Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula
Abstract Recent geophysical studies have highlighted the potential utility of integrating both seismic and infrasound data to improve source characterization and event discrimination efforts. However, the influence of each of these data types within an integrated framework is not yet well‐understood...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-07-01
|
| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL109404 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849725638013878272 |
|---|---|
| author | Miro Ronac Giannone Stephen Arrowsmith Junghyun Park Brian Stump Chris Hayward Eric Larson Il‐Young Che |
| author_facet | Miro Ronac Giannone Stephen Arrowsmith Junghyun Park Brian Stump Chris Hayward Eric Larson Il‐Young Che |
| author_sort | Miro Ronac Giannone |
| collection | DOAJ |
| description | Abstract Recent geophysical studies have highlighted the potential utility of integrating both seismic and infrasound data to improve source characterization and event discrimination efforts. However, the influence of each of these data types within an integrated framework is not yet well‐understood by the geophysical community. To help elucidate the role of each data type within a merged structure, we develop a neural network which fuses seismic and infrasound array data via a gated multimodal unit for earthquake‐explosion discrimination within the Korean Peninsula. Model performance is compared before and after adding the infrasound branch. We find that the seismoacoustic model outperforms the seismic model, with the majority of the improvements stemming from the explosions class. The influence of infrasound is quantified by analyzing gated multimodal activations. Results indicate that the model relies comparatively more on the infrasound branch to correct seismic predictions. |
| format | Article |
| id | doaj-art-7dfb5d45c2e9455f82f5549339fa5c1f |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-7dfb5d45c2e9455f82f5549339fa5c1f2025-08-20T03:10:25ZengWileyGeophysical Research Letters0094-82761944-80072024-07-015114n/an/a10.1029/2024GL109404Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean PeninsulaMiro Ronac Giannone0Stephen Arrowsmith1Junghyun Park2Brian Stump3Chris Hayward4Eric Larson5Il‐Young Che6Department of Earth Science Southern Methodist University Dallas TX USADepartment of Earth Science Southern Methodist University Dallas TX USADepartment of Earth Science Southern Methodist University Dallas TX USADepartment of Earth Science Southern Methodist University Dallas TX USADepartment of Earth Science Southern Methodist University Dallas TX USADepartment of Computer Science Southern Methodist University Dallas TX USAKorea Institute of Geoscience and Mineral Resources Daejeon South KoreaAbstract Recent geophysical studies have highlighted the potential utility of integrating both seismic and infrasound data to improve source characterization and event discrimination efforts. However, the influence of each of these data types within an integrated framework is not yet well‐understood by the geophysical community. To help elucidate the role of each data type within a merged structure, we develop a neural network which fuses seismic and infrasound array data via a gated multimodal unit for earthquake‐explosion discrimination within the Korean Peninsula. Model performance is compared before and after adding the infrasound branch. We find that the seismoacoustic model outperforms the seismic model, with the majority of the improvements stemming from the explosions class. The influence of infrasound is quantified by analyzing gated multimodal activations. Results indicate that the model relies comparatively more on the infrasound branch to correct seismic predictions.https://doi.org/10.1029/2024GL109404deep learningseismologyinfrasound |
| spellingShingle | Miro Ronac Giannone Stephen Arrowsmith Junghyun Park Brian Stump Chris Hayward Eric Larson Il‐Young Che Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula Geophysical Research Letters deep learning seismology infrasound |
| title | Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula |
| title_full | Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula |
| title_fullStr | Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula |
| title_full_unstemmed | Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula |
| title_short | Deep Multimodal Learning for Seismoacoustic Fusion to Improve Earthquake‐Explosion Discrimination Within the Korean Peninsula |
| title_sort | deep multimodal learning for seismoacoustic fusion to improve earthquake explosion discrimination within the korean peninsula |
| topic | deep learning seismology infrasound |
| url | https://doi.org/10.1029/2024GL109404 |
| work_keys_str_mv | AT miroronacgiannone deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula AT stephenarrowsmith deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula AT junghyunpark deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula AT brianstump deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula AT chrishayward deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula AT ericlarson deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula AT ilyoungche deepmultimodallearningforseismoacousticfusiontoimproveearthquakeexplosiondiscriminationwithinthekoreanpeninsula |