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				<h1 class="stroke-text" style="font-size: 28pt; padding-top: 20px; color: rgb(37, 158, 203);">AI/ML for interpretation and analysis of geochemical and geophysical data</h1>
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						<h1><span style="color: orange; font-weight: bold">GeoML</span>: AI/ML for interpretation and analysis of geochemical and geophysical data</h1>
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						<!-- <img src="logo/geothermal ver0.1.png" style="width: 100px;" /> -->
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							<span style="color: orange; font-weight: bold">GeoML</span> provides methods and tools to better understand and utilize geologic subsurface for energy production and storage.
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							<span style="color: orange; font-weight: bold">GeoML</span> addresses geologic complexities, technology advancements, and energy needs.
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							<span style="color: orange; font-weight: bold">GeoML</span> relies on cloud-based artificial intelligence and machine learning (AI/ML) tools.
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							<span style="color: orange; font-weight: bold">GeoML</span> prototype version can be accessed <a href="http://73.42.119.232:8988" target="_blank">here</a>.
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							<span style="color: orange; font-weight: bold">GeoML</span> facilitates the interactions between the industry, regulators, stakeholders, and end-users.
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							<span style="color: orange; font-weight: bold">GeoML</span> merges data, computational methods, knowledge, expertise, and experience.
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							<span style="color: orange; font-weight: bold">GeoML</span> provides fast processing, analysis and dissemination of geologic information related to subsurface energy storage and production.
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							<span style="color: orange; font-weight: bold">GeoML</span> help us understand and predict complex subsurface processes associated with energy extraction and storage in the subsurface.
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							Efficeinty utilization of geologic subsurface for energy extraction and storage will help decarbonize our nation to reduce energy costs, and boost energy independence.
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											<h2>Energy Storage</h2>
											<h4>Building Blocks for Energy Storage</h4>
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											<h2>Energy Storage</h2>
											<h4>How Borehole Thermal Storage Works</h4>
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											<h2>Energy Storage</h2>
											<h4>Underground Heat Storage with Geosolar Systems</h4>
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											<h2>Energy Storage</h2>
											<h4>How Aquifer Energy Storage Works</h4>
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						<h2>Recent relevent publications</h2>
						<ul>
							<li>
								Mudunuru, M., Ahmmed, B., Rau, E., Vesselinov, V.V., Karra, S., Machine Learning for Geothermal Resource Exploration in Tularosa Basin, New Mexico, Energies, 2023. <a href="https://gitlab.com/monty/monty.gitlab.io/raw/master/papers/Mudunuru et al 2023 Machine Learning for Geothermal Resource Exploration in Tularosa Basin.pdf">PDF</a>
							</li>
							<li>
								Mudunuru, M., Vesselinov, V.V., Ahmmed, B., Geothermalcloud: Machine learning for geothermal resource exploration, Journal of Machine Learning for Modeling and Computing, 10.1615/JMachLearnModelComput.2022046445, 2022. <a href="https://gitlab.com/monty/monty.gitlab.io/raw/master/papers/Mudunuru et al 2022 Geothermalcloud - Machine learning for geothermal resource exploration.pdf">PDF</a>
							</li>
						</ul>
						<div class="line-dec"></div>
						<h2>Recent relevent presentations</h2>
						<ul>
							<li>
								Kliphuis, T.L., Bluehouse, M., Vesselinov, V.V., GeoML: Equitable and Inclusive Tool for Community-Based Geothermal, Stanford Geothermal Workshop, Stanford, CA, February 6-8, 2023, (not presented). <a style="color: orange;" href="https://gitlab.com/monty/monty.gitlab.io/raw/master/presentations/Kliphuis et al 2023 GeoML Equitable and Inclusive Tool for Community-Based Geothermal.pdf"  target="_blank">PDF</a>
							</li>
							<li>
								Vesselinov, V.V., Kliphuis, T.L., Novel machine learning methods and tools for geothermal and geochemical problems, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16, 2022. <a style="color: orange;" href="https://gitlab.com/monty/monty.gitlab.io/raw/master/presentations/Vesselinov & Kliphuis 2022 Novel machine learning methods and tools for geothermal and geochemical problems.pdf" target="_blank">PDF</a>
							</li>
						</ul>
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						<p>
							<span style="color: orange; font-weight: bold">GeoML</span> work is funded by a Small Business Innovation Research (SBIR) grant from the Department of Energy (DOE).
						</p>
						<img src="../logo/doe-logo.webp" style="width: 200px;" />
						<p>EnviTrace is also a <a href="https://edd.newmexico.gov/wp-content/uploads/2023/01/SBIRST-FY23-PR.pdf">2023 New Mexico SBIR Awardee</a>.
							The funding from <a href="https://edd.newmexico.gov/business-development/edd-programs-for-business/office-of-science-technology/">the Office of Science & Technology, Economic Development Department, State of New Mexico</a>, supplements the DOE support of our projects.
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						<p>
							Principal Investigator is Trace Kliphuis, Co-Founder and CEO of <span style="color: orange; font-weight: bold">EnviTrace LLC</span>.
						</p>
						<p>
							Project Manager is Velimir (monty) Vesselinov, Co-Founder, CTO, and CSO of <span style="color: orange; font-weight: bold">EnviTrace LLC</span>.
						</p>
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							More information about our AI/ML methods can be found <a href="../methods/ml.html" style="font-weight: bold; color: orange">here ...</a>
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						<h2>Our Company</h2>
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						<p><span style="color: orange; font-weight: bold">EnviTrace</span> develops cutting-edge artificial intelligence and machine learning (AI/ML) methods and tools.</p>
						<p><span style="color: orange; font-weight: bold">EnviTrace</span> applies AI/ML to find and analyze hidden pieces of information necessary to understand and solve energy and environmental challenges facing our world today and tomorrow.</p>
						<p><span style="color: orange; font-weight: bold">EnviTrace</span> experience and expertise include climate change, geothermal, carbon sequestration, oil/gas production, waste disposal, water/air contamination, water supply, wildfires, and air quality.</p>
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						<h2>Land Acknowledgement</h2>
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						<p>EnviTrace acknowledges that the place now called Santa Fe where our office is located sits on unceded ancestral Tewa Land and is still recognized as O'gha Po'oge, meaning White Shell Water Place.</p>
						<p>We recognize that this land is just one piece of a larger, boundless terrain for Indigenous peoples that include the Nambe Pueblo, the Tewa, and the Jicarilla Apache.</p>
						<p>O'gha Po'oge was once a thriving Pueblo village, and their descendants include the modern-day Tewa people who still live in Santa Fe and the local Pueblos of Nambe, Pojoaque, San Ildefonso, Ohkay Owingeh, Santa Clara and Tesuque.</p>
						<p>We also recognize the violence, displacement, migration, and colonization that haunt this place.</p>
						<p>We understand we are now stewards of this land, responsible for the care of water, air, and each other.</p>
						<p>In addition, we pledge to donate a percentage of our profit to a local non-profit organization supporting the education of native students.</p>
						<p>&nbsp;</p>
						<p>If you have feedback on this land acknowledgment statement please email us at <a href="mailto:info@envitrace.com" style="color: orange;">info@envitrace.com</a>.</p>
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				<h3>EnviTrace is a member of the American Indian Chamber of Commerce of New Mexico, the Santa Fe Green Chamber of Commerce, the Geothermal Rising, and Women in Geothermal.</h3>
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Creat By MiNi SheLL
Email: devilkiller@gmail.com