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REVIEW 4 major objections 5 minor 1 cited by

Real-time processing of distributed acoustic sensing data for earthquake monitoring operations

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Real-time DAS data can be folded into an operating earthquake monitoring system by treating selected fiber channels as standard seismic stations and feeding machine-learning phase picks into the existing AQMS pipeline.

desk verdict A real, well-engineered DAS-to-AQMS integration that deserves review, but its operational benefit is asserted more than demonstrated. read the letter →

arxiv 2505.24077 v1 pith:SMNGLJQL submitted 2025-05-29 physics.geo-ph

Ettore Biondi , Gabrielle Tepp , Ellen Yu , Jessie K. Saunders , Victor Yartsev , Michael Black , Michael Watkins , Aparna Bhaskaran
show 3 more authors
Rayomand Bhadha Zhongwen Zhan Allen L. Husker
This is my paper
classification physics.geo-ph
keywords distributedacousticsensingreal-timeearthquakemonitoringAQMSPhaseNet-DASEarthwormtraveltimepickingRidgecrestDASarrayfiber-opticseismology
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

The paper argues that distributed acoustic sensing (DAS) data can be folded into an operating earthquake monitoring center by treating a handful of fiber-optic channels as ordinary seismic stations and letting the deep-neural-network picker PhaseNet-DAS supply the phase arrivals. It presents a modular Python framework that streams DAS packets into 1-second Earthworm ring buffers under SEED channel metadata, while PhaseNet-DAS runs asynchronously on a rolling 70-second window and injects its traveltime picks into the AQMS associator. The demonstration is continuous: since August 2024 the 80-km Ridgecrest array has streamed 5,000 channels to a processing server with 0.6-second packet latency and better than 99.989% telemetry reliability, and the real-time pipeline detected more than 400 aftershocks of the August 2024 M5.2 Lamont sequence. If the approach holds in routine operations, monitoring networks can supplement sparse seismometer coverage with dense fiber-optic arrays without replacing their existing software stack.

What carries the argument

The carrying object is a translation-and-buffer pipeline: a StreamReader template that unpacks vendor DAS packets (demonstrated with an OptaSense interrogator), a RingBuffer that accumulates samples into 1-second Earthworm wavering rings and separately into a 70-second working window, and an asynchronous PhaseNet-DAS inference loop that sends picks to an Earthworm pick ring through the PyEarthWorm interface. SEED metadata definitions such as CI.DRS02..HS1 let existing AQMS tools treat a DAS channel as a standard station, and PhaseNet-DAS, a deep neural network trained on DAS data for P/S arrival-time picking, is the component that exploits dense spatial sampling. The sparsity choice of selecting channels every ~5 km keeps the DAS input commensurate with conventional station density, while the full 5,000-channel stream is still used for picking.

What would settle it

Compare every PhaseNet-DAS pick streamed into AQMS during a fixed one-month window against a reference catalog for events within 120 km of the Ridgecrest array; if the live pick stream misses a large share of catalog events at local magnitude 2 and above, or produces false associations that the associator cannot reject, the operational-accuracy assumption is refuted.

Watch

Extended reading notes

Core claim

The central claim is that an operational earthquake monitoring system such as AQMS can ingest real-time DAS data with no change to its internal logic, provided a translation layer buffers DAS samples into standard 1-second traces and only a sparse subset of well-coupled channels (roughly 5 km spacing) is forwarded. On top of that, the full DAS array can be exploited by running PhaseNet-DAS continuously on a rolling 70-second buffer over all streamed channels; picks from the selected channels are filtered to remove duplicates within 1 second and sent to an Earthworm pick ring for phase association. The paper reports this has been running since August 2024 on the Ridgecrest array, with 18 selected channels feeding AQMS and 8 already used in network monitoring operations. For events recorded between May and July 2023, pick signal-to-noise ratios increase with magnitude, and for the M5.2 Lamont sequence the real-time picks agree with nearby network detections while STA/LTA thresholding on the same DAS data misses many aftershocks and adds traffic-related mispicks. The system therefore claims to make DAS a practical supplement to conventional stations, not just a research instrument.

Load-bearing premise

The pre-trained PhaseNet-DAS model, applied to Ridgecrest data without fine-tuning, is accurate enough for real-time operational picking; the paper shows only qualitative examples and does not quantify detection rates, false positives, or pick latency against a reference catalog.

Editorial extensions

If this is right

  • Regional networks using AQMS can add DAS channels without rewriting associators or archive tools, since selected channels enter as standard SEED traces in Earthworm rings.
  • The demonstrated telemetry reliability (over 99.989% since August 2024) suggests dark-fiber arrays can support continuous operational data feeds, not just campaign experiments.
  • Real-time PhaseNet-DAS picks on the full array detect small aftershocks that STA/LTA thresholding on the same DAS data misses, pointing to better catalog completeness at low magnitudes.
  • Because picking runs asynchronously on a rolling buffer, the design can absorb faster or slower inference models and larger channel counts by adjusting buffer length and worker concurrency.
  • Archiving the DAS strain-rate waveforms lets analysts review and manually pick events in the same Jiggle interface used for conventional stations.

Reading between the lines

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

  • A natural extension the paper leaves implicit is feeding the dense picks from all 5,000 channels into a phase-association step rather than only the 18 selected channels, which could substantially improve event location and detection completeness.
  • The authors do not report end-to-end alert latency; if packet latency (~0.6 s) and picking time (~1 s) hold, the design sits near the threshold for earthquake early warning, but a quantified alert-latency budget would be needed to test that.
  • If the DAS amplitude calibration questions referenced in the paper are resolved, the same pick ring could carry strain-rate amplitudes, enabling real-time magnitude estimation directly from fiber-optic data without adding a new waveform path.
  • The framework's modularity implies it could be transferred to submarine cables or urban telecom networks, but transferability depends on the pre-trained PhaseNet-DAS generalizing to those installations, which is not established here.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents a modular Python software framework for integrating distributed acoustic sensing (DAS) data into the ANSS Quake Monitoring Software (AQMS) used by the Southern California Seismic Network. The system streams packets from a Ridgecrest, California DAS array into 1-second Earthworm wave rings, applies the PhaseNet-DAS machine-learning picker to a rolling 70-second buffer, and injects traveltime picks from 18 selected DAS channels into an Earthworm pick ring for ingestion by the AQMS associator. The demonstration claims continuous streaming since August 2024 with telemetry reliability exceeding 99.989% and detection of more than 400 aftershocks of the M5.2 Lamont sequence. The paper also provides implementation details, command-line usage examples, and a discussion of future amplitude-based capabilities.

Significance. If the claimed performance holds, this is a valuable engineering contribution: it is one of the first documented, open-source pipelines for passing DAS-derived picks into an operational seismic network's AQMS workflow. The paper's strengths include a detailed architecture description, use of standard SEED metadata, reproducible command examples, an accessible code repository, and a real-data deployment on an 80-km effective-length array. The central operational claim, however, rests on validation that is currently qualitative and partly performed on a different channel set from the one actually integrated into AQMS, so the quantitative impact on monitoring operations is not yet established.

major comments (4)
  1. [Real-time processing and earthquake sequence example] The statement that the real-time PhaseNet-DAS workflow 'generated more than 400 aftershocks' of the M5.2 Lamont sequence is not quantitatively validated. No reference-catalog comparison, detection-rate, precision/recall, or false-positive statistics are reported, and the assertion that the picks are 'in good agreement with the detections from nearby SCSN stations' is qualitative. Because this count is the principal evidence of operational value, it must be supported by a defined metric and a direct comparison against the SCSN/USGS catalog.
  2. [Real-time processing and earthquake sequence example] The AQMS integration is demonstrated with picks from only 18 selected channels, yet the 400+ aftershock example (Figure 5b) uses PhaseNet-DAS picks from all 5,000 streamed channels. The number of aftershocks detectable from the 18-channel subset—the configuration actually inserted into the AQMS pick ring—is not reported, and no AQMS-generated event list is presented. The central claim that this framework enables DAS to contribute to operational monitoring therefore applies to a configuration that is not the one quantitatively demonstrated.
  3. [Real-time processing and earthquake sequence example] The M1.0 example in Figure 3b contains misclassified P arrivals, and the text states that no phase-association step is included in the software. Since these picks are streamed directly to the Earthworm pick ring and hence to the AQMS associator, the paper should either quantify the expected false-pick rate for small events or add a preprocessing phase-association filter (as suggested via the cited Zhu et al. 2022 work) before claiming operational suitability.
  4. [Real-time DAS data streaming and processing] The 'real-time' claim lacks an end-to-end latency measure. The reported 0.6-second packet latency and approximately 1 second per picking task do not bound the delay between an earthquake's occurrence and the insertion of picks into the AQMS pick ring, because the 70-second rolling buffer introduces a window-dependent delay. Please report end-to-end detection-to-ingestion latency, for example by comparing pick timestamps with catalog origin times for the Lamont sequence.
minor comments (5)
  1. [Code description and Usage examples] The term 'wavering' is used repeatedly (e.g., 'inserted into an Earthworm wavering' and the '-wring' option) and appears to be a typo for 'wave ring' or the Earthworm ring name; please correct and define the intended Earthworm module.
  2. [Usage examples] In the command-line examples, the description for --strnRt reads 'Compute realt-time strain rate from strain'; fix the typo and explain what the flag computes.
  3. [Real-time processing and earthquake sequence example] Figure 4 is based on events from May-July 2023, before the continuous streaming operation that began August 2024; clarify that this SNR analysis used archived data and is not part of the real-time pipeline.
  4. [Data streaming and selected channel metadata] The text says 'The instrument transmits individual data packets at 100 Hz' after stating that the ping rate is 1 kHz and data are stored at 100 Hz; it would be clearer to distinguish the packet transmission rate from the decimated sample rate.
  5. [Real-time DAS data streaming and processing] The 5-km channel-spacing rule is described as chosen to be 'comparable with SCSN regions with dense station coverage'; a citation or quantitative justification for this value would help readers assess the trade-off between coverage and redundancy.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the software-integration claim rests on an implemented pipeline and measured telemetry, not on a fitted parameter or a self-referential derivation.

full rationale

The paper's central claim is that a modular software framework can ingest real-time DAS data into AQMS by mapping selected channels to SEED identifiers and Earthworm ring buffers, and by feeding PhaseNet-DAS traveltime picks into a pick ring. This is an engineering integration claim, not a derived scientific prediction. PhaseNet-DAS (W. Zhu et al., 2023) is used as an external, pre-trained component; the paper does not fit any of its parameters to the Ridgecrest data, nor does it derive the model's behavior from its own assumptions. The demonstrated outputs—telemetry reliability exceeding 99.989%, sub-second packet latency, and the count of more than 400 aftershock detections—are empirical results of running the system, not quantities that reduce to the paper's definitions. The paper explicitly discloses the limitations of the picks (misclassified P arrivals in the M1.0 event and the absence of a phase-association step), which are validation concerns rather than circularity. Self-citations such as Zhu et al. (2023) for the picking model and Biondi et al. (2023) for channel geolocation are used as tool/method references with independent published support, and they are not invoked to force the paper's conclusions. No step in the derivation chain is self-definitional, no fitted input is renamed a prediction, and no uniqueness argument is imported from the authors' prior work. The central integration claim is therefore self-contained with respect to the evidence presented, even though its operational value would be strengthened by quantitative detection-rate and pick-quality benchmarks against a reference catalog.

Assumptions & free parameters 3 free parameters · 2 assumptions · 0 invented entities

The central claim depends on no new physical entities and no fitted parameters. The free parameters are operational choices (channel spacing, buffer length, deduplication window). The axioms are domain assumptions that the paper itself flags or leaves unquantified.

free parameters (3)
  • Operational channel spacing = 5 km
    Chosen so DAS channel density is comparable to densely instrumented SCSN regions; affects the demonstration but is a design parameter, not fitted to a target result.
  • Picking buffer length = 70 seconds
    Rolling window for PhaseNet-DAS inference; determines context and processing latency.
  • Pick deduplication window = 1 second
    Filters picks within 1 second of a previously transmitted pick to avoid redundant detections.
assumptions (2)
  • domain assumption DAS instrument response is flat within the seismic frequency band, so raw phase can be converted directly to strain rate.
    Invoked in 'Data streaming and selected channel metadata' for the strain-rate conversion; the paper acknowledges emerging evidence of frequency dependence.
  • domain assumption PhaseNet-DAS generalizes to the Ridgecrest array without retraining.
    The pre-trained model is applied as-is to a new deployment; examples show misclassifications, but transferability is not quantitatively validated.

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Cite this review

Pith. "Pith review of Real-time processing of distributed acoustic sensing data for earthquake monitoring operations." pith.science (2026). https://pith.science/paper/SMNGLJQL

@misc{pith2026250524077,
  author       = {Pith},
  title        = {Pith review of: Real-time processing of distributed acoustic sensing data for earthquake monitoring operations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SMNGLJQL}},
  note         = {Machine review of arXiv:2505.24077}
}
read the original abstract

We introduce a modular software framework designed to integrate distributed acoustic sensing (DAS) data into operational earthquake monitoring systems. Building on the infrastructure of the Advanced National Seismic System (ANSS) and the Southern California Seismic Network (SCSN), which employs the ANSS Quake Monitoring Software (AQMS), our solution supports real-time DAS waveform streaming and machine-learning-based traveltime picking to leverage the dense spatial sampling of DAS arrays. To enable seamless compatibility with the AQMS, our approach uses standardized seismic data formats to incorporate predetermined DAS channels. We demonstrate the integration of data from a 100-km-long DAS array deployed in Ridgecrest, California, and provide a detailed description of the software components and deployment strategy. This work represents a step toward incorporating DAS into routine seismic monitoring and opens new possibilities for real-time hazard assessment using fiber-optic networks.

Figures

Figures reproduced from arXiv: 2505.24077 by the authors.

Figure 1
Figure 1. Schematic describing the currently operating data and processing flows for DAS and seismic stations for earthquake monitoring operations at the SCSN. AQMS = ANSS Quake Monitoring System; DAS = Distributed Acoustic Sensing; EQ = earthquake Supplementary materials Supplementary Figure S1 shows a Jiggle screen depicting a local M2.36 event (event ID 41153760) that occurred close to the Ridgecrest DAS array where P- and… view at source ↗
Figure 2
Figure 2. Location of the Ridgecrest DAS array (black line) with the considered DAS channels currently being tested for earthquake monitoring operations (green triangles). The instrument deployment position is depicted by the blue cross. The gray lines represent the local faults within the United States Geological Survey (USGS) catalog (Frankel et al., 2000), and the red dots depict the local seismicity that occurred between … view at source ↗
Figure 3
Figure 3. PhaseNet-DAS picking examples for a local M3.4 (event ID: 72110903, distance from array 52 km) (a) and an M1.0 (event ID: 72130223, distance from array 40 km)(b). P￾and S-wave picks are depicted by the red and blue dots, respectively. The black lines show the traces of the selected DAS channels whose amplitudes are normalized by their maximum for visualization purposes. –17– [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Signal-to-noise ratio (SNR) level and magnitude distribution of picked local-regional earthquakes using PhaseNet-DAS on the Ridgecrest array for events recorded between May 27 and July 31, 2023. –18– [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]
Figure 5
Figure 5. Figure 5: Real-time picks from the M5.2 Lamont earthquake. (a) Map of the array and of the main shock location (yellow star) and of its aftershock (red dots). (b) DAS streamed data with the corresponding phase picks. (c) Traveltime picks obtained by a thresholding approach of ST…

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Cited by 1 Pith paper

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  1. Vessel Detection and Localization Using Distributed Acoustic Sensing in Submarine Optical Fiber Cables

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.