{"id":625,"date":"2019-12-10T14:21:27","date_gmt":"2019-12-10T13:21:27","guid":{"rendered":"http:\/\/wp.unil.ch\/gaia\/?page_id=625"},"modified":"2021-05-27T20:42:03","modified_gmt":"2021-05-27T18:42:03","slug":"mps-book-multiple-point-geostatistics-stochastic-modeling-with-training-images","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/gaia\/mps\/mps-book-multiple-point-geostatistics-stochastic-modeling-with-training-images\/","title":{"rendered":"Reference book – Multiple-point Geostatistics: Stochastic Modeling with Training Images"},"content":{"rendered":"\n\n\n
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\"\"<\/a>This page accompanies the book entitled Multiple-point Geostatistics: Stochastic Modeling with Training Images<\/em>, First Edition, by Gregoire Mariethoz and Jef Caers, \u00a9 2014 John Wiley & Sons, Ltd. It can be purchased on the website of\u00a0Wiley\u00a0<\/a>or through other channels such as\u00a0Amazon<\/a>\u00a0or\u00a0Bookdepository<\/a>.<\/p>\n

On these pages you will find additional resources under the form of a library of training images, links to research codes and updated bibliographic references. These training images are the source files of the examples that we have used in the book, and are available to download and use for testing methods and for benchmarking computer codes.<\/p>\n

The book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Multiple-point geostatistics aims at bridging the gap between physical modelling\/realism and spatio-temporal stochastic modelling. The book provides an overview of this new field in three parts. Part I presents a conceptual comparison between traditional random function theory and stochastic modelling based on training images, where random function theory is not always used. Part II covers in detail various algorithms and methodologies starting from basic building blocks in statistical science and computer science. Concepts such as non-stationary and multi-variate modeling, consistency between data and model, the construction of training images and inverse modelling are treated. Part III covers three example application areas, namely, reservoir modelling, mineral resources modelling and climate model downscaling. This book will be an invaluable reference for students, researchers and practitioners of all areas of the Earth Sciences where forecasting based on spatio-temporal data is performed.<\/p>\n<\/div>\n

Book contents<\/h2>\n

Part I<\/p>\n

    \n
  1. Hiking in the Sierra Nevada<\/li>\n
  2. Spatial estimation based on random function theory<\/li>\n
  3. Universal kriging with training images<\/li>\n
  4. Stochastic simulations based on random function theory<\/li>\n
  5. Stochastic simulations without random function theory<\/li>\n
  6. Returning to the Sierra Nevada<\/li>\n<\/ol>\n

    Part II<\/p>\n

      \n
    1. Introduction<\/li>\n
    2. The algorithmic building blocks<\/li>\n
    3. Multiple-point geostatistics algorithms<\/li>\n
    4. Markov random fields<\/li>\n
    5. Nonstationary modeling with training images<\/li>\n
    6. Multivariate modeling with training images<\/li>\n
    7. Training image construction<\/li>\n
    8. Validation and quality control<\/li>\n
    9. Inverse problems with training images<\/li>\n
    10. Parallelization<\/li>\n<\/ol>\n

      Part III<\/p>\n

        \n
      1. Reservoir forecasting \u2013 the West Coast of Africa (WCA) reservoir<\/li>\n
      2. Geological resources modeling in mining<\/li>\n
      3. Climate modeling application \u2013 the case of the Murray\u2013Darling
        Basin<\/li>\n<\/ol>\n
        \n
        \u00a0<\/div>\n<\/div>\n

        Training images used in the book<\/h2>\n
        \u00a0<\/div>\n
        \n
        Below are some of the training images that are employed in the book. They are presented both graphically and in ASCII format. A more extensive training image library can be found on our Github repository<\/a>.<\/div>\n
        These images be\u00a0used freely for research purposes. All files are in GSLIB ASCII format. More details here:\u00a0https:\/\/www.gslib.com\/gslib_help\/format.html<\/a>.
        Large files are split\u00a0into several smaller zipped files. To extract them, download all parts to the same folder and then open the .zip file.
        Enjoy!<\/div>\n

        Part I<\/h2>\n

        \"Picture\"<\/a><\/p>\n

        Walker Lake exhaustive DEM\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        100 sample data extracted from the Walker Lake DEM\n

        Link to file in ASCII format
        <\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Rock density in a homogeneous layer of a carbonate reservoir (3D)\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Rock density in a heterogeneous deltaic reservoir (3D)\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Walker lake exhaustive DEM categorized\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Walker lake 100 sample categorized DEM data\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Training image, categorized\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \n

        Part II<\/h2>\n

        \"Picture\"<\/a><\/p>\n

        1D-temporal grid representing 120 years of daily rainfall measures in Sydney, Australia\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        2D grid of a satellite image of the Sundarbans region, Bengladesh\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        3D grid representing the hydrofacies in an alluvial aquifer in the Maules Creek valley, Australia.\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Red component, green component and blue component of an image.\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Channels training image (Strebelle 2002)\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Ganges delta, Bangladesh\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        2D grid of a satellite image of the Sundarbans region, Bengladesh – transformed in a binary variable.\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Two simple training images\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Lines with arrows\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Elementary training image: categorical 3D layers\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Elementary training image: continuous 3D layers\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        3D categorical folds\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        3D continuous folds\n

        Link to file in ASCII format <\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Multivariate training image
        Link to file in ASCII format
        <\/a><\/div>\n
        \u00a0<\/div>\n
        \"Picture\"<\/a>\n
        Herten training image (Bayer et al. 2011)\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        FLUVSIM object-based model\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Elementary training image – 3D checker\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Rotation-invariant simulation – 90 degrees tolerance\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a><\/p>\n

        Rotation-invariant simulation – 20 degrees tolerance\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Reconstruction of sea surface temperature over the Pacific Ocean
        Incomplete data and 5 realizations
        Link to file in ASCII format<\/a><\/div>\n
        \u00a0<\/div>\n
        \"Picture\"<\/a>\n
        Process-based model (FLUMY)\n

        Link to file in ASCII format\u00a0(part 1)<\/a>
        Link to file in ASCII format\u00a0(part 2)<\/a><\/p>\n<\/div>\n

        \"Picture\"<\/a>\n
        Stone wall training image\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \n

        Part III<\/h2>\n

        \"Picture\"<\/a><\/p>\n

        West Coast of Africa object-based training images\n

        Link to file in ASCII format<\/a><\/p>\n<\/div>\n

        \u00a0<\/div>\n<\/div>\n
        \u00a0<\/div>\n
        \u00a0<\/div>\n
        \u00a0<\/div>\n
        \u00a0<\/div>\n
        \n

        Additional resources<\/h2>\n

        \"Picture\"<\/a><\/p>\n

        The SGeMS software, which includes the SNESIM simulation algorithm<\/a><\/div>\n
        \n

        \"Picture\"<\/a><\/p>\n

        The Stanford Center for Reservoir Forecasting code repository<\/a><\/div>\n
        \n
        \u00a0<\/div>\n

        \"Picture\"<\/a><\/p>\n

        A Matlab implementation of Image quilting<\/a><\/div>\n
        \n

        \"Picture\"<\/a><\/p>\n

        The Direct Sampling algorithm available commercially on the Ocean Store (Petrel)<\/a><\/div>\n
        \n

        \"Picture\"<\/a><\/p>\n

        The IMPALA algorithm available commercially with Geovariances Isatis<\/a><\/div>\n
        \n

        \"Picture\"<\/a><\/p>\n

        A Matlab code for Direct Sampling and many other useful tools related to MPS<\/a><\/div>\n
        \u00a0<\/div>\n
        \u00a0<\/div>\n
        \n

        Bibliographic resources<\/span><\/h2>\n<\/div>\n
        \u00a0<\/div>\n
        \"Picture\"<\/a>\n
        An updated list of articles related to MPS, classified by theme<\/a><\/div>\n<\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n
        <\/div><\/div>\n\n\n\n

        <\/p>\n","protected":false},"excerpt":{"rendered":"

        This page accompanies the book entitled Multiple-point Geostatistics: Stochastic Modeling with Training Images, First Edition, by Gregoire Mariethoz and Jef Caers, \u00a9 2014 John Wiley & Sons, Ltd. It can be purchased on the website of\u00a0Wiley\u00a0or through other channels such as\u00a0Amazon\u00a0or\u00a0Bookdepository. On these pages you will find additional resources under the form of a library … <\/p>\n

        Continue reading “Reference book – Multiple-point Geostatistics: Stochastic Modeling with Training Images”<\/span><\/a><\/p>\n","protected":false},"author":1001689,"featured_media":0,"parent":197,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"class_list":["post-625","page","type-page","status-publish"],"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/625","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/users\/1001689"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/comments?post=625"}],"version-history":[{"count":0,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/625\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/pages\/197"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/gaia\/wp-json\/wp\/v2\/media?parent=625"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}