Last edited by Kir
Monday, July 20, 2020 | History

2 edition of Geostatistical analysis found in the catalog.

Geostatistical analysis

William L. Osburn

Geostatistical analysis

potentiometric network for the upper Floridan Aquifer in the St. Johns River Water Management District

by William L. Osburn

  • 300 Want to read
  • 10 Currently reading

Published by St. Johns River Water Management District in Palatka, Fla .
Written in English

    Places:
  • Floridan Aquifer.,
  • Florida,
  • Saint Johns River Region
    • Subjects:
    • Aquifers -- Florida -- Saint Johns River Region -- Statistical methods.,
    • Floridan Aquifer.

    • Edition Notes

      Includes bibliographical references (p. 75-80).

      Other titlesPotentiometric network for the Upper Florida Aquifer in the St. Johns River Water Management District
      Statementby W.L. Osburn.
      SeriesTechnical publication ;, SJ 2000-1, Technical publication (St. Johns River Water Management District (Fla.)) ;, SJ 2000-1.
      ContributionsSt. Johns River Water Management District (Fla.)
      Classifications
      LC ClassificationsGB1199.3.F6 O84 2000
      The Physical Object
      Paginationxvi, 155 p. :
      Number of Pages155
      ID Numbers
      Open LibraryOL6832263M
      LC Control Number00326013
      OCLC/WorldCa43968237

      Understanding geostatistical analysis Generating a continuous surface used to represent a particular attribute is a key capability required in most Geographic Information System (GIS) applications. Perhaps the most commonly used surface type is a digital elevation model of terrain. the availability of official books. is foremost Data Analysis and Spatial Continuity Model ing. Such analysis and modeling cannot be done without with the particular geostatistical twist, in the sense that the data used (the so-called "independent" variables of.

        Practical Geostatistics Book and CD, Isobel Clark and William. Harper () Practical Geostatistics Case Studies , Isobel Clark and William V Harper () Practical Geostatistics: Modeling and Spatial Analysis, Simon W. Houlding () Solved Problems in Geostatistics, Oy Leuangthong, K. Daniel Khan, and Clayton V. Deutsch (). What makes geostatistics powerful is its ability to characterize spatial variability through a consistent probabilistic model. Therefore, the predictions made using the geostatistical methods are tailored to the intrinsic structure of the variable and not only to the sampling quantity or geometric pattern.

        The Create Geostatistical Layer tool takes a model source as input. This model source can be a geostatistical layer (either a layer in ArcMap or saved as file on disk) or an XML model source. For your purposes, the XML will be easier to use. Geostatistical analysis and modeling performed during the current work did not rely on the interpreted stratigraphy of Anderson and others () except as it was used by Anderson and Liszewski () to assign a composite unit stratigraphy. Data Preparation. The stratigraphic data set compiled by Anderson and others () was in the form of a.


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A second family of interpolation. Understanding geostatistical analysis. Understanding geostatistical methods; Generating a continuous surface used to represent a particular attribute is a key capability required in most geographic information system (GIS) applications.

Perhaps the most commonly used surface type is a digital elevation model of terrain. Geostatistics deals with distributions in which the spatial and/or temporal dependence is the primary characteristic.

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