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A survey on theories and applications for self-driving cars based on deep learning methods
Self-driving cars are a hot research topic in science and technology, which has a great
influence on social and economic development. Deep learning is one of the current key …
influence on social and economic development. Deep learning is one of the current key …
Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples
The physics that describes the seismic response of an interval of saturated porous rocks with
known petrophysical properties is relatively well understood and includes rock physics …
known petrophysical properties is relatively well understood and includes rock physics …
Applications of deep neural networks in exploration seismology: A technical survey
Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …
controlled (active) source into the ground, and recorded by an array of seismic sensors …
An improved deep network-based scene classification method for self-driving cars
A self-driving car is a hot research topic in the field of the intelligent transportation system,
which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is …
which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is …
Imputation of missing well log data by random forest and its uncertainty analysis
Well logs are commonly used by geoscientists to infer and extrapolate physical properties of
subsurface rocks. However, at some depth intervals, well log values might be missing due to …
subsurface rocks. However, at some depth intervals, well log values might be missing due to …
Joint inversion of geophysical data for geologic carbon sequestration monitoring: A differentiable physics‐informed neural network model
Geophysical monitoring of geologic carbon sequestration is critical for risk assessment
during and after carbon dioxide (CO2) injection. Integration of multiple geophysical …
during and after carbon dioxide (CO2) injection. Integration of multiple geophysical …
An unsupervised deep-learning method for porosity estimation based on poststack seismic data
We propose to invert reservoir porosity from poststack seismic data using an innovative
approach based on deep-learning methods. We develop an unsupervised approach to …
approach based on deep-learning methods. We develop an unsupervised approach to …
Bayesian convolutional neural networks for seismic facies classification
The seismic response of geological reservoirs is a function of the elastic properties of porous
rocks, which depends on rock types, petrophysical features, and geological environments …
rocks, which depends on rock types, petrophysical features, and geological environments …
3D geological structure inversion from Noddy-generated magnetic data using deep learning methods
Using geophysical inversion for three-dimensional (3D) geological modeling is an effective
way to model underground geological structures. In this study, we propose and investigate a …
way to model underground geological structures. In this study, we propose and investigate a …
Inversion of 1D frequency-and time-domain electromagnetic data with convolutional neural networks
Inversion of electromagnetic data finds applications in many areas of geophysics. The
inverse problem is commonly solved with either deterministic optimization methods (such as …
inverse problem is commonly solved with either deterministic optimization methods (such as …