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Advancing Geophysical Data Training With MsPASS and GeoLab

Description: 

Cloud computing is revolutionizing the accessibility and scalability of geophysical research and education. As part of EarthScope's commitment to advancing the community's technical capabilities as operators of the NSF SAGE and GAGE facilities, the GeoLab JupyterHub environment has proven to be a key platform for hosting and facilitating short courses. A standout example is the use of MsPASS (Massive Parallel Analysis System for Seismologists) within GeoLab to provide a new hands-on training workshop in seismology data processing. MsPASS software utilizes modern container technology, an integrated data management system (MongoDB), and a parallel framework (Dask) to provide users with a workspace where they can quickly prototype and test parallelizable workflows that scale with the availability of cloud-based infrastructure. By using Python as the job control language, MsPASS provides seamless interaction with the cloud through Jupyter Notebooks. GeoLab’s built-in Dask Gateway enables MsPASS to take full advantage of the scalable framework it is built on and orchestrate massively parallelized workflows.

By leveraging GeoLab's pre-configured computational environment, the MsPASS short course eliminates technical barriers and enables participants to focus on skill development, with no requirement for individual students to have advanced local computing resources, install software packages, or configure their individual environment. With EarthScope instructional design support, participants gained practical experience in tackling real-world data challenges, leveraging cloud-computing resources, and developing reproducible workflows. The success of MsPASS in GeoLab highlights the scalability and potential of the platform as a model for delivering inclusive, high-impact geophysical training.

Session: Data-driven and Computational Characterization of Non-earthquake Seismoacoustic Sources [Poster]

Type: Poster

Date: 4/16/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Gillian

Student Presenter: No

Invited Presentation: 

Poster Number: 69

Authors