Добрый день, Коллеги. Важное сообщение, просьба принять участие. Музей Ферсмана ищет помощь для реставрационных работ в помещении. Подробности по ссылке

Автор(ы):Canu S., Maignan M., Parkin R., Pozdnukhov A., Демьянов В.В., Каневский М.Ф., Тимонин В.А.
Издание:2003 г., 11 стр.
Язык(и)Английский
Environmental data mining and modeling based on machine learning algorithms and geostatistics / Интеллектуальный анализ и моделирование экологических данных на основе алгоритмов машинного обучения и геостатистики

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model—machine learning (ML) residuals sequential simulations—MLRSS.

ТематикаГеостатистика, Экология
Автор(ы):Christman M.C., Hoenig J.M., Mann R., Walter J.F. III
Издание:2007 г., 16 стр.
Язык(и)Английский
Combining data from multiple years or areas to improve variogram estimation / Объединение данных за несколько лет или областей для улучшения оценки вариограммы

A requirement for geostatistical prediction is estimation of the variogram from the data. Often low sample size is a major impediment to elucidating a variogram even for a highly autocorrelated spatial process. This paper presents a methodology for improving variogram estimation when samples exist from multiple years or regions sharing a similar process for generating spatial autocorrelation.

ТематикаГеостатистика
Автор(ы):Herzfeld U.C.
Издание:Springer, 2004 г., 343 стр., ISBN: 978-3-642-62418-6
Язык(и)Английский
Atlas of Antarctica. Topographic maps from geostatistical analysis of satellite radar altimeter data / Атлас Антарктики. Топографическая карта по результатам геостатистического анализа спутниковой радарной высотной съемки

Although it is generally understood that the Antarctic Ice Sheet plays a critical role in the changing global system, there is to date still a lack of generally available information on the subject. Climatic change and the role of the polar areas are often discussed in the media.

ТематикаГеоинформационные системы, Геостатистика, Геодезия
Редактор(ы):Giraldo R., Mateu J.
Издание:Wiley, 2022 г., 450 стр., ISBN: 9781119387848
Язык(и)Английский
Geostatistical functional data analysis / Геостатистический функциональный анализ данных

Spatial statistics has developed rapidly during the last 30 years. We have seen an interesting progress both in theoretical developments and in practical studies. Some early applications were in mining, forestry, and hydrology. It seems to be honest to remark that the increasing availability of computer power and skillful computer software has stimulated the ability to solve increasingly complex problems.

ТематикаГеостатистика
Автор(ы):Welhan J.
Издание:Idaho Geological Survey, 2004 г., 48 стр.
Язык(и)Английский
Geostatistics and spatial modeling. Lecture notes / Геостатистика и пространственное моделирование. Лекционные заметки

An introduction to the description, analysis, and modeling of geospatial data and of the resulting uncertainty in the models. Theory and its correct application will be integrated with the use of various software tools (including GIS) and appropriate examples to emphasize the crossdisciplinary applicability of geostatistical analysis and modeling.

ТематикаГеоинформационные системы, Геостатистика
Том 2
Редактор(ы):Chambers R.L., Coburn T.C., Yarus J.M.
Издание:American Association of Petroleum Geologists, 2006 г., 312 стр., ISBN: 0891817042
Язык(и)Английский
Stochastic modeling and geostatistics. Principles, methods and case studies. Volume 2 / Стохастическое моделирование и геостатистика. Принципы, методы и тематические исследования. Часть 2

Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.

ТематикаГеоинформационные системы, Геостатистика
Автор(ы):Bardossy A.
Язык(и)Английский
Introduction to Geostatistics / Введение в геостатистику

Most of the natural phenomena we study are variable both in space and time. Considering a topographic surface or a groundwater contamination one can observe high variability within small distances. The variability is a result of natural processes, thus deterministic. As most of these processes are sensitive and the conditions under which the they took place are not fully known, it is not possible to describe them based on physical and chemical laws completely. <...>

ТематикаГеостатистика
Автор(ы):Beck L.R., Dungan J.L., Rossi R.E.
Издание:1994 г., 9 стр.
Язык(и)Английский
Kriging in the shadows: Geostatistical interpolation for remote sensing / Кригинг в тени: геостатистическая интерполяция для дистанционного зондирования

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator krigingo is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into  pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used. <...>

ТематикаГеоинформационные системы, Геостатистика, Дистанционные методы
Автор(ы):Emery X., Ortiz J.M.
Издание:2006 г., 8 стр.
Язык(и)Английский
Geostatistical estimation of mineral resources with soft geological boundaries: a comparative study / Геостатистическая оценка минеральных ресурсов с неявными геологическими границами: сравнительное исследование

Mineral resource evaluation requires defining geological domains that differentiate the types of mineralogy, alteration and lithology. Usual practice is to consider the domain boundaries as hard, i.e. data from across the boundaries are disregarded when estimating the grades within a given domain. This practice may hinder the quality of the estimates when a significant spatial correlation of the grades exists across the domain boundaries.

Автор(ы):Babish G.
Издание:Ecological Research Division Environmental Conservation Branch, 2006 г., 115 стр.
Язык(и)Английский
Geostatistics without tears. A practical guide to surface interpolation, geostatistics, variograms and kriging / Геостатистика без слез. Практическое руководство по интерполяции поверхности, геостатистике, вариограммам и кригингу

Geostatistics (also known as kriging) was developed for the mining industry during the 1960s and 1970s to estimate changes in ore grade. The principles of geostatistics are now applied to many applications that require statistically based interpolation techniques. Geostatistics provides a data value estimate for locations that cannot be sampled directly by examining data taken at locations that can be sampled.

ТематикаГеоинформационные системы, Геостатистика
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