Conditional Recovery Estimation Through Probability Kriging — Theory and Practice View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1984

AUTHORS

Jeff Sullivan

ABSTRACT

The probability kriging technique is an improvement on the distribution free indicator kriging technique for obtaining conditional recoverable reserves. Probability kriging is similar to indicator kriging in that both techniques utilize indicator data and no assumption concerning the shape of the conditional distribution is made. Indicator kriging however does not utilize some easily obtainable information which causes, in certain cases, the indicator kriging estimator to be smoothed, conditionally biased, and in general a poor local estimator. The cases where indicator kriging performs poorly will be identified and it will be shown that by including additional information, through the probability kriging estimator, that the quality of the estimator will be improved. The probability kriging technique is then tested on a gold deposit and the results are presented. More... »

PAGES

365-384

References to SciGraph publications

  • 1984. The Place of Non-Parametric Geostatistics in GEOSTATISTICS FOR NATURAL RESOURCES CHARACTERIZATION
  • Book

    TITLE

    Geostatistics for Natural Resources Characterization

    ISBN

    978-94-010-8157-3
    978-94-009-3699-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22

    DOI

    http://dx.doi.org/10.1007/978-94-009-3699-7_22

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010762596


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Stanford University", 
              "id": "https://www.grid.ac/institutes/grid.168010.e", 
              "name": [
                "Department of Applied Earth Sciences, Stanford University School of Earth Sciences, Stanford, California, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sullivan", 
            "givenName": "Jeff", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-94-009-3699-7_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050704907", 
              "https://doi.org/10.1007/978-94-009-3699-7_19"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1984", 
        "datePublishedReg": "1984-01-01", 
        "description": "The probability kriging technique is an improvement on the distribution free indicator kriging technique for obtaining conditional recoverable reserves. Probability kriging is similar to indicator kriging in that both techniques utilize indicator data and no assumption concerning the shape of the conditional distribution is made. Indicator kriging however does not utilize some easily obtainable information which causes, in certain cases, the indicator kriging estimator to be smoothed, conditionally biased, and in general a poor local estimator. The cases where indicator kriging performs poorly will be identified and it will be shown that by including additional information, through the probability kriging estimator, that the quality of the estimator will be improved. The probability kriging technique is then tested on a gold deposit and the results are presented.", 
        "editor": [
          {
            "familyName": "Verly", 
            "givenName": "Georges", 
            "type": "Person"
          }, 
          {
            "familyName": "David", 
            "givenName": "Michel", 
            "type": "Person"
          }, 
          {
            "familyName": "Journel", 
            "givenName": "Andre G.", 
            "type": "Person"
          }, 
          {
            "familyName": "Marechal", 
            "givenName": "Alain", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-94-009-3699-7_22", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-94-010-8157-3", 
            "978-94-009-3699-7"
          ], 
          "name": "Geostatistics for Natural Resources Characterization", 
          "type": "Book"
        }, 
        "name": "Conditional Recovery Estimation Through Probability Kriging \u2014 Theory and Practice", 
        "pagination": "365-384", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-94-009-3699-7_22"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "3345e61a0539ad6c9ba9683bd3b75de3c2f23241d1ad9320458b416b49aea489"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1010762596"
            ]
          }
        ], 
        "publisher": {
          "location": "Dordrecht", 
          "name": "Springer Netherlands", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-94-009-3699-7_22", 
          "https://app.dimensions.ai/details/publication/pub.1010762596"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T16:58", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000018.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-94-009-3699-7_22"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-94-009-3699-7_22'


     

    This table displays all metadata directly associated to this object as RDF triples.

    83 TRIPLES      23 PREDICATES      28 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-94-009-3699-7_22 schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author N55acc93bbd4a446fa5b3a787e2f41b19
    4 schema:citation sg:pub.10.1007/978-94-009-3699-7_19
    5 schema:datePublished 1984
    6 schema:datePublishedReg 1984-01-01
    7 schema:description The probability kriging technique is an improvement on the distribution free indicator kriging technique for obtaining conditional recoverable reserves. Probability kriging is similar to indicator kriging in that both techniques utilize indicator data and no assumption concerning the shape of the conditional distribution is made. Indicator kriging however does not utilize some easily obtainable information which causes, in certain cases, the indicator kriging estimator to be smoothed, conditionally biased, and in general a poor local estimator. The cases where indicator kriging performs poorly will be identified and it will be shown that by including additional information, through the probability kriging estimator, that the quality of the estimator will be improved. The probability kriging technique is then tested on a gold deposit and the results are presented.
    8 schema:editor N431ed6fbe83b409d838f0d250eb0f9a3
    9 schema:genre chapter
    10 schema:inLanguage en
    11 schema:isAccessibleForFree false
    12 schema:isPartOf Ne242c5145b324ffd9c9dfb24415a65ec
    13 schema:name Conditional Recovery Estimation Through Probability Kriging — Theory and Practice
    14 schema:pagination 365-384
    15 schema:productId N16a392dbfc8d40e1998f05c958cf94e8
    16 N72791f7ee8a643ef9ff8dbe42f0071cb
    17 Ncd6105852b8a4246b6a800199dedc4f6
    18 schema:publisher N8688605508bd48c8a395b6408b00f318
    19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010762596
    20 https://doi.org/10.1007/978-94-009-3699-7_22
    21 schema:sdDatePublished 2019-04-15T16:58
    22 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    23 schema:sdPublisher Nc2536225a20a42e0b8a4c1b6c3813c54
    24 schema:url http://link.springer.com/10.1007/978-94-009-3699-7_22
    25 sgo:license sg:explorer/license/
    26 sgo:sdDataset chapters
    27 rdf:type schema:Chapter
    28 N16a392dbfc8d40e1998f05c958cf94e8 schema:name doi
    29 schema:value 10.1007/978-94-009-3699-7_22
    30 rdf:type schema:PropertyValue
    31 N2350dc9f0e8949be83f594cfc4a10517 rdf:first Nf40fc0a0a4b0486ab86d91b8e9b69af1
    32 rdf:rest N6e51e13e23104685879815f2aef9ae95
    33 N2668a1a4c55d4f40b0b91e0559ae3217 schema:familyName Marechal
    34 schema:givenName Alain
    35 rdf:type schema:Person
    36 N2a5641b983444dbc8cf4c5c297674b39 schema:affiliation https://www.grid.ac/institutes/grid.168010.e
    37 schema:familyName Sullivan
    38 schema:givenName Jeff
    39 rdf:type schema:Person
    40 N431ed6fbe83b409d838f0d250eb0f9a3 rdf:first N9d04e3b51f8740e69235347ce81c0fd1
    41 rdf:rest N2350dc9f0e8949be83f594cfc4a10517
    42 N55acc93bbd4a446fa5b3a787e2f41b19 rdf:first N2a5641b983444dbc8cf4c5c297674b39
    43 rdf:rest rdf:nil
    44 N5fe1104a65ea44558c28f2ae02219142 schema:familyName Journel
    45 schema:givenName Andre G.
    46 rdf:type schema:Person
    47 N6e51e13e23104685879815f2aef9ae95 rdf:first N5fe1104a65ea44558c28f2ae02219142
    48 rdf:rest Nff2bb473441a4a2bbbdb740e6d1da372
    49 N72791f7ee8a643ef9ff8dbe42f0071cb schema:name dimensions_id
    50 schema:value pub.1010762596
    51 rdf:type schema:PropertyValue
    52 N8688605508bd48c8a395b6408b00f318 schema:location Dordrecht
    53 schema:name Springer Netherlands
    54 rdf:type schema:Organisation
    55 N9d04e3b51f8740e69235347ce81c0fd1 schema:familyName Verly
    56 schema:givenName Georges
    57 rdf:type schema:Person
    58 Nc2536225a20a42e0b8a4c1b6c3813c54 schema:name Springer Nature - SN SciGraph project
    59 rdf:type schema:Organization
    60 Ncd6105852b8a4246b6a800199dedc4f6 schema:name readcube_id
    61 schema:value 3345e61a0539ad6c9ba9683bd3b75de3c2f23241d1ad9320458b416b49aea489
    62 rdf:type schema:PropertyValue
    63 Ne242c5145b324ffd9c9dfb24415a65ec schema:isbn 978-94-009-3699-7
    64 978-94-010-8157-3
    65 schema:name Geostatistics for Natural Resources Characterization
    66 rdf:type schema:Book
    67 Nf40fc0a0a4b0486ab86d91b8e9b69af1 schema:familyName David
    68 schema:givenName Michel
    69 rdf:type schema:Person
    70 Nff2bb473441a4a2bbbdb740e6d1da372 rdf:first N2668a1a4c55d4f40b0b91e0559ae3217
    71 rdf:rest rdf:nil
    72 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    73 schema:name Information and Computing Sciences
    74 rdf:type schema:DefinedTerm
    75 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    76 schema:name Information Systems
    77 rdf:type schema:DefinedTerm
    78 sg:pub.10.1007/978-94-009-3699-7_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050704907
    79 https://doi.org/10.1007/978-94-009-3699-7_19
    80 rdf:type schema:CreativeWork
    81 https://www.grid.ac/institutes/grid.168010.e schema:alternateName Stanford University
    82 schema:name Department of Applied Earth Sciences, Stanford University School of Earth Sciences, Stanford, California, USA
    83 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...