54 citations · 54 across the 3 of their papers we have counts for
3 papers
In-context learning enables continental-scale subsurface temperature prediction from sparse local observations
Daniel O'Malley, Christopher W. Johnson, Javier E. Santos +10
Continental-scale knowledge of subsurface temperature is limited by the cost and sparsity of borehole measurements, but such information is essential for geothermal resource assess…
The temporal limits of predicting fault failure
Kun Wang, Christopher W. Johnson, Kane C. Bennett +1
Machine learning models using seismic emissions can predict instantaneous fault characteristics such as displacement in laboratory experiments and slow slip in Earth. Here, we addr…
Predicting Fault Slip via Transfer Learning
Kun Wang, Christopher W. Johnson, Kane C. Bennett +1
Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly due to large training data sets. In Earth…