Inference and machine learning across the spatial scales in geophysics
<p>The Anthropocene and the concomitant Great Acceleration – defined by super-exponential growth rates in a collection of measures of human activity – are having a tremendous impact on Earth and its ecosystems. To better understand the consequences of our activity, we must increase our knowled...
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| Format: | Thesis |
| Language: | English |
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2021
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| _version_ | 1797107267898703872 |
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| author | Szenicer, A |
| author2 | Nissen-Meyer, T |
| author_facet | Nissen-Meyer, T Szenicer, A |
| author_sort | Szenicer, A |
| collection | OXFORD |
| description | <p>The Anthropocene and the concomitant Great Acceleration – defined by super-exponential growth rates in a collection of measures of human activity – are having a tremendous impact on Earth and its ecosystems. To better understand the consequences of our activity, we must increase our knowledge of all the elements in the Geosystem, loosely defined as Earth, the oceans, atmosphere, and the Sun, and in constant evolution as human activity ties in new elements, e.g. the Moon or Mars. In this thesis, I am focusing on improving our knowledge of elements pertaining to geophysics, while also showcasing the diversity of thematics in the Geosystem. I also emphasize the common grounds and cross-cutting themes in our sundry of projects, in particular the importance of a data-centric approach, including the creation of new sensors, and the necessity of both methodological improvements to our numerical tools, and the adoption of novel methods from machine learning to help us exploit the newly available vast amount of data.</p>
<p>I begin with wildlife conservation, where, with collaborators, we prototype an elephant monitoring system based on the classification of animal generated seismic signals with deep learning. We achieve promising results, with high accuracies in classification of elephants and their behaviour, and with further research I believe our method will bring about a seismic shift to wildlife monitoring. Next, I present a novel method for the computation of Frechet sensitivity kernels for full waveform inversion based on the discrete adjoint approach, resulting in a more advantageous frequency scaling of the computational cost than traditional methods. This discount is crucial to enable us to image the interior of the Earth at higher resolutions, which is necessary to unlock the answers to some of the biggest questions in Earth science, e.g. related to the mechanisms of mantle convection and plate tectonics. Finally, I introduce how, with my teammates, we developed a deep learning replacement to a now defunct Sun-monitoring spectrograph onboard a NASA satellite. The best performing model, a combined linear-CNN architecture, achieves 1.6% median error, virtually reviving the spectrograph and restoring a vital data source, for instance for satellite operators to model atmospheric dynamics and avoid catastrophic collisions.</p> |
| first_indexed | 2024-03-07T07:13:39Z |
| format | Thesis |
| id | oxford-uuid:491b68af-08cf-4274-83f6-75173e19b8d1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:13:39Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | oxford-uuid:491b68af-08cf-4274-83f6-75173e19b8d12022-07-15T16:07:01ZInference and machine learning across the spatial scales in geophysicsThesishttp://purl.org/coar/resource_type/c_db06uuid:491b68af-08cf-4274-83f6-75173e19b8d1GeophysicsMachine learningEnglishHyrax Deposit2021Szenicer, ANissen-Meyer, T<p>The Anthropocene and the concomitant Great Acceleration – defined by super-exponential growth rates in a collection of measures of human activity – are having a tremendous impact on Earth and its ecosystems. To better understand the consequences of our activity, we must increase our knowledge of all the elements in the Geosystem, loosely defined as Earth, the oceans, atmosphere, and the Sun, and in constant evolution as human activity ties in new elements, e.g. the Moon or Mars. In this thesis, I am focusing on improving our knowledge of elements pertaining to geophysics, while also showcasing the diversity of thematics in the Geosystem. I also emphasize the common grounds and cross-cutting themes in our sundry of projects, in particular the importance of a data-centric approach, including the creation of new sensors, and the necessity of both methodological improvements to our numerical tools, and the adoption of novel methods from machine learning to help us exploit the newly available vast amount of data.</p> <p>I begin with wildlife conservation, where, with collaborators, we prototype an elephant monitoring system based on the classification of animal generated seismic signals with deep learning. We achieve promising results, with high accuracies in classification of elephants and their behaviour, and with further research I believe our method will bring about a seismic shift to wildlife monitoring. Next, I present a novel method for the computation of Frechet sensitivity kernels for full waveform inversion based on the discrete adjoint approach, resulting in a more advantageous frequency scaling of the computational cost than traditional methods. This discount is crucial to enable us to image the interior of the Earth at higher resolutions, which is necessary to unlock the answers to some of the biggest questions in Earth science, e.g. related to the mechanisms of mantle convection and plate tectonics. Finally, I introduce how, with my teammates, we developed a deep learning replacement to a now defunct Sun-monitoring spectrograph onboard a NASA satellite. The best performing model, a combined linear-CNN architecture, achieves 1.6% median error, virtually reviving the spectrograph and restoring a vital data source, for instance for satellite operators to model atmospheric dynamics and avoid catastrophic collisions.</p> |
| spellingShingle | Geophysics Machine learning Szenicer, A Inference and machine learning across the spatial scales in geophysics |
| title | Inference and machine learning across the spatial scales in geophysics |
| title_full | Inference and machine learning across the spatial scales in geophysics |
| title_fullStr | Inference and machine learning across the spatial scales in geophysics |
| title_full_unstemmed | Inference and machine learning across the spatial scales in geophysics |
| title_short | Inference and machine learning across the spatial scales in geophysics |
| title_sort | inference and machine learning across the spatial scales in geophysics |
| topic | Geophysics Machine learning |
| work_keys_str_mv | AT szenicera inferenceandmachinelearningacrossthespatialscalesingeophysics |