative seismic velocity changes at each location for the whole month. The results show a strong correlation of the velocity changes with the water level in the reservoir at all locations along the dam. We also observe systematic deviations from the average velocity change trend. We interpret these anomalies as the effects of the built-in defects placed at different positions in the bulk of the dam. The careful analysis of the residual velocity changes allows us to hypothesize on the position and nature of the defects.

Firn densification profiles are an important parameter for ice‐sheet mass balance and palaeoclimate studies. One conventional method of investigating firn profiles is using seismic refraction surveys, but these are difficult to upscale to large‐area measurements. Distributed acoustic sensing (DAS) presents an opportunity for large‐scale seismic measurements of firn with dense spatial sampling and easy deployment, especially when seismic noise is used. We study the feasibility of seismic noise interferometry (SI) on DAS data for characterizing the firn layer at the Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to come from anthropogenic noise and shear‐margin crevasses. The DAS cross‐correlation interferometry yields noisy Rayleigh wave signals. To overcome this, we present two strategies for cross‐correlations: (a) hybrid instruments—correlating a geophone with DAS, and (b) stacking of selected cross‐correlation panels picked in the tau‐p domain. These approaches are validated with results derived from an active survey. Using the retrieved Rayleigh wave dispersion curve, we inverted for a high‐resolution 1D S‐wave velocity profile down to a depth of 100 m. The profile shows a “kink” (velocity gradient inflection) at ∼12 m depth, resulting from a change of compaction mechanism. A triangular DAS array is used to investigate directional variation in velocity, which shows no evident variations thus suggesting a lack of azimuthal anisotropy in the firn. Our results demonstrate the potential of using DAS and SI to image the near‐surface and present a new approach to derive S‐velocity profiles from surface wave inversion in firn studies.

This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator. 2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data. 3. Refraction P wave travel time from a geophone array refraction survey.

Distributed Acoustic Sensing (DAS) can play a critical role in monitoring industrial subsurface activities such as Carbon Capture and Storage (CCS), due to its relative low cost and high spatial resolution. Despite significant developments in the technology, there remain some key challenges which need to be addressed before routine deployed for seismic monitoring purposes. In this study we assess the suitability of DAS to monitor the shallow subsurface using active and passive seismic datasets. These were acquired at the Containment and Monitoring Institute’s (CaMI) Field Research Station (FRS), Canada between 6th and 10th September 2021. Field surveys were recorded on a linear and helical fibre-optic array, seismic nodes, and broadband seismometers, using both active (mini-Vibroseis and impulse sources) and passive seismic measurements. During this period, 452kg of CO2 was also injected into the Basal Belly River Formation at a depth of about 285m. We find that the linear fibre is more sensitive than the helical fibre to the active sources, while overall DAS produces a poorer response to direct waves but a stronger response to surface waves when compared to the node array. A ‘terracing effect’ at the shot location, combined with the poor response to first arrivals, limits the effectiveness of seismic refraction methods. Instead, we image shallow velocity structure using surface-wave methods and derive frequency velocity plots showing both fundamental and higher modes. Using the passive dataset, we apply Seismic Noise Interferometry methods and produce stable cross-correlations from noise originating from an irrigation dam and CO2 injection. We observe slight travel times variations in direct waves originating from the dam, which were retrieved by cross-correlating broadband and seismic nodes. From the DAS array we produce velocity-frequency plots during the CO2 injection period, which are comparable to those generated by the active surveys. Inverting these produces a velocity profile extending to a depth of 40m with velocities increasing from 200m/s to 400m/s and demonstrates the potential of using DAS to image and detect near-surface leaks using DAS.