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.

中文翻译：

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地震声融合深度多模态学习改善朝鲜半岛地震爆炸判别


最近的地球物理研究强调了整合地震和次声数据的潜在效用，以改善震源表征和事件辨别工作。然而，地球物理学界尚未充分理解综合框架内每种数据类型的影响。为了帮助阐明合并结构中每种数据类型的作用，我们开发了一种神经网络，通过门控多模态单元融合地震和次声阵列数据，以区分朝鲜半岛内的地震爆炸。比较了添加次声分支前后的模型性能。我们发现地震声学模型优于地震模型，其中大部分改进源于爆炸类。通过分析门控多模态激活来量化次声的影响。结果表明，该模型相对更多地依赖次声分支来校正地震预测。