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Found 5 papers, 2 papers with code
Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon Storage
Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration.
Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination
This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions.
Predicting Wind-Driven Spatial Deposition through Simulated Color Images using Deep Autoencoders
We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0. 02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0. 93.
Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing.
Detecting Damage Building Using Real-time Crowdsourced Images and Transfer Learning
After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days.
