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New Software Tools for Geostatistics: GsTL and Simulacre Nicolas Remy
SCRF meeting 2003
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Introduction The GSLIB code has shown its limits
Need a new framework which provides exportable tools: convenient integration into other softwares implements geostatistics algorithms in their full generality fosters code reuse
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Methodology Consider all the algorithms to be implemented
Identify the key concepts they share and those that are unique to each of them Define the minimal set of properties each concept must have Implement the algorithms solely in term of the identified concepts and their properties
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Sequential Simulation
Cdf Estimator Cdf Simulation Path Simulation Path Neighborhood p Sampler
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Sequential Simulation
Simulation path Cdf sampling Random path Random sampling Spiral path Metropolis sampling Cdf estimation Gaussian Cdf kriging or cokriging based estimation Non-parametric cdf: Indicator kriging Search Tree Classification methods (e.g. Neural Network)
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Concept Requirements Path: has_more_nodes to_next_node cdfEstimator:
estimate_cdf(node, neighbors) Neighborhood: find_neighbors(node) Sampler: assign_value(node, cdf) Cdf: inverse(p)
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GsTL Algorithms Kriging (simple, ordinary, and with trend)
Cokriging (simple, ordinary, MM1, MM2, LMC) Sequential simulation (gaussian, indicator, snesim) Sequential cosimulation (gaussian, indicator, …) P-field simulation univariate statistics
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Main Concepts Simulation Path CDF Estimators Neighborhood Sampler
(random, structured,…) CDF Estimators (kriging, NN, search tree,…) Neighborhood (ellipsoid, template-based,…) Sampler (uniform-random, constant,…) GeoValue Correlation Measure (covariance) CDF (gaussian, exponential, non-parametric, …)
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Integration Into a Software
Proprietary Grid Data Structure (“Tsolid”) Geovalue property_value location is_informed Neighborhood find_neighbors Path Iterates on geovalues GsTL API SGSIM
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SGS on a T-Solid Continuous Discontinuous
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SGS on a faulted surface
Continuous Discontinuous
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Simulacre: a new geostat software
Algorithm selection Parameters for selected algorithm Visualization panel algorithm panel
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Conclusions Two example uses of GsTL
Implementing SGSIM into a commercial software using the GsTL tools Creating a new geostatistics software from scratch. It will serve two purposes: Be a possible GSLIB replacement into which new algorithms will be integrated Serve as an example of how to use the GsTL tools. Complete source code distributed.
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Conclusions GsTL is two-fold:
A framework, describing the fundamental properties required by the geostatistics algorithms a C++ implementation Using the GsTL framework: Enables to easily integrate newly developed algorithms when they are added to the GsTL API Does not mean that what already exists has to be re-written
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