Bauer, J. R., and Rose, K., 2015, Variable Grid Method: an Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty, Transactions in GIS. 19(3), p. 377-397. https://doi.org/10.1111/tgis.12158
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Bibliographies
Variable Grid Method: An Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty
Estimating Carbon Storage Resources in Offshore Geologic Environments
Cameron, E.; Thomas, R.; Bauer, J.; Bean, A.; DiGiulio, J.; Disenhof, C.; Galer, S.; Jones, K.; Mark-Moser, M.; Miller, R.; Romeo, L.; Rose, K. Estimating Carbon Storage Resources in Offshore Geologic Environments; NETL-TRS-14-2018; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Albany, OR, 2018; p 32. DOI: 10.18141/1464460 https://edx.netl.doe.gov/dataset/estimating-carbon-storage-resources-in-offshore-geologic-environments Â
Variable Grid Method: An Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty
Bauer, J. R., and Rose, K., 2015, Variable Grid Method: an Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty, Transactions in GIS. 19(3), p. 377-397. https://doi.org/10.1111/tgis.12158
Probabilistic Machine Learning for Integrated Social-Natural-Physical Assessment
Ghanem, R., Zhang, R., Rose, K., invited talk, Probabilistic Machine Learning for Integrated Social-Natural-Physical Assessment, AGU Annual Meeting 2020, Session: H027 – Artificial Intelligence and Machine Learning for Multiscale Model-Experimental Integration https://agu.confex.com/agu/fm20/prelim.cgi/Session/103051
Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry
Dyer, A., Zaengle, D., Mark-Moser, M., Duran, R., Suhag, A., Rose, K., Bauer, J. Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry. AGU Annual Fall Meeting (Virtual), 2020. Session: NH007 – Data Science and Machine Learning for Natural Hazard Sciences II Posters. https://www.osti.gov/servlets/purl/1779617
A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico
Duran, R., Dyer, A., Mark-Moser, M., Bauer, J., Rose, K., Zaengle. D., Wingo, P. 2020. A Geospatial Analytical Framework to Identify Seafloor Geohazards in the Northern Gulf of Mexico. AGU Annual Meeting 2020, Session: NH010 – Geohazards in Marine and Lacustrine Environments. https://ui.adsabs.harvard.edu/abs/2020AGUFMNH004..08D/abstract
Optimizing Prediction of Reservoir Properties with Artificial Intelligence, Big Data, and the Subsurface Trend Analysis Method
Mark-Moser, M., Suhag, A., Rose, K., Wingo, P. Â (2020, November 9). Optimizing prediction of reservoir properties with artificial intelligence, big data, and the Subsurface Trend Analysis method [Conference presentation]. Machine Learning for Oil and Gas 2020, Nov. 9-11, Virtual. https://edx.netl.doe.gov/sites/offshore/optimizing-prediction-of-reservoir-properties-with-artificial-intelligence-big-data-and-the-subsurface-trend-analysis-method/
Advanced Geospatial Analytics and Machine Learning for Offshore and Onshore Oil & Natural Gas Infrastructure
Justman D., Romeo, L., Barkhurst, A., Bauer, J., Duran, R., Dyer, A., Nelson, J., Sabbatino, M., Wingo, P., Wenzlick, M., Zaengle, D., Rose, K. (2020, October 6-7). Â Advanced geospatial analytics and machine learning for offshore and onshore oil & natural gas infrastructure. [Virtual conference presentation]. GIS Week 2020. https://www.osti.gov/servlets/purl/1767074
Enhancing Knowledge Discovery of Unstructured Data to Support Context in Subsurface-Modeling Predictions
Hoover, B., Mark-Moser, M., Wingo, P., Suhag, A., Rose, K. 2021. Enhancing knowledge discovery of unstructured data to support context in subsurface-modeling predictions. ACE/SEG21, Denver, Colorado, Sept. 26th-Oct. 1st. https://www.osti.gov/servlets/purl/1843422
Using AI/ML to Curate Thousands of Carbon Storage Data Assets via EDX
Morkner, P., Rowan, C., Rose, K., Bauer, J., Sabbatino, M., Barhurst, A. Using AI/ML to Curate Thousands of Carbon Storage Data Assets via EDX. NETL Carbon Storage Review Meeting. September 10, 2020. Virtual. https://netl.doe.gov/sites/default/files/netl-file/20CSVPR_Morkner.pdf
Assessing Offshore CO2 Saline Storage Potential with the NETL Calculator
Romeo, L., Rose, K., Thomas, R., Mark-Moser, M., Barkhurst, A., Wingo, P., Bean, A. 2020. Assessing Offshore CO2 Saline Storage Potential with the NETL Calculator. Carbon Storage Review Meeting. September 11, 2020. Virtual. https://netl.doe.gov/sites/default/files/netl-file/20CSVPR_Romeo_11.pdf
Building an Analytical Framework to Measure Offshore Infrastructure Integrity, Identify Risk, and Strategize Future Use for Oil and Gas
Dyer, A., Romeo, L., Wenzlick, M., Bauer, J., Nelson, J., Duran, R., Zaengle, D., Wingo, P., and Sabbatino, M. 2020. Building an Analytical Framework to Measure Offshore Infrastructure Integrity, Identify Risk, and Strategize Future Use for Oil and Gas. Esri User Conference, San Diego, CA, July 13-15, 2020. https://www.osti.gov/servlets/purl/1604638
Harnessing the Power of DOE Data Computing for End-user Analytics, SMART Webinar
Rose, K., Barkhurst, A., Mark-Moser, M., Romeo, L., and Wingo, P., 2020, Harnessing the Power of DOE Data Computing for End-user Analytics, SMART Webinar 6/25/2020, https://www.youtube.com/watch?v=G5oUWSb-kHc&feature=youtu.be