Uncertainty in the Predictive Capability of Detectors that Process Waveforms from Explosions

Joshua D Carmichael; Robert J Nemzek

arxiv: 1906.09350 · v1 · pith:JKQRYBL7new · submitted 2019-06-21 · ⚛️ physics.data-an · physics.geo-ph· stat.AP

Uncertainty in the Predictive Capability of Detectors that Process Waveforms from Explosions

Joshua D Carmichael , Robert J Nemzek This is my paper

Pith reviewed 2026-05-25 17:56 UTC · model grok-4.3

classification ⚛️ physics.data-an physics.geo-phstat.AP

keywords explosion monitoringwaveform detectorsperformance forecastingmagnitude discrepancyseismic acoustic radiogeophysical signalsdetection uncertaintymulti-phenomenological

0 comments

The pith

Empirically parameterized detectors forecast small explosion detection probabilities with fair-to-very-good accuracy using observed performance curves rather than theory.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper compares the predicted and observed performance of three digital detectors processing radio, acoustic, and seismic waveforms recorded from one small aboveground explosion. It introduces magnitude discrepancy as the measure of how much source magnitude must shift before two performance curves give the same detection probability in the moderate range. This comparison shows that detectors tuned on real data from noisy environments forecast detection rates reasonably well, that curves built from actual observations predict other curves better than theoretical models do, and that uncertainty in detection remains bounded by the physical magnitude of the source.

Core claim

By deriving and comparing predicted versus observed performance of three digital detectors on radio, acoustic and seismic data from a single small aboveground explosion, the work demonstrates that empirically parameterized detectors operating in variable noisy environments provide fair-to-very good forecasting capability to detect small explosions, that the observed performance of a particular waveform detector can better forecast performance curves constructed from different observations when compared to theoretical performance curves, and that an upper bound on detection uncertainty exists in terms of a physical source attribute (magnitude).

What carries the argument

Magnitude discrepancy, defined as the peak range in source magnitude over which different performance curves report the same probability of detection within a moderate-probability interval.

If this is right

Empirically parameterized detectors can anticipate trigger rates for small explosions in operational multi-phenomenological monitoring.
Observed performance curves from one detector should be used to forecast curves from other observations rather than relying on theoretical models.
Detection uncertainty in waveform-based explosion monitoring can be expressed as an upper bound tied to source magnitude.
Forecasting capability improves when detectors are tuned directly to data collected in variable noise rather than to idealized models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same empirical-versus-theoretical comparison could be repeated on data from larger or buried explosions to test whether the forecasting advantage persists.
Operational monitoring networks might reduce uncertainty by maintaining libraries of observed performance curves for each sensor type and environment.
The magnitude-discrepancy approach could be adapted to quantify predictive uncertainty in other signal-detection tasks that use multiple sensor modalities.

Load-bearing premise

That performance measured on a single small aboveground explosion and three specific detectors generalizes to the forecasting of hypothetical explosions in operational settings.

What would settle it

New performance measurements from several additional small explosions in which theoretical curves forecast observed detection rates more closely than empirical curves do, across the moderate probability range, would falsify the central claims.

Figures

Figures reproduced from arXiv: 1906.09350 by Joshua D Carmichael, Robert J Nemzek.

**Figure 1.** Figure 1: Waveforms recorded from an 11.6 kg COMP-B explosive detonated at a 4 m HoB over dry sand, ∼ 2 km from source (mechanical waveforms) and ∼ 120 m from source (electric waveform). plosions of magnitude m with probability PrD, what is the observed, absolute range of magnitudes ∆m (magnitude discrepancy) that the detector actually identifies explosions, for that same probability? 2. Does the predicted-versus-ob… view at source ↗

**Figure 2.** Figure 2: Notational examples of absolute and relative magnitude discrepancy ∆m. Thin, black curves compare the theoretical performance of a hypothetical signal detector with an F-distributed detection statistic against source magnitude. Thicker black curves show their upper and lower performance bounds. The blue curve shows the average of the four “theoretical” performance curves Pr¯ Pre D , whereas the red stair-c… view at source ↗

**Figure 3.** Figure 3: Los Alamos National Laboratory material testing ranges. Includes the Minie shot pit, RF antennae, and the seismo-acoustic recording sites. over pairs of performance curves: ∆m (t1, t2) = X i,j ∆m (ti , tj ) √ij X i,j q −1 i −1 j , where: t1 ≤ ti < tj ≤ t2 (9) as an error weighted-estimate of average performance curve accuracy. The error terms i and j in Equation 9 are the same error terms from Equa… view at source ↗

**Figure 4.** Figure 4: Electric field data x recorded during over the first ∼1 ms proceeding the detonation of an 8’ cylindrical COMP-B charge (11.6 kg) at a 4m HoB over dry sand and processed with our SNR detector. Left, Top: Peak-normalized, bandpass filtered (20 -150 MHz) electric waveforms (gray) superimposed with detection statistic e (x) (purple). The red, horizontal dashed line shows an SNR-threshold for event declaration… view at source ↗

**Figure 5.** Figure 5: Semi-empirical and theoretical performance curves for the SNR-detector that processed radio emissions measured 117 m from the Minie Shot pit, displayed as waveform count number versus relative source magnitude. Data include 68 electric field (V m−1 ) waveforms that record 11.6 kg COMP-B charges detonated 4 m over dry ground, which was infused into 12 different days of identically processed noise records. … view at source ↗

**Figure 6.** Figure 6: Semi-empirical and theoretical performance curves as shown in [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

**Figure 7.** Figure 7: A 12 s, three channel waveform template u recording the east (top), north (middle) and vertical (bottom) component of ground motion at station LOSL that was triggered by a 11.6 kg COMP B solid charge detonated at a 4m HoB over dry sand. Data are bandpass filtered to 1.5 − 7.5Hz with a minimum phase 4-pole Butterworth filter [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗

**Figure 8.** Figure 8: Semi-empirical and theoretical performance curves as shown in [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗

**Figure 9.** Figure 9: Absolute magnitude discrepancy between performance curves for the radio emission, SNR detector. Each data point shows ∆m (t1, t2) between either a pair of predicted-versus-observed performance curves (blue, circular markers), or observed-versus-observed performance curves (red, square markers). The lower left gray box includes discrepancy estimates that compare same-day observations and predictions. We ex… view at source ↗

**Figure 10.** Figure 10: Error weighted estimates of absolute magnitude discrepancy that compare predicted-versusobserved radio emission curves (blue bars), and observed-versus-observed curves (red bars), averaged with Equation 9. Numbers above each bar count the discrepancy observations and exclude time-averaged performance curves Pr¯ Pre D and Pr¯ Obs D . 25 [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗

**Figure 11.** Figure 11: Error weighted averages of the absolute magnitude discrepancy (Equation 9) as shown in [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗

**Figure 12.** Figure 12: Error weighted averages of the absolute magnitude discrepancy (Equation 9) as shown in [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗

**Figure 13.** Figure 13: A log-log comparison between true values of noncentrality parameter λ and average estimates λˆ (blue, rectangular markers) for the acoustic emission STA/LTA detector. The vertical, gray dashed line shows the minimum estimated value of λˆ. The divergence of our estimates for λˆ from the red, one-to-one line indicates a positive bias at low magnitude values, where λˆ ≥ λ, and PrPre D over-predicts true dete… view at source ↗

read the original abstract

Explosions near ground generate multiple geophysical waveforms in the radiation-dominated range of their signature fields. Multi-phenomological explosion monitoring (MultiPEM) at these ranges requires the predictive capability to forecast trigger rates of digital detectors that process such waveform data, and thereby accurately anticipate the probability that hypothetical explosions can be identified in operations. To confront this challenge, we derive and compare the predicted and observed performance of three digital detectors that process radio, acoustic and seismic waveform data that record a small, aboveground explosion. We measure this comparison with the peak range in magnitude (magnitude discrepancy) over which different performance curves report the same probability of detection, within an interval of moderate detection probability, and thereby quantify solutions to three topical monitoring questions. In particular, our solutions (1) demonstrate how empirically parameterized detectors that operate in a variable noisy environments provide fair-to-very good forecasting capability to detect small explosions, (2) show that the observed performance of a particular waveform detector can better forecast performance curves constructed from different observations, when compared to theoretical performance curves, and (3) provide an upper bound on detection uncertainty, in terms of a physical source attribute (magnitude)

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Applies the magnitude discrepancy metric to performance curves from one small explosion and three detectors to show empirical curves forecast better than theoretical ones, but the single-event data keeps the generalization claims narrow.

read the letter

The main thing to know is that this paper takes data from a single small aboveground explosion recorded by radio, acoustic, and seismic detectors, then uses the magnitude discrepancy to compare how observed performance curves stack up against theoretical ones for forecasting detection probabilities. It reports that empirical parameterization works reasonably well in noisy settings and that the observed curves do a better job predicting other curves than the theoretical versions, while also giving an upper bound on uncertainty expressed in magnitude terms. That directly tackles the three monitoring questions laid out in the abstract. The application is straightforward and gives concrete numbers for this case, which is the useful part for anyone who needs an example of how to quantify detector forecasting in multi-phenomenological work. No new framework or derivation appears; it is an application of existing performance evaluation ideas to this scenario. The soft spot is exactly the one the stress-test flags. Everything rests on one event, so the noise field, propagation, and source coupling are fixed to whatever happened there. That makes the claimed superiority of observed over theoretical curves and the uncertainty bound specific to those conditions rather than a general result for operational settings. If other explosions see different variability, the forecasting advantage may not carry over. The abstract gives no methods details on how the curves were built or how parameters were set, so circularity from fitting to the same data cannot be ruled out from what is visible. This paper is for specialists in explosion monitoring who already work with waveform detectors and want a worked example of the magnitude discrepancy metric on real data. A reader outside that niche will not find new techniques or broad implications. It deserves a serious referee. The practical questions are worth checking in detail, and a review can test whether the single-event limitation is handled properly and whether the derivations hold up once the full methods are examined.

Referee Report

1 major / 2 minor

Summary. The manuscript analyzes waveforms from a single small aboveground explosion recorded by three detectors (radio, acoustic, seismic). It derives observed and theoretical performance curves, quantifies their comparison via a magnitude-discrepancy metric over moderate detection-probability intervals, and uses this to claim that empirically parameterized detectors achieve fair-to-very-good forecasting in variable noise, that observed curves forecast better than theoretical ones, and that an upper bound on detection uncertainty exists in terms of source magnitude.

Significance. If the single-event results generalize, the work would supply a practical, data-driven route to bounding detection uncertainty for multi-phenomenological monitoring, highlighting the advantage of observed over purely theoretical performance curves in operational settings.

major comments (1)

[Abstract] Abstract: All three claims rest on performance curves derived from recordings of one small aboveground explosion. The magnitude-discrepancy comparison and the asserted forecasting superiority therefore depend on the untested assumption that the noise statistics, propagation conditions, and source coupling realized in this single realization are representative of the ensemble of hypothetical explosions; without additional events or a statistical argument for representativeness, the generalization to variable operational environments remains unsupported.

minor comments (2)

[Abstract] Abstract: 'Multi-phenomological' is a typographical error and should read 'Multi-phenomenological'.
[Abstract] Abstract: 'variable noisy environments' should be 'a variable noisy environment' for grammatical consistency.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review. The central concern is that the three claims rest on data from a single explosion without demonstrated representativeness. We respond point by point below and indicate the revisions we will make.

read point-by-point responses

Referee: [Abstract] Abstract: All three claims rest on performance curves derived from recordings of one small aboveground explosion. The magnitude-discrepancy comparison and the asserted forecasting superiority therefore depend on the untested assumption that the noise statistics, propagation conditions, and source coupling realized in this single realization are representative of the ensemble of hypothetical explosions; without additional events or a statistical argument for representativeness, the generalization to variable operational environments remains unsupported.

Authors: We agree that the analysis uses recordings from only one small aboveground explosion and that the manuscript does not supply additional events or a formal statistical argument establishing that the realized noise, propagation, and coupling conditions are representative of an ensemble. The three numbered claims in the abstract are therefore framed more broadly than the single-event data can strictly support. We will revise the abstract and the final section to state explicitly that the results constitute a detailed case study of one event, that the magnitude-discrepancy metric quantifies agreement for that event, and that the method itself supplies a template whose generalization requires further events. These changes will remove the unsupported generalization while preserving the technical contribution of the comparison between observed and theoretical curves. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison uses independent observed data against theoretical baselines

full rationale

The paper derives detector performance curves from direct processing of recorded waveforms from one explosion event and compares them to separately constructed theoretical curves using the magnitude-discrepancy metric. No equations or steps reduce a claimed prediction to a fit on the same inputs by construction, nor do self-citations supply load-bearing uniqueness theorems. The forecasting claims rest on the empirical-versus-theoretical contrast rather than tautological re-use of fitted parameters as outputs. The single-event limitation affects generalizability but does not create definitional circularity within the reported derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; full paper likely contains additional fitted parameters and domain assumptions not visible here.

axioms (1)

domain assumption Magnitude discrepancy is a valid and sufficient metric for quantifying predictive capability between performance curves
Invoked to compare predicted and observed detection probabilities within moderate probability intervals.

pith-pipeline@v0.9.0 · 5738 in / 1258 out tokens · 32309 ms · 2026-05-25T17:56:48.326836+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

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Generation of E lectric and M agnetic F ields by A ir, S urface, and U nderground E xplosions

VV Adushkin and SP Soloviev. Generation of E lectric and M agnetic F ields by A ir, S urface, and U nderground E xplosions. Combustion, Explosion and Shock Waves, 40 0 (6): 0 649--657, 2004

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Joshua D Carmichael. A W aveform D etector T hat T argets T emplate- D ecorrelated S ignals and A chieves I ts P redicted P erformance, P art I : D emonstration with I ms D ata. Bulletin of the Seismological Society of America, 106 0 (5): 0 1998--2012, 2016

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T hreshold M agnitudes for a M ultichannel C orrelation D etector in B ackground S eismicity

Joshua D Carmichael and Hans Hartse. T hreshold M agnitudes for a M ultichannel C orrelation D etector in B ackground S eismicity. Bulletin of the Seismological Society of America, 106 0 (2): 0 478--498, 2016. doi:10.1785/0120150191

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Carmichael, Robert Nemzek, Stephen J

Joshua D. Carmichael, Robert Nemzek, Stephen J. Arrowsmith, and Kari Sentz. F using G eophysical S ignatures of L ocally R ecorded S urface E xplosions to I mprove B last D etection. Geophysical Journal International, 204 0 (3): 0 1838--1842, 2016. doi:10.1093/gji/ggw006. URL http://gji.oxfordjournals.org/content/204/3/1838.abstract

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Partitioning of seismoacoustic energy and estimation of yield and height-of-burst/depth-of-burial for near-surface explosions

Sean R Ford, Arthur J Rodgers, Heming Xu, Dennise C Templeton, Philip Harben, William Foxall, and Robert E Reinke. Partitioning of seismoacoustic energy and estimation of yield and height-of-burst/depth-of-burial for near-surface explosions. Bulletin of the Seismological Society of America, 2014

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Steven J. Gibbons, Mathilde Bottger Sorensen, David B. Harris, and Frode Ringdal. The D etection and L ocation of L ow M agnitude E arthquakes in N orthern N orway using M ulti- C hannel W aveform C orrelation at R egional D istances. Physics of the Earth and Planetary Interiors, 160 0 (3-4): 0 285--309, MAR 16 2007. PT: J; TC: 14; UT: WOS:000244572200007

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An A nalysis of S eismic and A coustic S ignals M easured from a S eries of A tmospheric and N ear- S urface E xplosions

IO Kitov, JR Murphy, OP Kusnetsov, BW Barker, and NI Nedoshivin. An A nalysis of S eismic and A coustic S ignals M easured from a S eries of A tmospheric and N ear- S urface E xplosions. Bulletin of the Seismological Society of America, 87 0 (6): 0 1553--1562, 1997

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On the E ffects of N on- N ormality on the D istribution of the S ample P roduct- M oment C orrelation C oefficient

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E xtracting C hanges in A ir T emperature using A coustic C oda P hase D elays

Omar Marcillo, Stephen Arrowsmith, Rod Whitaker, Emily Morton, and W Scott Phillips. E xtracting C hanges in A ir T emperature using A coustic C oda P hase D elays. The Journal of the Acoustical Society of America, 136 0 (4): 0 EL309--EL314, 2014

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The D etection of W ind- T urbine N oise in S eismic R ecords

Omar E Marcillo and Joshua Carmichael. The D etection of W ind- T urbine N oise in S eismic R ecords. Seismological Research Letters, 89 0 (5): 0 1826--1837, 2018

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N ear- F ield R ayleigh W aves from S urface E xplosions

John R Murphy. N ear- F ield R ayleigh W aves from S urface E xplosions. Bulletin of the Seismological Society of America, 71 0 (1): 0 223--248, 1981

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Transmission and R eflection of F undamental- M ode R g S ignals from A tmospheric and U nderground E xplosions

Vanessa J Napoli and David R Russell. Transmission and R eflection of F undamental- M ode R g S ignals from A tmospheric and U nderground E xplosions. Bulletin of the Seismological Society of America, 108 0 (6): 0 3590--3597, 2018

work page 2018
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Massive L andslide at U tah C opper M ine G enerates W ealth of G eophysical D ata

Kristine L Pankow, Jeffrey R Moore, J Mark Hale, Keith D Koper, Tex Kubacki, Katherine M Whidden, and Michael K McCarter. Massive L andslide at U tah C opper M ine G enerates W ealth of G eophysical D ata. GSA Today, 24 0 (1): 0 4--9, 2014

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I mprovements to D etection C apability by C ross- C orrelating for S imilar E vents: a C ase S tudy of the 1999 X iuyan, C hina, S equence and S ynthetic S ensitivity T ests

David Schaff. I mprovements to D etection C apability by C ross- C orrelating for S imilar E vents: a C ase S tudy of the 1999 X iuyan, C hina, S equence and S ynthetic S ensitivity T ests. Geophysical Journal International, 180 0 (2): 0 829--846, 2010

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Semiempirical S tatistics of C orrelation- D etector P erformance

David P Schaff. Semiempirical S tatistics of C orrelation- D etector P erformance. Bulletin of the Seismological Society of America, 98 0 (3): 0 1495--1507, 2008

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Schaff, Wo-Young Kim, and Paul G

David P. Schaff, Wo-Young Kim, and Paul G. Richards. S eismological C onstraints on P roposed L ow- Y ield N uclear T esting in P articular R egions and T ime P eriods in the P ast, with C omments on `` R adionuclide E vidence for L ow- Y ield N uclear T esting in N orth K orea in A pril/ M ay 2011'' by L ars- E rik D e G eer. Science and Global Security:...

work page doi:10.1080/08929882.2012.711183 2011
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Q uadrupolar E lectromagnetic F ield from D etonation of H igh E xplosive C harges on the G round S urface

SP Soloviev, VV Surkov, and JJ Sweeney. Q uadrupolar E lectromagnetic F ield from D etonation of H igh E xplosive C harges on the G round S urface. Journal of Geophysical Research: Solid Earth, 107 0 (B6): 0 ESE--4, 2002

work page 2002

[1] [1]

Generation of E lectric and M agnetic F ields by A ir, S urface, and U nderground E xplosions

VV Adushkin and SP Soloviev. Generation of E lectric and M agnetic F ields by A ir, S urface, and U nderground E xplosions. Combustion, Explosion and Shock Waves, 40 0 (6): 0 649--657, 2004

work page 2004

[2] [2]

A W aveform D etector T hat T argets T emplate- D ecorrelated S ignals and A chieves I ts P redicted P erformance, P art I : D emonstration with I ms D ata

Joshua D Carmichael. A W aveform D etector T hat T argets T emplate- D ecorrelated S ignals and A chieves I ts P redicted P erformance, P art I : D emonstration with I ms D ata. Bulletin of the Seismological Society of America, 106 0 (5): 0 1998--2012, 2016

work page 1998

[3] [3]

T hreshold M agnitudes for a M ultichannel C orrelation D etector in B ackground S eismicity

Joshua D Carmichael and Hans Hartse. T hreshold M agnitudes for a M ultichannel C orrelation D etector in B ackground S eismicity. Bulletin of the Seismological Society of America, 106 0 (2): 0 478--498, 2016. doi:10.1785/0120150191

work page doi:10.1785/0120150191 2016

[4] [4]

Carmichael, Robert Nemzek, Stephen J

Joshua D. Carmichael, Robert Nemzek, Stephen J. Arrowsmith, and Kari Sentz. F using G eophysical S ignatures of L ocally R ecorded S urface E xplosions to I mprove B last D etection. Geophysical Journal International, 204 0 (3): 0 1838--1842, 2016. doi:10.1093/gji/ggw006. URL http://gji.oxfordjournals.org/content/204/3/1838.abstract

work page doi:10.1093/gji/ggw006 2016

[5] [5]

Partitioning of seismoacoustic energy and estimation of yield and height-of-burst/depth-of-burial for near-surface explosions

Sean R Ford, Arthur J Rodgers, Heming Xu, Dennise C Templeton, Philip Harben, William Foxall, and Robert E Reinke. Partitioning of seismoacoustic energy and estimation of yield and height-of-burst/depth-of-burial for near-surface explosions. Bulletin of the Seismological Society of America, 2014

work page 2014

[6] [6]

Gibbons, Mathilde Bottger Sorensen, David B

Steven J. Gibbons, Mathilde Bottger Sorensen, David B. Harris, and Frode Ringdal. The D etection and L ocation of L ow M agnitude E arthquakes in N orthern N orway using M ulti- C hannel W aveform C orrelation at R egional D istances. Physics of the Earth and Planetary Interiors, 160 0 (3-4): 0 285--309, MAR 16 2007. PT: J; TC: 14; UT: WOS:000244572200007

work page 2007

[7] [7]

P hysical P roperties of C onventional E xplosives D educed from R adio F requency E missions

Jeremiah Harlin and Robert Nemzek. P hysical P roperties of C onventional E xplosives D educed from R adio F requency E missions. Propellants, Explosives, Pyrotechnics, 34 0 (6): 0 544--550, 2009

work page 2009

[8] [8]

An A nalysis of S eismic and A coustic S ignals M easured from a S eries of A tmospheric and N ear- S urface E xplosions

IO Kitov, JR Murphy, OP Kusnetsov, BW Barker, and NI Nedoshivin. An A nalysis of S eismic and A coustic S ignals M easured from a S eries of A tmospheric and N ear- S urface E xplosions. Bulletin of the Seismological Society of America, 87 0 (6): 0 1553--1562, 1997

work page 1997

[9] [9]

On the E ffects of N on- N ormality on the D istribution of the S ample P roduct- M oment C orrelation C oefficient

Charles J Kowalski. On the E ffects of N on- N ormality on the D istribution of the S ample P roduct- M oment C orrelation C oefficient. Journal of the Royal Statistical Society: Series C (Applied Statistics), 21 0 (1): 0 1--12, 1972

work page 1972

[10] [10]

E xtracting C hanges in A ir T emperature using A coustic C oda P hase D elays

Omar Marcillo, Stephen Arrowsmith, Rod Whitaker, Emily Morton, and W Scott Phillips. E xtracting C hanges in A ir T emperature using A coustic C oda P hase D elays. The Journal of the Acoustical Society of America, 136 0 (4): 0 EL309--EL314, 2014

work page 2014

[11] [11]

The D etection of W ind- T urbine N oise in S eismic R ecords

Omar E Marcillo and Joshua Carmichael. The D etection of W ind- T urbine N oise in S eismic R ecords. Seismological Research Letters, 89 0 (5): 0 1826--1837, 2018

work page 2018

[12] [12]

N ear- F ield R ayleigh W aves from S urface E xplosions

John R Murphy. N ear- F ield R ayleigh W aves from S urface E xplosions. Bulletin of the Seismological Society of America, 71 0 (1): 0 223--248, 1981

work page 1981

[13] [13]

Transmission and R eflection of F undamental- M ode R g S ignals from A tmospheric and U nderground E xplosions

Vanessa J Napoli and David R Russell. Transmission and R eflection of F undamental- M ode R g S ignals from A tmospheric and U nderground E xplosions. Bulletin of the Seismological Society of America, 108 0 (6): 0 3590--3597, 2018

work page 2018

[14] [14]

Massive L andslide at U tah C opper M ine G enerates W ealth of G eophysical D ata

Kristine L Pankow, Jeffrey R Moore, J Mark Hale, Keith D Koper, Tex Kubacki, Katherine M Whidden, and Michael K McCarter. Massive L andslide at U tah C opper M ine G enerates W ealth of G eophysical D ata. GSA Today, 24 0 (1): 0 4--9, 2014

work page 2014

[15] [15]

I mprovements to D etection C apability by C ross- C orrelating for S imilar E vents: a C ase S tudy of the 1999 X iuyan, C hina, S equence and S ynthetic S ensitivity T ests

David Schaff. I mprovements to D etection C apability by C ross- C orrelating for S imilar E vents: a C ase S tudy of the 1999 X iuyan, C hina, S equence and S ynthetic S ensitivity T ests. Geophysical Journal International, 180 0 (2): 0 829--846, 2010

work page 1999

[16] [16]

Semiempirical S tatistics of C orrelation- D etector P erformance

David P Schaff. Semiempirical S tatistics of C orrelation- D etector P erformance. Bulletin of the Seismological Society of America, 98 0 (3): 0 1495--1507, 2008

work page 2008

[17] [17]

Schaff, Wo-Young Kim, and Paul G

David P. Schaff, Wo-Young Kim, and Paul G. Richards. S eismological C onstraints on P roposed L ow- Y ield N uclear T esting in P articular R egions and T ime P eriods in the P ast, with C omments on `` R adionuclide E vidence for L ow- Y ield N uclear T esting in N orth K orea in A pril/ M ay 2011'' by L ars- E rik D e G eer. Science and Global Security:...

work page doi:10.1080/08929882.2012.711183 2011

[18] [18]

Q uadrupolar E lectromagnetic F ield from D etonation of H igh E xplosive C harges on the G round S urface

SP Soloviev, VV Surkov, and JJ Sweeney. Q uadrupolar E lectromagnetic F ield from D etonation of H igh E xplosive C harges on the G round S urface. Journal of Geophysical Research: Solid Earth, 107 0 (B6): 0 ESE--4, 2002

work page 2002