63 research outputs found
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions
Traditional ground-penetrating radar (GPR) data inversion leverages iterative
algorithms which suffer from high computation costs and low accuracy when
applied to complex subsurface scenarios. Existing deep learning-based methods
focus on the ideal homogeneous subsurface environments and ignore the
interference due to clutters and noise in real-world heterogeneous
environments. To address these issues, a two-stage deep neural network (DNN),
called DMRF-UNet, is proposed to reconstruct the permittivity distributions of
subsurface objects from GPR B-scans under heterogeneous soil conditions. In the
first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1)
is built to remove the clutters due to inhomogeneity of the heterogeneous soil.
Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan
to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns
the inverse mapping relationship and reconstructs the permittivity distribution
of subsurface objects. To avoid information loss, an end-to-end training method
combining the loss functions of two stages is introduced. A wide range of
subsurface heterogeneous scenarios and B-scans are generated to evaluate the
inversion performance. The test results in the numerical experiment and the
real measurement show that the proposed network reconstructs the
permittivities, shapes, sizes, and locations of subsurface objects with high
accuracy. The comparison with existing methods demonstrates the superiority of
the proposed methodology for the inversion under heterogeneous soil conditions
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar
(GPR) data is of great importance for mapping subsurface environments and
inspecting underground structural integrity. Traditional iterative 3D
reconstruction algorithms suffer from strong non-linearity, ill-posedness, and
high computational cost. To tackle these issues, a 3D deep learning scheme,
called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR
C-scans. The proposed scheme leverages a prior 3D convolutional neural network
with a feature attention mechanism to suppress the noise in the C-scans due to
subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder
network with multi-scale feature aggregation modules is designed to establish
the optimal inverse mapping from the denoised C-scans to 3D permittivity maps.
Furthermore, a three-step separate learning strategy is employed to pre-train
and fine-tune the networks. The proposed scheme is applied to numerical
simulation as well as real measurement data. The quantitative and qualitative
results show the network capability, generalizability, and robustness in
denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface
objects
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
Generative adversarial networks review in earthquake-related engineering fields
Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions
3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function
Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed
Aeronautical Engineering, a continuing bibliography with indexes, supplement 173
This bibliography lists 704 reports, articles and other documents introduced into the NASA scientific and technical information system in March 1984
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