Distributed Acoustic Sensing Research Articles

Classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals

Abstract Distributed acoustic sensing (DAS) is an optoelectronic technology that utilises fibre optic cables to detect disturbances caused by seismic waves. Using DAS, seismologists can monitor geophysical phenomena at high spatial and temporal resolutions over long distances in inhospitable environments. Field experiments using DAS, are typically associated with large volumes of observations, requiring algorithms for efficient processing and monitoring capabilities. In this study, we present a supervised classifier trained to recognise seismic activity from other sources of hydroacoustic energy. Our classifier is based on a 2D convolutional neural network architecture. The 55km-long ocean-bottom fibre optic cable, located off Cape Muroto in south-west of Japan, was interrogated using DAS. Data were collected during two different monitoring time-periods. Optimisation of the model’s hyperparameters using Gaussian Processes Regression was necessary to prevent issues associated with small sizes of training data. Using a test set of 100 annotated images, we have shown that the top performing model is around $92\%$ accurate in classifying geophysical data from other sources of hydroacoustic energy and ambient noise.

Measurement of ocean currents by seafloor distributed optical-fiber acoustic sensing.

Ocean current measurements play a crucial role in aiding our understanding of ocean dynamics and circulation systems. Traditional methods, such as drifters and ocean buoys, are sparsely distributed and of limited effectiveness due to the nature of the marine environment and high operating expenses. Distributed acoustic sensing (DAS) is an emerging technology using submarine optical-fiber (OF) cables as dense seismo-acoustic arrays, offering a new perspective for ocean observations. Here, in situ observations of ocean surface gravity waves (OSGWs) and ocean currents by DAS were made along a pre-existing 33.6 km seafloor OF cable. The average current velocity and water depth along the cable were determined from observed OSGW-induced seafloor noise (0.05-0.2 Hz) using ambient-noise interferometry and frequency-domain beamforming. Variations in current velocity were derived at high spatiotemporal resolution using the frequency-domain waveform-stretching method. The inverted current velocity was verified by nearby ocean buoy observations and forecasting results. The observations demonstrate the effectiveness of DAS-instrumented OF cables in monitoring ocean currents.

Monitoring Slug Flow Using Distributed Acoustic Sensing Technology with Different Sensing Cable Configurations

Summary Monitoring slug flow in real time over long distances is essential for facility design, operation, and flow assurance management. In this study, we investigated the use of distributed acoustic sensing (DAS) technology for slug flow monitoring, exploring various cable designs and deployment methods. The experimentation encompassed the use of five distinctive fiber-optic cable deployment methods: three internal cables of varying designs (thin, flat, and thick), supplemented by two external cables—one placed straight atop the pipeline while the other was helically wrapped around it. All cables were connected consecutively and successfully captured the dynamic variation of the slugging phenomenon along the pipe. We introduce different data processing algorithms for slug flow characterization, including standard deviation (SD) downsampling, slug frequency determination, and semblance for velocity extraction. The experimental results indicate a higher slug frequency but smaller slug sizes near the inlet of upward inclined pipe, where most slug structures originate. The structure velocity shows a positive correlation with DAS amplitude or the maximum strain rate, which could be related to the slug size. The flat cable exhibited a heightened amplitude response to slugs, while the internal thin and the external straight cables provided the most distinct delineation of slug patterns. The internal thick cable provided the least sensitivity among all due to its design. Although the linear deployment of fiber-optic cables is more practical compared to helically wrapped ones, their resolution limits detailed analysis of more intricate slug characteristics for short pipelines, such as precise velocity measurements of slugs. Data from the helically wrapped cable can address these limitations, providing more comprehensive insights.

Performance of synthetic DAS as a function of array geometry

Distributed Acoustic Sensing (DAS) can record acoustic wavefields at high sampling rates and with dense spatial resolution difficult to achieve with seismometers. Using optical scattering induced by cable deformation, DAS can record strain fields with ones of meters spatial resolution. However, many experiments utilizing DAS have relied on unused, dark telecommunication fibers. As a result, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. This limits our understanding of guiding principles in array design to deploy DAS effectively and efficiently in the field. A better quantitative understanding of DAS array behavior can help improve the quality of the data recorded by guiding the DAS array design. Here we use array response functions as well as beamforming and back-projection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A regular heptagon DAS array demonstrated improved capabilities for recording regional sources over segmented linear arrays, with potential improvements in recording and locating local sources. These results reveal DAS array performance as a function of geometry and can guide future DAS deployments.

Quasi-distributed acoustic sensing based on optical path difference demodulation for high-frequency and large-amplitude using an optical fiber interferometer array.

The phase-sensitive optical time domain reflectometry scheme has attracted great research interest in distributed acoustic sensing (DAS) but suffers from a trade-off between the dynamic range and signal frequency. In this paper, an optical path difference (OPD) demodulation method is applied to an interferometer array composed of an ultra-weak fiber Bragg grating (UWFBG) array and an imbalanced Michelson interferometer to solve this problem. As far as we know, this is the first time that OPD demodulation has been applied to DAS. The UWFBG array is interrogated by a frequency-modulated pulse, and the acoustic signal sensed by the fiber between any two adjacent UWFBGs can be retrieved by demodulating the variation of residual OPD of the interferometer formed by them. The proposed method is analyzed theoretically and validated experimentally; compared with the phase demodulation method, a 100-times boost in bandwidth is achieved for a signal with an amplitude of 0.4 µε. Results also show that the proposed method offers increasing signal-to-noise ratios as the frequency increases.

Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation

Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.

Inclined bending seismic reflection layer in the crust illuminated by distributed fibre-optic-sensing measurements in western Japan

Distributed acoustic sensing (DAS), which enables a single fibre-optic cable to function as multiple sensors, is a technique to measure the strain rate distributed along the cable. This technique is applied to record ground motions in western Japan via a 50 km-long fibre-optic cable beneath a national road. The measured values are strain changes along the cable every 5 m, corresponding to 9788 sensor deployments. This high-density measurement along the long cable successfully recorded the 2021 M2.8 and M3.2 earthquakes that occurred in the crust within the distance of the cable in southern Kyoto. The direct S waves were followed by seismic waves approximately 8–14 s later, which were reflected by lower crustal structures. These waveforms were previously reported by observing many earthquakes via multiple seismometers, but the DAS observations clearly illuminate reflected wavefields from single earthquake observations for the first time. The numerical simulation of the strain-rate wavefields of these earthquakes reveals the existence of a north-dipping thin layer with a slow seismic velocity in the lower crust, which becomes steeper in the shallower part. This layer might represent the path of slab-derived fluid to the shallow fault zone.

A real scale application of a novel set of spatial and similarity features for detection and classification of natural seismic sources from Distributed Acoustic Sensing data

Summary Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude >0.4 and quarry blasts of magnitude >1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.

An adaptive parameter-free seismic data denoising approach by combining general cross-validation thresholding and pixel connectivity in synchrosqueezed domain

High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising.Graphical

Suppression of the interference fading in heterodyne Φ-OTDR with fast signal synthesis algorithm

Abstract With the increasing diversity of application scenarios, there is a growing demand for distributed acoustic sensing (DAS) systems to monitor the environment without false alarms. In DAS system based on phase-sensitive optical time-domain reflectometry (Φ-OTDR), interference fading significantly lead to a large number of false alarms in the system, which affecting the system’s monitoring capability. Before the phase demodulation process, Φ-OTDR system generally requires additional time for interference fading suppression. Therefore, the rapid acquisition of a fading-free signal before phase demodulation benefits the use of Φ-OTDR in real-time demanding scenarios. In this paper, based on the time-domain stitching and complex-field moving average, a fast signal synthesis (FSS) algorithm is proposed, enabling the quick synthesis of signals while maintaining fading suppression performance. Experimental results show that the signal intensity obtained by FSS algorithm fluctuates within 27 dB, while the intensity of the conventional denoising algorithm fluctuates more than 50 dB. Meanwhile, this method is more rapid than the rotated-vector-sum (RVS) algorithm, and the time consumption of signal synthesis is only 26.58% of that of the RVS algorithm. FSS algorithm provides a new and faster way to synthesize signals for interference fading suppression. Benefiting from this, FSS algorithm further improves the real-time performance of the Φ-OTDR system.

Geophysical Monitoring Technologies for the Entire Life Cycle of CO2 Geological Sequestration

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir.

Research on gas pipeline leakage identification based on SE-2DCNN with ultra-weak fiber Bragg grating distributed acoustic sensing system

Abstract A new method for identifying gas pipeline leaks using an ultra-weak fiber Bragg grating (UWFBG) distributed acoustic sensing (DAS) system is proposed. To enhance the accuracy of gas leakage detection, an improved convolutional neural network (CNN) incorporating a two-dimensional structure based on the squeeze-and-excitation (SE) attention mechanism was introduced. The principle of acoustic leakage sensing using this technology is explained in detail, and an experimental setup simulating gas pipeline leaks is constructed. During this process, 9,340 DAS data points across varying leakage volumes and pipeline pressures were collected to create ten distinct datasets. The Mel-frequency cepstral coefficients (MFCC) are employed as the feature input for the optimized SE-2DCNN model, which performs identification tasks. The results show that this optimized model achieves an average leakage identification accuracy of 95.33%, demonstrating superior performance over other methods. This approach offers a robust reference for accurately detecting gas pipeline leakages.

Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network

Distributed acoustic sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signals compared with the background noise, especially in challenging installation environments. In this study, we develop a new approach to denoise DAS data that leverages an unsupervised deep learning (DL) model, eliminating the need for labeled training data. The input DAS data undergo band-pass filtering to eliminate high-frequency content. Subsequently, a continuous wavelet transform (CWT) is performed, and the finest scale is used to guide the DL model in reconstructing the DAS signal. First, we extract 2D patches from the band-pass filtered data and the CWT scale of the data. Then, these patches are converted using an unrolling mechanism into 1D vectors to form the input of the DL model. A self-attention layer is included in each layer to extract the spatial relation between the band-pass filtered data and the CWT scale. Through an iterative process, the DL model tunes its parameters to suppress DAS noise, with the band-pass filtered data serving as the target for the network. The denoising performance of our framework is validated using field examples from the San Andreas Fault Observatory at Depth and Frontier Observatory for Research in Geothermal Energy data sets, where the data are recorded by a fiber-optic cable. Comparative analyses against three benchmark methods reveal the robust denoising performance of our framework.

Modelling uncertainty in P-wave arrival-times retrieved from DAS data: case-studies from 15 fibre optic cables

SUMMARY Distributed acoustic sensing (DAS) technology enables the detection of waves generated by seismic events, generally as uniaxial strain/strain rate time-series observed for dense, subsequent, portions of a Fibre Optic Cable (FOC). Despite the advantages in measurement density, data quality is often affected by uniaxial signal polarization, site effects and cable coupling, beyond the physical energy decay with distance. To better understand the relative importance of these factors for data inversion, we attempt a first modelling of noise patterns affecting DAS arrival times for a set of seismic events. The focus is on assessing the impact of noise statistics, together with the geometry of the problem, on epicentral location uncertainties. For this goal, we consider 15 ‘real-world’ cases of DAS arrays with different geometry, each associated with a seismic event of known location. We compute synthetic P-wave arrival times and contaminate them with four statistical distributions of the noise. We also estimate P-wave arrival times on real waveforms using a standard seismological picker. Eventually, these five data sets are inverted using a Markov chain Monte Carlo method, which offers the evaluation of the relative event location differences in terms of posterior probability density (PPD). Results highlight how cable geometry influences the shape, extent and directionality of the PPDs. However, synthetic tests demonstrate how noise assumptions on arrival times often have important effects on location uncertainties. Moreover, for half of the analysed case studies, the observed and synthetic locations are more similar when considering noise sources that are independent of the geometrical characteristics of the arrays. Thus, the results indicate that axial polarization, site conditions and cable coupling, beyond other intrinsic features (e.g. optical noise), are likely responsible for the complex distribution of DAS arrival times. Overall, the noise sensitivity of DAS suggests caution when applying geometry-only-based approaches for the a priori evaluation of novel monitoring systems.

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.

Observation of the modal propagation of T waves using distributed acoustic sensing

T waves are low-frequency acoustic-waves (< 100 Hz) generated by earthquakes and propagating in the watercolumn. It is largely accepted that T waves propagate as modes. However, the direct observations of this modal propagation in experimental data remain surprisingly rare. Observation of T waves using distributed acoustic sensing (DAS) makes possible space-sampling with one sensor every few meters over tens of kilometers. Using a simple 2D Fourier transform, the time-space pressure-field can be turned into a frequency-wavenumber field where the propagating wave dispersion curves are revealed. Using freely accessible data from a DAS experiment on the coast of Oregon (USA), we show that the dispersion curves of T waves are consistent with the presence of four propagating modes. The number of modes decreases as expected as T waves propagate shoreward with decreasing depths. The T-wave mode dispersion curves can be fitted by using environmental parameters obtained through ambient noise Scholte wave inversion (also visible in the data).

Far-field radiation patterns of distributed acoustic sensing in anisotropic media with an explosive source and vertically straight fiber

Distributed acoustic sensing (DAS) is increasingly used in seismic exploration owing to its wide frequency range, dense sampling and real-time monitoring. DAS radiation patterns help to understand angle response of DAS records and improve the quality of inversion and imaging. In this paper, we solve the 3D vertical transverse isotropic (VTI) Christoffel equation and obtain the analytical, first-order, and zero-order Taylor expansion solutions that represent P-, SV-, and SH-wave phase velocities and polarization vectors. These analytical and approximated solutions are used to build the P/S plane-wave expression identical to the far-field term of seismic wave, from which the strain rate expressions are derived and DAS radiation patterns are thus extracted for anisotropic P/S waves. We observe that the gauge length and phase angle terms control the radiating intensity of DAS records. Additionally, the Bond transformation is adopted to derive the DAS radiation patterns in title transverse isotropic (TTI) media, which exhibits higher complexity than that of VTI media. Several synthetic examples demonstrate the feasibility and effectiveness of our theory.

High-resolution observations of shallow-water acoustic propagation with distributed acoustic sensing.

Distributed acoustic sensing (DAS), converting fiber-optic cables into dense acoustic sensors, is a promising technology that offers a cost-effective and scalable solution for long-term, high-resolution studies in ocean acoustics. In this paper, the telecommunication cable of Martha's Vineyard Coastal Observatory (MVCO) is used to explore the feasibility of cable localization and shallow-water sound propagation with a mobile acoustic source. The MVCO DAS array records coherent, high-quality acoustic signals in the frequency band of 105-160 Hz, and a two-step inversion method is used to improve the location accuracy of DAS channels, reducing the location uncertainty to ∼2 m. The DAS array with refined channel positions enables the high-resolution observation of acoustic modal interference. Numerical simulations that reproduce the observed interference pattern suggest a compressional speed of 1750 m/s in the sediment, which is consistent with previous in situ geoacoustic measurements. These findings demonstrate the long-term potential of DAS for high-resolution ocean acoustic studies.

Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning

AbstractEffective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over . It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

Enhancing the performance of DAS using a dual-wavelength UWFBG array with alternating arrangement

We propose a novel distributed acoustic sensor (DAS) based on a dual-wavelength ultra-weak fiber Bragg grating (UWFBG) array. The UWFBGs in the array have two wavelengths that are arranged alternately. Two double-pulses with different wavelengths and staggered launching times are used for sensing in the UWFBG array. This approach successfully overcomes the trade-off between spatial resolution and sensing distance in the traditional UWFBG-based DAS and effectively doubles the maximum achievable sampling rate constrained by the sensing distance. The crosstalk of different wavelengths is avoided by time division multiplexing. Comprehensive analyses and simulations are conducted to validate the performance enhancements, compared to the traditional method. In our proof-of-concept experiments, employing a 2 km alternating arranged dual-wavelength UWFBG array, we achieve a spatial resolution enhancement from 10 m to 5 m, expand the dynamic strain measurement range from 34.2 rad to 68.2 rad, and increase the frequency response from 9 kHz to 19 kHz.