Pith. sign in

REVIEW 2 major objections 5 minor 59 references

The Roles of Low-Noise Stations, Arrays and Ocean-Bottom Seismometers in Monitoring UK Offshore Seismicity associated with Subsurface Storage of Carbon Dioxide

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Of the tested monitoring upgrades, only seabed seismometers substantially improve where small induced earthquakes at offshore CO2 storage sites are located: 2-3 sensors cover Endurance, 3-5 all North Sea licences.

desk verdict A useful applied Bayesian OED paper for UK offshore CCS monitoring, but the OBS-over-land ranking is more sensitive to the assumed velocity-uncertainty model than the paper lets on—though the authors do flag this. read the letter →

arxiv 2506.08560 v1 pith:2SFG7RDE submitted 2025-06-10 physics.geo-ph

Dominik Strutz , Andrew Curtis This is my paper
classification physics.geo-ph MSC 62K0562F1586A15
keywords inducedseismicitymonitoringcarboncaptureandstorageBayesianexperimentaldesignexpectedinformationgainocean-bottomseismometersmicroseismicNorthSeaseismicnetworkoptimisation
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 asks how the UK should upgrade its earthquake monitoring so that small induced earthquakes near offshore carbon dioxide storage sites in the North Sea can be detected and, more importantly, located. Using Bayesian experimental design, it compares four candidate additions to the existing land-based British network: a deep low-noise station in Boulby mine, the North York Moors seismic array, an optimally placed extra onshore station, and ocean-bottom seismometers (OBS). Its central claim is that land-based additions mainly lower the detection threshold for magnitude 0-2 events while barely improving location accuracy, whereas a small OBS network sharpens locations substantially because the sensors sit close to the expected events. If the paper is right, operators should direct monitoring money at small seabed deployments rather than further onshore stations; the bottleneck that remains is uncertainty in the seismic velocity model, not station coverage.

What carries the argument

The engine of the comparison is the expected information gain (EIG) of Bayesian experimental design: the expected reduction in Shannon entropy of the event-location posterior, estimated by nested Monte Carlo with 10,000 outer samples and 500 likelihood samples. Two modelling choices carry the argument. Picking uncertainty follows the Shannon-Hartley theorem, $\sigma_{\mathrm{pick}}^2 = \left[2 f_{\max} \log_2\left(1 + \mathrm{SNR}/K\right)\right]^{-2}$, with $K=10$ calibrated to empirical pick-error studies and $f_{\max}=30$ Hz; this term makes an event effectively undetectable as the signal-to-noise ratio approaches one and nearly constant above $\mathrm{SNR}=5$. Velocity-model uncertainty is modelled as $\sigma_{\mathrm{vel}}^2 = t\, \zeta_{\mathrm{vel}}^2$, scaling the differential travel time $t$ by a factor $\zeta_{\mathrm{vel}}=0.083$ fitted to 25 CRUST1.0 crustal models beneath the North Sea, making velocity error the dominant uncertainty wherever events are detectable. Signal amplitudes come from published magnitude-distance relations for P- and S-waves, which is why a seabed sensor's proximity to the expected events raises SNR and thereby both detectability and location precision; the EIG values are converted into an approximate posterior standard deviation in kilometres by assuming an isotropic Gaussian posterior.

What would settle it

Deploy a network of two to three ocean-bottom seismometers plus the existing land stations at a North Sea storage site, record calibration sources with known locations such as airgun shots, and compare the mislocation errors: the paper predicts the seabed network should roughly halve location uncertainty for magnitude 1-2 events while an additional land station would barely change it. Alternatively, re-run the analysis with picking-error statistics measured from modern automated phase pickers on real local data, or with the velocity-uncertainty factor replaced by local tomographic estimates; if magnitude 0-2 events then locate as well from land as from the seabed, the central ranking would be overturned.

Watch

Extended reading notes

Core claim

Stated on the paper's own terms, the result is a ranking of monitoring strategies for the Endurance site and the wider UK North Sea. The existing land network already detects and adequately locates magnitude-2 and larger events, so the gap is the magnitude 0-2 range, where events are either missed or seen but cannot be pinned down. A low-noise mine station such as Boulby, or an onshore array such as North York Moors, primarily converts undetectable small events into detectable ones without improving where they occurred, and an optimally located additional land station adds little beyond detection. An ocean-bottom seismometer behaves differently: proximity raises signal amplitudes, so even one OBS at ordinary noise levels improves location uncertainty for magnitude-1 and larger events, and optimised networks of two to three OBS stations deliver most of the achievable information gain for Endurance. Across all licence areas off England's east coast, three to five OBS stations give robust monitoring, with a standalone three-station OBS network performing nearly as well as one combined with the onshore network. Velocity-model uncertainty remains the dominant error source in every configuration, so reducing it, for example by local seismic tomography, is predicted to shrink location uncertainty by a factor of two to three.

Load-bearing premise

Everything hinges on two assumed error models, neither measured directly at the site: how sharply arrival-time picking accuracy degrades as the signal-to-noise ratio falls (a Shannon-Hartley formula with hand-chosen $K=10$ and $f_{\max}=30$ Hz) and how large the regional velocity-model error is taken to be (a factor $\zeta_{\mathrm{vel}}=0.083$ fitted from coarse crustal models and scaled by travel time), because together these determine why land stations are judged 'detectable but poorly located' while seabed stations are judged 'well located'.

Editorial extensions

If this is right

  • Events of magnitude 2 and above near Endurance are already detected and located adequately by the existing UK network, so the meaningful monitoring gap is the magnitude 0-2 range.
  • Adding a Boulby-type low-noise station or an onshore array would primarily lengthen the catalogue of small detected events without saying where they occurred, making land additions a detection strategy rather than a location strategy.
  • An optimised network of two to three OBS stations near Endurance captures most of the achievable location information, with diminishing returns beyond three stations and the onshore network contributing little once two OBS are in place.
  • Across all UK North Sea carbon storage licence areas off eastern England, three to five OBS stations give robust monitoring, and a standalone three-station OBS network performs nearly as well as a combined onshore-plus-OBS network.
  • Reducing velocity-model uncertainty, for example through local seismic tomography, is predicted to reduce location uncertainty by a factor of two to three across all configurations.

Reading between the lines

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

  • A cost-conscious operator could read the results as a two-tier strategy: keep the existing land network for magnitude-2-plus events and add two to three OBS for locating smaller induced events, rather than paying for ultra-quiet mine installations or wide seabed arrays; the paper itself only notes that the OBS route is likely more expensive.
  • The framework amounts to a quantitative forecast that a field deployment could test: with two to three OBS at Endurance, location errors for magnitude 1-2 events should fall toward the predicted posterior standard deviation of roughly 1.7-2 km, whereas the land network alone should scatter them much more widely.
  • Because the velocity-uncertainty factor is fitted from coarse regional crustal models, sites that already have detailed three-dimensional seismic velocity models may see better land-station performance than this study's conservative estimate suggests, which would narrow the OBS advantage; the paper acknowledges the factor may be conservative where local imaging exists.
  • The same machinery could be re-run as a cost-benefit optimisation: instead of asking which network gives most information, ask which network maximises information per pound, or whether near-coastal or mixed onshore-offshore stations reproduce OBS performance at lower cost, an option the paper flags because several optimal OBS locations lie close to the coast.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The manuscript uses Bayesian experimental design to compare the value of low-noise onshore stations, onshore arrays, an optimally placed land station, and ocean-bottom seismometers (OBS) for monitoring induced seismicity at the Endurance CCS site and neighbouring North Sea licence areas. The data likelihood combines SNR-dependent picking uncertainty with a travel-time-proportional velocity-model uncertainty, and the expected information gain (EIG) about source location is estimated with nested Monte Carlo. The main conclusions are that land-based additions mostly improve detection of small events (M 0–2) without greatly improving location accuracy, while small OBS networks provide substantially better location accuracy, with 2–3 OBS recommended for Endurance and 3–5 OBS for all licence areas. The paper also concludes that velocity-model uncertainty is the key limiter on location precision once events are detected.

Significance. If the results are robust, the paper offers practically useful guidance for monitoring offshore CO2 storage: it quantifies the detection-vs-location distinction for land stations and arrays, and it gives specific OBS station-count recommendations. The methodology is a standard Bayesian OED framework used carefully, with a nested Monte Carlo estimator and convergence tests (Appendix C), an externally calibrated velocity-uncertainty factor derived from CRUST1.0 models, and explicit sensitivity analyses for the maximum frequency and the velocity-uncertainty factor. These strengths make the paper a credible contribution to the CCS monitoring literature, provided the robustness of the headline OBS recommendations to the calibrated noise-model parameters is demonstrated.

major comments (2)
  1. [§3.3.2, §3.3.3, Figs. 14 and 16] The headline station-count recommendations (2–3 OBS for Endurance, 3–5 OBS for all licence areas) are computed only at the nominal velocity-uncertainty factor ζ_vel = 0.083. The paper's own sensitivity analysis in §3.1.1 and §3.3.1 (Figs. 10 and 12) shows that reducing ζ_vel makes a distant low-noise station nearly as informative as a nearby OBS, and the Discussion concedes that local subsurface imaging may make the CRUST1.0-derived value conservative. Because the multi-OBS optimisations are not repeated at smaller ζ_vel, the reader cannot assess whether the recommended OBS counts, and the claim that onshore stations add negligible information beyond one OBS, are robust to the least certain component of the uncertainty model. Please repeat the optimisations at reduced ζ_vel (for example 0.04 and 0), or state the ζ_vel threshold at which the relative ranking of OBS versus onshore designs changes.
  2. [§2.2, Eq. (4)] The picking-uncertainty model is specified with K = 10 and f_max = 30 Hz, but no sensitivity test is reported for K. Because K controls the SNR level at which pick uncertainty rises sharply, and because the relative value of OBS versus land-based low-noise stations depends on how many small (M = 0–2) events are detectable, the configuration rankings could shift if K were changed, for example to the K = 20 value used in the original Aki/Fuggi formulation. Please add a sensitivity analysis for K, or at least discuss quantitatively how reasonable variations in K would affect the relative EIG of the compared designs.
minor comments (5)
  1. [Eq. (4)] Please ensure the typesetting places 2 f_max inside the inverse bracket, i.e. σ²_pick = [2 f_max log2(1 + SNR/K)]^{-2}, to match Figure 4 and the text's statement that lower frequencies increase picking uncertainty; the current layout could be misread as having the opposite frequency dependence.
  2. [Eq. (1)] The truncated Gutenberg-Richter density appears to contain a spurious '+M_lower' term; the expression should be exp[-γ(M_l - M_lower)] / (1 - exp[-γ(M_upper - M_lower)]) for M_l in [M_lower, M_upper].
  3. [Figure 12] The y-axis label reads 'noise level at Boulby site' but the caption and text refer to an OBS station; this should be corrected.
  4. [Appendix D] The first sentence says 'This section supplements section D' but the appendix supplements Section 3.2; please correct the cross-reference.
  5. [§3.3.2] The Bayesian optimisation setup (acquisition function, number of iterations, search bounds, and treatment of the existing onshore stations) is not described; please add a short paragraph so the optimal designs in Figures 13 and 15 are reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the OBS-over-land ranking follows from externally calibrated uncertainty models, not from fitting the conclusion.

full rationale

The paper's derivation chain is self-contained and does not reduce any prediction to its inputs. The central comparison (OBS versus onshore stations) is computed from a Bayesian experimental design framework whose likelihood and prior are stated explicitly (Eqs. 1-16). The two key uncertainty inputs are calibrated to external data: sigma_vel^2 = t zeta_vel^2 (Eq. 8) uses zeta_vel = 0.083 fitted to travel-time perturbations across 25 CRUST1.0 crustal models (Laske et al., 2012), and the picking uncertainty (Eq. 4) uses K=10 chosen to match empirical estimates by Zelt and Forsyth (1994) and Pablo Canales et al. (2000). Neither parameter is fitted to the conclusion that OBS networks are preferable; the conclusion is a consequence of the resulting likelihood, which is computed rather than assumed. The statement that velocity-model uncertainty dominates is a quantitative outcome of comparing sigma_pick and sigma_vel in Figures 7-8, not a definition. The only self-citation (Strutz and Curtis, 2023) concerns optional algorithmic extensions and is not load-bearing for the main result. Figure 12's sensitivity to zeta_vel is an acknowledged model-dependence, not circularity: the paper states that the CRUST1.0-derived value may be conservative for local SCS sites where imaging is available. All such concerns are robustness or correctness issues, not circular reductions.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The central recommendations rest on a small set of calibrated constants (K, zeta_vel, f_max) and several domain priors. The most fragile are the hand-tuned K and the ad hoc velocity uncertainty scaling, because they determine the balance between detection and location accuracy.

free parameters (3)
  • K (Shannon-Hartley factor) = 10
    Hand-tuned in Eq. (4) instead of K=20 from Fuggi et al. (2024) and Aki (1976), to align pick uncertainty with Zelt and Forsyth (1994) and Canales et al. (2000). Controls the SNR threshold at which events become effectively undetectable.
  • zeta_vel (velocity model uncertainty factor) = 0.083
    Fitted in Section 2.2 as the square-root scaling of differential travel-time standard deviation across 25 CRUST1.0 models (Fig. 6). Directly sets location precision and drives the conclusion that velocity uncertainty dominates.
  • f_max (maximum signal frequency) = 30 Hz
    Chosen in Eq. (4) following Green et al. (2020). Sets the magnitude of pick uncertainty; lower frequencies increase pick uncertainty and would change the detection and accuracy trade-offs.
assumptions (7)
  • domain assumption Event locations follow a mixture of five Gaussians centered on injection wells, with 10 km horizontal and 0.5 km vertical standard deviation
    Section 2.1. This prior strongly shapes optimal sensor placement; if induced seismicity occurs farther away, the optimized OBS designs may need more spread.
  • domain assumption Event magnitudes follow a truncated Gutenberg-Richter distribution with b=1
    Section 2.1. The b-value is taken from Kettlety et al. (2024b).
  • domain assumption Data likelihood is multivariate Gaussian with diagonal covariance, ignoring correlations between differential arrival times
    Section 2.2. Justified by Callahan et al. (2024), but not verified for these specific designs.
  • domain assumption Signal amplitudes follow published P- and S-wave magnitude-distance relations (Eqs. 5 and 6)
    Section 2.2. These empirical relations set the SNR and thus the detection thresholds.
  • ad hoc to paper Velocity model uncertainty scales as sigma_vel^2 = t * zeta_vel^2, inspired by a Gaussian random walk
    Section 2.2, Eq. (8). The functional form is chosen for convenience and fitted to CRUST1.0 model spread.
  • domain assumption Station noise is stationary and measured from two-year PSD in the 10-30 Hz band
    Section 2.2, Eq. (7). Assumes noise does not vary with season or weather.
  • standard math Nested Monte Carlo EIG estimator with N=10,000 and Nlike=500 is unbiased enough for design comparisons
    Appendix C convergence tests. No error bars are propagated to the figures.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Roles of Low-Noise Stations, Arrays and Ocean-Bottom Seismometers in Monitoring UK Offshore Seismicity associated with Subsurface Storage of Carbon Dioxide." pith.science (2026). https://pith.science/paper/2SFG7RDE

@misc{pith2026250608560,
  author       = {Pith},
  title        = {Pith review of: The Roles of Low-Noise Stations, Arrays and Ocean-Bottom Seismometers in Monitoring UK Offshore Seismicity associated with Subsurface Storage of Carbon Dioxide},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2SFG7RDE}},
  note         = {Machine review of arXiv:2506.08560}
}
read the original abstract

Effective seismic monitoring of subsurface carbon dioxide storage (SCS) sites is essential for managing risks posed by induced seismicity. This is particularly challenging in offshore environments, such as the Endurance license area in the North Sea, where the UK's permanent land-based seismometer network offers limited monitoring capability due to its distance from the expected locations of seismic events. A Bayesian experimental design framework is used to assess enhancements of the network with a low-noise onshore station located at around 1~km depth in Boulby mine, the onshore North York Moors Seismic Array, an optimally-located additional on-shore monitoring site, and ocean bottom seismometers (OBS). We quantify the expected information gain about seismic source locations and introduce a practical method to incorporate signal-to-noise dependent detectability and velocity model uncertainty. We show that the Boulby station or an onshore array primarily lower the detection threshold for small-magnitude events (M=0-2), but offer limited improvement in location accuracy. An optimally-located additional land-based seismometer or local array provides little additional benefit. OBS deployments yield significant improvements in location accuracy due to their proximity to potential seismicity. Optimised networks of two to three OBS stations are effective for Endurance, while three to five OBS stations offer robust monitoring across North Sea carbon storage licence areas off England's east coast. Velocity model uncertainty remains a key limiting factor for location precision across all configurations. We conclude that deploying OBS networks is the most promising strategy for enhancing microseismic monitoring capabilities at offshore SCS sites, though potentially more expensive.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

59 extracted references · 43 canonical work pages

  1. [1]

    Abakumov, A

    I. Abakumov, A. Roeser, and S. A. Shapiro. Arrival-time picking uncertainty: Theoretical estimations and their application to microseismic data. Geophysics, 85 0 (4): 0 U65--U76, July 2020. ISSN 0016-8033. doi:10.1190/geo2019-0589.1

    work page doi:10.1190/geo2019-0589.1 2020

  2. [2]

    K. Aki. Signal to noise ratio in seismic measurements. Volcanoes and Tectonosphere, pages 187--192, 1976

    work page 1976

  3. [3]

    Berger, P

    J. Berger, P. Davis, and G. Ekstr \" o m. Ambient earth noise: A survey of the global seismographic network. Journal of Geophysical Research: Solid Earth, 109 0 (B11), Nov. 2004. ISSN 2156-2202. doi:10.1029/2004JB003408

    work page doi:10.1029/2004jb003408 2004

  4. [4]

    Beyreuther, R

    M. Beyreuther, R. Barsch, L. Krischer, T. Megies, Y. Behr, and J. Wassermann. ObsPy : A python toolbox for seismology. Seismol. Res. Lett., 81 0 (3): 0 530--533, May 2010. ISSN 0895-0695. doi:10.1785/gssrl.81.3.530

    doi:10.1785/gssrl.81.3.530 2010

  5. [5]

    Bormann and E

    P. Bormann and E. Wielandt. Seismic signals and noise, 2013

    work page 2013

  6. [6]

    Offshore environmental statement for the northern endurance partnership

    BP Exploration Operating Company Limited . Offshore environmental statement for the northern endurance partnership. https://www.bp.com/content/dam/bp/country-sites/en\_gb/united-kingdom/home/images/nep/NEP-Environmental-Statement-Part-A.pdf, Sept. 2023

    work page 2023

  7. [7]

    Great britain seismograph network, 1970

    British Geological Survey . Great britain seismograph network, 1970

    work page 1970

  8. [8]

    J. N. Brune. Tectonic stress and the spectra of seismic shear waves from earthquakes. Journal of Geophysical Research, 75 0 (26): 0 4997--5009, Sept. 1970. ISSN 2156-2202. doi:10.1029/JB075i026p04997

    work page doi:10.1029/jb075i026p04997 1970

Show all 59 references
  1. [9]

    Callahan, K

    J. Callahan, K. Monogue, R. Villarreal, and T. Catanach. Analysis and optimization of seismic monitoring networks with bayesian optimal experiment design. arXiv [stat.AP], Sept. 2024

    2024

  2. [10]

    Cerasi, A

    P. Cerasi, A. Stroisz, E. S nsteb , S. Stanchits, V. Oye, and R. Bauer. Experimental investigation of injection pressure effects on fault reactivation for CO2 storage. Int. J. Greenhouse Gas Control, 78: 0 218--227, Nov. 2018. ISSN 1750-5836,1878-0148. doi:10.1016/j.ijggc.2018.08.011

    2018 doi

  3. [11]

    R. A. Chadwick, P. Zweigel, U. Gregersen, G. A. Kirby, S. Holloway, and P. N. Johannessen. Geological reservoir characterization of a CO2 storage site: The utsira sand, sleipner, northern north sea. Energy, 29 0 (9-10): 0 1371--1381, July 2004. ISSN 0360-5442,1873-6785. doi:10...

    2004 doi

  4. [12]

    Coles and A

    D. Coles and A. Curtis. Efficient nonlinear bayesian survey design usingDNoptimization . Geophysics, 76 0 (2): 0 Q1--Q8, Mar. 2011. ISSN 0016-8033,1942-2156. doi:10.1190/1.3552645

    2011 doi

  5. [13]

    Endurance storage development plan

    Department for Business, Energy & Industrial Strategy . Endurance storage development plan. https://assets.publishing.service.gov.uk/media/629525a0d3bf7f036750aff5/NS051-SS-REP-000-00010-Storage\_Development\_Plan.pdf, Sept. 2023

    2023

  6. [14]

    Diehl, E

    T. Diehl, E. Kissling, S. Husen, and F. Aldersons. Consistent phase picking for regional tomography models: application to the greater alpine region. Geophys J Int, 176 0 (2): 0 542--554, Feb. 2009. ISSN 0956-540X. doi:10.1111/j.1365-246X.2008.03985.x

    2009 arXiv

  7. [15]

    Du and H

    X. Du and H. Wang. Efficient estimation of expected information gain in bayesian experimental design with multi-index monte carlo. Stat. Comput., 34 0 (6): 0 1--15, Dec. 2024. ISSN 0960-3174,1573-1375. doi:10.1007/s11222-024-10522-5

    2024 doi

  8. [16]

    Feng and Y

    C. Feng and Y. M. Marzouk. A layered multiple importance sampling scheme for focused optimal bayesian experimental design. arXiv [stat.CO], Mar. 2019

    2019

  9. [17]

    Foster, M

    A. Foster, M. Jankowiak, E. Bingham, P. Horsfall, Y. W. Teh, T. Rainforth, and N. Goodman. Variational bayesian optimal experimental design. Adv. Neural Inf. Process. Syst., 32, Mar. 2019. ISSN 1049-5258

    2019

  10. [18]

    Fuggi, S

    A. Fuggi, S. Re, G. Tango, S. Del Gaudio, A. Brovelli, and G. Cassiani. Assessment of earthquake location uncertainties for the design of local seismic networks. Earthq. Sci., 37 0 (5): 0 415--433, Oct. 2024. ISSN 1674-4519,1867-8777. doi:10.1016/j.eqs.2024.06.006

    2024 doi

  11. [19]

    D. D. Galloway. The british geological survey earthquake bulletin for 2023. Technical report, British Geological Survey Internal Report, 2024

    2023

  12. [20]

    T. Goda, T. Hironaka, and T. Iwamoto. Multilevel monte carlo estimation of expected information gains. Stoch. Anal. Appl., 38 0 (4): 0 581--600, July 2020. ISSN 0736-2994,1532-9356. doi:10.1080/07362994.2019.1705168

    2020

  13. [21]

    D. N. Green, R. Luckett, B. Baptie, and D. Bowers. A UK local seismic magnitude scale, MLP , using P -wave amplitudes. Geophys. J. Int., 223 0 (3): 0 2054--2065, Oct. 2020. ISSN 0956-540X,1365-246X. doi:10.1093/gji/ggaa438

    2020 doi

  14. [22]

    Gutenberg and C

    B. Gutenberg and C. F. Richter. Frequency of earthquakes in california. Bull. Seismol. Soc. Am., 34 0 (4): 0 185--188, Oct. 1944. ISSN 0037-1106,1943-3573. doi:10.1785/bssa0340040185

    1944 doi

  15. [23]

    C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. Del R\' o, M. Wiebe, P. Peterson, P. G\' e rard-Marchant, K. Sheppa...

    2020 doi

  16. [24]

    Heimann, M

    S. Heimann, M. Kriegerowski, M. Isken, S. Cesca, S. Daout, F. Grigoli, C. Juretzek, T. Megies, N. Nooshiri, A. Steinberg, H. Sudhaus, H. Vasyura-Bathke, T. Willey, and T. Dahm. Pyrocko - an open-source seismology toolbox and library, 2017

    2017

  17. [25]

    Huan and Y

    X. Huan and Y. M. Marzouk. Simulation-based optimal bayesian experimental design for nonlinear systems. J. Comput. Phys., 232 0 (1): 0 288--317, Jan. 2013. ISSN 0021-9991. doi:10.1016/j.jcp.2012.08.013

    2013 doi

  18. [26]

    Matplotlib: A 2D graphics environment

    Hunter . Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 9: 0 90--95, May 2007. ISSN 1521-9615. doi:10.1109/MCSE.2007.55

    2007 doi

  19. [27]

    H. A. Janiszewski, Z. Eilon, J. B. Russell, B. Brunsvik, J. B. Gaherty, S. G. Mosher, W. B. Hawley, and S. Coats. Broad-band ocean bottom seismometer noise properties. Geophys. J. Int., 233 0 (1): 0 297--315, Nov. 2022. ISSN 0956-540X,1365-246X. doi:10.1093/gji/ggac450

    2022 doi

  20. [28]

    A. E. Jerkins, A. K \" o hler, and V. Oye. On the potential of offshore sensors and array processing for improving seismic event detection and locations in the north sea. Geophys. J. Int., 233 0 (2): 0 1191--1212, Jan. 2023. ISSN 0956-540X,1365-246X. doi:10.1093/gji/ggac513

    2023 doi

  21. [29]

    S. Keil, J. Wassermann, T. Megies, and T. Kraft. Optimal network design for microseismic monitoring in urban areas - a case study in munich, germany. Seismica, 2 0 (2), Oct. 2023. ISSN 2816-9387,2816-9387. doi:10.26443/seismica.v2i2.1030

    2023 doi

  22. [30]

    Kettlety, J

    T. Kettlety, J. Asplet, T. Hudson, and J. M. Kendall. Using array methods for cost-effective onshore passive seismic monitoring for offshore CO2 storage projects. In 85th EAGE Annual Conference & Exhibition, volume 2024, pages 1--5. European Association of Geoscientists & Engi...

    2024

  23. [31]

    Kettlety, E

    T. Kettlety, E. Martuganova, D. K \" u hn, J. Schweitzer, C. Weemstra, B. Baptie, T. Dahl-Jensen, A. Jerkins, P. H. Voss, J. M. Kendall, and E. Skurtveit. A unified earthquake catalogue for the north sea to derisk european CCS operations. First Break, 42 0 (5): 0 31--36, May 2...

    2024 doi

  24. [32]

    Krischer, T

    L. Krischer, T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, and J. Wassermann. ObsPy : a bridge for seismology into the scientific python ecosystem. Comput. Sci. Discov., 8 0 (1): 0 014003, May 2015. ISSN 1749-4699,1749-4680. doi:10.1088/1749-4699/8/1/014003

    2015 doi

  25. [33]

    Laske, G

    G. Laske, G. Masters, Z. Ma, and M. E. Pasyanos. CRUST1 .0: An updated global model of earth's crust. EGUGA, page 3743, 2012

    2012

  26. [34]

    D. V. Lindley. On a measure of the information provided by an experiment, 1956

    1956

  27. [35]

    Lomax, A

    A. Lomax, A. Michelini, and A. Curtis. Earthquake location, direct, global-search methods. In Encyclopedia of Complexity and Systems Science, pages 1--33. Springer New York, New York, NY, 2014. ISBN 9783642277375. doi:10.1007/978-3-642-27737-5\_150-2

    2014 doi

  28. [36]

    Q. Long, M. Scavino, R. Tempone, and S. Wang. Fast estimation of expected information gains for bayesian experimental designs based on laplace approximations. Comput. Methods Appl. Mech. Eng., 259: 0 24--39, June 2013. ISSN 0045-7825. doi:10.1016/j.cma.2013.02.017

    2013 doi

  29. [37]

    Luckett, L

    R. Luckett, L. Ottem \" o ller, A. Butcher, and B. Baptie. Extending local magnitudeMl to short distances. Geophys. J. Int., 216 0 (2): 0 1145--1156, Feb. 2019. ISSN 0956-540X,1365-246X. doi:10.1093/gji/ggy484

    2019 doi

  30. [38]

    D. E. McNamara. Ambient noise levels in the continental united states. Bull. Seismol. Soc. Am., 94 0 (4): 0 1517--1527, Aug. 2004. ISSN 0037-1106,1943-3573. doi:10.1785/012003001

    2004 doi

  31. [39]

    Megies, M

    T. Megies, M. Beyreuther, R. Barsch, L. Krischer, and J. Wassermann. ObsPy -- what can it do for data centers and observatories? Ann. Geophys., 54 0 (1): 0 47--58, Apr. 2011. ISSN 1593-5213,2037-416X. doi:10.4401/ag-4838

    2011 doi

  32. [40]

    Mo c kus

    J. Mo c kus. On bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference Novosibirsk, July 1--7, 1974, Lecture notes in computer science, pages 400--404. Springer Berlin Heidelberg, Berlin, Heidelberg, 1975. ISBN 9783540071655,97835403749...

    1974 doi

  33. [41]

    Nogueira

    F. Nogueira. Bayesian optimization: Open source constrained global optimization tool for python, 2014

    2014

  34. [42]

    V. Oye, S. Stanchits, O. Babarinde, R. Bauer, A. M. Dichiarante, N. Langet, B. Goertz-Allmann, and S. Frailey. Cubic-meter scale laboratory fault re-activation experiments to improve the understanding of induced seismicity risks. Sci. Rep., 12 0 (1): 0 8015, May 2022. ISSN 204...

    2022 doi

  35. [43]

    Pablo Canales, J

    J. Pablo Canales, J. A. Collins, J. Escart\' n, and R. S. Detrick. Seismic structure across the rift valley of the mid-atlantic ridge at 23 20 ' ( MARK area): Implications for crustal accretion processes at slow spreading ridges. Journal of Geophysical Research: Solid Earth, 1...

    doi

  36. [44]

    Paszke, S

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. K \" o pf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala. PyTorch : An imperative style, hi...

    2019

  37. [45]

    J. R. Peterson. Observations and modeling of seismic background noise, 1993. ISSN 2331-1258

    1993

  38. [46]

    A. T. Ringler, J. Steim, D. C. Wilson, R. Widmer-Schnidrig, and R. E. Anthony. Improvements in seismic resolution and current limitations in the global seismographic network. Geophys. J. Int., 220 0 (1): 0 508--521, Jan. 2020. ISSN 0956-540X,1365-246X. doi:10.1093/gji/ggz473

    2020 doi

  39. [47]

    Rost and C

    S. Rost and C. Thomas. Array seismology: Methods and applications. Rev. Geophys., 2002. ISSN 8755-1209. doi:10.1029/2000RG000100

    2002 doi

  40. [48]

    F. Rozet. Zuko 0.2.0, Apr. 2023

    2023

  41. [49]

    K. J. Ryan. Estimating expected information gains for experimental designs with application to the random fatigue-limit model. J. Comput. Graph. Stat., 12 0 (3): 0 585--603, Sept. 2003. ISSN 1061-8600. doi:10.1198/1061860032012

    2003 doi

  42. [50]

    Shahriari, K

    B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas. Taking the human out of the loop: A review of bayesian optimization. Proc. IEEE Inst. Electr. Electron. Eng., 104 0 (1): 0 148--175, Jan. 2016. ISSN 0018-9219,1558-2256. doi:10.1109/jproc.2015.2494218

    2016

  43. [51]

    C. E. Shannon. A mathematical theory of communication. The Bell System Technical Journal, 27 0 (3): 0 379--423, July 1948. ISSN 0005-8580. doi:10.1002/j.1538-7305.1948.tb01338.x

    1948

  44. [52]

    Strutz and A

    D. Strutz and A. Curtis. Variational bayesian experimental design for geophysical applications: seismic source location, amplitude versus offset inversion, and estimating CO2 saturations in a subsurface reservoir. Geophys. J. Int., 236 0 (3): 0 1309--1331, Dec. 2023. ISSN 0956...

    2023 doi

  45. [53]

    Sutherland, D

    F. Sutherland, D. Lawrance, P. Legrand, and E. Wraith. Seismic monitoring for subsurface uncertainties at the endurance CO2 store. Lead. Edge, 41 0 (4): 0 253--258, Apr. 2022. ISSN 1070-485X. doi:10.1190/tle41040253.1

    2022 doi

  46. [54]

    Van Rossum and F

    G. Van Rossum and F. Drake. The python language reference manual. Network Theory, Bristol, England, Mar. 2011. ISBN 9781906966140

    2011

  47. [55]

    Vilarrasa, J

    V. Vilarrasa, J. Carrera, S. Olivella, J. Rutqvist, and L. Laloui. Induced seismicity in geologic carbon storage. Solid Earth, 10 0 (3): 0 871--892, June 2019. ISSN 1869-9510,1869-9529. doi:10.5194/se-10-871-2019

    2019 doi

  48. [56]

    o hler, P. Ringrose, L. Ottem \

    Z. Zarifi, A. K \" o hler, P. Ringrose, L. Ottem \" o ller, A.-K. Furre, F. Hansteen, A. Jerkins, V. Oye, R. Dehghan Niri, and R. Bakke. Background seismicity monitoring to prepare for large-scale CO2 storage offshore norway. Seismol. Res. Lett., 94 0 (2A): 0 775--791, Mar. 20...

    2023 doi

  49. [57]

    C. A. Zelt and D. A. Forsyth. Modeling wide-angle seismic data for crustal structure: Southeastern grenville province. Journal of Geophysical Research: Solid Earth, 99 0 (B6): 0 11687--11704, June 1994. ISSN 2156-2202. doi:10.1029/93JB02764

    1994 doi

  50. [58]

    Zhang, L

    L. Zhang, L. Fang, W. Wang, and Z. Lv. Seismic phase picking in china seismic array using a deep convolutional neuron network. Earthq. Sci., 33 0 (2): 0 72--81, Apr. 2020. ISSN 1674-4519,1867-8777. doi:10.29382/eqs-2020-0072-03

    2020 doi

  51. [59]

    Zhu and G

    W. Zhu and G. C. Beroza. PhaseNet : A deep-neural-network-based seismic arrival time picking method. Geophys. J. Int., 216 0 (1): 0 261--273, Oct. 2018. ISSN 0956-540X,1365-246X. doi:10.1093/gji/ggy423

    2018 doi

Pith tools

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