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Unsupervised seismic facies classification using deep convolutional autoencoder
Application of automatic methods for seismic facies interpretation could significantly reduce the manual labor and subjectivity of a particular interpreter present in conventional methods.
Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks
This approach is entirely data-driven, does not employ traditional gradient-based techniques and provides a guess to the model instantaneously.
Deep convolutional autoencoder for cryptocurrency market analysis
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder.
Deep learning electromagnetic inversion with convolutional neural networks
Examples with different survey geometry and noise levels confirm the feasibility of the deep learning inversion, opening the possibility to estimate the subsurface resistivity distribution in real time.
