ºÝºÝߣshows by User: TorbenBach / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: TorbenBach / Tue, 10 Oct 2017 10:10:39 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: TorbenBach 3d hydrogeological conceptual model building in denmark /slideshow/3d-hydrogeological-conceptual-model-building-in-denmark/80644875 slidesharebach-171010101039
Lessons learned from the Danish groundwater mapping campaign Presented October 4th at the 2017 Groundwater Resources Association meeting in Sacramento, CA Presenter: Torben Bach, I-GIS]]>
Lessons learned from the Danish groundwater mapping campaign Presented October 4th at the 2017 Groundwater Resources Association meeting in Sacramento, CA Presenter: Torben Bach, I-GIS]]> Tue, 10 Oct 2017 10:10:39 GMT /slideshow/3d-hydrogeological-conceptual-model-building-in-denmark/80644875 TorbenBach@slideshare.net(TorbenBach) 3d hydrogeological conceptual model building in denmark TorbenBach Lessons learned from the Danish groundwater mapping campaign Presented October 4th at the 2017 Groundwater Resources Association meeting in Sacramento, CA Presenter: Torben Bach, I-GIS <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesharebach-171010101039-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Lessons learned from the Danish groundwater mapping campaign Presented October 4th at the 2017 Groundwater Resources Association meeting in Sacramento, CA Presenter: Torben Bach, I-GIS
3d hydrogeological conceptual model building in denmark from Torben Bach
]]> 688 16 https://cdn.slidesharecdn.com/ss_thumbnails/slidesharebach-171010101039-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Smart Interpretation - Fast AEM Modelling - SAGEEP 2017 /slideshow/smart-interpretation-fast-aem-modelling-sageep-2017/74016881 sageepslideshare-170331001301
Presentation from SAGEEP 2017 showing lessons learned from using a semi-automated geological model picker for AEM data.]]>
Presentation from SAGEEP 2017 showing lessons learned from using a semi-automated geological model picker for AEM data.]]> Fri, 31 Mar 2017 00:13:01 GMT /slideshow/smart-interpretation-fast-aem-modelling-sageep-2017/74016881 TorbenBach@slideshare.net(TorbenBach) Smart Interpretation - Fast AEM Modelling - SAGEEP 2017 TorbenBach Presentation from SAGEEP 2017 showing lessons learned from using a semi-automated geological model picker for AEM data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sageepslideshare-170331001301-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation from SAGEEP 2017 showing lessons learned from using a semi-automated geological model picker for AEM data.
Smart Interpretation - Fast AEM Modelling - SAGEEP 2017 from Torben Bach
]]> 632 4 https://cdn.slidesharecdn.com/ss_thumbnails/sageepslideshare-170331001301-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fast modelling of Airborne EM data using "Smart Interpretation" /slideshow/fast-modelling-of-airborne-em-data-using-smart-interpretation/63599282 si2016-160630083639
When using airborne geophysical measurements in e.g. groundwater mapping, an overwhelming amount of data is collected. Increasingly larger survey areas, denser data collection and limited resources, combines to an increasing problem of building geological models that use all the available data in a manner that is consistent with the geologists knowledge about the geology of the survey area. In the ERGO project, funded by The Danish National Advanced Technology Foundation, we address this problem, by developing new, usable tools, enabling the geologist utilize her geological knowledge directly in the interpretation of the AEM data, and thereby handle the large amount of data, In the project we have developed the mathematical basis for capturing geological expertise in a statistical model. Based on this, we have implemented new algorithms that have been operationalized and embedded in user friendly software. In this software, the machine learning algorithm, Smart Interpretation, enables the geologist to use the system as an assistant in the geological modelling process. As the software ‘learns’ the geology from the geologist, the system suggest from new features in the data. In this presentation we demonstrate the application of the results from the ERGO project, including the proposed modelling workflow utilized on a data example from Gotland, Sweden. ]]>
When using airborne geophysical measurements in e.g. groundwater mapping, an overwhelming amount of data is collected. Increasingly larger survey areas, denser data collection and limited resources, combines to an increasing problem of building geological models that use all the available data in a manner that is consistent with the geologists knowledge about the geology of the survey area. In the ERGO project, funded by The Danish National Advanced Technology Foundation, we address this problem, by developing new, usable tools, enabling the geologist utilize her geological knowledge directly in the interpretation of the AEM data, and thereby handle the large amount of data, In the project we have developed the mathematical basis for capturing geological expertise in a statistical model. Based on this, we have implemented new algorithms that have been operationalized and embedded in user friendly software. In this software, the machine learning algorithm, Smart Interpretation, enables the geologist to use the system as an assistant in the geological modelling process. As the software ‘learns’ the geology from the geologist, the system suggest from new features in the data. In this presentation we demonstrate the application of the results from the ERGO project, including the proposed modelling workflow utilized on a data example from Gotland, Sweden. ]]> Thu, 30 Jun 2016 08:36:39 GMT /slideshow/fast-modelling-of-airborne-em-data-using-smart-interpretation/63599282 TorbenBach@slideshare.net(TorbenBach) Fast modelling of Airborne EM data using "Smart Interpretation" TorbenBach When using airborne geophysical measurements in e.g. groundwater mapping, an overwhelming amount of data is collected. Increasingly larger survey areas, denser data collection and limited resources, combines to an increasing problem of building geological models that use all the available data in a manner that is consistent with the geologists knowledge about the geology of the survey area. In the ERGO project, funded by The Danish National Advanced Technology Foundation, we address this problem, by developing new, usable tools, enabling the geologist utilize her geological knowledge directly in the interpretation of the AEM data, and thereby handle the large amount of data, In the project we have developed the mathematical basis for capturing geological expertise in a statistical model. Based on this, we have implemented new algorithms that have been operationalized and embedded in user friendly software. In this software, the machine learning algorithm, Smart Interpretation, enables the geologist to use the system as an assistant in the geological modelling process. As the software ‘learns’ the geology from the geologist, the system suggest from new features in the data. In this presentation we demonstrate the application of the results from the ERGO project, including the proposed modelling workflow utilized on a data example from Gotland, Sweden. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/si2016-160630083639-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> When using airborne geophysical measurements in e.g. groundwater mapping, an overwhelming amount of data is collected. Increasingly larger survey areas, denser data collection and limited resources, combines to an increasing problem of building geological models that use all the available data in a manner that is consistent with the geologists knowledge about the geology of the survey area. In the ERGO project, funded by The Danish National Advanced Technology Foundation, we address this problem, by developing new, usable tools, enabling the geologist utilize her geological knowledge directly in the interpretation of the AEM data, and thereby handle the large amount of data, In the project we have developed the mathematical basis for capturing geological expertise in a statistical model. Based on this, we have implemented new algorithms that have been operationalized and embedded in user friendly software. In this software, the machine learning algorithm, Smart Interpretation, enables the geologist to use the system as an assistant in the geological modelling process. As the software ‘learns’ the geology from the geologist, the system suggest from new features in the data. In this presentation we demonstrate the application of the results from the ERGO project, including the proposed modelling workflow utilized on a data example from Gotland, Sweden.
Fast modelling of Airborne EM data using "Smart Interpretation" from Torben Bach
]]> 437 4 https://cdn.slidesharecdn.com/ss_thumbnails/si2016-160630083639-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-TorbenBach-48x48.jpg?cb=1660641270 International Sales | R&D Project Development | Product Portfolio Management https://cdn.slidesharecdn.com/ss_thumbnails/slidesharebach-171010101039-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/3d-hydrogeological-conceptual-model-building-in-denmark/80644875 3d hydrogeological con... https://cdn.slidesharecdn.com/ss_thumbnails/sageepslideshare-170331001301-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/smart-interpretation-fast-aem-modelling-sageep-2017/74016881 Smart Interpretation -... https://cdn.slidesharecdn.com/ss_thumbnails/si2016-160630083639-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/fast-modelling-of-airborne-em-data-using-smart-interpretation/63599282 Fast modelling of Airb...