Distributed Acoustic Sensing Array Research Articles

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.

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.

Ambient noise imaging for municipal solid waste landfill structure detection based on the common-midpoint two-station analysis with distributed acoustic sensing

SUMMARY Distributed acoustic sensing (DAS) enables high-density sampling of seismic wavefields at low cost compared to conventional geophones. This capability facilitates structural detection of a municipal solid waste (MSW) landfill, which is important for protecting the surrounding ecosystem. However, processing the vast amount of data from DAS array for ambient noise imaging can be computationally intensive. To address this, we employed the common-midpoint two-station (CMP-TS) analysis to enhance the efficiency of ambient noise imaging in the MSW landfill. CMP-TS analysis involves selecting pairs of traces at equal distances on both sides with the subarray midpoint as symmetry, which reduces the number of DAS array recordings for cross-correlation calculations. After positioning the DAS arrays linearly on top of the MSW landfill to automatically collect ambient noise, we used the CMP-TS analysis in the cross-correlation calculations to speed up the measurement of dispersion. The S-wave velocity structure of the study region was obtained quickly by inverting the extracted dispersion curves using the gradient optimization method. Ambient noise imaging based on CMP-TS analysis with DAS was applied to a test of an area-type MSW landfill. The resulting S-wave velocity section revealed a discontinuous low-velocity zone, validated by the high-density resistivity method. This low-velocity zone was interpreted as containing leachate from waste decomposition, and its discontinuity may be caused by excessive differences in the waste residues settling rates under compaction. Employing CMP-TS analysis in ambient noise data collected by DAS offers more cost-effective monitoring and a reliable basis for environmental pollution prevention and control.

Thousand‐Kilometer DAS Array Reveals an Uncatalogued Magnitude‐5 Dynamically Triggered Event After the 2023 Turkey Earthquake

AbstractLarge earthquakes can trigger smaller seismic events, even at significant distances. The process of earthquake triggering offers valuable insights into the evolution of local stress states, deepening our understanding of the mechanisms of earthquake nucleation. However, our ability to detect these triggered events is limited by the quality and spatial density of local seismometers, posing significant challenges if the triggered event is hidden in the signal of a nearby larger earthquake. Distributed acoustic sensing (DAS) has the potential to enhance the monitoring capability of triggered earthquakes through its high spatial sampling and large spatial coverage. Here, we report on an uncatalogued magnitude (M) 5.1 event in northeast Turkey, which was likely dynamically and instantaneously triggered by the 2023 M7.8 earthquake in southeast Turkey, located 400 km away. This event was initially discovered on ∼1,100 km of active DAS recordings that are part of an 1,850‐km linear array. Subsequent validation using local seismometers confirmed the event's precise time, location, and magnitude. Interestingly, this dynamically triggered event exhibited precursory signals preceding its P arrivals on the nearby seismometers. It can be interpreted as the signal from other nearby, uncatalogued, smaller triggered events. Our results highlight the potential of high‐spatial‐density DAS in enhancing the local‐scale detection and the detailed analysis of earthquake triggering.

Ice plate deformation and cracking revealed by an in situ-distributed acoustic sensing array

Abstract. Studying seismic sources and wave propagation in ice plates can provide valuable insights into understanding various processes, such as ice structure dynamics, migration, fracture mechanics and mass balance. However, the harsh environment makes it difficult to conduct in situ dense seismic observations. Consequently, our understanding of the dynamic changes within the ice sheet remains insufficient. We conducted a seismic experiment using a distributed acoustic sensing (DAS) array on a frozen lake, exciting water vibrations through underwater airgun shots. By employing an artificial intelligence method, we were able to detect seismic events that include both high-frequency icequakes and low-frequency events. The icequakes clustered along ice fractures and their activity correlated with local temperature variations. The waveforms of low-frequency events exhibit characteristics of flexural-gravity waves, which offers insights into the properties of the ice plate. Our study demonstrates the effectiveness of an DAS array as an in situ dense seismic network for investigating the internal failure process and dynamic deformation of ice plates such as the ice shelf, which may contribute to an enhanced comprehension and prediction of ice shelf disintegration.

Distributed Acoustic Sensing for Crowd Motion and Firecracker Explosions in the Fireworks Show

Abstract Urban seismology has recently emerged as a vibrant scientific field, driven by the growing interest in seismic signals generated by major public events, sports gatherings, and transportation services. However, deploying dense traditional seismometers in economically active, densely populated urban areas with heavy traffic poses significant challenges. In this study, we conducted a field experiment utilizing distributed acoustic sensing (DAS) technology during a fireworks display in Guangzhou on 5 February 2023. About 572 m of optical fiber was turned into 286 seismic sensors and deployed on LingShan Island to monitor various vibration signals generated during the fireworks show. Our analysis revealed substantial correlations between crowd motions during different phases of the event and ambient noise features recorded by DAS. Moreover, the cross-correlation functions of the ambient noise with its dispersion characteristics pointed to near-field pedestrian activity as the primary noise source. Real-time heat maps of human crowd motions were reconstructed from DAS recording, offering significant insights into the variations of activity intensity across different locations. Discerning fireworks events on the DAS array is more effective than on a scattered seismometer array, because it is easier to ensure that the same event is picked for all the sites in the DAS dense linear configuration. The DAS data inspection allowed us to pick up a total of 549 firecracker explosions in comparison to the seismometer data that only allowed us to detect 116 firecracker events. The heights of fireworks were located by the grid-search method and predominantly distributed at 75–300 m, closely aligning with actual fireworks explosion locations. Our findings underscore that the DAS technology can monitor crowd motion and detect vibration signals in the air, bridging the gap between fundamental earth science research and human social activities.

Impact of Lossy Compression Errors on Passive Seismic Data Analyses

Abstract New technologies such as low-cost nodes and distributed acoustic sensing (DAS) are making it easier to continuously collect broadband, high-density seismic monitoring data. To reduce the time to move data from the field to computing centers, reduce archival requirements, and speed up interactive data analysis and visualization, we are motivated to investigate the use of lossy compression on passive seismic array data. In particular, there is a need to not only just quantify the errors in the raw data but also the characteristics of the spectra of these errors and the extent to which these errors propagate into results such as detectability and arrival-time picks of microseismic events. We compare three types of lossy compression: sparse thresholded wavelet compression, zfp compression, and low-rank singular value decomposition compression. We apply these techniques to compare compression schemes on two publicly available datasets: an urban dark fiber DAS experiment and a surface DAS array above a geothermal field. We find that depending on the level of compression needed and the importance of preserving large versus small seismic events, different compression schemes are preferable.

3D near-surface P-wave velocity structure imaging with Distributed Acoustic Sensing and electric hammer source

Distributed Acoustic Sensing (DAS) is an emerging technique for ultra-dense seismic observation, which provides a new method for high-resolution sub-surface seismic imaging. Recently a large number of linear DAS arrays have been used for two-dimensional S-wave near-surface imaging in urban areas. In order to explore the feasibility of three-dimensional (3D) structure imaging using a DAS array, we carried out an active source experiment at the Beijing National Earth Observatory. We deployed a 1 ​km optical cable in a rectangular shape, and the optical cable was recast into 250 sensors with a channel spacing of 4 ​m. The DAS array clearly recorded the P, S and surface waves generated by a hammer source. The first-arrival P wave travel times were first picked with a Short-Term Average/Long-Term Average (STA/LTA) method and further manually checked. The P-wave signals recorded by the DAS are consistent with those recorded by the horizontal components of short-period seismometers. At shorter source-receiver distances, the picked P-wave arrivals from the DAS recording are consistent with vertical component recordings of seismometers, but they clearly lag behind the latter at greater distances. This is likely due to a combination of the signal-to-noise ratio and the polarization of the incoming wave. Then, we used the TomoDD software to invert the 3D P-wave velocity structure for the uppermost 50 ​m with a resolution of 10 ​m. The inverted P-wave velocity structures agree well with the S-wave velocity structure previously obtained through ambient noise tomography. Our study indicates the feasibility of 3D near-surface imaging with the active source and DAS array. However, the inverted absolute velocity values at large depths may be biased due to potential time shifts between the DAS recording and seismometer at large source-receiver distances.

DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data

SUMMARY This paper 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 data set 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 s of data recorded at a sampling frequency of 1000 Hz over 985 channels (approximately 1 km of fibre) 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.

Three-dimensional distributed acoustic sensing at the Sanford Underground Research Facility

Distributed acoustic sensing (DAS) is a valuable tool for monitoring seismic signals as it provides high spatial and temporal resolution strain sensing along the length of a fiber-optic cable. DAS records the seismic wavefields essentially synchronously at each sensing location (i.e., sensing channel with gauge lengths) because the interrogator senses the distributed strain at the speed of light in the fiber. Unlike traditional seismic sensors, DAS has an intrinsic directional sensitivity to the axial strain change along the fiber, leading to difficulties when using standard seismic analysis and interpretations that rely on 3D particle velocity sensing. In addition, cable deployments on the surface can be dominated by high-amplitude wind or urban noise, impeding the detection of low-amplitude distant seismic sources. Here we investigate the capabilities of a unique 3D array with spiral-like portions in the Sanford Underground Research Facility (SURF), the former Homestake Mine, between 1250 m (4100 ft) and 1488 m (4850 ft) in depth for detecting local, regional, and teleseismic sources of ground vibrations. Our pilot array finds that DAS records high frequency (above 5 Hz) vibration sources well, such as mine activities and local and regional blasting events. Furthermore, our deployment method (fiber resting on the surface with rocks placed every meter or so) may contribute to low-frequency noise that contaminates the interpretation of teleseismic waves, particularly lower frequency S-wave arrivals. Nevertheless, this 3D DAS array provides significant data for future analysis as well as the basis for improving and expanding the array in SURF.

Wavefield Modeling and Analysis of Lightning Quakes Measured by a Distributed Acoustic Sensing Array

ABSTRACT Lightning-induced seismic waves, termed “lightning quakes,” are frequent natural sources in many storm-prone regions. Lightning quakes have been clearly observed in numerous environments by both seismic and acoustic instruments, for example, by distributed acoustic sensing (DAS) array. Despite these numerous observations, the physical nature of lightning quake wavefields detected by ground-based arrays remains poorly understood. The possibility of electroseismic (ES) conversion due to lightning’s powerful electromagnetic fields was, until now, unstudied. This investigation uses 3D numerical simulations of acoustic-to-seismic and ES wavefields alongside a novel data-driven azimuthal strain-rate variation analysis technique to robustly reveal the complex nature of lightning quakes. We show that lightning quakes begin as airborne acoustic waves before coupling with the solid earth as air-coupled Rayleigh waves and Love waves that are generated by local sources near the receiver, such as topography or urban infrastructure. These conclusions suggest thunder observations from a DAS array can be used to infer the structure of the near surface around the receiver, but care needs to be taken in understanding the role of local sources. An estimate of the Rayleigh- and Love-wave phase velocities is produced using a novel data analysis method unique to DAS. Furthermore, we demonstrate that electroseismic coupling does not play a significant role in the lightning quake wavefields. Although these simulations do not fully capture the realistic frequency of the electroseismic coupled wavefield, theory suggests that the wavefield is high frequency and thus quickly attenuated in the saturated near-surface soils. Electroseismic coupled wavefields from lightning may be detectable very close to the bolt.

Earthquake location based on Distributed Acoustic Sensing (DAS) as a seismic array

We present a simple and fast method to estimate preliminary earthquake location coordinates using Distributed Acoustic Sensing (DAS). Strain rate data is recorded on a 25-km long fibre-optic cable located in the northern suburbs of Athens (Greece). We apply frequency - wavenumber analysis on two segments of the cable forming an L-shaped seismic array, which offers good azimuthal sensitivity, whereas, the spatial resolution (spacing) and signal coherency control the ability to reliably resolve the apparent slowness of an incoming wavefield, and hence, to estimate the hypocentral distance to the array. We attempt to locate a local earthquake (2021/9/23, ML=3.4, Thiva) NW to the DAS array (approximately 50 km NW of Athens), and a regional earthquake (2021/10/12, MW=6.3, off the East coast of Crete) using S-wave onsets filtered within two frequency bands (0.5 Hz - 2.5 Hz and 1.0 Hz - 5.0 Hz). The obtained backazimuths agree with the observed backazimuths based on the locations reported by the Institute of Geodynamics, National Observatory of Athens (NOA) for both earthquakes, with our location for the local earthquake being roughly 10 km South of the NOA location, whereas, the location that we obtained for the regional earthquake suggested larger errors in distance, projected in our slowness estimation, possibly due to the array spatial resolution and the complex structure of the Hellenic subduction zone.

Real-Data Testing of Distributed Acoustic Sensing for Offshore Earthquake Early Warning

Abstract We present a real-data test for offshore earthquake early warning (EEW) with distributed acoustic sensing (DAS) by transforming submarine fiber-optic cable into a dense seismic array. First, we constrain earthquake locations using the arrival-time information recorded by the DAS array. Second, with site effects along the cable calibrated using an independent earthquake, we estimate earthquake magnitudes directly from strain rate amplitudes by applying a scaling relation transferred from onshore DAS arrays. Our results indicate that using this single 50 km offshore DAS array can offer ∼3 s improvement in the alert time of EEW compared to onshore seismic stations. Furthermore, we simulate and demonstrate that multiple DAS arrays extending toward the trench placed along the coast can uniformly improve alert times along a subduction zone by more than 5 s.

Deep clustering analysis for data exploration and anomaly detection in distributed acoustic sensing (DAS) systems

Distributed acoustic systems (DAS) offer a unique opportunity to sense seismic and acoustic waves at high sampling rates and spatial resolutions using fiber optic cables. The prevalence of fiber optic cables crossing seabeds around the world make them particularly attractive sensors of opportunity for acoustical and geophysical applications. However, the high spatial and temporal resolutions of these systems pose significant challenges to efficient data processing and analysis. To overcome this, we propose a two-step exploratory analysis procedure in which the dimensionality of the data is first reduced into a latent space containing its salient information, and then perform clustering analysis of the data in the latent space. Spectrograms of detected events are encoded into the latent space using a convolutional autoencoder, and clustering is performed using Gaussian mixture model clustering. We demonstrate our technique on experimental data collected from a DAS array deployed at a geothermal energy research site. With this technique, we show a data-driven approach to exploratory data analysis, enabling identification of dominant types of detections in the data and identification and removal of anomalous detections due to instrument noise.

Spatiotemporal evaluation of Rayleigh surface wave estimated from roadside dark fiber DAS array and traffic noise

Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.

Monitoring seismicity triggered by geothermal site shutdown with a surface DAS array at Brady Hot Springs

SUMMARY Seismicity induced by fluid injection including wastewater injection, hydrofracking and enhanced geothermal system (EGS) site production draws public attention. Dense arrays have been deployed to improve monitoring capability. In 2016 March, the PoroTomo experiment deployed an 8.6-km-long fibre-optic cable for distributed acoustic sensing (DAS) in the geothermal field at Brady Hot Springs, Nevada, covering an area of 1.5 by 0.5 km. The goal was to assess an integrated technology for characterizing and monitoring temporal changes in the rock mechanical properties of an EGS reservoir in three dimensions. We applied a neural network designed for earthquake detection called ADE-Net2 to the DAS data set to detect seismic events in continuous records. We were able to detect a total of 90 earthquakes, which included 21 events that had not been reported by a previous template-matching study. Additionally, we were able to successfully detect almost all of the active source signals, with only seven events being missed. We used the STA/LTA (short-/long-term average) method to pick arrivals and a clustering method to remove outliers. We initially tried a standard event location algorithm, but the low signal-to-noise ratio resulted in significant picking uncertainty that is up to ∼0.5 s, leading to large location uncertainty. Therefore, we developed a new location method based on the similarity between the theoretical traveltime curve and picked moveout. A grid search scheme was adopted to find the optimal point at which the traveltime curve is most similar to the picked one. Most newly detected earthquakes locate southwest of the DAS array, where five earthquakes were reported by a local seismic network. The plant began shutting down at 19:15 UTC on the March 13, and most earthquakes occurred on the March 14, indicating a relationship between the seismicity and the pressure changes caused by the shutdown of the plant. The pressure changes at epicentres obtained from a simplified model range from 71 to 157 kPa, exceeding a typical earthquake trigger threshold of 10 kPa.

Near-surface characterization using distributed acoustic sensing in an urban area: Granada, Spain

SUMMARY The Granada Basin in southeast Spain is an area of moderate seismicity. Yet, it hosts some of the highest seismic hazards in the Iberian Peninsula due to the presence of shallow soft sediments amplifying local ground motion. In urban areas, seismic measurements often suffer from sparse instrumentation. An enticing alternative to conventional seismometers is the distributed acoustic sensing (DAS) technology that can convert fibre-optic telecommunication cables into dense arrays of seismic sensors. In this study, we perform a shallow structure analysis using the ambient seismic field interferometry method. We conduct a DAS array field test in the city of Granada on the 26 and 27 August 2020, using a telecommunication fibre. In addition to the existing limitations of using DAS with unknown fibre-ground coupling conditions, the complex geometry of the fibre and limited data recording duration further challenge the extraction of surface-wave information from the ambient seismic field in such an urban environment. Therefore, we develop a processing scheme that incorporates a frequency–wavenumber (f−k) filter to enhance the quality of the virtual shot gathers and related multimode dispersion images. We are able to use this data set to generate several shear-wave velocity (VS) profiles for different sections of the cable. The shallow VS structure shows a good agreement with different geological conditions of soil deposits. This study demonstrates that DAS could provide insights into soil characterization and seismic microzonation in urban areas. In addition, the results contribute to a better understanding of local site response to ground motion.

Distributed acoustic sensing for shallow structure imaging using mechanical noise: A case study in Guangzhou, China

Distributed Acoustic Sensing (DAS) is a novel seismic detection technology that transforms fiber-optic cables into densely sampled seismic stations, showing immense potential in both urban seismology and the oil and gas industry. We deployed a ~ 182-m-long single-core fiber-optic cable in a campus under construction to record human activity and demonstrated the application for seismic imaging. The spectrogram analysis revealed significant differences in ambient noise between the daytime and nighttime at the construction site. During the daytime, the noise energy exhibited dominant frequencies ranging from 2 to 11 Hz, approximately one order of magnitude higher than that at night, which had frequencies ranging from 2 to 5 Hz. Beamforming analysis demonstrated that noise sources exhibited distinct directional characteristics and propagation velocities during daytime and nighttime periods. We extracted the cross-correlation functions between DAS channels from the mechanical noise generated by a large excavator and enhanced signal-to-noise ratio by using the phase-weighted stacking method. The multichannel analysis of surface waves (MASW) method was then employed to obtain 2D near-surface shear-wave velocity. Based on five 60-m-deep boreholes drilled along the DAS array, we established the correlation between shear-wave velocity information and shallow structure. We obtained the unbiased cross-correlation function in the presence of continuous mechanical noise along the DAS array direction, demonstrating the importance of site noise source distribution analysis in ambient noise tomography. Our research is informative for research related to the construction of accurate, high-resolution near-surface shear wave velocity models.

Earthquake focal mechanisms with distributed acoustic sensing

Earthquake focal mechanisms provide critical in-situ insights about the subsurface faulting geometry and stress state. For frequent small earthquakes (magnitude< 3.5), their focal mechanisms are routinely determined using first-arrival polarities picked on the vertical component of seismometers. Nevertheless, their quality is usually limited by the azimuthal coverage of the local seismic network. The emerging distributed acoustic sensing (DAS) technology, which can convert pre-existing telecommunication cables into arrays of strain/strain-rate meters, can potentially fill the azimuthal gap and enhance constraints on the nodal plane orientation through its long sensing range and dense spatial sampling. However, determining first-arrival polarities on DAS is challenging due to its single-component sensing and low signal-to-noise ratio for direct body waves. Here, we present a data-driven method that measures P-wave polarities on a DAS array based on cross-correlations between earthquake pairs. We validate the inferred polarities using the regional network catalog on two DAS arrays, deployed in California and each comprising ~ 5000 channels. We demonstrate that a joint focal mechanism inversion combining conventional and DAS polarity picks improves the accuracy and reduces the uncertainty in the focal plane orientation. Our results highlight the significant potential of integrating DAS with conventional networks for investigating high-resolution earthquake source mechanisms.

Multiwell Fiber Optic Sensing Reveals Effects of CO2 Flow on Triggered Seismicity

Abstract Induced seismicity is one of the main risks for gigaton-scale geological storage of carbon dioxide (CO2). Thus, passive seismic monitoring is often recommended as a necessary component of the monitoring systems for CO2 storage projects, with a particular forcus on risk mitigation. We present the first field study, CO2CRC Otway Project Stage 3 (Victoria, Australia), where distributed acoustic sensing (DAS) enabled high-precision tracking of the induced seismicity triggered by a small CO2 injection and also informed the reservoir models. In 610 days of passive seismic monitoring of the Stage 3 injection, we detected 17 microseismic events (maximum moment magnitude Mw 0.1) using five deep boreholes equipped with enhanced-sensitivity optical fiber. The DAS array has sensitivity sufficient for detection and location of induced events with Mw∼−2 in a monitoring borehole located up to 1500 m away. Thanks to the dense spatial sampling by the DAS, we were able to estimate the focal mechanisms for events with Mw&amp;gt;−1.5; although the monitoring boreholes provided very limited angular coverage. The main cluster of the events has the same location and source mechanism as the one triggered by the previous CO2 injection at the Otway Project site, Stage 2C. Surprizingly, the Stage 2C and Stage 3 events closely followed the actual movement of the CO2 saturation plume front (not the pressure front), as observed using controlled-source reflection seismic images. The nature of the plume-fault interaction remains unclear, but some alteration of the fault gouge by CO2 might be responsible for the faults’ reactivation by the pressure perturbation. Importantly, the seismogenic fault could not be identified in the seismic images and was only revealed by DAS observations, which also demonstrated the signature of fluid–rock interaction, that may control the CO2 flow.