19-3. Application of machine learning to forecasting ground motion characteristics and their evolution

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Would you like to forecast how the ground will move during earthquakes? Curious about applications of machine learning? Apply for USGS Mendenhall postdoc 19-3: (1/3)

physical phenomena using models that can quickly be computed or queried in real time. Machine learning techniques have recently been applied to several problems in seismology and have been particularly successful at automatic waveform processing tasks such as signal versus noise discrimination and seismic phase classification.

Applicants should consider how to apply machine learning to develop new techniques for rapidly estimating shaking characteristics , with greater accuracy than existing methods that estimate shaking from source information and a ground motion model.

 

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With this research opportunity you would apply novel machine learning techniques to understand ground motion during a large earthquake. Your work could improve earthquake early warning, earthquake scenarios, or ShakeMap as shown here for the 2019 M7.1 Ridgecrest quake. (2/3)

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