Digital geoscience and engineering tools related to subsurface characterization, modeling and simulation are being leveraged in many industries to improve our understanding of the subsurface and the efficiency of interpretation workflows. In oil & gas, groundwater monitoring, and geo-hazard mitigation, data science is being applied at an unprecedented rate to enable a rapid and multidisciplinary approach to processing enormous volumes of data. However, there is a need to integrate the new data-centric technology tools with our in-depth understanding and numerical modeling capabilities of the geology and physics of the Earth, wave-propagation phenomena, and reservoir systems. Additionally, advanced subsurface data analytics requires specialized data management tools because subsurface data includes complex spatial and temporal measurement data (e.g., seismic; well logs, INSAR, etc.) not found in other industries.
Stanford Earth is well positioned to take a lead role in the application of subsurface data science for three main reasons: our theoretical understanding of Earth systems; our relationship with industry through affiliate programs and collaborating partners; and our strong ties to Silicon Valley and many energy technology companies. In this inaugural Stanford Subsurface Data Science workshop, we will:
Stanford is roughly equidistant from the San Francisco and San Jose airports. Three major airports serve the San Francisco Bay Area, including San Francisco International (SFO), San Jose Mineta International (SJC), and Oakland International (OAK). Although San Francisco is the largest of the area airports and offers the most airlines and flights, some visitors find that San Jose’s smaller size makes it a somewhat more convenient alternative, especially for domestic flights.
Visit Stanford's lodging guide which includes website links, rates, and contact information of local hotels, B&Bs and motels.
Stanford has two properties for lodging:
The nearest visitor parking lot is located next to the Tresidder Union, between Mayfield Ave. and Lagunita Dr. Other parking lots can be found here. Please note that parking on campus is very challenging and we recommend visitors to use public transportation or shared rides.