June 2024: Applying Maching Learning as a competent Engineer or Geoscientist скачать в хорошем качестве

June 2024: Applying Maching Learning as a competent Engineer or Geoscientist

Abstract: The subsurface resource industry has a long history of working with large, complex geoscience and engineering datasets. Our community has been dealing with ‘big data’ for decades, which has driven the development and establishment of the geostatistical toolbox. As a result, we are uniquely positioned in the current digital revolution, being further along the path of data-driven workflow development. This gives us valuable insights and experiences to share with other scientific and engineering domains. Over the decades, we have built a strong foundation of standards for the data-driven integration and application of geostatistics, domain expertise, and modeling choices. These standards cater to the unique aspects of the subsurface, including spatial continuity, multiple scales, and sources of uncertainty. Drawing on this extensive experience, I will elucidate connections between established robust geostatistics and data analytics best practices, and the emerging integration of machine learning. This will frame suggestions and warnings for the application of machine learning in our field by competent engineers and geoscientists. Bio: Michael Pyrcz is a professor in the Cockrell School of Engineering and the Jackson School of Geosciences, at The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also the principal investigator of the Energy Analytics freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin, an associate editor for Computers and Geosciences, and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences. Michael has written over 60 peer-reviewed publications, a Python package for spatial data analytics, and co-authored a textbook on spatial data analytics, ‘Geostatistical Reservoir Modeling’. All of Michael’s university lectures are available on his YouTube channel with links to 100’s of Python interactive dashboards and well-documented workflows, to support his students and working professionals with evergreen content. To find out more about Michael’s work and shared educational resources visit his website. YouTube Channel:    / geostatsguylectures   GitHub Repositories: https://github.com/GeostatsGuy/ Personal Website with Inventory of Shared Resources: www.michaelpyrcz.com