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

鸟场

书目名称3D Geoscience Modeling影响因子(影响力)




书目名称3D Geoscience Modeling影响因子(影响力)学科排名




书目名称3D Geoscience Modeling网络公开度




书目名称3D Geoscience Modeling网络公开度学科排名




书目名称3D Geoscience Modeling被引频次




书目名称3D Geoscience Modeling被引频次学科排名




书目名称3D Geoscience Modeling年度引用




书目名称3D Geoscience Modeling年度引用学科排名




书目名称3D Geoscience Modeling读者反馈




书目名称3D Geoscience Modeling读者反馈学科排名

AGOG

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.

Parley

全部逛商店

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

不可救药

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

Irksome

Nonthreatening

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

peak-flow

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

Aprope

Endometrium

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