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Preparing (for) Seismic Data in the Cloud

Session: Applications and Technologies in Large-Scale Seismic Analysis

Type: Oral

Date: 4/23/2021

Presentation Time: 02:30 PM Pacific

Description: 

As research in seismology continues to identify valuable new signals in ever-growing data streams, it becomes more important to explore tools and platforms that can scale from small exploratory analyses to large survey-style applications. The commercial cloud offers a diverse and powerful platform to quickly perform large-scale research, but it also comes with a number of practical challenges. Much existing research software in seismology was not designed for a remote distributed system like the cloud, there is a significant learning curve in using a such a system, and common seismic formats, such as miniSEED, SAC, or ASDF may not be optimal for access on distributed systems where the balance between compression, file size, and network communication is different compared to local or HPC systems. In this work, we use the Xarray, Dask, and Zarr libraries in the Python software ecosystem to address some of the challenges outlined above. We perform a seismic noise analysis using the Amazon Web Services cloud platform to demonstrate an interactive and fully in-cloud research workflow that accelerates time-to-result.

Presenting Author: Jonathan K. MacCarthy

Student Presenter: No

Authors