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Titlebook: 3D Geoscience Modeling; Computer Techniques Simon W. Houlding Book 1994 Springer-Verlag Berlin Heidelberg 1994 3D Computermodellierung.3D

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發(fā)表于 2025-3-21 17:07:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱3D Geoscience Modeling
期刊簡稱Computer Techniques
影響因子2023Simon W. Houlding
視頻videohttp://file.papertrans.cn/101/100719/100719.mp4
圖書封面Titlebook: 3D Geoscience Modeling; Computer Techniques  Simon W. Houlding Book 1994 Springer-Verlag Berlin Heidelberg 1994 3D Computermodellierung.3D
影響因子This book is a result of a career spent developing and applying computer techniques for the geosciences. The need for a geoscience modeling reference became apparent during participation in several workshops and conferences on the subject in the last three years. For organizing these, and for the lively discussions that ensued and inevitably contributed to the contents, I thank Keith Turner, Brian Kelk, George Pflug and Johnathan Raper. The total number of colleagues who contributed in various ways over the preceding years to the concepts and techniques presented is beyond count. The book is dedicated to all of them. Compilation of the book would have been impossible without assistance from a number of colleagues who contributed directly. In particular, Ed Rychkun, Joe Ringwald, Dave Elliott, Tom Fisher and Richard Saccany reviewed parts of the text and contributed valuable comment. Mohan Srivastava reviewed and contributed to some of the geostatistical presentations. Mark Stoakes, Peter Dettlaff and Simon Wigzell assisted with computer processing of the many application examples. Anar Khanji and Randal Crombe assisted in preparation of the text and computer images. Klaus Lamers as
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https://doi.org/10.1007/978-981-13-3399-6e is waste site characterization, contamination assessment, groundwater flow simulation, mineral resource evaluation, reservoir engineering or tunnel design, our common denominator is a concern with investigating and characterizing the geological subsurface.
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Hypoxic and Anoxic Brain Damagezation and management of large quantities of data of various types and structures are critical to successful computerization of the geological characterization process. On a typical characterization project we must deal with anywhere from 20 to several thousand borehole logs, each of which may conta
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https://doi.org/10.1007/978-4-431-54189-9e . step. The principal objective is to obtain an in- depth appreciation of the available information that will dictate the course of the characterization process. The primary tools available for this purpose are ., .. Visualization techniques and statistical analysis are familiar to most of us and
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ABZ 2014: The Landing Gear Case Studyese relatively simple situations there are much less input-intensive ways of achieving a 3D characterization of geology, provided we have sufficient information to define the principal geological surfaces. We can then instruct the computer to fill the spaces between surfaces with suitably shaped vol
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Solutions of Problems: Synchronous Machines,grid data structure discussed in Chapter 4. The methodology comprises whatever prediction technique, prediction model(s) and parameters we select as a result of our analysis of spatial variability, as discussed in Chapter 5. Spatial control of prediction is provided by our interpretation of relevant
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Problems: Sinusoidal Steady-State Analysis, in more detail. The first half of this chapter is devoted to a discussion of the implications of uncertainty to spatial prediction, what it means in real terms, and how it can be applied to sampling control and risk assessment. The latter half of the chapter attempts to deal with some of the more i
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