Distributed Multichannel Acoustic Sensing (DMAS)

Updated 27 January 2026

DMAS is a distributed sensing paradigm that leverages fiber-optic DAS interrogators to deliver spatially continuous, high-channel acoustic and vibration measurements.
It is applied in seismology, underwater acoustics, and geotechnical analyses by integrating multichannel data acquisition with sophisticated signal processing.
Key challenges include managing high data throughput, addressing channel variability, and ensuring spatiotemporal fidelity through robust denoising and channel selection.

Distributed Multichannel Acoustic Sensing (DMAS) is an array-based sensing paradigm that leverages distributed acoustic sensing (DAS) technologies to perform spatially continuous, high-channel-count measurement of acoustic and vibrational phenomena. DMAS systems exploit the high spatial resolution, extended coverage, and multichannel data acquisition of fiber-optic DAS interrogators, enabling applications in seismic monitoring, underwater acoustics, environmental sensing, geophysics, and machine audition. They are characterized by high data throughput, significant channel variability, and stringent requirements for spatiotemporal fidelity, signal quality, and robustness against channel degradation and environmental noise.

1. Physical Principles, Sensing Model, and Array Configuration

The core of DMAS is the DAS interrogator, which injects narrow laser pulses into single-mode optical fibers and monitors Rayleigh backscatter. External acoustic or vibrational perturbations induce local strain ε(z, t) in the fiber over gauge-length segments, modulating the returning optical phase Δφ(z, t) according to

$\Delta\phi(z, t) = \frac{4\pi n L_g}{\lambda} \epsilon(z, t)$

where n is the core refractive index, L_g is the gauge length (typ. 1–50 m), and λ is the laser wavelength (Xenaki et al., 25 Feb 2025, Rychen et al., 2023).

DMAS deployments typically reconstruct virtual sensor arrays:

Channel count $N = \lfloor L_{\rm tot}/\Delta z \rfloor$ , with $L_{\rm tot}$ the fiber length and channel spacing Δz (commonly 1–2 m) (Vantassel et al., 2022, Xenaki et al., 25 Feb 2025).
Each channel acts as a spatially localized (but finite-extent) strain sensor, with data acquired at rates from 50 Hz (seismic) to >5 kHz (underwater acoustics) (Rychen et al., 2023, Biondi et al., 29 May 2025).
Gauge length L_g and Δz define both spatial resolution and the anti-aliasing limit for measurable wavelengths.

Both configuration and physical coupling (e.g. burial depth, strain-transmitting sheath) affect amplitude fidelity, sensitivity, and channel-to-channel variability (Vantassel et al., 2022, Rychen et al., 2023).

2. Signal Processing Architecture and Channel Selection

Given the large volume and spatial redundancy in DMAS data, channel selection and efficient processing are central. The ORION framework exemplifies near-real-time, quality-guided channel selection (Bozzi et al., 9 Dec 2025):

Spatial clustering: Using cable trace coordinates, channels are partitioned into azimuth-coherent clusters via a contiguity-constrained DBSCAN variant. Local azimuth α_i is calculated from segmental trace derivatives, and spatial proximity (meters) and azimuthal similarity (degrees) underpin clustering.
Quality metrics: For each detected event, candidate channels are scored with normalized signal-to-noise ratio (SNR), median local coherence (MCC), and pre-event RMS noise. Outlier rejection and normalization ensure robustness.
Selection algorithm: Top-ranked channels (one or few per cluster/section) are retained, with optional global Qi thresholding to enforce minimum quality. Super-channel stacking (over gauge-length) provides further SNR enhancement.
Performance: ORION reduces data volume by up to two orders of magnitude; spatially distributed selection preserves azimuthal coverage and yields improved source localization compared to uniform subsampling (e.g., median location error cut from 10.69 km to 3.98 km in field tests) (Bozzi et al., 9 Dec 2025).

This design is compatible with rapid workflows (1–3 min/event for 5,000 channels) and extensible to more complex attributes or multi-cable systems.

3. Applications: Seismology, Oceanography, and Acoustic Event Analysis

DMAS is deployed in applications requiring dense, distributed measurements:

Earthquake and environmental monitoring: Long-baseline arrays (e.g., 80 km, 10,000 channels, 100 Hz) are integrated with operational seismic networks via standardized software frameworks (e.g., AQMS/Earthworm), allowing machine-learning-based picking (PhaseNet-DAS), continuous raw data streaming (via WebSocket), and near-real-time event detection (Biondi et al., 29 May 2025). DMAS matches or exceeds traditional seismometer performance for spatial correlation and Newtonian-noise cancellation (residuals of 0.11 at 20 Hz) (Rading et al., 17 Jul 2025).
Marine and underwater acoustics: In hybrid DAS–hydrophone deployments, DAS arrays (1 km, 946 channels, 5 kHz, 10 m L_g) enable beamforming-based source localization and structural monitoring. Controlled lake tests confirm meter-scale accuracy and sub-10 dB channel stability across 100 Hz–2.5 kHz (Rychen et al., 2023). For ocean observatories, OOI datasets achieve N≈32,600 virtual channels, SNR down to −134 dB re 1 µPa, and beamform to locate whale/vessel/T-wave arrivals (Xenaki et al., 25 Feb 2025).
Geotechnical MASW and Vs profiling: Active-source MASW paired with meter-scale DMAS arrays reproduces surface wave dispersion and shear velocity profiles previously achievable only by dense geophone deployments. DAS uncertainties, resolution, and profile agreement (to CPTU, <5%) are achieved when $g_l$ and Δx are chosen such that $g_l \leq \lambda_{\rm min}$ , $\Delta x \leq \lambda_{\rm min}/2$ (Vantassel et al., 2022).

4. Noise, Anomaly Detection, and Denoising in High-Channel Arrays

High channel count brings channel-dependent sensitivity, noise, and nonstationary artifacts:

Self-supervised denoising (DAS-N2N): Exploits “noise2noise” U-Net trained on independently spliced fiber pairs: $x_1(t) = s(t) + n_1(t)$ , $x_2(t) = s(t) + n_2(t)$ , both sharing the signal $s(t)$ . MSE loss forces reconstruction of the distribution mean, effectively retrieving $s(t)$ and suppressing random noise. DAS-N2N outperforms conventional filtering (Butterworth, Wiener) in SNR enhancement (>5× improvement) and achieves sub-second processing for 985-channel, 30 s data (Lapins et al., 2023).
Low-rank/self-supervised denoising: A Tucker low-rank constraint is combined with explicit per-channel bias/gain learning ( $\beta, \gamma$ with entropy regularization). This approach explicitly adapts to channel-dependent SNR, providing interpretable channel weights and yielding $\approx$ 3.4 dB PSNR gain over classical low-rank methods, robust to rank selection (Tonami et al., 2023).
Online anomaly/stripe detection: Real-time multichannel wavelet models (MvLSW) segment DMAS streams via scale-localized time-varying coherence, efficiently detecting anomalous “stripes” with $O(1)$ per-sample updates. Performance (V-measure >0.85, TPR >0.90) is validated on synthetic and real DAS (Wilson et al., 2018).
Physics-based signal restoration: Reverse time migration (RTM) inpainting reconstructs 3D scene images and projects back to the sensor coordinates, yielding layout-agnostic, SNR-driven equalization for sound event classification. RTM improves accuracy by up to 13.1 points on challenging nonuniform/partially degraded layouts, directly aligning attention to high-SNR channels (Tonami et al., 20 Jan 2026).

5. Data Fusion, Distributed Inference, and Network-Level Algorithms

DMAS frameworks interface with external sensors or internal multichannel arrays:

Hybrid arrays and calibration: Synchronization of DAS with co-located arrays (e.g., hydrophones) is achieved by GPS-locked clocks and consolidated HDF5 datasets. Cross-modal beamforming enables improved soundsource localization, with O(1 m) spatial precision (Rychen et al., 2023, Xenaki et al., 25 Feb 2025).
Multichannel fusion for noise cancellation: Multichannel Wiener filtering exploits the dense spatial sampling for adaptive noise suppression, leading to noise cancellation factors comparable or superior to geophones, especially as more DAS channels are added (Rading et al., 17 Jul 2025).
Node-specific distributed estimation (iDANSE): In wireless acoustic sensor network (WASN) contexts, DMAS-style multichannel nodes participate in “iterationless” MMSE estimation using single-round fused-signal exchanges. Under non-overlapping latent subspaces, iDANSE achieves one-shot convergence to centralized performance, reducing communication and latency (Didier et al., 2024).
Channel selection and geometry-aware fusion: Modular design (e.g., ORION, AQMS) allows dynamic adaptation to channel quality, geometry, and user-imposed constraints (Bozzi et al., 9 Dec 2025, Biondi et al., 29 May 2025).

6. Deployment Considerations, Limitations, and Future Directions

Robust DMAS operation involves both hardware and algorithmic design choices:

Cable specification and deployment: For surface MASW and geotechnical work, tightly-buffered, strain-sensing fibers with shallow burial ensure efficient coupling. Gauge length and channel spacing fundamentally limit resolved wavelengths and amplitude fidelity (Vantassel et al., 2022).
Computational and streaming architecture: Real-time operation is feasible for O(10³–10⁴) channels using modern compute resources (GPU or high-core CPUs), with modular streaming, rolling buffers, and observer-driven or machine-learned pickers (Biondi et al., 29 May 2025, Bozzi et al., 9 Dec 2025).
Channel variability: Physical coupling, installation heterogeneity, and environmental conditions impart nonuniform channel SNRs and frequency responses, necessitating adaptive or spatially aware denoising and selection (Tonami et al., 2023, Rychen et al., 2023).
Generalization and hybridization: Physics-informed preprocessing (e.g., RTM) yields strong generalization across layouts and environmental shifts, complementing learning-based methods and facilitating robust SEC and localization (Tonami et al., 20 Jan 2026).
Extensions: Ongoing work includes generalizing spatial clustering to 3D cable geometries, integration of DMAS with multicore/hybrid arrays, multi-source triangulation, and utilization of DMAS as environmental monitors for gravitational wave observatories and hazard warning networks (Rading et al., 17 Jul 2025, Biondi et al., 29 May 2025).

Limitations remain regarding cable type dependencies, calibration nonstationarities, maximum attainable bandwidth (for, e.g., odontocete clicks), and the need for rapid adaptation to partial or complete channel failure in harsh environments.

References

(Bozzi et al., 9 Dec 2025) Near real-time channel selection for Distributed Acoustic Sensing technology
(Biondi et al., 29 May 2025) Real-time processing of distributed acoustic sensing data for earthquake monitoring operations
(Xenaki et al., 25 Feb 2025) Distributed acoustic sensing for ocean applications
(Rading et al., 17 Jul 2025) Distributed Acoustic Sensing for Environmental Monitoring, and Newtonian Noise Mitigation: Comparable Sensitivity to Seismometers
(Rychen et al., 2023) Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sound sources
(Vantassel et al., 2022) Extracting High-Resolution, Multi-Mode Surface Wave Dispersion Data from Distributed Acoustic Sensing Measurements using the Multichannel Analysis of Surface Waves
(Lapins et al., 2023) DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean data
(Tonami et al., 2023) Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensing
(Wilson et al., 2018) Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series
(Tonami et al., 20 Jan 2026) Event Classification by Physics-informed Inpainting for Distributed Multichannel Acoustic Sensor with Partially Degraded Channels
(Didier et al., 2024) One-Shot Distributed Node-Specific Signal Estimation with Non-Overlapping Latent Subspaces in Acoustic Sensor Networks