<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-3322</journal-id>
<journal-title><![CDATA[Boletín de la Sociedad Geológica Mexicana]]></journal-title>
<abbrev-journal-title><![CDATA[Bol. Soc. Geol. Mex]]></abbrev-journal-title>
<issn>1405-3322</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Geológica Mexicana A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-33222016000300553</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Neural networks for defining spatial variation of rock properties in sparsely instrumented media]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Benítez]]></surname>
<given-names><![CDATA[Silvia Raquel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Molina]]></surname>
<given-names><![CDATA[Jorge Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castellanos Pedroza]]></surname>
<given-names><![CDATA[Valentín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,UNAM Instituto de Ingeniería ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Comisión Federal de Electricidad División de Inyecciones y Mecánica de Rocas ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2016</year>
</pub-date>
<volume>68</volume>
<numero>3</numero>
<fpage>553</fpage>
<lpage>570</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-33222016000300553&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-33222016000300553&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-33222016000300553&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Reliable information of the three-dimensional distribution of rock mass properties improves the design of secure and cost-effective civil structures. In this paper, a recurrent neural network is presented as an alternative to predict the spatial variation of some index properties of rock in sparsely instrumented media. The neural technique, from statistical learning models, is used to approximate functions that can depend on a large number of inputs that are generally unknown. From a reasonably simple neuronal model of two inhomogeneous rock volumes, the limited measured information is extrapolated and the properties in the entire mass can be estimated. Comparisons between in situ explorations versus the 3D-neuronal definition confirm the potential of the proposed method for characterizing the properties of masses with inhomogeneous properties. Such a representation is useful for design of economic realistic numerical modelling of rock volumes, maximizing information while minimizing cost.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La información confiable de la distribución tridimensional de las propiedades del macizo rocoso mejora el diseño de estructuras civiles seguras y rentables. En este trabajo, se presenta una red neuronal recurrente como una alternativa para predecir la variación espacial de algunas propiedades índice de roca en medios escasamente instrumentados. La técnica neuronal, que forma parte de los modelos de aprendizaje estadístico, se utiliza para aproximar funciones que pueden depender de un gran número de entradas y que generalmente son desconocidas. Con un modelo neuronal, razonablemente simple, de dos volúmenes de roca no homogéneos, se extrapola la escasa información levantada en campo y se estiman las propiedades en toda la masa. Las comparaciones entre la exploración in situ y la definición neuro-3D confirma el potencial del método propuesto para la caracterización de propiedades de las masas con propiedades no homogéneas. Esta representación es útil para el modelado numérico realista y económico de volúmenes de roca, maximizando la información mientras se reducen los costos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[spatial variation analysis]]></kwd>
<kwd lng="en"><![CDATA[index properties of rock, artificial intelligence, back propagation, recurrent neural networks]]></kwd>
<kwd lng="es"><![CDATA[análisis de variación espacial]]></kwd>
<kwd lng="es"><![CDATA[propiedades índice de rocas, inteligencia artificial, retro-propagación, redes neuronales recurrentes]]></kwd>
</kwd-group>
</article-meta>
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