An intrinsic model of coregionalization that solves variance inflation in collocated cokriging

Babak, Olena; Deutsch, Clayton V.

doi:10.1016/j.cageo.2008.02.025

Cited by 30 publications

(17 citation statements)

References 5 publications

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“…Since Y and Z are standard Gaussian vectors, must be equal to (1 − ρ 2 )I in order to ensure a unit variance for the components of Y. Particular cases occur if C Y is diagonal (Y j is uncorrelated with Y i ) or if |ρ| = 1 (Z i perfectly screens out Y j in the co kriging system) [4].…”

Section: Discussionmentioning

confidence: 98%

Assessing the accuracy of sequential Gaussian simulation and cosimulation

Emery

Peláez

2011

Comput Geosci

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Sequential Gaussian simulation is widespread in Earth Science applications to quantify the uncertainty about regionalized properties. Its practical implementation relies on the screen effect approximation in order to determine the successive conditional distributions by considering only the information available in the neighborhood of the target location. A methodology is presented to assess the accuracy of sequential Gaussian simulation, by calculating the theoretical moments (expectation and variance-covariance matrix) of the simulated random vector and comparing them with the moments of the underlying model. The methodology can be applied in both the conditional and non-conditional contexts, as well as for univariate or multivariate simulation. It is helpful to determine appropriate implementation parameters, in particular about the visiting sequence and the design of the moving neighborhood for selecting relevant conditioning information, prior to performing simulation.

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Section: Discussionmentioning

confidence: 98%

Assessing the accuracy of sequential Gaussian simulation and cosimulation

Emery

Peláez

2011

Comput Geosci

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“…We account for this dichotomy by using secondary variables in the SGS or DS (Babak, 2009a;Babak & Deutsch, 2009b;Mariethoz et al, 2012). The main signal results from tropospheric perturbations due to variations of water vapor, pressure, and temperature.…”

Section: 1029/2018ea000533mentioning

confidence: 99%

Generative Modeling of InSAR Interferograms

Rongier

Rude

Herring

et al. 2019

Earth and Space Science

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Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post-processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles.

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“…CCK, however, has a longstanding problem with variance inflation that leads to problems in histogram reproduction. As a solution to the variance inflation, a new approach referred to as intrinsic collocated cokriging (ICCK) was recently proposed by Babak and Deutsch (2009a). The context of this paper is the intrinsic collocated cokriging and modeling using this technique under a Gaussian model.…”

Section: Introduction: Problem Settingmentioning

confidence: 98%