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Geostatistics is a branch of statistics focused on analyzing and interpreting spatial or spatiotemporal data. It employs techniques such as kriging to make predictions about unknown values based on the spatial correlation of observed data points. Commonly used in fields like geology, environmental science, and mining, geostatistics helps in modeling phenomena that vary across geographic space, enabling better decision-making in resource management and environmental assessments.
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When was geostatistics theory developed?
Geostatistics theory was developed in the 1950s by French engineer Georges Matheron while working in the mining industry. Matheron's work laid the foundation for geostatistics as a statistical approach for analyzing spatial data and has since been widely applied in various fields such as geology, ecology, and environmental science.
What are the importance of geostatistics in mining industry?
Geostatistics in the mining industry helps to improve resource estimation accuracy, optimize mine planning and design, and assess risk and uncertainty in decision-making processes. By incorporating spatial variability and relationships within data, geostatistics enables more informed and data-driven decision-making, ultimately leading to more efficient and profitable mining operations.
What has the author George Christakos written?
George Christakos has written: 'Modern Spatiotemporal Geostatistics - Studies in Mathematical Geology, 6. -'
What is the name of the country that developed geostatistics?
no country developed it, of course, but maybe you could say that the work of Krige started the field. He was South African.
What is regression effect in geostatistics?
The regression effect in geostatistics refers to the phenomenon where extreme values in a dataset tend to be followed by more moderate values upon subsequent measurements or observations. This effect is often observed in spatial data, where the spatial correlation can lead to an underestimation or overestimation of values in areas with high or low extremes. Essentially, it highlights the tendency of measurements to gravitate towards the mean, leading to a smoothing of extreme observations in spatial predictions. This concept is crucial for understanding and improving the accuracy of geostatistical models and predictions.
What has the author Joseph A Hevesi written?
Joseph A. Hevesi has written: 'Precipitation estimation in mountainous terrain using multivariate geostatistics' -- subject(s): Statistical methods, Geology, Precipitation (Meteorology), Precipitation forecasting, Measurement 'Preliminary estimates of spatially distributed net infiltration and recharge for the Death Valley Region, Nevada-California' -- subject(s): Groundwater flow, Seepage
Geostatistics method of reserve estimation?
That's a big question, because it depends on alot of things. If I were you, I'd try to get into a course. These tend to be expensive and hard to find though. If you forgo the official certification, you can still access a few geostats courses (including worked examples) on Edumine by paying for a monthly access fee (something like $40 CDN). You and I are in the same boat, actually! Good luck!
What has the author Charles V Eidsvik written?
Charles V Eidsvik is a notable authority in the field of reservoir characterization and modeling. He has written several research papers and books focusing on geostatistics, mathematical modeling, and uncertainty quantification in reservoir engineering. Eidsvik has also made significant contributions to the study of stochastic modeling in the context of subsurface hydrocarbon reservoirs.
What is lateral correlation?
Lateral correlation is the relationship between two adjacent points or data values within a system or dataset. It is used to analyze spatial patterns, such as how similar or dissimilar neighboring values are in a given context, like in geostatistics or image processing. Lateral correlation helps identify trends or patterns that exist horizontally or laterally across the data.
What is trend in variogram?
A trend in a variogram refers to a systematic increase or decrease in the spatial variance of a dataset as the distance between sampled points increases. This trend can indicate underlying patterns in the data, such as a directional bias or a gradual change in the mean value across the study area. In geostatistics, it's important to identify and model trends before fitting a variogram, as they can affect the interpretation of spatial correlation and the accuracy of predictions. By accounting for trends, analysts can improve the reliability of spatial analyses and modeling efforts.
Two studies that come under Earth?
Take your pick from any of the following: atmospheric chemistry, climatology, meteorology, hydrometeorology, paleoclimatology, biogeography, paleontology, palynology, micropaleontology, geomicrobiology, geoarchaeology, hydrology, geohydrology, limnology, oceanography, chemical oceanography, physical oceanography, biological oceanography, geological oceanography, paleoceanography, geology, economic geology, engineering geology, environmental geology, quaternary geology, planetary geology, sedimentology, stratigraphy, structural geology, geography, physical geography, geochemistry, geomorphology, geophysics, geochronology, geodynamics, geomagnetism, gravimetry, seismology, glaciology, hydrogeology, mineralogy, crystallography, gemology, petrology, speleology, volcanology, soil science, edaphology, pedology, cartography, geoinformatics, geostatistics and geodesy, to name but a few.
What is the importance of statistics in geography?
IMPORTANCE OF STUDING STATISTICS IN GEOGRAPHY. In recent years, statistics has occupied a dominant place in society. In the light of its significance, its scope as well as importance highlighted Importance in Defense and War: Statistical tools are very useful in the fields of defense and war because it helps to compare the military strength of different countries in terms of man power, tanks, war-aeroplanes, missiles etc. Moreover, it helps in planning future military strategy of the country. It helps to estimate the loss due to war. It helps to arrange the war finance. Statistics and Economic Planning: Modern age is the age of planning and without statistics planning is inconceivable. The days of laissez faire had gone and state intervention in every walk of life has become universal in character. Our future depends on proper planning. Thus, planning is only successful on accurate analysis of complex statistical data. In India, the various plans that have been prepared or implemented, planners have made use of statistical data. Moreover, in our country, National Sample Survey Scheme was introduced to collect the statistical data for the use of planning. Statistical apparatus are employed not only to construct the plans but the success of every plan is judged by the use of statistical tools. Statistics and State: Statistics are the eyes of state as they help in administration. In the ancient times, the ruling kings and chiefs have to rely heavily on statistics to frame suitable military and fiscal policies. Similarly, modern states make tremendous use of statistical tools on various problems. Before, implementing any policy, a state has to examine its pros and cons. For instance, before suggesting any remedial measures of the evil of crime, the state requires to make a deep statistical investigation of the problem. Similarly, state conducts the population census to estimate the figures of national income and the prosperity of the country. In this way, state is the most single unit which not only collects the largest amount of statistics but also needs statistics on a very extensive scale. statistics may only have valid interpretations for the area and subarea configuration over which they are calculated. Boundary delineation The location of a study area boundary and the positioning of internal boundaries affect various descriptive statistics. With respect to measures such as the mean or standard deviation, the study area size alone may have large implications; consider a study of per capita income within a city, if confined to the inner city, income levels are likely to be lower because of a less affluent population, if expanded to include the suburbs or surrounding communities, income levels will become greater with the influence of homeowner populations. Because of this problem, absolute descriptive statistics such as the mean, standard deviation, and variance should be evaluated comparatively only in relation to a particular study area. In the determination of internal boundaries this is also true, as these statistics may only have valid interpretations for the area and subarea configuration over which they are calculated. Descriptive spatial statistics See main article Spatial descriptive statistics For summarizing point pattern analysis, a set of descriptive spatial statistics has been developed that are areal equivalents to nonspatial measures. Since geographers are particularly concerned with the analysis of locational data, these descriptive spatial statistics (geostatistics) are often applied to summarize point patterns and to describe the degree of spatial variability of some phenomena. Spatial measures of central tendency An example here is the idea of a center of population, of which a particular example is the mean center of U.S. population. Several different ways of defining a center are available: • Mean center: The mean is an important measure of central tendency, which when extended to a set of points, located on a Cartesian coordinate system, the average location, centroid or mean center, can be determined. • The weighted mean center is analogous to frequencies in the calculation of grouped statistics, such as the weighted mean. A point may represent a retail outlet, while its frequency will represent the volume of sales within the particular store. • Median center or Euclidean center and in the median center of United States population. This is related to the Manhattan distance. . Statistics and Business: Statistics is extremely useful in modern activities of business. Business is full of risks and uncertainties. According to Boddington, "A successful businessman is one whose estimates most closely approach accuracy". Every success in business depends on precision in forecasting. Thus, a businessman must make a proper analysis of the past records to forecast the future business conditions. Moreover, every business man has to make use of the statistical tools to estimate the trend of prices and of economic activities. In short, business involves risk and when there is risk, it is better to have a calculated risk. References • Duncan, Otis Dudley, Raymond Paul Cuzzort and Beverly Duncan (1977). Statistical Geography: Problems in Analyzing Areal Data. Greenwood Press. ISBN 0-8371-9676-0. • Dickinson, G.C. (1973). Statistical mapping and the presentation of statistics. Edward Arnold. ISBN 0-7131-5641-4.
