A Triangulated Irregular Network Constrained Ordinary Kriging Method for Three-Dimensional Modeling of Faulted Geological Surfaces

Three-dimensional (3D) modeling of geological surfaces, such as coal seams and strata horizons, from sparsely sampled data collected in the field, is a crucial task in geological modeling. Interpolation is a common approach for this task to construct continuous geological surface models. However, this problem becomes challenging considering the impact of the faults on geological surfaces. Existing methods tend to solve this problem through three steps, including interpolating stratum and fault surface, applying a fault modeling method to modify the geological surface, and optimizing the modified surface to pass sample points fallen into the fault displacement zone. This paper presents a more concise method to generate a faulted geological surface, in which 1) a constrained Delaunay triangulated irregular network (CD-TIN) is constructed to facilitate the neighborhood search process of the ordinary kriging (OK) interpolation, 2) the CD-TIN is also directly constrained by horizon cut-off lines formed from theoretical fault displacement profiles, and 3) subsequently, neighbors of the location to be estimated are selected effectively in the CD-TIN considering the fault topology. The proposed method significantly improves the time efficiency of the OK interpolation by utilizing the CD-TIN and incorporates fault effects directly into the interpolation process by inserting fault horizontal cut-off lines into CD-TIN. Moreover, by integrating the fault effects directly into the interpolation process, the surface modeling process is accomplished in a single stage instead of two separate stages of interpolation first and then modifying the surface in the fault area. By this strategy, the proposed method significantly improves the time efficiency of the OK interpolation algorithm and achieves more accurate modeling of the faulted geological surface. Experiments were designed to compare the performance of our method with several commonly used approaches, and the results indicate that the proposed TIN-constrained OK method achieves better accuracy and efficiency in modeling faulted geological surfaces than other methods. This method could also be used in geospatial interpolation studies, such as meteorological data interpolation.

[1]  Clayton V. Deutsch,et al.  Programs for kriging and sequential Gaussian simulation with locally varying anisotropy using non-Euclidean distances , 2011, Comput. Geosci..

[2]  J. Chilès,et al.  Geological modelling from field data and geological knowledge. Part I. Modelling method coupling 3D potential-field interpolation and geological rules , 2008 .

[3]  Kurt J. Marfurt,et al.  A workflow to skeletonize faults and stratigraphic features , 2017 .

[4]  Pierre Thierry,et al.  3D geological modelling at urban scale and mapping of ground movement susceptibility from gypsum dissolution: The Paris example (France) , 2009 .

[5]  Hans-Peter Seidel,et al.  A multi-scale approach to 3D scattered data interpolation with compactly supported basis functions , 2003, 2003 Shape Modeling International..

[6]  Pierre Thore,et al.  Structural uncertainties: Determination, management, and applications , 2002 .

[7]  J. Walsh,et al.  Faults and fault properties in hydrocarbon flow models , 2010 .

[8]  A. R. Syversveen,et al.  Fault displacement modelling using 3D vector fields , 2010, Computational Geosciences.

[9]  Travis Losser,et al.  Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree , 2014, International journal of environmental research and public health.

[10]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH Courses.

[11]  Mao Pan,et al.  GSIS: A 3D geological multi-body modeling system from netty cross-sections with topology , 2010, Comput. Geosci..

[12]  Guillaume Caumon,et al.  Three-Dimensional Implicit Stratigraphic Model Building From Remote Sensing Data on Tetrahedral Meshes: Theory and Application to a Regional Model of La Popa Basin, NE Mexico , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Defu Che,et al.  Effective coal seam surface modeling with an improved anisotropy-based, multiscale interpolation method , 2019, Comput. Geosci..

[14]  Gang Ai,et al.  Implementation of a Parallel GPU-Based Space-Time Kriging Framework , 2018, ISPRS Int. J. Geo Inf..

[15]  B. Freeman,et al.  Fault correlation during seismic interpretation , 1990 .

[16]  Liangliang Xu,et al.  Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search , 2016, SpringerPlus.

[17]  G. Caumon,et al.  Surface-Based 3D Modeling of Geological Structures , 2009 .

[18]  Yi-Hsing Tseng,et al.  SEMIAUTOMATED BUILDING EXTRACTION BASED ON CSG MODEL-IMAGE FITTING , 2003 .

[19]  G. Caumon,et al.  A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data , 2018 .

[20]  L. Paul Chew,et al.  Constrained Delaunay triangulations , 1987, SCG '87.

[21]  Richard W. Allmendinger,et al.  Inverse and forward numerical modeling of trishear fault‐propagation folds , 1998 .

[22]  Hua Xu,et al.  A 3D modeling approach to complex faults with multi-source data , 2015, Comput. Geosci..

[23]  Jason Franklin,et al.  Efficient spatiotemporal interpolation with spark machine learning , 2018, Earth Science Informatics.

[24]  W. K. Hamblin Origin of “Reverse Drag” on the Downthrown Side of Normal Faults , 1965 .

[25]  M. Jessell,et al.  A parametric method to model 3D displacements around faults with volumetric vector fields , 2013 .

[26]  N. Cardozo,et al.  Kinematic modeling of folding above listric propagating thrusts , 2014 .

[27]  Kurt J. Marfurt,et al.  Display and enhancement of volumetric fault images , 2016 .

[28]  Aurèle Parriaux,et al.  Geological uncertainties associated with 3-D subsurface models , 2006, Comput. Geosci..

[29]  Rachida Bouhlila,et al.  3D geological modeling of the Kasserine Aquifer System, Central Tunisia: New insights into aquifer-geometry and interconnections for a better assessment of groundwater resources , 2016 .

[30]  J. Chilès,et al.  Geostatistics: Modeling Spatial Uncertainty , 1999 .

[31]  Jean-Laurent Mallet,et al.  Discrete smooth interpolation in geometric modelling , 1992, Comput. Aided Des..

[32]  Guillaume Caumon,et al.  3D geomodelling combining implicit surfaces and Voronoi-based remeshing: A case study in the Lorraine Coal Basin (France) , 2015, Comput. Geosci..

[33]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[34]  Tom Manzocchi,et al.  Faulting and fault sealing in production simulation models: Brent Province, northern North Sea , 2007, Petroleum Geoscience.

[35]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[36]  J. Walsh,et al.  Displacement Geometry in the Volume Containing a Single Normal Fault , 1987 .

[37]  Christopher B. Jones,et al.  Three-dimensional reconstruction of geoscientific objects from serial sections , 1995, The Visual Computer.

[38]  Zhihui Zhu,et al.  Methods to enhance seismic faults and construct fault surfaces , 2017, Comput. Geosci..

[39]  A. Cruden,et al.  Regional dome evolution and its control on ore-grade distribution: Insights from 3D implicit modelling of the Navachab gold deposit, Namibia , 2015 .

[40]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[41]  X. Emery The kriging update equations and their application to the selection of neighboring data , 2009 .

[42]  Xiaojun Li,et al.  Coal seam surface modeling and updating with multi-source data integration using Bayesian Geostatistics , 2013 .