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Global Change Master Directory (GCMD)
Stanford Geostatistical Earth Modeling Software (S-GEMS)
Entry ID: SGEMS


Summary
Abstract: The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a user-friendly interface, an interactive 3-D visualization, and a wide selection of algorithms.

Related URL
Link: DOWNLOAD SOFTWARE
Description: Download the Geostatistical Earth Modeling Software.


Link: PROJECT HOME PAGE
Description: Geostatistical Earth Modeling Software project home page.

ISO Topic Category
ELEVATION
ENVIRONMENT


Keywords
Geostatistics
Modeling
Multi-variate kriging
Sequential Gaussian simulation
Sequential indicator simulation
Multiple-point statistics simulation
Earth Analysis


Personnel
TYLER B. STEVENS
Role: SERF AUTHOR
Phone: 301-851-8113
Email: Tyler.B.Stevens at nasa.gov
Contact Address:
5700 Rivertech Court
City: Riverdale
Province or State: MD
Postal Code: 20737
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.
Extended Metadata Properties
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Creation and Review Dates
SERF Creation Date: 2005-04-21
SERF Last Revision Date: 2018-08-15



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