Results 31 to 40 of about 19,252 (217)

Wavelets and the Generalization of the Variogram

Mathematical Geology, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bosch, E. H., Oliver, M. A., Webster, R.
openaire   +1 more source

Kriging Interpolating Cosmic Velocity Field

, 2015
[abridged] Volume-weighted statistics of large scale peculiar velocity is preferred by peculiar velocity cosmology, since it is free of uncertainties of galaxy density bias entangled in mass-weighted statistics.
Jing, Yipeng, Yu, Yu, Zhang, Jun, Zhang, Pengjie   +3 more
core   +1 more source

Leveraging Deep Learning for Automated Experimental Semivariogram Fitting

Atmosphere
The variogram function is a crucial tool for quantifying spatial correlation and a key component of Kriging interpolation, directly influencing the accuracy of interpolation results.
Siyu Yu, Lifang Zhao, Shaohua Li
doaj   +1 more source

Development of a variogram procedure to identify spatial outliers using a supplemental digital elevation model

Journal of Hydrology X, 2019
When using a ground water elevation dataset for the development of a ground water model, it is prudent to first evaluate the quality of the data before using it in a ground water model.
Zane D. Helwig, Joe Guggenberger, Andrew Curtis Elmore, Rachel Uetrecht   +3 more
doaj   +1 more source

Spatial distribution pattern analysis using variograms over geographic and feature space

Geo-spatial Information Science
Variogram analysis is effective in revealing the spatial distribution patterns of geographic variable(s). Yet, existing variograms mostly focus on the impact of spatial correlation on the variable variation and neglect the impact of geographic ...
Fang-He Zhao, A-Xing Zhu, Cheng-Zhi Qin
doaj   +1 more source

Graph Variogram: A novel tool to measure spatial stationarity

, 2018
Irregularly sampling a spatially stationary random field does not yield a graph stationary signal in general. Based on this observation, we build a definition of graph stationarity based on intrinsic stationarity, a less restrictive definition of ...
Girault, Benjamin, Ortega, Antonio, Serrano, Alexander   +2 more
core   +1 more source

Variogram estimation in the presence of trend [PDF]

Statistics and Its Interface, 2012
Estimation of covariance function parameters of the error process in the presence of an unknown smooth trend is an important problem because solving it allows one to estimate the trend nonparametrically using a smoother corrected for dependence in the errors.
Bliznyuk, Nikolay, Carroll, Raymond J., Genton, Marc G., Wang, Yuedong   +3 more
openaire   +3 more sources

Critical Analysis of the Snow Survey Network According to the Spatial Variability of Snow Water Equivalent (SWE) on Eastern Mainland Canada

Hydrology, 2019
In Eastern Canada, the snow survey network is highly optimized at the operational scale. However, it is commonly accepted that the network is limited when it comes to studying the spatial variability of the snow water equivalent (SWE), which forms ...
Yawu Noumonvi Sena, Karem Chokmani, Erwan Gloaguen, Monique Bernier   +3 more
doaj   +1 more source

Autism and Cortical Thickness Deviation From Neurotypical Controls: Evidence for a Spatial Association With Serotonin Receptors

Autism Research, EarlyView.
ABSTRACT Cortical thickness (CT) differences between autistic individuals (AI) and neurotypical controls have been consistently reported, yet the neurochemical mechanisms underlying these differences remain insufficiently understood. Neurotransmitter receptor systems exhibit distinct spatial distributions across the cortex and influence synaptic ...
Livio Tarchi, Arne Doose, Julius Hennig, Fabio Bernardoni, Joseph A. King, Tiziana Pisano, Giovanni Castellini, Valdo Ricca, Inge Kamp‐Becker, Stefan Ehrlich   +9 more
wiley   +1 more source

Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization

Frontiers in Earth Science
Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling.
Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal   +6 more
doaj   +1 more source