Joel Sotelo Flores

About

Joel Sotelo Flores

Undergraduate researcher in computational and physical volcanology at Washington and Lee University. I build computer-vision and scientific-software workflows that turn volcanic imagery and micro-CT data into reproducible quantitative measurements.

Joel Sotelo Flores standing on the volcanic landscape of Mauna Loa.

Research focus

  • computational volcanology
  • physical volcanology
  • computer vision for eruption imagery
  • machine learning for scientific image segmentation
  • volcanic micro-CT analysis
  • pore-network and permeability analysis
  • scientific software development

Methodological approach

My research combines physics and Earth science with scientific imaging, machine learning, quantitative analysis, and software development to study volcanic systems. This work connects field observation and laboratory data to reproducible computational methods that extract physical measurements from imagery and micro-CT volumes.

Current projects span lava-fountain computer vision and pyroclast micro-CT analysis, applying similar end-to-end workflows — collect data, segment structures, quantify physical properties — to different volcanic systems.

Current work

Active

Kīlauea Lava-Fountain Computer Vision

Computer vision and scientific software for segmenting lava-fountain video and supporting quantitative extraction of eruption parameters.

Joel is developing a computer-vision pipeline to segment Kīlauea lava-fountain video and support quantitative extraction of eruption parameters from field footage. The project combines U-Net segmentation, manual labeling, dataset preparation, metadata organization, and field video collection under variable lighting, viewing geometry, and field conditions.

  • computational volcanology
  • physical volcanology
  • computer vision
Active

Ijen Pyroclast Micro-CT and Pore-Network Analysis

Deep-learning segmentation and stereometric analysis of pyroclastic materials from the 1817 Kawah Ijen eruption.

Joel conducts computational volcanology research on pyroclastic micro-CT volumes from the 1817 Kawah Ijen eruption. The work uses U-Net-based deep-learning segmentation, stereometric analysis, Dragonfly, OpenPNM, electron microprobe data, and scanning electron microscopy to investigate vesicularity, pore connectivity, permeability, anisotropy, mineral chemistry, fractionation trends, and pressure-temperature constraints.

  • computational volcanology
  • pyroclast textures
  • micro-CT

Contact

Lexington, Virginia