Distributed Acoustic Sensing for Environmental Monitoring, and Newtonian Noise Mitigation:Comparable Sensitivity to Seismometers

Fracensca Badaracco; Katharina Sophie Isleif; Paul Ophardt; Reinhardt Rading; Spiridon Beis; the WAVE Collaboration; Wanda Vossius

REVIEW 4 major objections 5 minor 1 cited by

Distributed Acoustic Sensing for Environmental Monitoring, and Newtonian Noise Mitigation:Comparable Sensitivity to Seismometers

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Distributed acoustic sensing, using ordinary optical fiber, cancels a co-located seismometer's noise to the same residual factor as a geophone array (0.11 at 20 Hz), making it a scalable witness-sensor option for gravitational wave…

desk verdict First DAS-based cancellation of a colocated seismometer's noise is a real experimental step, but the headline residual is in-sample and internally inconsistent. read the letter →

arxiv 2507.13523 v1 pith:U5BYWUAC submitted 2025-07-17 astro-ph.IM eess.SPgr-qc

Reinhardt Rading , Fracensca Badaracco , Spiridon Beis , Katharina Sophie Isleif , Paul Ophardt , Wanda Vossius , the WAVE Collaboration This is my paper · ORCID

classification astro-ph.IMeess.SPgr-qc

keywords distributedacousticsensingNewtoniannoisemitigationgravitationalwavedetectorsWienerfilterseismometercomparisongeophonearraycoherencelengthenvironmentalmonitoring

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Distributed acoustic sensing (DAS) turns a standard optical fiber into thousands of closely spaced strain sensors, and this paper argues that those sensors can match conventional seismometers and geophones for cancelling Newtonian noise—the gravity-gradient disturbance that limits the low-frequency sensitivity of gravitational wave detectors. Dense seismometer arrays are expensive and spatially sparse, while fiber is already installed at many observatory sites, so a comparable DAS performance would make witness-sensor networks far easier to scale. The evidence is a direct comparison on a research campus: DAS and colocated seismometer recordings correlate above 0.8 in the 3–20 Hz band, DAS-predicted geophone signals keep coherence above 0.7, and a multichannel Wiener filter fed by six DAS channels reduces a colocated seismometer's vertical-component noise to a residual factor of 0.11 at 20 Hz, equal to the geophone array. If the result holds, DAS becomes a practical complement to traditional seismic instruments for both environmental monitoring and low-frequency noise suppression in current and next-generation detectors.

What carries the argument

The mechanism is the equivalence between DAS strain-rate and ground velocity: $d\varepsilon/dt = [v(x+L_g/2)-v(x-L_g/2)]/L_g$, with the conversion $\varepsilon = du/dx = \pm (1/s)\,du/dt$, where $s$ is the local apparent slowness estimated by semblance analysis, a coherence-based scan over trial slowness values. Dividing the DAS strain-rate by $s$ yields ground acceleration comparable to a seismometer. On this sits the multichannel Wiener filter, whose residual is $R(\omega)=1-\vec{C}_{DS}^{\dagger}(\omega)\vec{C}_{DD}^{-1}(\omega)\vec{C}_{DS}(\omega)/C_{SS}(\omega)$; the square root $\sqrt{R}$ is the reported noise-reduction factor. The gauge length $L_g$ acts as a spatial averaging scale, and the dense channel spacing lets the array reconstruct wavelengths longer than a single gauge length.

What would settle it

Train the multichannel Wiener filter on the first half of the one-hour dataset and apply it to the second half, then recompute the residual factor at 20 Hz for the vertical seismometer component using six DAS channels; a held-out residual substantially above 0.11 (for example, above 0.3) would show the headline cancellation factor does not generalize.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that DAS is not just a detector of seismic events but a quantitatively equivalent seismic instrument. After converting DAS strain-rate to ground acceleration through the apparent slowness, the DAS waveforms, power spectra, and Bland–Altman agreement match a colocated broadband seismometer. Used as witness channels in a multichannel Wiener filter, six DAS sensors cancel a seismometer's vertical noise to a residual factor of 0.11 at 20 Hz, exactly matching six geophone channels, and outperform the geophones on the horizontal component (0.04 versus 0.15). The paper also reports that this high colocated correlation contradicts a prior theoretical expectation that strain meters must be placed far from the test mass to be useful for Newtonian-noise cancellation.

Load-bearing premise

The cancellation filter is tuned and evaluated on the same one-hour dataset, with no separate test period mentioned, so the reported residual factors may be optimistically biased by in-sample fitting.

Editorial extensions

If this is right

Existing fiber infrastructure can be turned into dense seismic arrays, reducing the deployment cost of witness sensors for Newtonian-noise cancellation at gravitational wave observatories.
DAS matches a geophone array for vertical-component cancellation (0.11 residual at 20 Hz with six channels) and exceeds it on the horizontal component (0.04 versus 0.15).
DAS can reconstruct geophone signals with an average correlation of 0.97 and coherence above 0.7 above 3 Hz, allowing it to substitute for or supplement sparse geophone networks.
DAS coherence lengths of about 11 m at 3–5 Hz and 23 m at 6–10 Hz resolve ground-motion structure finer than typical 25 m geophone spacing, which is relevant near detector test masses.
Combining DAS with traditional seismometers and geophones is identified as a promising direction for further improving Newtonian-noise cancellation and environmental monitoring.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A held-out evaluation is the natural next step: the paper's residual factors are in-sample, so a train/test split would give a realistic bound on DAS cancellation performance.
Because DAS measures strain only along the fiber direction, the vertical-component improvement with more channels suggests that helical or multi-orientation cable layouts could push vertical cancellation below the quoted 0.11; that is a direct, testable extension.
The reported coherence lengths of roughly 11 m and 23 m in the 3–10 Hz band imply an optimal witness-channel spacing for cancellation; arrays much denser than that may add little information while raising processing cost.
If the colocated high correlation survives at quieter detector sites, existing telecommunication fiber around gravitational wave observatories could be repurposed for Newtonian-noise monitoring, with the open engineering question being real-time slowness estimation and adaptive filter updates.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The new element here is the first reported case of DAS actually cancelling a colocated seismometer's noise, with residual factors comparable to a geophone array, plus coherence-length measurements from a 19-km fiber at DESY. That is a genuine experimental step, not just another equivalence study. The paper also does some things well: the vibrotruck and earthquake events give real waveforms, the PSD and Bland–Altman checks are reasonable for sensor comparison, and the coherence-length numbers (11 m and 23 m in the two bands) are concrete and match Gaussian fits.

The problem is the evaluation of the cancellation residual. The Wiener filter coefficients in Eq. (13) are estimated from the same 1-hour dataset used to report the residual. There is no train/test split, no cross-validation, nothing in Sections 4.2, 4.3, or 5.2 that says the filter was applied to a held-out segment. So the headline “residual noise factor 0.11” at 20 Hz is an in-sample number, and with up to six channels and many frequency bins it will be optimistically biased. The geophone comparison is subject to the same issue, so the claim of comparability is not yet established out of sample.

There is also an internal inconsistency: the abstract and Table 1 give 0.11 for the DAS vertical residual at 20 Hz with six channels, while Section 5.2.1 states 0.14 for the same configuration. That needs reconciliation. Less serious, but still relevant: the DAS channel selection is not specified (which channels, how many, how chosen), and the slowness used for DAS-to-acceleration conversion is itself fitted from the DAS data, adding another free parameter. No error bars or statistical significance are given for the residuals.

The central concept is sound and the experiment is real. The in-sample estimate is the main soft spot, not a fatal flaw—it can be fixed by re-estimating the filter on a training segment and evaluating on a test segment. The coherence-length analysis and the environmental monitoring sections are more straightforward and hold up.

Who is this for? People working on Newtonian noise mitigation for Einstein Telescope or LIGO upgrades, and DAS applications in seismology. It deserves a serious referee. I'd send it to peer review with a clear request for out-of-sample validation and a resolution of the 0.11/0.14 discrepancy. The paper's usefulness depends on whether the residuals survive out-of-sample testing.

Referee Report

4 major / 5 minor

Summary. The paper argues that distributed acoustic sensing (DAS) is a viable and scalable alternative to seismometers and geophones for environmental monitoring and Newtonian-noise mitigation in gravitational-wave detectors. Using field data from the DESY campus (vibrotruck, earthquake, thunderstorm, HVAC), the authors convert DAS strain-rate measurements to ground acceleration using a semblance-estimated slowness, compare waveforms and power spectra with co-located broadband seismometers, use multichannel Wiener filtering to cancel the vertical and east components of a seismometer with DAS and with geophones, and report residual noise factors down to 0.11 at 20 Hz. They also report a 0.97 correlation for Wiener-filter prediction of geophone signals from DAS, DAS coherence lengths of roughly 11 m and 23 m in the 3-10 Hz band, and several environmental monitoring examples.

Significance. If the central cancellation claim is established out of sample, the paper would provide a practically important result: DAS could serve as a scalable witness sensor for Newtonian-noise cancellation in current and next-generation gravitational-wave detectors, using existing fiber infrastructure and dense spatial sampling. The paper also contributes useful field demonstrations of DAS-to-seismometer conversion, coherence-length measurements, and environmental monitoring. However, the headline cancellation performance is currently supported only by in-sample Wiener-filter residuals, and the paper contains an internal inconsistency in the reported DAS vertical residual (0.11 vs 0.14). Those issues directly affect the central claim, so the significance of the result is not yet established.

major comments (4)

[Section 4.3, Eq. (13); Section 5.2] The residual factors reported in Table 1 and the abstract are computed from cross-spectral matrices CDS, CDD, and CSS estimated with Daniell's method on the same 1-hour dataset used for evaluation; no train/test split or cross-validation is described. Since the Wiener filter coefficients are fitted to the same noise realization that is being cancelled, the in-sample residual sqrt(R(ω)) is optimistically biased, and the headline 'residual noise factor of 0.11 at 20 Hz' is not established as an out-of-sample cancellation factor. Please report out-of-sample residuals (or k-fold cross-validation) with uncertainties, and specify the number of frequency bins/taps used in the Wiener filter.
[Abstract; Table 1; Section 5.2.1] The abstract and Table 1 report a DAS vertical-component residual of 0.11 at 20 Hz with six channels, while Section 5.2.1 states that 'DAS achieved 0.14' for the same configuration; Section 5.2 also gives 0.14 for six DAS channels at 20 Hz. This internal inconsistency affects the central comparison and must be resolved before the headline claim can be accepted.
[Section 4.2; Section 5.3] The geophone-prediction correlation of 0.97 and the coherence 'above 0.7' are computed with Wiener filter coefficients estimated from the same data used for the prediction, so this metric is a fit-quality measure rather than a predictive validation. Please provide an out-of-sample evaluation and specify the Wiener filter length or number of taps, the frequency smoothing, and the dataset split.
[Section 4.1; Section 3.1.1; Section 6] The DAS-to-acceleration conversion relies on an apparent slowness estimated from the DAS data itself via semblance (Eq. 8), and the selected DAS channels used for cancellation are not specified in Section 4.3 or Table 1. Because both the slowness and the channel selection are adapted to the record being analyzed, they can inflate the reported correlation and cancellation performance; please describe the channel-selection criteria, report sensitivity to slowness and channel choice, and ideally validate the conversion with an independent slowness estimate from the geophone array.

minor comments (5)

[Section 6.0.1] The Gaussian correlation length Lc is a fitted parameter, and the reported coherence lengths (11 m and 23 m) come from a single vibrotruck event; please report the fit uncertainty and state whether the result is stable across events and frequency bands.
[Equation (11)] The wavenumber vector is written as k = 2π s(f)·(sinθ, cosθ, 0), which appears dimensionally inconsistent with the slowness definition in Eq. (6); please define f and s explicitly and check the units.
[References] Reference [20] is cited for LPSD, but the citation appears to point to a software package rather than the original LPSD algorithm; please cite the original algorithm and provide a version/commit for the software if used.
[Throughout] There are several typographical and nomenclature issues, including 'Fracensca' in the author list, 'Deutches' in the acknowledgments, 'compliment' for 'complement' in Section 6, and the irregular spacing in 'W A VE'; these should be corrected.
[Figure 13] The PDF plot of residuals should specify how the residuals are aggregated over frequency and channels; the statement that the maximum lies between 0.00 and 0.02 needs error bars or sample counts to be meaningful.

Circularity Check

2 steps flagged · score 6.0 of 10

In-sample Wiener filtering turns the headline residual 0.11 and geophone-prediction correlation 0.97 into fit statistics; no train/test split is described.

fitted input called prediction [Section 4.2 (Wiener filter training), Section 5.3 and Figure 14 (reported correlation)]
"In this study, we used data from a 10-channel DAS array, down-sampled to 200 Hz, over a period of 1 hour. ... We assumed stationarity within the data set needed for Wiener filtering. ... The Wiener filter successfully estimated the geophone data from the DAS measurements, demonstrating the potential of DAS systems for seismic signal reconstruction. The correlation coefficient between the estimated and true geophone signals averaged 0.97, indicating a strong linear relationship between the two."

The Wiener filter coefficients h(t) in Eq. (12) are estimated from the same 1-hour DAS/geophone segment on which the 0.97 correlation is later reported. No train/test split or cross-validation is described. Since the optimal Wiener filter minimizes the mean-square error on the training segment by construction, the 'predicted' geophone trace is an in-sample reconstruction. The high correlation is therefore a fit statistic, not an out-of-sample prediction, despite being presented as evidence that DAS can 'predict' or 'estimate' geophone signals.
fitted input called prediction [Section 4.3, Eq. (13); Section 5.2, 5.2.1; Table 1; abstract]
"The noise cancellation technique is based on the multichannel Wiener filter and utilizes cross-spectral matrices, expressed as [19]: ... . Residual p R(ω) represents the noise reduction factor that DAS is able to achieve. ... Cross-spectral and auto-spectral densities were estimated using Daniell's method [20]."

Eq. (13) defines the residual as the theoretical output of the optimal Wiener filter. The cross-spectral matrices CDD, CDS, and CSS are estimated on the same 1-hour dataset used to report the residuals in Section 5.2 and Table 1; no train/test split is mentioned. With up to six DAS channels and many frequency bins, the in-sample optimal residual is optimistically biased. Calling √R 'the noise reduction factor that DAS is able to achieve' and quoting 0.11 at 20 Hz presents this in-sample fit as achieved cancellation. The geophone comparison uses the same in-sample protocol, so the symmetry does not validate the absolute factor.

full rationale

The paper's two central quantitative claims—geophone prediction and noise-cancellation residuals—are computed with Wiener filters whose coefficients or cross-spectral matrices are estimated from the same 1-hour dataset on which the results are evaluated. Section 4.2 describes a 1-hour DAS/geophone dataset and assumes stationarity; Section 4.3 estimates cross-spectral densities with Daniell's method and then Eq. (13) reports the optimal residual. No train/test split, cross-validation, or independent validation segment is described. The 0.97 correlation and the 0.11 residual at 20 Hz are therefore in-sample fit statistics, making the headline 'prediction' partially reduce to a fit. There is independent non-circular content: the DAS-to-seismometer conversion uses slowness estimated from DAS moveout, not from the seismometer, and the waveform/PSD/Bland-Altman comparisons are external agreement checks. Self-citations [3] and [19] are not load-bearing because Eq. (13) is a standard multichannel Wiener formula and the geophone comparison is recomputed in this paper. An unrelated data-integrity concern is that the abstract and Table 1 give a DAS vertical residual of 0.11 at 20 Hz with six channels, while Sections 5.2 and 5.2.1 state 0.14; this inconsistency does not affect the circularity score but should be resolved. Overall, the central cancellation factor and geophone-prediction correlation are partly forced by the in-sample fitting procedure, so the score is 6.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The central claim rests on data-fitted parameters (slowness, Wiener filter coefficients, channel selection) and on domain assumptions about plane-wave propagation and stationarity.

free parameters (4)

Apparent slowness s = estimated per time-frequency via semblance
Eq. (6) converts DAS strain rate to acceleration by dividing by s; s is chosen by maximizing semblance across DAS channels, so it is a data-fitted parameter.
Wiener filter coefficients h(t) = frequency-domain optimal filter
Eq. (12) coefficients minimize MSE between DAS and geophone; fitted to the same 1-hour dataset used for evaluation.
Number and selection of DAS channels = 2-6 channels
Section 5.2 varies channel count; selection criteria are not specified, so channel choice is a free modeling parameter.
Gaussian correlation length Lc in coherence fits = 11.76 m and 23.55 m
Section 6 fits the spatial correlation function with a Gaussian; the fitted standard deviation sets Lcoh via Eq. (14).

assumptions (4)

domain assumption Ground motion can be modeled as plane waves with a single apparent slowness s at each time/frequency.
Section 3, Eq. (6), Section 3.1 use this to convert DAS strain rate to ground acceleration; the paper notes slowness varies in space and time.
domain assumption DAS strain rate is related to seismometer velocity by a finite-difference approximation over the gauge length.
Section 3, Eq. (5) assumes the strain-rate equals [v(x+Lg/2)-v(x-Lg/2)]/Lg, ignoring wavefield curvature and coupling effects.
domain assumption The noise field and signal are stationary over the 1-hour analysis window, so Wiener filtering is valid.
Section 4.2 states 'We assumed stationarity within the data set needed for Wiener filtering.'
domain assumption The vertical seismometer channel is a valid proxy for test-mass displacement noise in Newtonian noise cancellation.
Section 4.3 says vertical is emphasized as analogous to test mass motion, then notes this holds only for surface detectors.

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Cite this review

Pith. "Pith review of Distributed Acoustic Sensing for Environmental Monitoring, and Newtonian Noise Mitigation:Comparable Sensitivity to Seismometers." pith.science (2026). https://pith.science/paper/U5BYWUAC

@misc{pith2026250713523,
  author       = {Pith},
  title        = {Pith review of: Distributed Acoustic Sensing for Environmental Monitoring, and Newtonian Noise Mitigation:Comparable Sensitivity to Seismometers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U5BYWUAC}},
  note         = {Machine review of arXiv:2507.13523}
}

read the original abstract

Newtonian noise limits the low-frequency sensitivity of ground-based gravitational wave detectors. While seismometers and geophones are commonly employed to monitor ground motion for Newtonian noise cancellation, their limited spatial coverage and high deployment costs hinder scalability. In this study, we demonstrate that distributed acoustic sensing offers a viable and scalable alternative, providing performance comparable to that of conventional seismic instruments. Using data from acoustic sensing and colocated seismometers during both natural and controlled events, we observe a strong correlation, exceeding 0.8, between the two sensor types in the 3 to 20 Hz frequency band relevant for Newtonian noise. Moreover, when distributed acoustic sensing data are used to predict geophone signals, the correlation remains high, above 0.7, indicating that distributed acoustic sensing accurately captures both the spatial and spectral features of ground motion. As a case study, we apply distributed acoustic sensing data to cancel noise recorded by the vertical component of a seismometer and compare the results with those obtained using geophone data for the same task. Both distributed acoustic sensing and geophone-based cancellations yield a residual noise factor of 0.11 at 20 Hz. These findings confirm the feasibility of using distributed acoustic sensing for Newtonian noise mitigation and highlight its potential, in combination with traditional seismic sensors, to improve environmental monitoring and noise suppression in current and next-generation gravitational wave observatories.

Figures

Figures reproduced from arXiv: 2507.13523 by the authors.

**Figure 1.** Schematic representation of a distributed optical fiber sensor. The fiber can be defined as consisting of discrete virtual sensing channels (points) with a defined channel spacing (spatial resolution) and gauge length. Local phase changes are estimated by differentiating the phase at each fiber channel with respect to the preceding one, using the first channel as a reference. Repeating this process over time enables… view at source ↗

**Figure 2.** Illustration of the displacement measured by the DAS system at a given position x, showing the change between two time steps, t + ∆t. The strain rate can be converted to strain by integrating over time. This strain is a one-dimensional projection of the full strain tensor ε. The strain tensor is derived from the displacement field ui(x, t) as: εij = 1 2 dui dxj + duj dxi , (3) where ui and uj are displacement co… view at source ↗

**Figure 3.** Distributed Acoustic Sensing (DAS) recording the Morrocan earthquake over 4000 km from Hamburg showing clear arrivals of both compressional (P) and shear (S) waves. The Pwave, arriving first, is characterized by lower amplitude and higher velocity, followed by the higher amplitude S-wave. by aggregating measurements across multiple channels. In effect, while each individual channel behaves as a spatially averaged s… view at source ↗

Figures from the paper (15 more)

**Figure 4.** Figure 4: Illustration of the semblance algorithm. The algorithm functions as a spatial noise filter based on amplitude scaling, enhancing signal components that are coherent across the sensor array. This filtering is essential for suppressing localized noise that does not exhib…

**Figure 5.** Figure 5: Illustration of the conversion process used to compare seismometer and DAS measurements in terms of ground acceleration. The DAS output, given as strain rate, is converted to acceleration by dividing by the slowness s. The seismometer output, originally in velocity, i…

**Figure 6.** Figure 6: Left: Block diagram illustrating the process of seismometer noise cancellation† . Right: Map of the DESY campus showing the locations of the seismometer, geophones, and the DAS array, as well as the ground excitation points produced by the vibrotruck. The DAS sensor ne…

**Figure 7.** Figure 7: Left: Vibrotruck used during the campus experiment to generate controlled ground motion. The vehicle excited the ground continuously for a duration of five minutes, providing a repeatable seismic source for comparison between DAS and seismometer recordings. Right: Time…

**Figure 8.** Figure 8: Comparison of DAS output with the vertical component of a co-located seismometer. The right panel shows a zoomed-in view of the vibrotruck event, where the oscillatory behavior is clearly visible in both recordings. The similarity in frequency content and waveform conf…

**Figure 9.** Figure 9: Comparison of DAS output with the east component of a co-located seismometer. The right panel shows a zoomed-in view of the vibrotruck event, where the oscillatory behavior is clearly visible in both recordings. The similarity in frequency content and waveform confirms…

**Figure 10.** Figure 10: Left: PSD comparison between the DAS output and the east, vertical, and north components of a co-located seismometer. The spectral shapes exhibit strong agreement, with the DAS showing higher amplitude due to spatial averaging, which enhances the SNR. Right: Bland–Al…

**Figure 11.** Figure 11: Noise reduction factor as a function of the number of DAS channels used for cancelling seismometer noise. Left: Cancellation performance for the eastern component of the seismometer between 1 Hz and 20 Hz. Right: Cancellation performance for the vertical component of…

**Figure 12.** Figure 12: Noise reduction factor comparison between geophones and DAS as a function of the number of sensors used, geophones are the dashed with the symbol plots. Left: Cancellation performance for the eastern component of the seismometer between 1 Hz and 20 Hz. Right: Cancell…

**Figure 13.** Figure 13: Residuals plotted as a function of frequency for the noise cancellation process when 6 channels are used. The PDF of the DAS residuals shows a sharp peak near zero, indicating that most residuals are small and concentrated around zero. The peak occurs between 0.00 and…

**Figure 14.** Figure 14: Comparison of DAS-predicted geophone signals with actual geophone data. Left: PSD plot showing strong agreement across all frequencies between the Wiener filter output and the real geophone signal. Right: Coherence remains above 0.7 for frequencies above 3 Hz, indicat…

**Figure 15.** Figure 15: Coherence length analysis of DAS in the 3–6 Hz band. Left: Coherence plots at 3, 4, 5, and 6 Hz showing a consistent coherence length of approximately 11 meters across the DAS array. Right: Comparison between the DAS-derived correlation profile and the corresponding G…

**Figure 16.** Figure 16: Coherence length analysis of DAS in the 7–10 Hz band. Left: Coherence plots at 7, 8, 9, and 10 Hz indicating a coherence length of approximately 23 meters. Right: Comparison between the DAS correlation and the Gaussian correlation model, with the Gaussian correlation …

**Figure 17.** Figure 17: Illustration showing vibrations generated by HVAC systems (fans) within the European XFEL facility, as recorded by DAS. These persistent narrowband signals highlight DAS sensitivity to mechanical infrastructure. The right side displays DAS data capturing a controlled …

**Figure 18.** Figure 18: Top: Illustration demonstrating the capability of DAS to detect atmospheric acoustic events. Between sections 6000 and 10000, the wave arrival times are earlier compared to sections 500 to 6000. This time difference is attributed to faster wave propagation along the s…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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