Lingna Chen

Bio: Lingna Chen is an academic researcher from Jilin University. The author has contributed to research in topics: Signal processing & White noise. The author has an hindex of 3, co-authored 3 publications receiving 66 citations.

GPR Signal Denoising and Target Extraction With the CEEMD Method

Abstract: In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.

Recursive impedance inversion of ground-penetrating radar data in stochastic media

Abstract: The travel time and amplitude of ground-penetrating radar (GPR) waves are closely related to medium parameters such as water content, porosity, and dielectric permittivity. However, conventional estimation methods, which are mostly based on wave velocity, are not suitable for real complex media because of limited resolution. Impedance inversion uses the reflection coefficient of radar waves to directly calculate GPR impedance and other parameters of subsurface media. We construct a 3D multiscale stochastic medium model and use the mixed Gaussian and exponential autocorrelation function to describe the distribution of parameters in real subsurface media. We introduce an elliptical Gaussian function to describe local random anomalies. The tapering function is also introduced to reduce calculation errors caused by the numerical simulation of discrete grids. We derive the impedance inversion workflow and test the calculation precision in complex media. Finally, we use impedance inversion to process GPR field data in a polluted site in Mongolia. The inversion results were constrained using borehole data and validated by resistivity data.

Estimation of mixed soil water content by impedance inversion of GPR data

Abstract: In the vadose zone, soil has become an object of research due to its importance for environmental issues. Description and estimation of the mixed soil water content or dielectric parameter is the essential condition and the key to improving soil investigation with GPR detection. In this paper, first of all, a way to describe 3D random media which the preferred orientation of the multi-scale inhomogeneity is proposed and the importance of reducing the numerical errors with tapering function is stated. Then, we apply the FDTD method to simulate the GPR signal response of random model and use S-transform to test the simulation accuracy. For the complex random soil media, conventional method likes transmission wave method provide model parameter estimation of limited resolution only. Here, we apply a novel reflection amplitude inversion workflow for GPR data which is capable of resolving the subsurface dielectric permittivity and related water content distribution with markedly improved resolution. The synthetic results demonstrate that this method has extensive applicability in complex mixed random soil media detection and physics parameters estimation.

Spectral Decomposition for Hydrocarbon Detection Based on VMD and Teager–Kaiser Energy

Abstract: Hydrocarbons can cause anomalies in the energy density of seismic signals when seismic waves pass through them. Teager–Kaiser energy (TKE) is an important attribute that can be utilized to depict the energy density of a seismic signal and the energy distribution of a seismic wavefield. In this letter, a novel spectral decomposition-based approach for hydrocarbon detection is proposed that applies the variational mode decomposition (VMD) associated with TKE to seismic data, which is called the VMDTKE algorithm. The proposed method not only possesses the better performance of TKE in focusing instantaneous energy, but also inherits the merit of high time–frequency resolution from VMD. The Marmousi2 example is used to demonstrate that the VMDTKE approach is capable of depicting the location and extent of strong anomalies which correlate to hydrocarbons more clearly. We compare the spectral decomposition results with that from the conventional VMD-based method. Application on field data further confirms the potential of the VMDTKE algorithm in delineating strong amplitude anomalies that are associated with hydrocarbon reservoirs.

A Novel Hydrocarbon Detection Approach via High-Resolution Frequency-Dependent AVO Inversion Based on Variational Mode Decomposition

Abstract: Amplitude-versus-offset (AVO) inversion always plays an important role in reservoir fluid identification, which allows the estimation of various rock and fluid properties from prestack seismic data. In this paper, we propose a new method for discrimination of hydrocarbon accumulation that combines frequency-dependent AVO inversion scheme and variational mode decomposition (VMD). VMD is a recently developed algorithm for adaptive signal decomposition that is able to nonrecursively decompose a multicomponent signal into a number of quasi-orthogonal intrinsic mode functions and avoid mode mixing effectively. VMD is superior to other state-of-the-art approaches in obtaining high-resolution and high-fidelity local time–frequency depiction performance. Two synthetic signals are employed to illustrate that VMD achieves higher temporal and frequency resolution than the conventional continuous wavelet transform (CWT) decomposition. Other synthetic examples, elastic and dispersive, are utilized to demonstrate that the proposed method is more reliable for the detection of hydrocarbon saturation and a comparison is made with the CWT-based inverted results. Application on field data has further shown that the proposed approach has the potential in identifying the reservoir related to hydrocarbon.

Simulation of the Lunar Regolith and Lunar-Penetrating Radar Data Processing

Abstract: Lunar-penetrating radar (LPR) was conducted by the “Yutu” rover of China's Chang-E 3 lunar mission to study the shallow subsurface of the Moon Both regolith modeling and numerical simulation can provide a reliable reference for data processing of the Moon In this study, a 3-D lunar regolith model that considered some key factors including terrain, rocks, randomness of medium, and permittivity change with depth is built Based on LPR numerical simulation, v(z) f-k migration, with high accuracy for vertical velocity variations, is carried out Compared with Stolt f-k migration, which is limited to constant velocity, v(z) f-k migration performs better We have designed a workflow for LPR data of Chang-E 3 mission, such as v(z) f-k migration, filters, and gain Finally, according to the LPR real data result, we estimate the thickness of the regolith, the location and physical parameters of several rocks, and randomness of medium Besides, the present study provides a good reference for further understanding of lunar near-surface geological information

Through-Wall Detection of the Moving Paths and Vital Signs of Human Beings

Abstract: Detection of human activities in complex environments such as through wall by ultrawideband radar has many important applications in security, vital rescue, and so on. It is much more difficult to detect vital signs of moving human beings than static ones. In this letter, we build a model for moving targets and apply the time domain finite element method to simulate single-input multiple-outputs (SIMO) radar data. Human respiration is modeled by changing body size and physical parameters. The background removal is performed for radar data. Then, we use the back projection to reconstruct the consecutive target locations, which constitute the moving path, leading to a curve carrying vital signs in the radar image. Since SIMO radar data are multivariate, we use multivariate empirical mode decomposition (MEMD) and fast Fourier transform to separate and extract the respiratory characteristic frequencies. The reconstructed frequency coincides with that in the original model. The result shows that the combination of SIMO radar and MEMD can effectively identify the moving path of the human being behind the wall and extract vital signs.