Seismology in the cloud: guidance for the individual researcher
The commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
McGill University
2023-08-01
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| Series: | Seismica |
| Online Access: | https://seismica.library.mcgill.ca/article/view/979 |
| _version_ | 1797622943913607168 |
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| author | Zoe Krauss Yiyu Ni Scott Henderson Marine Denolle |
| author_facet | Zoe Krauss Yiyu Ni Scott Henderson Marine Denolle |
| author_sort | Zoe Krauss |
| collection | DOAJ |
| description | The commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a reproducible earthquake detection and association workflow that runs on Microsoft Azure. The Python-based workflow runs on continuous time-series data using both template matching and machine learning. We provide tutorials for constructing cloud resources (both storage and computing) through a desktop portal and deploying the code both locally and remotely on the cloud resources. We report on scaling of compute times and costs to show that CPU-only processing is generally inexpensive, and is faster and simpler than using GPUs. When the workflow is applied to one year of continuous data from a mid-ocean ridge, the resulting earthquake catalogs suggest that template matching and machine learning are complementary methods whose relative performance is dependent on site-specific tectonic characteristics. Overall, we find that the commercial cloud presents a steep learning curve but is cost-effective. This report is intended as an informative starting point for any researcher considering migrating their own processing to the commercial cloud. |
| first_indexed | 2024-03-11T09:18:26Z |
| format | Article |
| id | doaj.art-ed6f24e901834dd1b43ef1ea7ce261f8 |
| institution | Directory Open Access Journal |
| issn | 2816-9387 |
| language | English |
| last_indexed | 2024-03-11T09:18:26Z |
| publishDate | 2023-08-01 |
| publisher | McGill University |
| record_format | Article |
| series | Seismica |
| spelling | doaj.art-ed6f24e901834dd1b43ef1ea7ce261f82023-11-16T18:31:58ZengMcGill UniversitySeismica2816-93872023-08-012210.26443/seismica.v2i2.979Seismology in the cloud: guidance for the individual researcherZoe Krauss0Yiyu Ni1Scott Henderson2Marine Denolle3University of WashingtonUniversity of WashingtonUniversity of WashingtonUniversity of WashingtonThe commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a reproducible earthquake detection and association workflow that runs on Microsoft Azure. The Python-based workflow runs on continuous time-series data using both template matching and machine learning. We provide tutorials for constructing cloud resources (both storage and computing) through a desktop portal and deploying the code both locally and remotely on the cloud resources. We report on scaling of compute times and costs to show that CPU-only processing is generally inexpensive, and is faster and simpler than using GPUs. When the workflow is applied to one year of continuous data from a mid-ocean ridge, the resulting earthquake catalogs suggest that template matching and machine learning are complementary methods whose relative performance is dependent on site-specific tectonic characteristics. Overall, we find that the commercial cloud presents a steep learning curve but is cost-effective. This report is intended as an informative starting point for any researcher considering migrating their own processing to the commercial cloud.https://seismica.library.mcgill.ca/article/view/979 |
| spellingShingle | Zoe Krauss Yiyu Ni Scott Henderson Marine Denolle Seismology in the cloud: guidance for the individual researcher Seismica |
| title | Seismology in the cloud: guidance for the individual researcher |
| title_full | Seismology in the cloud: guidance for the individual researcher |
| title_fullStr | Seismology in the cloud: guidance for the individual researcher |
| title_full_unstemmed | Seismology in the cloud: guidance for the individual researcher |
| title_short | Seismology in the cloud: guidance for the individual researcher |
| title_sort | seismology in the cloud guidance for the individual researcher |
| url | https://seismica.library.mcgill.ca/article/view/979 |
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