GP Workbench Manual: Technical Manual, User's Guide, and Software Guide

Oden, Charles P.; Moulton, Craig W.

doi:10.3133/ofr20061365

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…GMPE and GMICE selections follow those currently used by the regional seismic networks to provide as much consistency as possible with other USGS products (e.g., ShakeMap): Chiou and Youngs (2008) and Worden et al (2012) for the Pacific Northwest and Boore and Atkinson (2008) and Wald et al (1999) for northern and southern California. These implementations closely follow those in ShakeMap v.3.5, as documented in Worden and Wald (2016), though because of the paucity of source characterization information available on the early warning timescale, many terms are unused or simplified (e.g., fault type, aftershock terms; see Thakoor et al, 2019, for all implementation details).…”

Section: Eqinfo2gm Ground-motion Estimation Algorithmmentioning

confidence: 99%

“…This component examines the combination of algorithm source parameters, GMPE uncertainty, and alert thresholds on the performance of EEW alerting. For this test, we followed the method of Cochran et al (2018) to convert EPIC and FinDer source parameter estimates from the test with historic earthquakes (excluding all data latencies) into groundmotion estimates using the ShakeMap 3.5 software (Worden and Wald, 2016). An example is shown in Figure 2 for the 24 August 2014 M 6.0 South Napa, California, earthquake, which shows a gridpoint-by-gridpoint comparison between predicted and observed ShakeMaps that have been calculated using the same GMPEs, with grid points classified by their alerting success.…”

Section: Ground-motion Assessmentmentioning

confidence: 99%

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Earthquake Early Warning ShakeAlert 2.0: Public Rollout

Kohler

Smith

Andrews

et al. 2020

Seismological Research Letters

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The ShakeAlert earthquake early warning system is designed to automatically identify and characterize the initiation and rupture evolution of large earthquakes, estimate the intensity of ground shaking that will result, and deliver alerts to people and systems that may experience shaking, prior to the occurrence of shaking at their location. It is configured to issue alerts to locations within the West Coast of the United States. In 2018, ShakeAlert 2.0 went live in a regional public test in the first phase of a general public rollout. The ShakeAlert system is now providing alerts to more than 60 institutional partners in the three states of the western United States where most of the nation’s earthquake risk is concentrated: California, Oregon, and Washington. The ShakeAlert 2.0 product for public alerting is a message containing a polygon enclosing a region predicted to experience modified Mercalli intensity (MMI) threshold levels that depend on the delivery method. Wireless Emergency Alerts are delivered for M 5+ earthquakes with expected shaking of MMI≥IV. For cell phone apps, the thresholds are M 4.5+ and MMI≥III. A polygon format alert is the easiest description for selective rebroadcasting mechanisms (e.g., cell towers) and is a requirement for some mass notification systems such as the Federal Emergency Management Agency’s Integrated Public Alert and Warning System. ShakeAlert 2.0 was tested using historic waveform data consisting of 60 M 3.5+ and 25 M 5.0+ earthquakes, in addition to other anomalous waveforms such as calibration signals. For the historic event test, the average M 5+ false alert and missed event rates for ShakeAlert 2.0 are 8% and 16%. The M 3.5+ false alert and missed event rates are 10% and 36.7%. Real-time performance metrics are also presented to assess how the system behaves in regions that are well-instrumented, sparsely instrumented, and for offshore earthquakes.

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Section: Eqinfo2gm Ground-motion Estimation Algorithmmentioning

confidence: 99%

Section: Ground-motion Assessmentmentioning

confidence: 99%

Earthquake Early Warning ShakeAlert 2.0: Public Rollout

Kohler

Smith

Andrews

et al. 2020

Seismological Research Letters

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“…Discrimination between probable UXO items and harmless scrap metal that could be left in the ground is the goal. An inversion algorithm for ALLTEM data has been developed as part of a processing and inversion package called GP Workbench (Oden, 2006;Oden and Moulton, 2006). The algorithm uses a physicsbased non-linear inversion method that minimizes an objective function that is a measure of the difference between experimental data and forward modeled data using a Gauss-Newton method (Gill et al, 1986).…”

Section: Inversionmentioning

confidence: 99%

ALLTEM UXO detection and discrimination

Asch

Wright

Moulton

et al. 2008

SEG Technical Program Expanded Abstracts 2008

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ALLTEM is a multi-axis electromagnetic induction system designed for unexploded ordnance (UXO) applications. It uses a continuous triangle-wave excitation and provides good late-time signal-to-noise ratio (SNR) especially for ferrous targets. Multi-axis transmitter (Tx) and receiver (Rx) systems such as ALLTEM provide a richer data set from which to invert for the target parameters required to distinguish between clutter and UXO. Inversions of field data over the Army's UXO Calibration Grid and Blind Test Grid at the Yuma Proving Ground (YPG), Arizona in 2006 produced polarizability moment values for many buried UXO items that were reasonable and generally repeatable for targets of the same type buried at different orientations and depths. In 2007 a test stand was constructed that allows for collection of data with varying spatial data density and accurate automated position control. The behavior of inverted ALLTEM test stand data as a function of spatial data density, sensor SNR, and position error has been investigated. The results indicate that the ALLTEM inversion algorithm is more tolerant of sensor noise and position error than has been reported for single-axis systems. A high confidence level in inversion-derived target parameters is required when a target is declared to be harmless scrap metal that may safely be left in the ground. Unless high confidence can be demonstrated, state regulators will likely require that targets be dug regardless of any "no-dig" classifications produced from inversions, in which case remediation costs would not be decreased.

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“…Many algorithms have been created to estimate the shape of UXO targets from EM induction measurements [6,[10][11][12], and passive magnetic measurements [13,14] based on the target's equivalent induced dipole moments. It has been shown that the uncertainty in estimated target shapes is reduced by analyzing datasets from both EM induction sensors and passive magnetometers [10,15].…”

Section: Introductionmentioning

confidence: 99%

Combining Advances in Em Induction Instrumentation and Inversion Schemes for Uxo Characterization

Oden¹

2012

PIER B

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Abstract-Several experimental time-domain EM induction instruments have recently been developed for unexploded ordnance (UXO) detection and characterization that use multiple transmitting and receiving coil combinations. One such system, the US Geological Survey's ALLTEM system, is unique in that it measures both the electrodynamic response (i.e., induced eddy currents) and the magneto-static response (i.e., induced magnetization). This allows target characterization based on the dyadic polarizability of both responses. This paper examines the numerical response of the ALLTEM instrument due to spheroidal, conductive, and permeable UXO targets; and to conductive and optionally viscous magnetic earth. An inversion scheme is presented for spheroidal targets that incorporates fully polarimetric measurements for both magneto-static and electro-dynamic excitations. The performance of the inversion algorithm is evaluated using both simulated and surveyed data. The results are examined as a function of the number of coil combinations, number of instrument locations, and uncertainty in sensor location and orientation. Results from the specific cases tested (prolate spheroids lying horizontally) show that 1) that collecting data from more than 12 sensor locations or from more than four coil combinations reduced the chances that inversion solutions would be from a local minimum, and 2) that uncertainties in position greater than 3 cm or in orientation greater than 10 degrees cause errors in the estimated spheroid principal lengths of greater than 100%. Soil conductivities less than 1 S/m contribute negligible interference to the target response, but viscous magnetic soils with permeabilities greater than 10 −6 MKS units do cause detrimental interference.

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GP Workbench Manual: Technical Manual, User's Guide, and Software Guide

Cited by 8 publications

References 8 publications

Earthquake Early Warning ShakeAlert 2.0: Public Rollout

Earthquake Early Warning ShakeAlert 2.0: Public Rollout

ALLTEM UXO detection and discrimination

Combining Advances in Em Induction Instrumentation and Inversion Schemes for Uxo Characterization

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