Direct multi-modal inversion of geophysical logs using deep learning. (arXiv:2201.01871v1 [physics.geo-ph])

Geosteering of wells requires fast interpretation of geophysical logs which
is a non-unique inverse problem. Current work presents a proof-of-concept
approach to multi-modal probabilistic inversion of logs using a single
evaluation of an artificial deep neural network (DNN). A mixture density DNN
(MDN) is trained using the "multiple-trajectory-prediction" (MTP) loss
functions, which avoids mode collapse typical for traditional MDNs, and allows
multi-modal prediction ahead of data. The proposed approach is verified on the
real-time stratigraphic inversion of gamma-ray logs. The …

arxiv deep learning learning logs physics