Training the Next Generation of Seismologists: Delivering Research-Grade Software Education for Cloud and HPC Computing through Diverse Training Modalities

TL;DR

This paper discusses the development and delivery of diverse training workshops aimed at equipping seismologists with research-grade skills in cloud and HPC computing, emphasizing open science and sustainable education methods.

Contribution

It introduces a comprehensive curriculum and practical guidelines for training seismologists in large-scale computing and data analysis using open, reproducible science principles.

Findings

Participants gained advanced skills in HPC and cloud computing.

Workshops improved understanding of seismic data processing and modeling.

Lessons learned inform future training program improvements.

Abstract

With the rise of data volume and computing power, seismological research requires more advanced skills in data processing, numerical methods, and parallel computing. We present the experience of conducting training workshops over various forms of delivery to support the adoption of large-scale High-Performance Computing and Cloud computing to advance seismological research. The seismological foci were on earthquake source parameter estimation in catalogs, forward and adjoint wavefield simulations in 2 and 3 dimensions at local, regional, and global scales, earthquake dynamics, ambient noise seismology, and machine learning. This contribution describes the series of workshops that were delivered as part of research projects, the learning outcomes of the participants, and lessons learned by the instructors. Our curriculum was grounded on open and reproducible science, large-scale scientific computing and data mining, and computing infrastructure (access and usage) for HPC and the cloud. We also describe the types of teaching materials that have proven beneficial to the instruction and the sustainability of the program. We propose guidelines to deliver future workshops on these topics.