188-4 GeoLab: A Cloud-Compute Platform for Geophysics Data Analysis and Educational Accessibility

Session: Technology and tools for 3D visualization of field and lab data in teaching and research

Presenting Author:

Sarah Wilson

Authors:

Wilson, Sarah¹, Weekly, Robert², Bravo, Tammy³, Parafina, Sophia⁴, Sweet, Justin⁵, Hubenthal, Michael⁶, Trabant, Chad⁷

(1) Earthscope Consortium, USA, (2) Earthscope Consortium, USA, (3) EarthScope Consortium, USA, (4) EarthScope Consortium, USA, (5) EarthScope Consortium, USA, (6) EarthScope Consortium, USA, (7) EarthScope Consortium, USA,

Abstract:

Modern geoscience visualization increasingly relies on specialized software, cloud-hosted datasets, and reproducible computational workflows. Installing, configuring, and managing authentication for these tools can create significant barriers for both research and instruction. GeoLab, a cloud-computing environment provided by the NSF National Geophysical Facility operated by EarthScope, is a browser-based JupyterHub environment that provides immediate access to the NGF’s petabyte-scale seismic and geodetic data archives. As the archives are lifted into the cloud, new cloud compute resources and strategies create opportunities for researchers to conduct larger analyses on a more rapid scale, and share them more easily with students and collaborators. 

GeoLab is designed to minimize cloud configuration barriers with free, browser-based access. The platform supports interactive visualization workflows using PyGMT, ObsPy, and other Python-based libraries, allowing users to explore seismic waveforms, geodetic time series, mapping tools, and derived spatial products within a homogenized environment. The default environment is optimized for Python notebooks and pre-loaded with geophysics domain-specific libraries, while offering the flexibility for users to extend their environments with additional Python packages during their compute session. More complex software, such as QGIS or R-based environments, can be installed in custom images that can be reused and shared with collaborators. GeoLab also includes the EarthScope API and SDK, which streamline credential management and programmatic data access. For users with heavier compute needs, a Dask Gateway, scalable CPU and memory allocations, and GPU-backed servers are available upon request.

Reproducible and customizable environments have made GeoLab an invaluable tool for teaching. Over the past two years, the platform has hosted eleven educational workshops and technical courses serving over 625 learners on topics ranging from GNSS strain accumulation and inversion modeling to machine learning and massive parallelization frameworks. Prior to each course, instructors configure the required software and computational resources, then distribute identical cloud environments to every participant through a web browser. This creates a common, reproducible starting point for every participant, reducing technical barriers due to differences in local installations or individual computers’ hardware specs, and ensuring all learners can immediately engage with the course activities. By combining cloud-native computing with reusable instructional environments, GeoLab enables visualization tools developed for research to be used directly in classrooms, workshops, and self-paced instruction, strengthening the connection between research and geoscience education.

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