A survey on theories and applications for self-driving cars based on deep learning methods

J Ni, Y Chen, Y Chen, J Zhu, D Ali, W Cao - Applied Sciences, 2020‏ - mdpi.com
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 …

Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples

D Grana, L Azevedo, L De Figueiredo, P Connolly… - Geophysics, 2022‏ - library.seg.org
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 …

Applications of deep neural networks in exploration seismology: A technical survey

SM Mousavi, GC Beroza, T Mukerji, M Rasht-Behesht - Geophysics, 2024‏ - library.seg.org
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 …

An improved deep network-based scene classification method for self-driving cars

J Ni, K Shen, Y Chen, W Cao… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
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 …

Imputation of missing well log data by random forest and its uncertainty analysis

R Feng, D Grana, N Balling - Computers & Geosciences, 2021‏ - Elsevier
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 …

Joint inversion of geophysical data for geologic carbon sequestration monitoring: A differentiable physics‐informed neural network model

M Liu, D Vashisth, D Grana… - Journal of Geophysical …, 2023‏ - Wiley Online Library
Geophysical monitoring of geologic carbon sequestration is critical for risk assessment
during and after carbon dioxide (CO2) injection. Integration of multiple geophysical …

An unsupervised deep-learning method for porosity estimation based on poststack seismic data

R Feng, T Mejer Hansen, D Grana, N Balling - Geophysics, 2020‏ - library.seg.org
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 …

Bayesian convolutional neural networks for seismic facies classification

R Feng, N Balling, D Grana… - … on Geoscience and …, 2021‏ - ieeexplore.ieee.org
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 …

3D geological structure inversion from Noddy-generated magnetic data using deep learning methods

J Guo, Y Li, MW Jessell, J Giraud, C Li, L Wu, F Li… - Computers & …, 2021‏ - Elsevier
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 …

Inversion of 1D frequency-and time-domain electromagnetic data with convolutional neural networks

V Puzyrev, A Swidinsky - Computers & geosciences, 2021‏ - Elsevier
Inversion of electromagnetic data finds applications in many areas of geophysics. The
inverse problem is commonly solved with either deterministic optimization methods (such as …