https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Shi%2A_2024a Shi* 2024a - Revision history 2026-05-09T18:57:33Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Shi*_2024a&diff=302960&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 280739538 to Shi* 2024a 2024-06-10T11:03:39Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_280739538" class="mw-redirect" title="Draft Sanchez Pinedo 280739538">Draft Sanchez Pinedo 280739538</a> to <a href="/public/Shi*_2024a" title="Shi* 2024a">Shi* 2024a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 11:03, 10 June 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez https://www.scipedia.com/wd/index.php?title=Shi*_2024a&diff=302959&oldid=prev JSanchez at 11:03, 10 June 2024 2024-06-10T11:03:35Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 11:03, 10 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Subsurface stratigraphy is an indispensable component of geotechnical site characterization and primarily deals with the interpretation of geological interfaces from site-specific measurements, such as boreholes. Traditional geological profiling methods often rely on engineering judgement for manual drawing or entirely depend on parametric models for interpolation. Both approaches face challenges when dealing with limited geo-data. To effectively address the dilemma, a new machine learning paradigm is proposed in this study to combine valuable prior geological knowledge and sparse site-specific measurements for data-driven predictions of both two-dimensional geological cross-sections and threedimensional geological domains. The valuable prior knowledge is quantitatively represented as training images, which are compiled and stored in a training image database that is further enriched and augmented by employing deep generative models. Subsequently, the optimal training images that are compatible with the available site-specific data are adaptively selected for onward stochastic predictions under the framework of non-parametric Bayesian analysis. The method has been successfully applied to tackle geological profiling challenges in Hong Kong. The proposed framework is demonstrated to be capable of not only predicting the most probable geological patterns but also effectively quantifying associated stratigraphic uncertainty. The framework holds great potential of revolutionizing current engineering practices</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Subsurface stratigraphy is an indispensable component of geotechnical site characterization and primarily deals with the interpretation of geological interfaces from site-specific measurements, such as boreholes. Traditional geological profiling methods often rely on engineering judgement for manual drawing or entirely depend on parametric models for interpolation. Both approaches face challenges when dealing with limited geo-data. To effectively address the dilemma, a new machine learning paradigm is proposed in this study to combine valuable prior geological knowledge and sparse site-specific measurements for data-driven predictions of both two-dimensional geological cross-sections and threedimensional geological domains. The valuable prior knowledge is quantitatively represented as training images, which are compiled and stored in a training image database that is further enriched and augmented by employing deep generative models. Subsequently, the optimal training images that are compatible with the available site-specific data are adaptively selected for onward stochastic predictions under the framework of non-parametric Bayesian analysis. The method has been successfully applied to tackle geological profiling challenges in Hong Kong. The proposed framework is demonstrated to be capable of not only predicting the most probable geological patterns but also effectively quantifying associated stratigraphic uncertainty. The framework holds great potential of revolutionizing current engineering practices</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_280739538320.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:302957:newid:302959 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Shi*_2024a&diff=302957&oldid=prev JSanchez at 11:03, 10 June 2024 2024-06-10T11:03:33Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 11:03, 10 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Subsurface stratigraphy is an indispensable component of geotechnical site characterization and primarily deals with the interpretation of geological interfaces from site-specific measurements, such as boreholes. Traditional geological profiling methods often rely on engineering judgement for manual drawing or entirely depend on parametric models for interpolation. Both approaches face challenges when dealing with limited geo-data. To effectively address the dilemma, a new machine learning paradigm is proposed in this study to combine valuable prior geological knowledge and sparse site-specific measurements for data-driven predictions of both two-dimensional geological cross-sections and threedimensional geological domains. The valuable prior knowledge is quantitatively represented as training images, which are compiled and stored in a training image database that is further enriched and augmented by employing deep generative models. Subsequently, the optimal training images that are compatible with the available site-specific data are adaptively selected for onward stochastic predictions under the framework of non-parametric Bayesian analysis. The method has been successfully applied to tackle geological profiling challenges in Hong Kong. The proposed framework is demonstrated to be capable of not only predicting the most probable geological patterns but also effectively quantifying associated stratigraphic uncertainty. The framework holds great potential of revolutionizing current engineering practices</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:302956:newid:302957 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Shi*_2024a&diff=302956&oldid=prev JSanchez: Created blank page 2024-06-10T11:03:31Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez