2025-02-12 ロスアラモス国立研究所(LANL)
＜関連情報＞
- https://www.lanl.gov/media/news/0212-voice-to-text-ai
- https://www.nature.com/articles/s41467-025-55994-9
自動音声認識で同時期の地震断層変位を予測 Automatic speech recognition predicts contemporaneous earthquake fault displacement
Christopher W. Johnson,Kun Wang & Paul A. Johnson
Nature Communications Published:27 January 2025
DOI:https://doi.org/10.1038/s41467-025-55994-9
Abstract
Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.
