A Science Gateway for the Repeatable Analysis of Machine Learning Predicted Gravity Anomalies Article Swipe
Jacob Arndt
,
Jason Wohlgemuth
,
H. Lexie Yang
,
Jordan Bowman
,
Dalton Lunga
,
Dawn M. King
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/lgrs.2024.3441322
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/lgrs.2024.3441322
In recent years, deep learning has become an increasingly popular alternative for modeling in geoscience applications due to its scalability and efficiency. However, the interpretability, compute, data volume, and hyperparameter tuning requirements of deep learning models make development and monitoring difficult. Furthermore, model explainability and communicating results obtained by these models to users or domain experts is a challenge, as domain experts in geoscience also need to have a deep understanding of how those models function in order to support their scientific works. Here, we describe a science gateway and machine learning pipeline for predicting gravity anomalies from geophysical data. The gateway, built on open-source technologies, provides a holistic view of the pipeline through interactive visualizations aimed at enabling efficient exploratory data analysis. The repeatability, reproducibility, and monitoring capabilities of this overall system allow us to iterate and analyze at scale. Using this pipeline and gateway, we can repeatedly produce accurate high-resolution gravity anomaly datasets. By describing the underlying technologies, implementation, and results, here we provide a foundation for the broader adoption of science gateways into cross-cutting geoscience and machine learning research projects as a means to improve the scientific discovery and collaboration in the geophysics and computational sciences community.
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A Science Gateway for the Repeatable Analysis of Machine Learning Predicted Gravity AnomaliesWork title
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2024Year of publication
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2024-01-01Full publication date if available
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Jacob Arndt, Jason Wohlgemuth, H. Lexie Yang, Jordan Bowman, Dalton Lunga, Dawn M. KingList of authors in order
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https://doi.org/10.1109/lgrs.2024.3441322Publisher landing page
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/2441103Direct OA link when available
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.aimed | 116 |
| abstract_inverted_index.allow | 133 |
| abstract_inverted_index.built | 102 |
| abstract_inverted_index.data. | 99 |
| abstract_inverted_index.means | 185 |
| abstract_inverted_index.model | 42 |
| abstract_inverted_index.order | 77 |
| abstract_inverted_index.their | 80 |
| abstract_inverted_index.these | 49 |
| abstract_inverted_index.those | 73 |
| abstract_inverted_index.users | 52 |
| abstract_inverted_index.become | 6 |
| abstract_inverted_index.domain | 54, 60 |
| abstract_inverted_index.models | 35, 50, 74 |
| abstract_inverted_index.recent | 1 |
| abstract_inverted_index.scale. | 140 |
| abstract_inverted_index.system | 132 |
| abstract_inverted_index.tuning | 30 |
| abstract_inverted_index.works. | 82 |
| abstract_inverted_index.years, | 2 |
| abstract_inverted_index.analyze | 138 |
| abstract_inverted_index.anomaly | 153 |
| abstract_inverted_index.broader | 170 |
| abstract_inverted_index.experts | 55, 61 |
| abstract_inverted_index.gateway | 88 |
| abstract_inverted_index.gravity | 95, 152 |
| abstract_inverted_index.improve | 187 |
| abstract_inverted_index.iterate | 136 |
| abstract_inverted_index.machine | 90, 179 |
| abstract_inverted_index.overall | 131 |
| abstract_inverted_index.popular | 9 |
| abstract_inverted_index.produce | 149 |
| abstract_inverted_index.provide | 165 |
| abstract_inverted_index.results | 46 |
| abstract_inverted_index.science | 87, 173 |
| abstract_inverted_index.support | 79 |
| abstract_inverted_index.through | 113 |
| abstract_inverted_index.volume, | 27 |
| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.accurate | 150 |
| abstract_inverted_index.adoption | 171 |
| abstract_inverted_index.compute, | 25 |
| abstract_inverted_index.describe | 85 |
| abstract_inverted_index.enabling | 118 |
| abstract_inverted_index.function | 75 |
| abstract_inverted_index.gateway, | 101, 145 |
| abstract_inverted_index.gateways | 174 |
| abstract_inverted_index.holistic | 108 |
| abstract_inverted_index.learning | 4, 34, 91, 180 |
| abstract_inverted_index.modeling | 12 |
| abstract_inverted_index.obtained | 47 |
| abstract_inverted_index.pipeline | 92, 112, 143 |
| abstract_inverted_index.projects | 182 |
| abstract_inverted_index.provides | 106 |
| abstract_inverted_index.research | 181 |
| abstract_inverted_index.results, | 162 |
| abstract_inverted_index.sciences | 198 |
| abstract_inverted_index.analysis. | 122 |
| abstract_inverted_index.anomalies | 96 |
| abstract_inverted_index.datasets. | 154 |
| abstract_inverted_index.discovery | 190 |
| abstract_inverted_index.efficient | 119 |
| abstract_inverted_index.challenge, | 58 |
| abstract_inverted_index.community. | 199 |
| abstract_inverted_index.describing | 156 |
| abstract_inverted_index.difficult. | 40 |
| abstract_inverted_index.foundation | 167 |
| abstract_inverted_index.geophysics | 195 |
| abstract_inverted_index.geoscience | 14, 63, 177 |
| abstract_inverted_index.monitoring | 39, 127 |
| abstract_inverted_index.predicting | 94 |
| abstract_inverted_index.repeatedly | 148 |
| abstract_inverted_index.scientific | 81, 189 |
| abstract_inverted_index.underlying | 158 |
| abstract_inverted_index.alternative | 10 |
| abstract_inverted_index.development | 37 |
| abstract_inverted_index.efficiency. | 21 |
| abstract_inverted_index.exploratory | 120 |
| abstract_inverted_index.geophysical | 98 |
| abstract_inverted_index.interactive | 114 |
| abstract_inverted_index.open-source | 104 |
| abstract_inverted_index.scalability | 19 |
| abstract_inverted_index.Furthermore, | 41 |
| abstract_inverted_index.applications | 15 |
| abstract_inverted_index.capabilities | 128 |
| abstract_inverted_index.increasingly | 8 |
| abstract_inverted_index.requirements | 31 |
| abstract_inverted_index.collaboration | 192 |
| abstract_inverted_index.communicating | 45 |
| abstract_inverted_index.computational | 197 |
| abstract_inverted_index.cross-cutting | 176 |
| abstract_inverted_index.technologies, | 105, 159 |
| abstract_inverted_index.understanding | 70 |
| abstract_inverted_index.explainability | 43 |
| abstract_inverted_index.hyperparameter | 29 |
| abstract_inverted_index.repeatability, | 124 |
| abstract_inverted_index.visualizations | 115 |
| abstract_inverted_index.high-resolution | 151 |
| abstract_inverted_index.implementation, | 160 |
| abstract_inverted_index.reproducibility, | 125 |
| abstract_inverted_index.interpretability, | 24 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.17074303 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |