High-performance computational and geostatistical experiments for testing the capabilities of 3-d electrical tomography

PDF Version Also Available for Download.

Description

This project explores the feasibility of combining geologic insight, geostatistics, and high-performance computing to analyze the capabilities of 3-D electrical resistance tomography (ERT). Geostatistical methods are used to characterize the spatial variability of geologic facies that control sub-surface variability of permeability and electrical resistivity Synthetic ERT data sets are generated from geostatistical realizations of alluvial facies architecture. The synthetic data sets enable comparison of the �truth� to inversion results, quantification of the ability to detect particular facies at particular locations, and sensitivity studies on inversion parameters

Physical Description

1.1 Megabytes

Creation Information

Carle, S F; Daily, W D; Newmark, R L; Ramirez, A & Tompson, A January 19, 1999.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Sponsor

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

This project explores the feasibility of combining geologic insight, geostatistics, and high-performance computing to analyze the capabilities of 3-D electrical resistance tomography (ERT). Geostatistical methods are used to characterize the spatial variability of geologic facies that control sub-surface variability of permeability and electrical resistivity Synthetic ERT data sets are generated from geostatistical realizations of alluvial facies architecture. The synthetic data sets enable comparison of the �truth� to inversion results, quantification of the ability to detect particular facies at particular locations, and sensitivity studies on inversion parameters

Physical Description

1.1 Megabytes

Source

  • The 12th Annual Symposium on the Application of Geophysics to Environmental and Engineering Problems (SAGEEP), Oakland, CA, March 14-18, 1999

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Other: DE00007711
  • Report No.: UCRL-JC-132943
  • Grant Number: W-7405-Eng-48
  • Office of Scientific & Technical Information Report Number: 7711
  • Archival Resource Key: ark:/67531/metadc724875

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • January 19, 1999

Added to The UNT Digital Library

  • Sept. 29, 2015, 5:31 a.m.

Description Last Updated

  • Feb. 23, 2016, 7:56 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 2

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Carle, S F; Daily, W D; Newmark, R L; Ramirez, A & Tompson, A. High-performance computational and geostatistical experiments for testing the capabilities of 3-d electrical tomography, article, January 19, 1999; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc724875/: accessed August 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.