dv/v processing parameters and best practices by application

A community-literature survey of how the dv/v knobs are set, and why

What this page is A literature-derived synthesis of the processing parameters used in ambient-noise / coda seismic velocity-change (\(\delta v/v\)) monitoring — frequency band, coda (lapse-time) window, \(\delta v/v\) method, station geometry, and depth sensitivity — organized by science application (volcanoes, earthquakes/faults, landslides, groundwater, cryosphere, geothermal). It distills 103 studies into recommended value ranges and rules-of-thumb, so a new study can choose sound parameters and know which choices bias the result.

This survey backs the measurement-design scoring in codameter’s uq_measurement module: the parameters tabulated here are exactly the knobs scored there. Full citations with DOIs are on the references page; the complete per-study parameter table (CSV + Markdown, one row per paper) lives in the literature/ folder of the repository.

Citations below link to the reference list at the foot of this page (each with a clickable DOI); the full, per-study citations with DOIs are on the references page. n/r in the per-study table means a value was not found in the source as scanned — it was never guessed.

Provenance. The four measurement fields (frequency band, coda window, estimator, uncertainty treatment) were re-checked against the full text for 82 of the 103 studies (open access, plus AGU/Wiley titles read through a UW Wiley text-and-data-mining token). Those rows are flagged full text in the table’s measurement_source column, and an n/r there means the value is genuinely not stated in the paper.

The remaining rows carry abstract scan (19) or abstract only (paywalled) (2): these are the studies published outside the Wiley token’s coverage (Elsevier, AAAS/Science, Springer, SEG/SSA, one EGU abstract), for which full text was not available here. Their fields come from the abstract, so an n/r means “not found in the abstract,” not “not reported” — the value is very likely stated in the methods and awaits a manual full-text check (institutional VPN or a supplied PDF). Treat the coda-window and frequency-band gaps on these rows as a lower bound on what the literature reports, not a finding about the field.


Cross-cutting methodology rules

These hold across all applications (sources: (Snieder et al. 2002; Sens-Schönfelder and Wegler 2006; Bensen et al. 2007; Hadziioannou et al. 2009; Hadziioannou et al. 2011; Clarke et al. 2011; Weaver et al. 2011; Obermann et al. 2013, 2016; Zhan et al. 2013; Lecocq et al. 2014; Mikesell et al. 2015; Stehly et al. 2015; Daskalakis et al. 2016; Wang et al. 2017; Obermann and Hillers 2019; Jiang and Denolle 2020; Wang and Yao 2020; Yuan et al. 2021)).

  1. Coda time shift equals −dv/v for a homogeneous change, and sensitivity grows with lapse time (dt = −t·dv/v). Later coda amplifies tiny velocity changes — the physical basis for using coda over direct waves (Snieder et al. 2002; Obermann et al. 2013).
  2. A fully reconstructed Green’s function is NOT required — temporally stable noise sources are. Partial GF retrieval is acceptable provided the noise-source distribution and correlation waveforms are stable in time (Hadziioannou et al. 2009).
  3. Use the canonical 4-phase preprocessing: time-domain normalization (one-bit or running-mean) + spectral whitening → correlate → stack → SNR-based QC (Bensen et al. 2007), as implemented in MSNoise (Lecocq et al. 2014) and NoisePy (Jiang and Denolle 2020).
  4. Stretching is more robust than MWCS/cross-spectral methods at low SNR and for uniform changes; cross-spectral methods cycle-skip at large dv/v. DTW and wavelet methods help at low SNR / large perturbations (Mikesell et al. 2015; Yuan et al. 2021; Mao et al. 2020).
  5. Quantify dv/v uncertainty explicitly: coherence-weighted per-window phase errors for MWCS (Clarke et al. 2011); the analytic RMS formula based on coherence loss, bandwidth, and coda-window length for stretching (Weaver et al. 2011). SNR is a usable quality proxy.
  6. Depth is set by frequency band and coda lapse time — not assumed. Early coda / higher frequency → shallow surface-wave sensitivity; later coda → deeper body-wave sensitivity. Select band and window for the target depth using lapse-time-dependent 3-D kernels (Obermann et al. 2013, 2016).
  7. Reference-stack choice and stacking-window length bias the long-term trend and seasonal amplitude. Use a long, representative reference and a consistent stacking scheme for cross-station comparability (Wang et al. 2017).
  8. Beware spurious dv/v from non-stationary noise. Temporal changes in the noise-source spectrum (Zhan et al. 2013) and seasonal source fluctuations (Daskalakis et al. 2016) create apparent velocity changes; verify spectral stationarity, restrict to stable bands, and normalize the CCFs.
  9. Boost temporal resolution via denoising/clustering, not just longer stacks: curvelet denoising (Stehly et al. 2015) and waveform clustering by noise-source condition (Hadziioannou et al. 2011) reach daily resolution from short stacks.
  10. Cross-check with multiple estimators and reproducible, parameter-transparent software (MSNoise, NoisePy); each method has distinct failure modes (Obermann and Hillers 2019).

Volcano

Parameter Typical / recommended Notes
Frequency band ~0.1–2 Hz (deep/edifice) up to 1–4 Hz, >5 Hz for coda Multi-band stacking is the emerging standard for depth discrimination (Feng et al. 2020; Donaldson et al. 2019; Takano et al. 2017).
Coda / lapse window ~5–120 s Short (5–35 s) for near-field single-station; up to ~100–120 s for station pairs (min lag set by inter-station distance).
dv/v method Stretching (default); MWCS for error bars; wavelet for environmental separation CWI of repeating earthquakes when noise unstable during crises (Hotovec-Ellis).
Station config Pairs/arrays where dense; single-station (cross-comp > autocorr) for sparse networks Single-station survives when only one station remains during an eruption (De Plaen).
Depth sensitivity Shallow, upper ~0.3–3 km; occasionally mid-crustal magma (~3–10 km) dv/v dominated by compliant, crack-rich shallow edifice.

Key rule: environmental (rainfall) correction is necessary — hydrological dv/v is comparable in amplitude to the pre-eruptive signal (Rivet et al. 2015).


Earthquake / Fault

Parameter Typical / recommended Notes
Frequency band 0.1–2 Hz (crustal); 0.06–0.9 Hz for mid/deep crust; 4–12 Hz for shallow damage Band selects the depth probed.
Coda / lapse window ~5–30 s (crustal noise); ~3 s for high-freq aftershock autocorrelations Later coda → deeper/larger volume but lower SNR.
dv/v method Stretching (tolerates coseismic waveform change); MWCS / wavelet for frequency-resolved precision Wavelet handles cycle-skipping at large dv/v (Mao et al. 2020; Sheng et al. 2022).
Station config Single-station autocorr for dense mapping; pairs/arrays for deep & slow-slip Autocorr maximizes coverage; pairs give long-period sensitivity.
Depth sensitivity Coseismic damage shallow (top ~100 m–few km); deep change needs long-period / tiltmeters Strong-motion softening saturates near surface (Rubinstein & Beroza; Nakata; Sawazaki).

Key rule: coseismic velocity reductions are dominantly a shallow (top ~100 m) nonlinear site effect — separate near-surface from genuine fault-zone change before interpreting (Rubinstein and Beroza 2005).


Landslide

Parameter Typical / recommended Notes
Frequency band ~2–20 Hz (clayey precursors cluster 4–12 Hz) Far higher than volcano/tectonic — shallow bodies.
Coda / lapse window ~0.05–2 s Inter-sensor distances of tens of m; usable coda arrives within ~1–2 s.
dv/v method Stretching dominates; SPAC / MASW-interferometry for structure MWCS comparatively rare.
Station config Short-distance pairs for a single failure plane; dense / borehole arrays for 4-D imaging & early warning Liu 2025 (28-stn array); Whiteley 2026 (10-stn array); de Wit 2026 (borehole).
Depth sensitivity Very shallow, top few m to ~40 m Failure surfaces and pore-pressure changes are near-surface.

Differs from volcano/tectonic: higher frequencies, shallower depth, sub-second coda, denser/closer geometry, larger dv/v (several % to ±10 %), and rainfall/freeze-thaw forcings with multi-day lags that must be removed before precursor detection.


Groundwater / Hydrology

Parameter Typical / recommended Notes
Frequency band ~2–4 Hz (shallow aquifer); ~0.1–2 Hz multi-band for deep/depth-resolved Lower frequency → greater depth.
Coda / lapse window ~2–8 s (single-station autocorr); ~15–100 s (coda-wave) Low-coherence early lags discarded.
dv/v method Stretching (default); MWCS (Gräfenberg); multi-band coda inversion for depth Mao 2022/2025; Fokker 2023.
Station config Single-station autocorr/cross-comp for cheap dense aquifer monitoring; arrays for depth/spatial resolution
Depth sensitivity Upper ~50–500 m (high-freq) to ~200–700 m (low-freq multi-band) Enables explicit shallow-vs-deep-aquifer separation.

Separating hydrologic from thermoelastic/tidal: fit/remove a thermoelastic component (Tsai 2011 framework) and model the hydrologic part as drained-poroelastic precipitation / pore-pressure diffusion. Diagnostic = seasonal phase lag (thermoelastic lags temperature; hydrologic lags/anticorrelates with precipitation & groundwater level); multi-year trends → net groundwater storage change (Clements and Denolle 2018; Clements and Denolle 2023; Wang et al. 2017).


Cryosphere (permafrost, glaciers, freeze-thaw)

Parameter Typical / recommended Notes
Frequency band ~1.5–30 Hz (active layer / rock glacier 4–14 Hz); lower for ice sheets Targets shallow few meters.
Coda / lapse window ~0.3–0.8 s for shallow high-freq arrays
dv/v method Stretching; spectral-resonance/modal as complement Guillemot 2021.
Station config Pairs; single-station autocorr at sparse permafrost/polar sites; dense arrays Lindner 2021 (single 3-comp); Luo 2023 (polar autocorr).
Depth sensitivity ~0–10 m (active layer / firn) Large seasonal swings (e.g. +3 % to −8 %, James 2019).

Geothermal / Reservoir / CO2 / mining

Parameter Typical / recommended Notes
Frequency band ~0.25–3.5 Hz (mining as low as 0.6–1.2 Hz) Lower band for depth penetration to reservoir.
Coda / lapse window ~20 s coda windows for km-scale reservoirs Tie window to depth-sensitivity (lapse-time) analysis.
dv/v method Stretching; MWCS for larger borehole arrays (Salton Sea)
Station config Pairs for reservoir localization; dense arrays for tailings dams / mine slopes
Depth sensitivity Hundreds of m to a few km; coda-wave kernels attribute location Caveat: surface arrays at CO2 sites (Ketzin) sense the shallow subsurface, not the deep plume (Gassenmeier et al. 2014).

Key rule: ambient noise can reveal aseismic reservoir response invisible to standard microseismic monitoring (Obermann et al. 2015; Hillers et al. 2015).


Survey compiled from a structured scan of the published literature (2026). Scope is dv/v passive monitoring only; fluvial seismology and landslide detection (power-spectral methods) are out of scope. See the references page for full citations and the repository literature/ folder for the machine-readable table and provenance.


References

Bensen, G. D., M. H. Ritzwoller, M. P. Barmin, et al. 2007. “Processing Seismic Ambient Noise Data to Obtain Reliable Broad-Band Surface Wave Dispersion Measurements.” Geophysical Journal International 169 (3): 1239–60. https://doi.org/10.1111/j.1365-246X.2007.03374.x.
Clarke, D., L. Zaccarelli, N. M. Shapiro, and F. Brenguier. 2011. “Assessment of Resolution and Accuracy of the Moving Window Cross Spectral Technique for Monitoring Crustal Temporal Variations Using Ambient Seismic Noise.” Geophysical Journal International 186 (2): 867–82. https://doi.org/10.1111/j.1365-246X.2011.05074.x.
Clements, T., and M. A. Denolle. 2023. “The Seismic Signature of California’s Earthquakes, Droughts, and Floods.” Journal of Geophysical Research: Solid Earth 128. https://doi.org/10.1029/2022jb025553.
Clements, Timothy, and Marine A. Denolle. 2018. “Tracking Groundwater Levels Using the Ambient Seismic Field.” Geophysical Research Letters 45 (13): 6459–65. https://doi.org/10.1029/2018GL077706.
Daskalakis, E., C. P. Evangelidis, J. Garnier, N. S. Melis, G. Papanicolaou, and C. Tsogka. 2016. “Robust Seismic Velocity Change Estimation Using Ambient Noise Recordings.” Geophysical Journal International 205: 1926–36. https://doi.org/10.1093/gji/ggw142.
Donaldson, C., T. Winder, C. Caudron, and R. S. White. 2019. “Crustal Seismic Velocity Responds to a Magmatic Intrusion and Seasonal Loading in Icelands Northern Volcanic Zone.” Science Advances 5. https://doi.org/10.1126/sciadv.aax6642.
Feng, Kuan-Fu, Hsin-Hua Huang, and Yih-Min Wu. 2020. “Detecting Pre-Eruptive Magmatic Processes of the 2018 Eruption at Kilauea, Hawaii Volcano with Ambient Noise Interferometry.” Earth, Planets and Space 72. https://doi.org/10.1186/s40623-020-01199-x.
Gassenmeier, M., C. Sens-Schönfelder, M. Delatre, and M. Korn. 2014. “Monitoring of Environmental Influences on Seismic Velocity at the Geological Storage Site for CO2 in Ketzin (Germany) with Ambient Seismic Noise.” Geophysical Journal International 200: 524–33. https://doi.org/10.1093/gji/ggu413.
Hadziioannou, Céline, Eric Larose, Olivier Coutant, Philippe Roux, and Michel Campillo. 2009. “Stability of Monitoring Weak Changes in Multiply Scattering Media with Ambient Noise Correlation: Laboratory Experiments.” The Journal of the Acoustical Society of America 125: 3688–95. https://doi.org/10.1121/1.3125345.
Hadziioannou, C., E. Larose, A. Baig, P. Roux, and M. Campillo. 2011. “Improving Temporal Resolution in Ambient Noise Monitoring of Seismic Wave Speed.” Journal of Geophysical Research 116. https://doi.org/10.1029/2011jb008200.
Hillers, Gregor, Stephan Husen, Anne Obermann, Thomas Planès, Eric Larose, and Michel Campillo. 2015. “Noise-Based Monitoring and Imaging of Aseismic Transient Deformation Induced by the 2006 Basel Reservoir Stimulation.” Geophysics 80: KS51–68. https://doi.org/10.1190/geo2014-0455.1.
Jiang, Chengxin, and Marine A. Denolle. 2020. NoisePy: A New High-Performance Python Tool for Ambient-Noise Seismology.” Seismological Research Letters 91 (3): 1853–66. https://doi.org/10.1785/0220190364.
Lecocq, Thomas, Corentin Caudron, and Florent Brenguier. 2014. MSNoise, a Python Package for Monitoring Seismic Velocity Changes Using Ambient Seismic Noise.” Seismological Research Letters 85 (3): 715–26. https://doi.org/10.1785/0220130073.
Mao, Shujuan, Aurélien Mordret, Michel Campillo, Hongjian Fang, and Robert D. van der Hilst. 2020. “On the Measurement of Seismic Traveltime Changes in the Time–Frequency Domain with Wavelet Cross-Spectrum Analysis.” Geophysical Journal International 221 (1): 550–68. https://doi.org/10.1093/gji/ggz495.
Mikesell, T. Dylan, Alison E. Malcolm, Di Yang, and Matthew M. Haney. 2015. “A Comparison of Methods to Estimate Seismic Phase Delays: Numerical Examples for Coda Wave Interferometry.” Geophysical Journal International 202 (1): 347–60. https://doi.org/10.1093/gji/ggv138.
Obermann, A., T. Kraft, E. Larose, and S. Wiemer. 2015. “Potential of Ambient Seismic Noise Techniques to Monitor the St. Gallen Geothermal Site (Switzerland).” Journal of Geophysical Research: Solid Earth 120: 4301–16. https://doi.org/10.1002/2014jb011817.
Obermann, Anne, Thomas Planès, Céline Hadziioannou, and Michel Campillo. 2016. “Lapse-Time-Dependent Coda-Wave Depth Sensitivity to Local Velocity Perturbations in 3-d Heterogeneous Elastic Media.” Geophysical Journal International 207 (1): 59–66. https://doi.org/10.1093/gji/ggw264.
Obermann, Anne, Thomas Planès, Eric Larose, Christoph Sens-Schönfelder, and Michel Campillo. 2013. “Depth Sensitivity of Seismic Coda Waves to Velocity Perturbations in an Elastic Heterogeneous Medium.” Geophysical Journal International 194 (1): 372–82. https://doi.org/10.1093/gji/ggt043.
Obermann, and Hillers. 2019. “Seismic Time-Lapse Interferometry Across Scales.” Advances in Geophysics, 65–143. https://doi.org/10.1016/bs.agph.2019.06.001.
Rivet, Diane, Florent Brenguier, and Frédéric Cappa. 2015. “Improved Detection of Preeruptive Seismic Velocity Drops at the Piton de La Fournaise Volcano.” Geophysical Research Letters 42: 6332–39. https://doi.org/10.1002/2015gl064835.
Rubinstein, Justin L., and Gregory C. Beroza. 2005. “Depth Constraints on Nonlinear Strong Ground Motion from the 2004 Parkfield Earthquake.” Geophysical Research Letters 32. https://doi.org/10.1029/2005gl023189.
Sens-Schönfelder, Christoph, and Ulrich Wegler. 2006. “Passive Image Interferometry and Seasonal Variations of Seismic Velocities at Merapi Volcano, Indonesia.” Geophysical Research Letters 33 (21): L21302. https://doi.org/10.1029/2006GL027797.
Sheng, Y., A. Mordret, K. Sager, et al. 2022. “Monitoring Seismic Velocity Changes Across the San Jacinto Fault Using TrainGenerated Seismic Tremors.” Geophysical Research Letters 49. https://doi.org/10.1029/2022gl098509.
Snieder, Roel, Alexandre Grêt, Huub Douma, and John Scales. 2002. “Coda Wave Interferometry for Estimating Nonlinear Behavior in Seismic Velocity.” Science 295 (5563): 2253–55. https://doi.org/10.1126/science.1070015.
Stehly, L., B. Froment, M. Campillo, Q. Y. Liu, and J. H. Chen. 2015. “Monitoring Seismic Wave Velocity Changes Associated with the Mw 7.9 Wenchuan Earthquake: Increasing the Temporal Resolution Using Curvelet Filters.” Geophysical Journal International 201 (3): 1939–49. https://doi.org/10.1093/gji/ggv110.
Takano, Tomoya, Takeshi Nishimura, and Hisashi Nakahara. 2017. “Seismic Velocity Changes Concentrated at the Shallow Structure as Inferred from Correlation Analyses of Ambient Noise During Volcano Deformation at IzuOshima, Japan.” Journal of Geophysical Research: Solid Earth 122: 6721–36. https://doi.org/10.1002/2017jb014340.
Wang, QingYu, Florent Brenguier, Michel Campillo, Albanne Lecointre, Tetsuya Takeda, and Yosuke Aoki. 2017. “Seasonal Crustal Seismic Velocity Changes Throughout Japan.” Journal of Geophysical Research: Solid Earth 122: 7987–8002. https://doi.org/10.1002/2017jb014307.
Wang, Qing-Yu, and HuaJian Yao. 2020. “Monitoring of Velocity Changes Based on Seismic Ambient Noise: A Brief Review and Perspective.” Earth and Planetary Physics 4: 1–11. https://doi.org/10.26464/epp2020048.
Weaver, Richard L., Céline Hadziioannou, Eric Larose, and Michel Campillo. 2011. “On the Precision of Noise Correlation Interferometry.” Geophysical Journal International 185 (3): 1384–92. https://doi.org/10.1111/j.1365-246X.2011.05015.x.
Yuan, Congcong, Jared Bryan, and Marine Denolle. 2021. “Numerical Comparison of Time-, Frequency- and Wavelet-Domain Methods for Coda Wave Interferometry.” Geophysical Journal International 226 (2): 828–46. https://doi.org/10.1093/gji/ggab140.
Zhan, Zhongwen, Victor C. Tsai, and Robert W. Clayton. 2013. “Spurious Velocity Changes Caused by Temporal Variations in Ambient Noise Frequency Content.” Geophysical Journal International 194 (3): 1574–81. https://doi.org/10.1093/gji/ggt170.
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