Detecting tsunami-generated magnetic fields using machine learning

Conductive seawater moving through the Earth’s magnetic field generates electromagnetic fields. When this motion is driven by a tsunami, the phenomenon is referred to as a tsunami-generated electromagnetic field. Previous studies have explored its characteristics and potential applications for tsunami early warning systems. In this study, we present a novel approach utilizing machine learning to automatically detect tsunami-generated magnetic (TGM) fields at the seafloor. To train our model, we prepared a large dataset by combining simulated TGM signals with non-tsunami magnetic data observed at the seafloor of the Northwest Pacific Ocean and the Philippine Sea. A convolutional neural network was selected for the architecture of our model, successfully detecting TGM signals from the 2006 and 2007 Kuril earthquakes observed at the seafloor of the Northwest Pacific Ocean. Notably, the model also identified TGM signals from the 2009 Samoa earthquake at seafloor sites in French Polynesia—locations that were not included in the training data. These results demonstrate the effectiveness of applying machine learning to TGM signal detection. Furthermore, we evaluated our model’s performance based on the signal-to-noise (S/N) ratio and the signal duration in the input data, establishing quantitative criteria for detection. Our analysis showed that successful detection requires an S/N ratio greater than 2 for horizontal components and greater than 5 for the vertical component. As for the TGM signal points, our model requires a minimum TGM signal duration of 10 minutes for reliable detection. These criteria, which have not been proposed previously, provide valuable guidelines for detection of TGM signals at the seafloor.