Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning

We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noi...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2020-12, Vol.58 (12), p.8932-8939
Hauptverfasser:	Zhang, Xiaotian, Jia, Zhe, Ross, Zachary E., Clayton, Robert W.
Format:	Artikel
Sprache:	eng
Schlagworte:	Ambient noise Artificial neural networks Classification Convolutional networks Correlation Data models Deep learning Dispersion Dispersion curve analysis dispersion curves Frequency analysis Learning algorithms Machine learning Neural networks Seismograms Surface treatment Surface water waves Surface waves Training Transfer learning Wave dispersion
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