https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Marin_Moreno%2A_et_al_2024a 2026-05-11T21:01:22Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Marin_Moreno*_et_al_2024a&diff=301571&oldid=prev 2024-06-07T10:02:20Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_192354063" class="mw-redirect" title="Draft Sanchez Pinedo 192354063">Draft Sanchez Pinedo 192354063</a> to <a href="/public/Marin_Moreno*_et_al_2024a" title="Marin Moreno* et al 2024a">Marin Moreno* et al 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 10:02, 7 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=Marin_Moreno*_et_al_2024a&diff=301570&oldid=prev 2024-06-07T10:02:14Z

<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 10:02, 7 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>Offshore wind plays a pivotal role in enhancing Europe's energy security and achieving energy decarbonization goals. However, expediting offshore wind deployment necessitates efficient and economical site investigation surveys. To address this challenge, we introduce a novel approach utilising a deep neural network (DNN) to establish correlations between geotechnical cone penetrometer test (CPT) data and shear wave velocity (𝑉) from seismic CPT. Subsequently, porosity and P-wave velocity (𝑉) are derived using a 𝑉 to bulk density correlation and a dynamic poroelastic model. The DNN is trained and tested on a dataset comprising 5284 instances of public-domain geotechnical CPT test data, including depth, tip resistance, sleeve friction, and 𝑉 from seismic CPT. During testing, the DNN model demonstrates a mean absolute error of 55 m s-1 between predicted and measured 𝑉 values. The uncertainty in 𝑉 predictions is attributed to factors such as (i) limited training data for some soil types such as gravelly sands, (ii) intricate relationship between geotechnical CPT features and seismic properties influencing 𝑉, (iii) the presence of CPT features and 𝑉 combinations that lie well outside the region from most combinations (i.e. outliers), and (iv) CPT features and 𝑉 measurements that are averaged over different depth ranges. The derived porosity and 𝑉</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>Offshore wind plays a pivotal role in enhancing Europe's energy security and achieving energy decarbonization goals. However, expediting offshore wind deployment necessitates efficient and economical site investigation surveys. To address this challenge, we introduce a novel approach utilising a deep neural network (DNN) to establish correlations between geotechnical cone penetrometer test (CPT) data and shear wave velocity (𝑉) from seismic CPT. Subsequently, porosity and P-wave velocity (𝑉) are derived using a 𝑉 to bulk density correlation and a dynamic poroelastic model. The DNN is trained and tested on a dataset comprising 5284 instances of public-domain geotechnical CPT test data, including depth, tip resistance, sleeve friction, and 𝑉 from seismic CPT. During testing, the DNN model demonstrates a mean absolute error of 55 m s-1 between predicted and measured 𝑉 values. The uncertainty in 𝑉 predictions is attributed to factors such as (i) limited training data for some soil types such as gravelly sands, (ii) intricate relationship between geotechnical CPT features and seismic properties influencing 𝑉, (iii) the presence of CPT features and 𝑉 combinations that lie well outside the region from most combinations (i.e. outliers), and (iv) CPT features and 𝑉 measurements that are averaged over different depth ranges. The derived porosity and 𝑉</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_192354063248.pdf&lt;/pdf&gt;</ins></div></td></tr> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Marin_Moreno*_et_al_2024a&diff=301568&oldid=prev 2024-06-07T10:02:11Z

<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 10:02, 7 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;">Offshore wind plays a pivotal role in enhancing Europe's energy security and achieving energy decarbonization goals. However, expediting offshore wind deployment necessitates efficient and economical site investigation surveys. To address this challenge, we introduce a novel approach utilising a deep neural network (DNN) to establish correlations between geotechnical cone penetrometer test (CPT) data and shear wave velocity (𝑉) from seismic CPT. Subsequently, porosity and P-wave velocity (𝑉) are derived using a 𝑉 to bulk density correlation and a dynamic poroelastic model. The DNN is trained and tested on a dataset comprising 5284 instances of public-domain geotechnical CPT test data, including depth, tip resistance, sleeve friction, and 𝑉 from seismic CPT. During testing, the DNN model demonstrates a mean absolute error of 55 m s-1 between predicted and measured 𝑉 values. The uncertainty in 𝑉 predictions is attributed to factors such as (i) limited training data for some soil types such as gravelly sands, (ii) intricate relationship between geotechnical CPT features and seismic properties influencing 𝑉, (iii) the presence of CPT features and 𝑉 combinations that lie well outside the region from most combinations (i.e. outliers), and (iv) CPT features and 𝑉 measurements that are averaged over different depth ranges. The derived porosity and 𝑉</ins></div></td></tr> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Marin_Moreno*_et_al_2024a&diff=301567&oldid=prev 2024-06-07T10:02:09Z

<p>Created blank page</p> <p><b>New page</b></p><div></div>

JSanchez