Pith. sign in

REVIEW 4 major objections 5 minor 72 references

Near-real-time design of experiments for seismic monitoring of volcanoes

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A new open-source package lets volcano seismologists compute near-optimal monitoring networks in minutes by jointly optimizing travel-time, amplitude, and array observations.

desk verdict Useful open-source tool, but the EIG equations are internally inconsistent and must be fixed before the quantitative claims are reliable. read the letter →

arxiv 2411.11015 v1 pith:PUTN3JIF submitted 2024-11-17 physics.geo-ph

Dominik Strutz , Andrew Curtis This is my paper · ORCID
classification physics.geo-ph MSC 62K0562F1586A15 PACS 91.30.-f
keywords Bayesianexperimentaldesignvolcanoseismicmonitoringseismometernetworkoptimizationexpectedinformationgaintravel-timesourcelocationamplitudearrayback-azimuthgeneticalgorithm
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 claims that Bayesian experimental design can be made practical for volcano seismology: a code package lets any seismologist produce an optimal station layout in minutes using generic volcano databases, and refine it within hours. It is the first to optimize travel-time, amplitude, and array back-azimuth observations simultaneously, combining them into a single expected-information-gain objective. This matters because the network geometry determines how well earthquake locations can be inferred, and most existing networks are not optimized at all. Using Mount Etna as an example, the paper shows that optimal designs outperform random and space-filling layouts and quantifies how the number of receivers trades against expected location uncertainty.

What carries the argument

The engine is the expected information gain (EIG) written in data space as EIG(ξ) = E_{p(m)}[I[p(d|m,ξ)]] − I[p(d|ξ)], where p(m) is the prior over source locations, p(d|m,ξ) is the Gaussian data likelihood, and I is Shannon information. During optimization the EIG is evaluated with the DN method, which approximates the evidence p(d|ξ) as a multivariate Gaussian and uses the log-determinant of the data covariance, while the nested Monte Carlo (NMC) method provides an unbiased but slower estimate for validation. A genetic algorithm searches over a design space built from digital elevation models and user-defined constraints, and the prior is defined on a grid so each cell can be weighted by elevation or other volcano-specific information.

What would settle it

Take a volcano with two distinct plausible source regions (multimodal prior), optimize with both the fast Gaussian-evidence method and the slow exact method, and compare the resulting station layouts and their true expected information gain; if the fast layout differs substantially or performs clearly worse, the near-real-time design claim fails for that scenario.

Watch

Extended reading notes

Core claim

The central contribution is a unified Bayesian experimental design framework and its open-source implementation, which optimizes a seismic monitoring network for hybrid data types—travel times, amplitudes, and array back-azimuths—at the same time. The paper demonstrates that the fast DN approximation of expected information gain is accurate enough for near-real-time optimization, while the slower NMC method serves to validate the result. It also shows that the optimal design process is relatively robust to the choice of velocity model, and that going to a 3D heterogeneous model yields a modest improvement, mainly for deeper events. By translating the expected information gain into an expected posterior standard deviation, the paper gives practitioners an intuitive number for comparing designs and deciding how many stations they need.

Load-bearing premise

The fast design method used by default assumes that the spread of predicted data across all possible earthquake locations is roughly bell-shaped; if a volcano has several well-separated possible source regions, that assumption breaks and the quick designs can be misleading unless the slower, exact method is used.

Editorial extensions

If this is right

  • A volcano seismologist with no design expertise can obtain an initial optimal network layout within minutes and refine it within hours, using only public topography and volcano databases.
  • Optimal layouts consistently beat random and quasi-random space-filling layouts, matching the mean uncertainty of Sobol designs with one fewer receiver.
  • Optimizing travel-time, amplitude, and array back-azimuth data together produces designs suited to diverse event types, including long-period and tremor signals that lack clear phase arrivals.
  • The expected location uncertainty can be summarized by a single standard-deviation number, so practitioners can read off how many receivers are needed to reach a desired precision.
  • The choice of velocity model has modest influence on the optimal layout; a heterogeneous 3D model yields the best performance, mainly by improving resolution of deep events.

Reading between the lines

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

  • The authors' homogeneous-velocity assumption is likely the main hidden cost of the 'minutes' promise; moving to a 3D velocity model shifts computation from minutes to hours, so the speed claim applies most directly to the simplified forward model.
  • A natural testable extension is to back-test the optimizer on a volcano with an existing seismic catalog, comparing simulated location errors of the recommended network against those of the actual network.
  • Because the code treats an array as a single instrument measuring back-azimuth, it does not optimize the internal geometry of the array aperture itself, so users needing fine array control would have to extend the design vector beyond what the paper demonstrates.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 Bayesian experimental design framework and an accompanying Python package for optimizing seismic monitoring networks at volcanoes, combining travel-time, amplitude, and array back-azimuth observations in a single expected information gain (EIG) objective. The DN approximation is used for rapid design optimization and the NMC method for validation, and the workflow is demonstrated on Mount Etna with comparisons against random and Sobol reference designs, as well as an exploration of homogeneous, layered, and heterogeneous velocity models. The authors claim that this is the first simultaneous optimization of travel-time, amplitude, and array source location methods and that the package enables near-real-time design for any volcano through public databases.

Significance. If the software operates as described, it addresses a genuine operational need: rapid, accessible, and reproducible design of volcano monitoring networks. The integration of multiple data types and the connection to public topographic and volcanic databases are practical strengths. The authors also provide open-source code with a Jupyter notebook, which is commendable. However, the central quantitative results—EIG values, approximate standard deviations, and the receiver-count comparison in Figure 8—depend on information-theoretic equations that are internally inconsistent in the manuscript. The inconsistencies affect the sign and dimension of the evidence term, so the reported numbers cannot be considered reliable unless the code resolves the discrepancies. The paper's practical contribution is therefore potentially valuable but is not yet supported by a correct and unambiguous presentation of the objective function.

major comments (4)
  1. [2.1.2, Eqs. (6), (7), (10)] Equations (6) and (10) replace the term k/2(1+log(2π)) from Equation (7) with the constant 3/2(1+log(2π)), even though Equation (7) defines k as the dimension of the data space. In the main example (Section 3.0.5), three nodal stations and one array yield data dimension k = 9 (each nodal station contributes a travel time and an amplitude; the array contributes a travel time, an amplitude, and a back azimuth). Because k changes with the number of receivers, the error does not cancel when comparing designs of different sizes, as is done in Figure 8. Please correct the equations to use the actual data-space dimension and state explicitly which expression is implemented in the released code.
  2. [2.1.2 and Appendix A, Eqs. (4), (6), (15), (7)] The sign convention for Shannon information is inconsistent. Equation (15) defines I[p] = E_p[log p], which is the negative of the differential entropy, whereas Equation (7) gives the positive entropy of a multivariate Gaussian. Depending on which convention is used, the evidence term in Equations (4) and (6) has the opposite sign. The design objective is therefore not unambiguously specified. Please adopt a single convention, apply it consistently, and confirm that the code implements the corrected form.
  3. [3.0.4, Eq. (12)] The travel-time uncertainty formula σt(t)^2 = σp^2 + t·σv^2 is dimensionally inconsistent if σv is a dimensionless relative velocity uncertainty as stated in the text. The example (standard deviation of 0.1–0.2 s for a travel time of 1 s) implies the intended formula is σt^2 = σp^2 + (t·σv)^2. Please correct the equation and verify that the code implements the intended form, since this term enters the likelihood for all travel-time based data.
  4. [3.0.6, Eq. (14)] The derivation of the approximate standard deviation appears to contain an algebraic error. For a three-dimensional isotropic Gaussian posterior with variance σ^2, the entropy is H = (3/2)(1+log(2π)) + 3 log σ. Substituting this into the relation arI_post = EIG - I_prior and the exponential in Equation (14) yields arσ = exp( -arI_post/3 - 1/2(1+log(2π)) ), not arσ^2. Please re-derive Equation (14) and ensure that the values reported in Figures 5, 8, and 10 are correct.
minor comments (5)
  1. [2.2.3] The phrase "we define an array as seismic array" should read "we define an array as a seismic array".
  2. [3.0.6] The word "confimation" should be "confirmation".
  3. [Figure 8] The axis simultaneously shows EIG in nats and approximate standard deviation in meters, which makes the relative scales difficult to interpret. Consider using separate panels or a secondary axis.
  4. [3.0.6] The description of Sobol reference designs is somewhat confusing: it would be clearer to state that the Sobol sequence is scaled by a random factor sampled from 0 to 20 km, and to explain why this approximates a "reasonable, uniformly distributed random design."
  5. [7, Data Availability] The code is available only through a GitHub link. For reproducibility, please also provide a versioned archive (e.g., Zenodo DOI) and state the software license.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor tautological sanity check, no load-bearing circularity.

  1. other [Section 2.1.2 and Section 3.0.6, Eq. (3) and Fig. 8]
    "Using the EIG as our design objective function, the best design can be expressed mathematically as ... It is clear that the optimal designs are substantially better than the random designs, and that the Sobol designs are better than uniformly random designs."

    The paper defines the optimal design as the maximizer of EIG (Eq. 3), and then compares optimal, random, and Sobol designs using the same EIG-derived approximate standard deviation (Eq. 14, Fig. 8). Therefore, the reported superiority of optimal designs is entailed by the optimization objective itself; it is a consistency check of the optimizer against the DN criterion, not an independent empirical test of the resulting networks. This is a mild illustrative tautology rather than a fitted-parameter circularity, and it does not bear on the central software/design contribution.

full rationale

The derivation chain is largely self-contained. Equations (2)-(5) are standard Bayesian experimental-design algebra, and the DN approximation in Eqs. (6)-(10) follows from a stated multivariate-Gaussian evidence assumption, with prior, likelihood, and noise parameters fixed a priori from literature and scenario reasoning rather than fitted from the outputs. The NMC estimate is an unbiased Monte-Carlo realization of Eq. (4), and using DN for optimization while checking with NMC is a legitimate algorithmic choice, not circular validation. The self-citations to Strutz and Curtis (2023) support robustness caveats (e.g., DN failure under multimodal priors) but do not define the objective or force the design choice, so they are not load-bearing circularity. The only construction-reducing element is the optimal-versus-random/Sobol comparison in Fig. 8: because "optimal" is defined as EIG-maximizing and the comparison metric is EIG-derived, the result is an internal consistency check. This is minor and non-central. Separately, Eqs. (6) and (10) use a hard-coded 3/2 factor while Eq. (7) uses k/2 for the data dimension; for designs with different effective data dimensions this affects absolute EIG levels, but that is a technical consistency/reproducibility concern, not a circularity, and does not affect the score.

Assumptions & free parameters 8 free parameters · 7 assumptions · 0 invented entities

No new physical entities are introduced. All free parameters are user-supplied prior and forward-model constants for the worked example, not fitted to a target result. The main assumptions are Gaussian independent errors, homogeneous velocity in the default workflow, Gaussian approximation of the evidence in the DN method, and GA convergence.

free parameters (8)
  • Etna prior standard deviations and center (5 km horizontal, 8 km vertical, 2 km depth) = 5 km, 8 km, 2 km depth
    Chosen by hand for the example; affects the prior and therefore the resulting optimal design.
  • Travel-time picking uncertainty sigma_p = 0.01 s
    Assumed value in the forward model, not derived from data.
  • Relative velocity model uncertainty sigma_v = 0.1
    Selected as a reasonable starting point (paper suggests 0.1 to 0.2); affects likelihood widths.
  • Quality factor Q = 50
    Set for the amplitude forward model based on literature for the example volcano.
  • Frequency f = 2.0 Hz
    Chosen for the amplitude attenuation model.
  • Quality factor uncertainty sigma_Q = 10
    Assumed uncertainty used in linearized error propagation for amplitudes.
  • Back azimuth uncertainty sigma_theta = 6 degrees
    Taken from Inza et al. (2011) and applied to the array measurement.
  • Design space constraints (slope limits, safety margin, array flat area) = inclines <20 degrees nodes, <3 degrees arrays, 3 km safety margin, arrays in >=10 km2 flat areas
    Ad hoc constraints chosen for the Etna example to illustrate realistic deployment conditions.
assumptions (7)
  • standard math The posterior distribution is computed with Bayes' theorem and Shannon information as the utility.
    Bayesian experimental design foundation, Section 2.1.
  • domain assumption The data likelihood is multivariate Gaussian with a diagonal covariance matrix and independent errors.
    Explicitly stated in Section 2.1.1 and discussed as an assumption in Section 2.2.5.
  • domain assumption The forward model uses a homogeneous velocity model for the main workflow.
    Stated in Section 2.2.5: 'Unless we have a subsurface velocity model available and use a considerably more expensive ray tracing method, we assume a homogeneous velocity model.'
  • domain assumption The evidence distribution p(d|xi) is approximated as Gaussian in the DN method, giving an upper bound on EIG.
    Assumed in Section 2.1.2 and its failure mode for multimodal priors is acknowledged in Section 3.0.5.
  • domain assumption NMC and DN estimators provide adequate relative EIG values for design comparison.
    The paper relies on prior work (Bloem et al., 2020; Strutz and Curtis, 2023) and states that the difference between true EIG and estimates is mostly constant.
  • domain assumption The genetic algorithm converges to a sufficiently good design within the chosen population size and generations.
    No convergence guarantee is provided; the paper relies on observing only slight improvements after 200 generations.
  • domain assumption The prior distribution is represented on a discrete grid with uniform density within each cell.
    Described in Section 2.1.1 and used to define the Etna prior in Section 3.0.2.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Near-real-time design of experiments for seismic monitoring of volcanoes." pith.science (2026). https://pith.science/paper/PUTN3JIF

@misc{pith2026241111015,
  author       = {Pith},
  title        = {Pith review of: Near-real-time design of experiments for seismic monitoring of volcanoes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PUTN3JIF}},
  note         = {Machine review of arXiv:2411.11015}
}
read the original abstract

Monitoring the seismic activity of volcanoes is crucial for hazard assessment and eruption forecasting. The layout of each seismic network determines the information content of recorded data about volcanic earthquakes, and experimental design methods optimise sensor locations to maximise that information. We provide a code package that implements Bayesian experimental design to optimise seismometer networks to locate seismicity at any volcano, and a practical guide to make this easily and rapidly implementable by any volcano seismologist. This work is the first to optimise travel-time, amplitude and array source location methods simultaneously, making it suitable for a wide range of volcano monitoring scenarios. The code-package is designed to be straightforward to use and can be adapted to a wide range of scenarios, and automatically links to existing global databases of topography and properties of volcanoes worldwide to allow rapid deployment. Any user should be able to obtain an initial design within minutes using a combination of generic and volcano-specific information to guide the design process, and to refine the design for their specific scenario within hours, if more specific prior information is available.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

72 extracted references · 54 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

    work page

  2. [2]

    Performance of the radial semblance method for the location of very long period volcanic signals

    Almendros, J. Performance of the radial semblance method for the location of very long period volcanic signals. Bull. Seismol. Soc. Am., 93 0 (5): 0 1890--1903, Oct. 2003. doi:10.1785/0120020143

    work page doi:10.1785/0120020143 1903

  3. [3]

    and Aki, K

    Battaglia, J. and Aki, K. Location of seismic events and eruptive fissures on the Piton de la Fournaise volcano using seismic amplitudes. J. Geophys. Res., 108 0 (B8), Aug. 2003. doi:10.1029/2002jb002193

    work page doi:10.1029/2002jb002193 2003

  4. [4]

    Experimental design for fully nonlinear source location problems: which method should I choose? Geophys

    Bloem, H., Curtis, A., and Maurer, H. Experimental design for fully nonlinear source location problems: which method should I choose? Geophys. J. Int., 223 0 (2): 0 944--958, July 2020. doi:10.1093/gji/ggaa358

    work page doi:10.1093/gji/ggaa358 2020

  5. [5]

    G., Gonz\' a lez, P

    Cannav\` o , F., Camacho, A. G., Gonz\' a lez, P. J., Mattia, M., Puglisi, G., and Fern\' a ndez, J. Real time tracking of magmatic intrusions by means of ground deformation modeling during volcanic crises. Sci. Rep., 5 0 (1): 0 10970, June 2015. doi:10.1038/srep10970

    work page doi:10.1038/srep10970 2015

  6. [6]

    Geophysical indications of magma uprising at Mt Etna during the December 2005 to January 2006 non-eruptive period

    Carbone, D., Zuccarello, L., and Saccorotti, G. Geophysical indications of magma uprising at Mt Etna during the December 2005 to January 2006 non-eruptive period. Geophys. Res. Lett., 35 0 (6), Mar. 2008. doi:10.1029/2008gl033212

    work page doi:10.1029/2008gl033212 2005

  7. [7]

    S., Green, R

    Caudron, C., White, R. S., Green, R. G., Woods, J., \' A g\' u stsd\' o ttir, T., Donaldson, C., Greenfield, T., Rivalta, E., and Brandsd\' o ttir, B. Seismic Amplitude Ratio Analysis of the 2014--2015 b\' a rarbunga-Holuhraun dike propagation and eruption. J. Geophys. Res. Solid Earth, 123 0 (1): 0 264--276, Jan. 2018. doi:10.1002/2017jb014660

    work page doi:10.1002/2017jb014660 2014

  8. [8]

    Magmas near the critical degassing pressure drive volcanic unrest towards a critical state

    Chiodini, G., Paonita, A., Aiuppa, A., Costa, A., Caliro, S., De Martino, P., Acocella, V., and Vandemeulebrouck, J. Magmas near the critical degassing pressure drive volcanic unrest towards a critical state. Nat. Commun., 7 0 (1): 0 13712, Dec. 2016. doi:10.1038/ncomms13712

    doi:10.1038/ncomms13712 2016

Show all 72 references
  1. [9]

    and Curtis, A

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

    2011 doi

  2. [10]

    Optimal nonlinear design of marine borehole seismic surveys

    Coles, D., Yang, Y., Djikpesse, H., Prange, M., and Osypov, K. Optimal nonlinear design of marine borehole seismic surveys. Geophysics, 78 0 (3): 0 WB17--WB29, May 2013. doi:10.1190/geo2012-0265.1

    2013 doi

  3. [11]

    Cover, T. M. and Thomas, J. A. Elements of Information Theory. John Wiley & Sons, Nashville, TN, 2 edition, July 2006. doi:10.1002/047174882x

    2006 doi

  4. [12]

    Application of near real-time radial semblance to locate the shallow magmatic conduit at Kilauea Volcano, Hawaii

    Dawson, P., Whilldin, D., and Chouet, B. Application of near real-time radial semblance to locate the shallow magmatic conduit at Kilauea Volcano, Hawaii. Geophys. Res. Lett., 31 0 (21), Nov. 2004. doi:10.1029/2004gl021163

    2004 doi

  5. [13]

    L., and Scarpa, R

    Di Lieto, B., Saccorotti, G., Zuccarello, L., Rocca, M. L., and Scarpa, R. Continuous tracking of volcanic tremor at Mount Etna, Italy. Geophys. J. Int., 169 0 (2): 0 699--705, May 2007. doi:10.1111/j.1365-246x.2007.03316.x

    2007 arXiv

  6. [14]

    and Neri, M

    Falsaperla, S. and Neri, M. Seismic footprints of shallow dyke propagation at Etna, Italy. Sci. Rep., 5 0 (1): 0 11908, July 2015. doi:10.1038/srep11908

    2015 doi

  7. [15]

    Variational Bayesian Optimal Experimental Design

    Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Whye Teh, Y., Rainforth, T., and Goodman, N. Variational Bayesian Optimal Experimental Design. In Advances in Neural Information Processing Systems. Curran Associates, Inc., Mar. 2019

    2019

  8. [16]

    Gad, A. F. PyGAD : an intuitive genetic algorithm Python library. Multimed. Tools Appl., 83 0 (20): 0 58029--58042, Dec. 2023. doi:10.1007/s11042-023-17167-y

    2023 doi

  9. [17]

    R., Millman, K

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

    2020 doi

  10. [18]

    Holland, J. H. Adatptation Natural Artificial Systems. University Michigan Press, Ann Arbor, 1975

    1975

  11. [19]

    and Hamman, J

    Hoyer, S. and Hamman, J. xarray: N - D labeled Arrays and Datasets in Python. J. Open Res. Softw., 5 0 (1): 0 10, Apr. 2017. doi:10.5334/jors.148

    2017 doi

  12. [20]

    and Marzouk, Y

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

    2013 doi

  13. [21]

    Matplotlib: A 2D Graphics Environment

    Hunter . Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng., 9: 0 90--95, May 2007. doi:10.1109/MCSE.2007.55

    2007 doi

  14. [22]

    A., Mars, J

    Inza, L. A., Mars, J. I., M\' e taxian, J. P., O'Brien, G. S., and Macedo, O. Seismo-volcano source localization with triaxial broad-band seismic array: 3C seismic antenna on volcano. Geophys. J. Int., 187 0 (1): 0 371--384, Oct. 2011. doi:10.1111/j.1365-246x.2011.05148.x

    2011 arXiv

  15. [23]

    D., Thompson, G., and Norton, G

    Jolly, A. D., Thompson, G., and Norton, G. E. Locating pyroclastic flows on Soufriere Hills Volcano, Montserrat, West Indies, using amplitude signals from high dynamic range instruments. J. Volcanol. Geotherm. Res., 118 0 (3-4): 0 299--317, Nov. 2002. doi:10.1016/s0377-0273(02)00299-8

    2002 doi

  16. [24]

    and Shan, S.-J

    Kao, H. and Shan, S.-J. The Source-Scanning Algorithm: mapping the distribution of seismic sources in time and space. Geophys. J. Int., 157 0 (2): 0 589--594, Apr. 2004. doi:10.1111/j.1365-246x.2004.02276.x

    2004 arXiv

  17. [25]

    Aso94 : Aso seismic observation with broadband instruments

    Kawakatsu, H., Kaneshima, S., Matsubayashi, H., Ohminato, T., Sudo, Y., Tsutsui, T., Uhira, K., Yamasato, H., Ito, H., and Legrand, D. Aso94 : Aso seismic observation with broadband instruments. J. Volcanol. Geotherm. Res., 101 0 (1-2): 0 129--154, Aug. 2000. doi:10.1016/s0377...

    doi

  18. [26]

    Ku, H. H. Notes on the use of propagation of error formulas. J. Res. Natl. Bur. Stand., 70C 0 (4): 0 263, Oct. 1966. doi:10.6028/jres.070c.025

    1966 doi

  19. [27]

    and Leibler, R

    Kullback, S. and Leibler, R. A. On Information and Sufficiency. Ann. Math. Stat., 22 0 (1): 0 79--86, 1951

    1951

  20. [28]

    B., and Nakano, M

    Kumagai, H., Palacios, P., Maeda, T., Castillo, D. B., and Nakano, M. Seismic tracking of lahars using tremor signals. J. Volcanol. Geotherm. Res., 183 0 (1-2): 0 112--121, May 2009. doi:10.1016/j.jvolgeores.2009.03.010

    2009 doi

  21. [29]

    Ascending seismic source during an explosive eruption at Tungurahua volcano, Ecuador

    Kumagai, H., Placios, P., Ruiz, M., Yepes, H., and Kozono, T. Ascending seismic source during an explosive eruption at Tungurahua volcano, Ecuador. Geophys. Res. Lett., 38 0 (1), Jan. 2011. doi:10.1029/2010gl045944

    2011 doi

  22. [30]

    S., Yamashina, T., Ruiz, M., Palacios, P., Ortiz, H., and Yepes, H

    Kumagai, H., Lacson, Jr, R., Maeda, Y., Figueroa, II, M. S., Yamashina, T., Ruiz, M., Palacios, P., Ortiz, H., and Yepes, H. Source amplitudes of volcano-seismic signals determined by the amplitude source location method as a quantitative measure of event size. J. Volcanol. Ge...

    2013 doi

  23. [31]

    Continuous kurtosis-based migration for seismic event detection and location, with application to piton de la fournaise volcano, la reunion

    Langet, N., Maggi, A., Michelini, A., and Brenguier, F. Continuous kurtosis-based migration for seismic event detection and location, with application to piton de la fournaise volcano, la reunion. Bull. Seismol. Soc. Am., 104 0 (1): 0 229--246, Feb. 2014. doi:10.1785/0120130107

    2014 doi

  24. [32]

    u mpker, G., and W \

    Leva, C., R \" u mpker, G., and W \" o lbern, I. Multi-array analysis of volcano-seismic signals at Fogo and Brava, Cape Verde. Solid Earth, 13 0 (8): 0 1243--1258, Aug. 2022. doi:10.5194/se-13-1243-2022

    2022 doi

  25. [33]

    Lindley, D. V. On a Measure of the Information Provided by an Experiment, 1956. doi:10.1214/aoms/1177728069

    1956

  26. [34]

    Precise, absolute earthquake location under Somma-Vesuvius volcano using a new three-dimensional velocity model

    Lomax, A., Zollo, A., Capuano, P., and Virieux, J. Precise, absolute earthquake location under Somma-Vesuvius volcano using a new three-dimensional velocity model. Geophys. J. Int., 146 0 (2): 0 313--331, Aug. 2001. doi:10.1046/j.0956-540x.2001.01444.x

    2001 arXiv

  27. [35]

    Earthquake location, direct, global-search methods

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

    2014 doi

  28. [36]

    and Bebbington, M

    Marzocchi, W. and Bebbington, M. S. Probabilistic eruption forecasting at short and long time scales. Bull. Volcanol., 74 0 (8): 0 1777--1805, Oct. 2012. doi:10.1007/s00445-012-0633-x

    2012 doi

  29. [37]

    and Boerner, D

    Maurer, H. and Boerner, D. E. Optimized and robust experimental design: a non-linear application to EM sounding. Geophys. J. Int., 132 0 (2): 0 458--468, Feb. 1998. doi:10.1046/j.1365-246x.1998.00459.x

    1998

  30. [38]

    Maurer, H., Curtis, A., and Boerner, D. E. Recent advances in optimized geophysical survey design. Geophysics, 75 0 (5): 0 75A177--75A194, Sept. 2010. doi:10.1190/1.3484194

    2010 doi

  31. [39]

    McNutt, S. R. Volcanic seismology. Annu. Rev. Earth Planet. Sci., 33 0 (1): 0 461--491, May 2005. doi:10.1146/annurev.earth.33.092203.122459

    2005 arXiv

  32. [40]

    Locating sources of volcanic tremor and emergent events by seismic triangulation: Application to Arenal volcano, Costa Rica

    M\' e taxian, J.-P., Lesage, P., and Valette, B. Locating sources of volcanic tremor and emergent events by seismic triangulation: Application to Arenal volcano, Costa Rica. J. Geophys. Res., 107 0 (B10): 0 ECV 10--1--ECV 10--18, Oct. 2002. doi:10.1029/2001jb000559

    2002 doi

  33. [41]

    Theoretical basis of the amplitude source location method for volcano-seismic signals

    Morioka, H., Kumagai, H., and Maeda, T. Theoretical basis of the amplitude source location method for volcano-seismic signals. J. Geophys. Res. [Solid Earth], 122 0 (8): 0 6538--6551, Aug. 2017. doi:10.1002/2017jb013997

    2017 doi

  34. [42]

    S., Lokmer, I., De Barros, L., Bean, C

    O'Brien, G. S., Lokmer, I., De Barros, L., Bean, C. J., Saccorotti, G., Metaxian, J.-P., and Patane, D. Time reverse location of seismic long-period events recorded on Mt Etna. Geophys. J. Int., 184 0 (1): 0 452--462, Jan. 2011. doi:10.1111/j.1365-246x.2010.04851.x

    2011 arXiv

  35. [43]

    and Yomogida, K

    Ogiso, M. and Yomogida, K. Migration of tremor locations before the 2008 eruption of Meakandake Volcano, Hokkaido, Japan. J. Volcanol. Geotherm. Res., 217-218: 0 8--20, Mar. 2012. doi:10.1016/j.jvolgeores.2011.12.005

    2008 doi

  36. [44]

    Descent of tremor source locations before the 2014 phreatic eruption of Ontake volcano, Japan

    Ogiso, M., Matsubayashi, H., and Yamamoto, T. Descent of tremor source locations before the 2014 phreatic eruption of Ontake volcano, Japan. Earth Planets Space, 67 0 (1), Dec. 2015. doi:10.1186/s40623-015-0376-y

    2014 doi

  37. [45]

    A., Dawson, P., and Kedar, S

    Ohminato, T., Chouet, B. A., Dawson, P., and Kedar, S. Waveform inversion of very long period impulsive signals associated with magmatic injection beneath Kilauea volcano, Hawaii. J. Geophys. Res., 103 0 (B10): 0 23839--23862, Oct. 1998. doi:10.1029/98jb01122

    1998 doi

  38. [46]

    and McNutt, S

    Pallister, J. and McNutt, S. R. Synthesis of volcano monitoring. In The Encyclopedia of Volcanoes, pages 1151--1171. Elsevier, 2015. doi:10.1016/b978-0-12-385938-9.00066-3

    2015 doi

  39. [47]

    Tomographic images and 3D earthquake locations of the seismic swarm preceding the 2001 Mt

    Patane, D., Chiarabba, C., Cocina, O., De Gori, P., Moretti, M., and Boschi, E. Tomographic images and 3D earthquake locations of the seismic swarm preceding the 2001 Mt. Etna eruption: Evidence for a dyke intrusion. Geophysical Research Letters, 29 0 (10): 0 135--1--135--4, M...

    2001 doi

  40. [48]

    and Steinberg, D

    Rabinowitz, N. and Steinberg, D. M. Optimal configuration of a seismographic network: A statistical approach. Bull. Seismol. Soc. Am., 80 0 (1): 0 187--196, Feb. 1990. doi:10.1785/BSSA0800010187

    1990 doi

  41. [49]

    and Thomas, C

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

    2002 doi

  42. [50]

    Ryan, K. J. 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. doi:10.1198/1061860032012

    2003 doi

  43. [51]

    and Lokmer, I

    Saccorotti, G. and Lokmer, I. Chapter 2 - A review of seismic methods for monitoring and understanding active volcanoes. In Papale, P., editor, Forecasting and Planning for Volcanic Hazards, Risks, and Disasters, volume 2, pages 25--73. Elsevier, Jan. 2021. doi:10.1016/B978-0-...

    2021 doi

  44. [52]

    J., Di Grazia, G., and Patan\` e , D

    Saccorotti, G., Lokmer, I., Bean, C. J., Di Grazia, G., and Patan\` e , D. Analysis of sustained long-period activity at Etna Volcano, Italy. J. Volcanol. Geotherm. Res., 160 0 (3-4): 0 340--354, Feb. 2007. doi:10.1016/j.jvolgeores.2006.10.008

    2007 doi

  45. [53]

    C., Newman, A

    Saltogianni, V., Stiros, S. C., Newman, A. V., Flanagan, K., and Moschas, F. Time-space modeling of the dynamics of Santorini volcano (Greece) during the 2011-2012 unrest. J. Geophys. Res. Solid Earth, 119 0 (11): 0 8517--8537, Nov. 2014. doi:10.1002/2014jb011409

    2011 doi

  46. [54]

    and Gallagher, K

    Sambridge, M. and Gallagher, K. Earthquake hypocenter location using genetic algorithms. Bull. Seismol. Soc. Am., 83 0 (5): 0 1467--1491, Oct. 1993. doi:10.1785/bssa0830051467

    1993 doi

  47. [55]

    Operational short-term volcanic hazard analysis

    Selva, J., Marzocchi, W., Sandri, L., and Costa, A. Operational short-term volcanic hazard analysis. In Volcanic Hazards, Risks and Disasters, pages 233--259. Elsevier, Jan. 2015. doi:10.1016/b978-0-12-396453-3.00009-5

    2015 doi

  48. [56]

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

    1948

  49. [57]

    o rnsson, H., and Feigl, K. L. Intrusion triggering of the 2010 Eyjafjallaj \

    Sigmundsson, F., Hreinsd\' o ttir, S., Hooper, A., Arnad\' o ttir, T., Pedersen, R., Roberts, M. J., Oskarsson, N., Auriac, A., Decriem, J., Einarsson, P., Geirsson, H., Hensch, M., Ofeigsson, B. G., Sturkell, E., Sveinbj \" o rnsson, H., and Feigl, K. L. Intrusion triggering ...

    2010 doi

  50. [58]

    o rnsson, H., P\' a lsson, F., Sturkell, E., Bean, C. J., M \

    Sigmundsson, F., Hooper, A., Hreinsd\' o ttir, S., Vogfj \" o rd, K. S., \' O feigsson, B. G., Heimisson, E. R., Dumont, S., Parks, M., Spaans, K., Gudmundsson, G. B., Drouin, V., \' A rnad\' o ttir, T., J\' o nsd\' o ttir, K., Gudmundsson, M. T., H \" o gnad\' o ttir, T., Fri...

    2015

  51. [59]

    Sobol', I. M. On the distribution of points in a cube and the approximate evaluation of integrals. Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 7 0 (4): 0 784--802, 1967

    1967

  52. [60]

    M., Rabinowitz, N., Shimshoni, Y., and Mizrachi, D

    Steinberg, D. M., Rabinowitz, N., Shimshoni, Y., and Mizrachi, D. Configuring a seismographic network for optimal monitoring of fault lines and multiple sources. Bull. Seismol. Soc. Am., 85 0 (6): 0 1847--1857, Dec. 1995. doi:10.1785/BSSA0850061847

    1995 doi

  53. [61]

    and Curtis, A

    Strutz, D. and Curtis, A. 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. doi:10.1...

    2023 doi

  54. [62]

    M., and Ferrazzini, V

    Taisne, B., Brenguier, F., Shapiro, N. M., and Ferrazzini, V. Imaging the dynamics of magma propagation using radiated seismic intensity. Geophys. Res. Lett., 38 0 (4), Feb. 2011. doi:10.1029/2010gl046068

    2011 doi

  55. [63]

    Inverse problems= quest for information

    Tarantola , Valette , and Others . Inverse problems= quest for information. J. Geophys., 50 0 (1): 0 159--170, 1982

    1982

  56. [64]

    T., Smith, W

    Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., and Wessel, P. Global bathymetry and topography at 15 arc sec: SRTM15+ . Earth Space Sci., 6 0 (10): 0 1847--1864, Oct. 2019. doi:10.1029/2019ea000658

    2019 doi

  57. [65]

    and Drake, F

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

    2011

  58. [66]

    Global Volcanism Program [Database] Volcanoes of the World (v

    Venzke, E. Global Volcanism Program [Database] Volcanoes of the World (v. 5.2.1; 3 Jul 2024) Distributed by Smithsonian Institution, compiled by Venzke, E . https://doi.org/10.5479/si.GVP.VOTW5-2024.5.2, 2024. doi:10.5479/si.GVP.VOTW5-2024.5.2. Accessed: 2024-7-23

    2024 doi

  59. [67]

    M., Filippi, L., De Luca, G., Scarpa, R., Patan\` e , G., and Vinciguerra, S

    Villase\ n or, A., Benz, H. M., Filippi, L., De Luca, G., Scarpa, R., Patan\` e , G., and Vinciguerra, S. Three-dimensional P -wave velocity structure of Mt. Etna, Italy. Geophysical Research Letters, 25 0 (11): 0 1975--1978, June 1998. doi:10.1029/98GL01240

    1975 doi

  60. [68]

    E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S

    Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J....

    2020

  61. [69]

    White, M. C. A., Fang, H., Nakata, N., and Ben-Zion, Y. PyKonal : A Python Package for Solving the Eikonal Equation in Spherical and Cartesian Coordinates Using the Fast Marching Method. Seismol. Res. Lett., 91 0 (4): 0 2378--2389, July 2020. doi:10.1785/0220190318

    2020 doi

  62. [70]

    S., and Brandsd\' o ttir, B

    Woods, J., Winder, T., White, R. S., and Brandsd\' o ttir, B. Evolution of a lateral dike intrusion revealed by relatively-relocated dike-induced earthquakes: The 2014--15 B\' a r arbunga--Holuhraun rifting event, Iceland. Earth Planet. Sci. Lett., 506: 0 53--63, Jan. 2019. do...

    2014 doi

  63. [71]

    Quantitative analysis of pyroclastic flows using infrasonic and seismic data at unzen volcano, japan

    Yamasato, H. Quantitative analysis of pyroclastic flows using infrasonic and seismic data at unzen volcano, japan. J. Phys. Earth, 45 0 (6): 0 397--416, 1997. doi:10.4294/jpe1952.45.397

    1997 doi

  64. [72]

    Zobin, V. M. Fundamentals of volcanic seismology. In Introduction to Volcanic Seismology, pages 35--60. Elsevier, 2017. doi:10.1016/b978-0-444-63631-7.00003-0

    2017 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.