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Electrical resistivity measurements for nondestructive evaluation of chloride-induced deterioration of reinforced concrete—a review
The objective of this study is to review, evaluate, and compare the existing research and
practices on electrical resistivity as a nondestructive technique in evaluating chloride …
practices on electrical resistivity as a nondestructive technique in evaluating chloride …
Wavefield reconstruction inversion via physics-informed neural networks
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization
problem to reduce cycle skip** in full-waveform inversion (FWI). WRI is often implemented …
problem to reduce cycle skip** in full-waveform inversion (FWI). WRI is often implemented …
Unsupervised 3-D random noise attenuation using deep skip autoencoder
Effective random noise attenuation is critical for subsequent processing of seismic data,
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …
GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings
A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of
map** the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of …
map** the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of …
Deep-learning seismic full-waveform inversion for realistic structural models
Velocity model inversion is one of the most important tasks in seismic exploration. Full-
waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion …
waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion …
Deep learning for 3-D magnetic inversion
The difficulty of 3-D magnetic inversion is to use 2-D magnetic anomaly data to obtain 3-D
magnetic susceptibility structure. The contribution of the underground medium to the …
magnetic susceptibility structure. The contribution of the underground medium to the …
Denoising of distributed acoustic sensing data using supervised deep learning
Distributed acoustic sensing (DAS) is an emerging technology for acquiring seismic data
due to its high-density and low-cost advantages. Because of the harsh acquisition …
due to its high-density and low-cost advantages. Because of the harsh acquisition …
Recovering 3D basement relief using gravity data through convolutional neural networks
Gravity surveys in regional geophysical research can be used to estimate the depth of the
sediment‐basement interface. In this study, we investigate a novel method using the …
sediment‐basement interface. In this study, we investigate a novel method using the …
[HTML][HTML] TBM penetration rate prediction based on the long short-term memory neural network
B Gao, RR Wang, C Lin, X Guo, B Liu, W Zhang - Underground Space, 2021 - Elsevier
Tunnel boring machines (TBMs) are widely used in tunnel engineering because of their
safety and efficiency. The TBM penetration rate (PR) is crucial, as its real-time prediction can …
safety and efficiency. The TBM penetration rate (PR) is crucial, as its real-time prediction can …
Deep learning for 3-D inversion of gravity data
L Zhang, G Zhang, Y Liu, Z Fan - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Three-dimensional (3-D) gravity inversion obtains the density distribution of subsurface
geological bodies through observed gravity anomalies. Recently, data-driven methods …
geological bodies through observed gravity anomalies. Recently, data-driven methods …