| Autor: |
Ronac Giannone, Miro, Arrowsmith, Stephen, Park, Junghyun, Stump, Brian, Hayward, Chris, Larson, Eric, Che, Il‐Young |
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| Zdroj: |
Geophysical Research Letters; 7/28/2024, Vol. 51 Issue 14, p1-9, 9p |
| Abstrakt: |
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. Plain Language Summary: Earthquakes and explosions can produce energy that travel as waves through the ground, seismic, and the air, infrasound. As these waves travel to the station where they are detected, they can be changed so drastically by the medium that it makes it difficult to determine what caused them. In these instances, it has been shown that using both seismic and infrasound data works better to characterize an event than using them independent of one another. However, due to the differences in how the air and ground influence the movement of energy, it is not well‐known how these types of data work in unison to give us more information about an event. In this study, we use a machine learning model trained on both seismic and infrasound data to help us better understand how they can be used together to determine their source. Key Points: Discrimination performance within the Korean Peninsula is improved after fusing seismoacoustic data within a deep learning architectureNeural network framework provides insight into how information in multimodal data combine to distinguish between different event types [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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