Integrating Prior Knowledge and Locally Varying Parameters with Moving-GeoStatistics: Methodology and Application to Bathymetric Mapping

Magneron, C.; Jeannée, Nicolas; Moine, Olivier Le; Bourillet, Jean‐François

doi:10.1007/978-90-481-2322-3_35

Cited by 10 publications

(9 citation statements)

References 4 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is applied to rainfall, soil, and ambient radioactivity data. Magneron et al (2010) propose a methodology providing a set of optimization techniques for parameters involved in the local stationary spatial dependence structure, especially the structural parameters such as the range and the anisotropy direction. This approach is illustrated with bathymetric data in the Marenne-Oléron coast, western France.…”

Section: Moving Windowsmentioning

confidence: 99%

Second-order non-stationary modeling approaches for univariate geostatistical data

Fouedjio

2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

A fundamental decision to make during the analysis of geostatistical data is the modeling of the spatial dependence structure as stationary or non-stationary. Although second-order stationary modeling approaches have been successfully applied in geostatistical applications for decades, there is a growing interest in secondorder non-stationary modeling approaches. This paper provides a review of modeling approaches allowing to take into account the second-order non-stationarity in univariate geostatistical data. One broad distinction between these modeling approaches relies on the way that the secondorder non-stationarity is captured. It seems unlikely to prove that there would be the best second-order non-stationary modeling approach for all geostatistical applications. However, some of them are distinguished by their simplicity, interpretability, and flexibility.

show abstract

Section: Moving Windowsmentioning

confidence: 99%

Second-order non-stationary modeling approaches for univariate geostatistical data

Fouedjio

2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…Moving-GeoStatistics (M-GS) is an innovative technology which is fully dedicated to the local optimization of parameters involved in variogram-based models [9], [10].…”

Section: M-factorial Krigingmentioning

confidence: 99%

M-Factorial Kriging for Seismic Data Noise Attenuation

Magneron

Bourges²,

Jeannée³

2009

11th International Congress of the Brazilian Geophysical Society &Amp; EXPOGEF 2009, Salvador, Bahia, Brazil, 24-28 August 2009

Self Cite

View full text Add to dashboard Cite

In the last decade in the petroleum industry, geostatistical filtering solutions based on Factorial Kriging technique have been developed and applied to seismic data sets in various operational contexts. These solutions commonly assume stationarity for the underlying random function, which limits their efficiency as soon as the target area becomes large or involves complex structural patterns.In this paper we introduce M-Factorial Kriging models, which allow to account for non-stationary effects that are encountered within seismic data sets. In the framework of noise attenuation issues, sources of non-stationarity relate for example to signal absorption, geological structuration, spatial variations of signal-to-noise ratio or varying geometrical features of noise.M-Factorial Kriging models ensure a better efficiency of the resulting geostatistical filtering process. As a consequence, signal and noise are better separated. This is illustrated by applying M-Factorial Kriging to a noisy PSTM amplitude section.

show abstract

“…The estimation of these spatially varying parameters is a challenging problem. Paciorek and Schervish (2006) suggest to estimate these parameters as in the moving windows nonstationary geostatistical approach based on the variogram (Haas 1990a, b;Harris et al 2010;Magneron et al 2010;Machuca-Mory and Deutsch 2013). Anderes and Stein (2011) propose a weighted local likelihood approach where the influence of faraway observations is smoothly down-weighted.…”

Section: Introductionmentioning

confidence: 99%

Predictive Geological Mapping Using Closed-Form Non-stationary Covariance Functions with Locally Varying Anisotropy: Case Study at El Teniente Mine (Chile)

Fouedjio

Séguret

2016

Nat Resour Res

View full text Add to dashboard Cite

This paper is concerned with the problem of predicting the surface elevation of the Braden breccia pipe at the El Teniente mine in Chile. This mine is one of the worldÕs largest and most complex porphyry-copper ore systems. As the pipe surface constitutes the limit of the deposit and the mining operation, predicting it accurately is important. The problem is tackled by applying a geostatistical approach based on closed-form non-stationary covariance functions with locally varying anisotropy. This approach relies on the mild assumption of local stationarity and involves a kernel-based experimental local variogram a weighted local least-squares method for the inference of local covariance parameters and a kernel smoothing technique for knitting the local covariance parameters together for kriging purpose. According to the results, this non-stationary geostatistical method outperforms the traditional stationary geostatistical method in terms of prediction and prediction uncertainty accuracies.

show abstract

Integrating Prior Knowledge and Locally Varying Parameters with Moving-GeoStatistics: Methodology and Application to Bathymetric Mapping

Cited by 10 publications

References 4 publications

Second-order non-stationary modeling approaches for univariate geostatistical data

Second-order non-stationary modeling approaches for univariate geostatistical data

M-Factorial Kriging for Seismic Data Noise Attenuation

Predictive Geological Mapping Using Closed-Form Non-stationary Covariance Functions with Locally Varying Anisotropy: Case Study at El Teniente Mine (Chile)

Contact Info

Product

Resources

About