Events
EnviTrace hosts and convenes industry events advancing AI, geoscience, and energy innovation. Explore workshops that connect researchers, practitioners, and innovators solving critical subsurface challenges with physics-informed artificial intelligence.
AI for Earth Sciences Workshop 2026
A professional forum for advancing physics-informed AI in energy and Earth systems.
Dates
March 23 – 24, 2026
Location
Santa Fe Convention Center
Santa Fe, New Mexico
Participants
Industry, Academia, Government
National labs and leading institutions
Workshop Overview
EnviTrace organized and hosted a two-day technical workshop bringing together leading experts, researchers, and practitioners from national laboratories, universities, government agencies, and industry to examine physics-informed AI and machine learning applications across energy and Earth systems.
The workshop featured keynote presentations, technical talks, moderated panels, and facilitated discussions on real-world deployments of AI in geothermal prospecting, subsurface characterization, water management, carbon sequestration, seismic monitoring, and related domains.
- •20+ expert speakers from LANL, Sandia, NMT, USGS, and more
- •Two distinct tracks — foundational concepts and applied subsurface operations
- •Beginner-friendly primer on AI fundamentals for non-specialists
- •Networking and collaboration — breaks, receptions, and working sessions
Featured Speakers & Presenters
The workshop featured presentations and leadership from principal researchers at Los Alamos National Laboratory, Sandia National Laboratories, USGS, Stanford University, and EnviTrace. Key speakers included:
- • Christopher Johnson, PhD (LANL) — Seismic AI applications
- • Thushara Gunda, PhD (Sandia) — Water management ML
- • Daniel O'Malley, PhD (LANL) — Subsurface foundation models
- • Shubhendu Singh, PhD (LANL) — Reinforcement learning for reservoirs
- • Noah Hobbs, PhD (EnviTrace) — Contaminant plume prediction
- • Dunyu Liu (U of Texas) — Graph neural networks for earthquake dynamics
- • Alice Nuz (Stanford) — Real-time subsurface plume monitoring
- • Urbi Basu (NMT) — Seismic monitoring in New Mexico
- • Stanley Mordensky, PhD (USGS) - ML for hydrothermal resources
- • Adewale Amosu, PhD (NMT) — Geophysical subsurface modeling
- • Katrina Bennett, PhD (LANL) — Arctic snow and climate ML
- • Velimir 'Monty' Vesselinov, PhD (EnviTrace CTO)
- • Trais Kliphuis (EnviTrace CEO)
- • …and others from leading research institutions
Technical Topics & Applications
Why Santa Fe?
Santa Fe, New Mexico is a natural hub for geoscience and energy innovation. The region hosts Los Alamos National Laboratory and Sandia National Laboratories, two of the nation's premier research institutions, as well as USGS facilities and a vibrant ecosystem of scientific and technical talent. Hosting the workshop here enabled direct collaboration with world-leading experts in subsurface science, seismology, and applied AI.
Outcomes & Industry Impact
The 2026 AI for Earth Sciences Workshop successfully brought together leading researchers, practitioners, and innovators from national laboratories, universities, government agencies, and private industry. The event catalyzed meaningful collaboration across the sector, positioning physics-informed AI as a proven methodology for solving critical energy and Earth system challenges.
Collaboration & Joint Projects: Workshop attendees formed lasting partnerships and have since collaborated on joint pilot projects addressing real-world subsurface challenges. A direct outcome includes a strategic collaboration between Sandia National Laboratories and EnviTrace on a Department of Energy Genesis Mission application, combining Sandia's geothermal expertise with EnviTrace's physics-informed AI capabilities.
The workshop demonstrated the market demand for advanced AI solutions in energy and geosciences, validated applied approaches to hybrid modeling, and established EnviTrace as a trusted partner for clients seeking to integrate AI into subsurface workflows.
We are grateful to our sponsors, especially the New Mexico Economic Development Department (NM EDD), whose support enabled us to deliver a world-class event. We are committed to continuing this tradition of convening innovation in AI for Earth sciences.
Presentations, event photos, and speaker materials are available below.
Event Sponsors
We are grateful to our sponsors who generously supported the 2026 workshop, making it possible to convene world-leading experts and foster collaboration across industry, academia, and government. Their contributions advance innovation in physics-informed AI for energy and environmental solutions.
New Mexico Economic Development Department
Santa Fe Office of Economic Development
Agua Sulis
Java Joe's
Santa Fe Brewing Company
Bang Bite Food Truck
Event Photos
Highlights from the 2026 AI for Earth Sciences Workshop in Santa Fe. Featuring keynote presentations, panel discussions, networking sessions, and the offsite reception at The Brakeroom.
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Speaker Presentations & Materials
Explore technical presentations and materials from our 2026 workshop. Presenters share practical insights on deploying physics-informed AI and machine learning for geothermal prospecting, subsurface characterization, water management, carbon sequestration, seismic monitoring, and related subsurface applications. These materials provide actionable guidance for engineers and scientists implementing AI in energy and environmental workflows.
Monday, March 23 — AI Foundations for Earth Sciences
Welcome and Introduction
Trais Kliphuis
Intro to AI for Earth Sciences: A Primer
Monty Vesselinov, Noah Hobbs, Mikhael Semaan
Earthquake Dynamics from Graph Neural Networks
Dunyu Liu
Using AI/ML to Understand Arctic Snow
Katrina E. Bennett
AI Foundation Models for Complex Tectonic Applications
Christopher Johnson
The Transparent Earth: A Multimodal Foundation Model for the Earth's Subsurface
Daniel O'Malley
The What World: A Geoscientist's Guide to Building with GenAI
Nathan Suurmeyer
Solving Probabilistic Inverse Problems with Generative AI
Adam Rupe
Anatomy of a Seismic Time Series: Information, Prediction, and Limits on Learning
Mikhael Semaan
Multilayer Perceptron Model for Predicting Conservative Solute Transport in Streams and Rivers
Lina Rodriguez
Tuesday, March 24 — Applied AI for Subsurface Operations
From Rule Curves to Rewards: Deep Reinforcement Learning for Reservoir Management in Water-Stressed Systems
Shubhendu Singh
Harnessing AI and ML to Advance Water Management
Thushara Gunda
Solving Inverse Problems with Machine Learning for Real-Time Monitoring of Subsurface Plumes
Alice Nuz
Trusting AI Models for Contaminant Plume Prediction
Noah Hobbs
Artificial Intelligence for Geophysical Subsurface Modeling: Applications to Carbon Sequestration
Adewale Amosu
When Earthquakes Meet AI: Improving Seismic Monitoring in New Mexico
Urbi Basu
Single-Station Earthquake Early Warning System Using Machine Learning
Pablo Lara