NASA Home Page Goddard Space Flight Center Home Page
NASA Logo - Goddard Space Flight Center   + Visit NASA.gov
a directory of Earth science data and services Global Change Master Directory Web site
header 2 bullet Links bullet FAQ bullet Contact Us bullet Site Map
Home Data Sets Data Services Learn about GCMD_legacy's portal collaborations. Add new dataset or dataservice records to GCMD_legacy What's New Participate Calendar About GCMD_legacy
 
Record Search Query: ServiceParameters>MODELS>DIGITAL ELEVATION/DIGITAL TERRAIN MODELS

Stanford Geostatistical Earth Modeling Software (S-GEMS)
Entry ID: SGEMS
[ Get Service ]
[ Update this Record ]


Summary
Abstract: GEMS, the Geostatistical Earth Modeling Software, is an example of software built from scratch using the GsTL. The source code of GEMS serves as an example of how to use GsTL facilities.

GEMS was designed with two aims in mind. The first one, geared toward the enduser, is to provide a user-friendly software which offers a large range of geostatistics tools: the most common geostatistics algorithms are implemented, in addition to more recent developments such as multiple-point statistics simulation. The user-friendliness of GEMS mainly comes from its non-obtrusive graphical user interface, and the possibility to directly visualize data sets and results in a full 3-D interactive environment. The second objective was to design a software whose functionalities could conveniently be augmented. New features can be added into GEMS through a system of plug-ins, i.e. pieces of software which can not be run by themselves but complement a main software. In GEMS, plug-ins can be used to add new (geostatistics) tools, add new grid data structures (faulted stratigraphic grids for example) or define new import/export filters.

[Summary provided by Stanford University.]

Related URL
Link: GET SERVICE > GET SOFTWARE PACKAGE
Description: Download the Geostatistical Earth Modeling Software.

Link: VIEW RELATED INFORMATION
Description: Geostatistical Earth Modeling Software: User's Manual.
Service Citation
Originators: Stanford University
Title: Stanford Geostatistical Earth Modeling Software (S-GEMS)
Provider: Stanford University
URL: http://sgems.sourceforge.net/
ISO Topic Category
ELEVATION
ENVIRONMENT
GEOSCIENTIFIC INFORMATION
IMAGERY/BASE MAPS/EARTH COVER
PLANNING CADASTRE
Access Constraints
SGEMS is currently available on both Linux and Windows. It should be possible
to compile it on other Unix platforms and Mac OSX.

The code is distributed under the GNU General Public License (GPL).
Service Provider
Stanford Center for Reservoir Forecasting, School of Earth Sciences, Stanford University

Service Provider Personnel
Name: STANFORD UNIVERSITY CENTER FOR RESERVOIR FORECASTING
Phone: 1-650-725-2725
Fax: 1-650-725-2099
Contact Address:
Stanford University
City: Stanford
Province or State: CA
Postal Code: 94305
Country: USA
Distribution Media
Distribution_Media: Online
Fees: No fees
Personnel
TYLER B. STEVENS
Role: SERF AUTHOR
Phone: (301) 614-6898
Fax: 301-614-5268
Email: Tyler.B.Stevens at nasa.gov
Contact Address:
NASA Goddard Space Flight Center
Global Change Master Directory
City: Greenbelt
Province or State: MD
Postal Code: 20771
Country: USA

ANDRE G. JOURNEL
Role: TECHNICAL CONTACT
Phone: (650) 723-1594
Fax: (650) 725-2099
Email: journel at pangea.stanford.edu
Contact Address:
Department of Petroleum Engineering
Stanford University
City: Stanford
Province or State: CA
Postal Code: 94305
Country: USA
Publications/References
Almeida, A. and Journel, A.: 1994, Joint simulation of multiple variables with
a markovtype coregionalization model, Mathematical Geology 26(5), 565-588.

Chiles, J. and Delfiner, P.: 1999, Geostatistics: Modeling spatial
uncertainty, John Wiley & Sons, New York.

Deutsch, C. and Journel, A.: 1992, GSLIB: Geostatistical Software Library and
User's Guide, Oxford University Press, New York.

Goovaerts, P.: 1994, Comparative performance of indicator algorithms for
modeling conditional probability distribution functions, Mathematical Geology
26, 389-411.

Goovaerts, P.: 1997a, Geostatistics for natural resources evaluation, Oxford
University Press, New York.

Goovaerts, P.: 1997b, Geostatistics for natural resources evaluation, Oxford
University Press, New York.

Holden, L., Hauge, R., Skare, O. and Skorstad, A.: 1998, Modeling of fluvial
reservoirs with object models, Mathematical Geology 30(5), 473-496.

Journel, A.: 1999, Markov models for cross covariances, Mathematical Geology
31.

Journel, A.: 2002, Combining knowledge from diverse sources: An alternative to
traditional data independence hypotheses, Mathematical Geology 34(5), 573-596.

Mao, S. and Journel, A.: 1999, Generation of a reference petrophysical/seismic
data set: the stanford v reservoir, Report 12, Stanford Center for Reservoir
Forecasting, Stanford, CA.

Strebelle, S.: 2000, Sequential simulation drawing structures from training
images, PhD thesis, Stanford University, Stanford, CA.

Tjelmeland, H.: 1996, Stochastic models in reservoir characterization and
Markov random fields for compact objects, PhD thesis, Norwegian University of
Science and Technology, Trondheim, Norway.

Tran, T.: 1994, Improving variogram reproduction on dense simulation grids,
Computers and Geosciences 20(7), 1161-1168.

Zhu, H. and Journel, A.: 1993, Formatting and interpreting soft data:
Stochastic imaging via the markov-bayes algorithm, 1, 1-12.
Creation and Review Dates
SERF Creation Date: 2005-04-21
SERF Last Revision Date: 2011-05-20
[ Update this Record ]
USA dot gov - The U.S. Government's Official Web Portal NASA
Webmaster:  Monica Holland
Responsible NASA Official:  Lola Olsen
Last Updated:  April 2014