Computational & Physical Volcanology
I use computer vision and scientific software to extract quantitative measurements from volcanic imagery and micro-CT data, with interests in eruption dynamics, pyroclast textures, and image-based analysis.
Current Research
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.
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.
Quantitative analysis of eruptive behavior using field observations, video, and image-derived measurements.
Segmentation and temporal analysis of geoscientific imagery, particularly under variable observational conditions.
Using micro-CT, microscopy, stereology, and pore-network analysis to connect volcanic textures with physical processes.
Developing scientific software and analysis workflows that make image-based measurements more consistent and scalable.
About
My current work focuses on computational approaches to physical volcanology, particularly computer vision for eruption imagery and quantitative analysis of pyroclast textures.
About →Journal of Double Star Observations, vol. 21, no. 2, pp. 182-189
Questions about computational volcanology, scientific image analysis, or research software are welcome.