Geodetic Control of the Present Tectonic Deformation of the Betic Cordillera (Spain)

Alfaro, P.; Estévez, Antonio; Blázquez, Elízabeth; Borque, M.J.; Garrido, Marta; Gil, Antonio J.; Lacy, M. C.; Ruiz, Antonio M.; Giménez, J.; Molina, Sergio; Rodríguez‐Caderot, G.; Ruiz-Morales, M.; Galdeano, Carlos Sanz de

doi:10.1007/978-3-540-38596-7_25

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Several studies based on permanent and non-permanent GPS networks (e.g. Alfaro et al, 2006;Echeverria et al, 2013;Gárate et al, 2014;Gil et al, 2002) and high-precision levelling profiles have been carried out in SE Spain (e.g. Galindo-Zaldívar et al, 2013;Giménez et al, 2000;Marín-Lechado et al, 2005) revealing an on-going tectonic activity of this part of the Iberian Peninsula.…”

Section: Introductionmentioning

confidence: 99%

Geodetic evidence for continuing tectonic activity of the Carboneras fault (SE Spain)

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Geodetic evidence for continuing tectonic activity of the Carboneras fault (SE Spain)

et al. 2015

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“…This is indeed the approach that is usually followed in computing confidence regions for estimated deformations, see e.g. (Wieser, 2004;Alfaro et al, 2005;Shahar and Even-Tzur, 2005;Devoti et al, 2011;Dheenathayalan et al, 2016).…”

Section: Presentation Of Identified Deformationmentioning

confidence: 98%

“…There is a rich literature on the design and analysis of deformation monitoring systems for both man-made structures (such as a dam, a dike, or a bridge) and natural Earth structures (such as a volcano, a fault, or tectonic plates), see e.g. (Pelzer, 1971;van Mierlo, 1978;Niemeier, 1985;Caspary and Borutta, 1987;Chen et al, 1990;Alfaro et al, 2005;Devoti et al, 2011;Heunecke et al, 2013;Sabuncu and Ozener, 2014;Yigit et al, 2016;Scaioni et al, 2018;Yavaşo glu et al, 2018). Although these studies developed important and sophisticated statistical procedures for the individual detection and estimation of deformations, no description has yet been given on how to risk-evaluate the overall performance of the system's alert-function.…”

Section: Introductionmentioning

confidence: 99%

On the integrity of deformation monitoring

Teunissen

Zaminpardaz

Tiberius

2020

Geomatics, Natural Hazards and Risk

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In safety-critical applications, deformation monitoring systems are required to issue timely alerts when a deformation beyond a critical threshold occurs. Only a very small probability of failing to issue an alert when in fact one should have been given, is acceptable. This probability is referred to as integrity risk. In this contribution, we show how to evaluate this risk, thereby taking the intimate link between testing and estimation into account. Using a simple example, the basic integrity components of deformation monitoring are introduced and illustrated. The integrity risk is then formulated for the generalized case where multiple-hypothesis testing is involved. As monitoring systems, in addition to issuing timely alerts, are also required to provide deformation estimates, it is also crucial to assess their confidence levels. In doing so, the statistical testing, that preceded the estimation of the deformation parameters, needs to be accounted for. As this is not the customary procedure followed in practice, we show how the combined estimation and testing can be probabilistically accounted for, and thereby demonstrate that the customary practice can give a too optimistic outcome of the stated confidence levels. The presented methodology is worked out and numerically illustrated by means of two deformation examples.

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“…Then, the estimate of x is given byx i . With B x i ⊂ R n being a x-centered region, the approach that is usually followed in practice to determine the confidence level (CL) is to compute the probability ofx i ∈ B x i under the identified hypothesis as follows [1,[4][5][6]9] CL…”

Section: Confidence Levelmentioning

confidence: 99%

“…In this contribution we analyse the impact of statistical model selection on the quality of the estimators generated through combined testing-estimation procedures, and specifically concentrate on the confidence level. The approach that is usually followed in practice to determine the confidence level is to compute the probability that the estimator of the unknown parameters, directed by the testing outcome, is inside a region around its true value, without taking into account the statistical testing that preceded the estimation [1,[4][5][6]9]. Therefore, if H i is the identified hypothesis, then use is made of the distribution of the estimator under H i , sayx i , without regard to the conditioning process that led to the decision of accepting this hypothesis.…”

Section: Introductionmentioning

confidence: 99%