Qianci Ren

Memoryless quasi-Newton (MLQN) method for 2D acoustic full waveform inversion

Full waveform inversion (FWI) is an efficient way to solve parameter reconstruction problems, such as velocity, density, and viscosity coefficient. In this study, we apply a memoryless quasi-Newton (MLQN) method in FWI to invert velocity from surface seismic data for the first time. This method can attain acceptable results with low computational cost and small memory storage requirements. To ensure that the inverted velocity is maintained between the lower and upper boundaries of the velocity model, a nonlinear transformation is added to velocity as a priori information. To test the efficiency of the MLQN method in FWI, two synthetic models, a modified Marmousi model and a modified overthrust model, are examined from the surface seismic data with and without white Gaussian noise. For comparison, the conjugate gradient (CG) method is carried out for the same velocity models with the same parameters. We compare the inverted velocities by the two methods based on the aspects of memory storage requirements, computation time for each iteration, and error. By keeping the memory storage requirements and computation time in each iteration similar, the reconstructed velocity models obtained using the MLQN method are closer to the true velocity models than those obtained using the CG method. Our numerical tests show that the MLQN method is feasible and reliable in FWI. In this paper, a memoryless quasi-Newton (MLQN) method is applied in full waveform inversion to invert velocity from surface seismic data for the first time. This method can attain acceptable results with low computation cost and small memory storage requirements. Synthetic model tests show that the MLQN method is feasible and reliable.

Application of freeman decomposition to full polarimetric GPR

Full-polarimetric Ground-penetrating radar (GPR) is considered as a promising sensor for detecting buried targets. However, the polarimetric decomposition technique plays a crucial role in identifying and classifying targets which are buried in the sand under the surface. The decomposition techniques of full-polarimetric Ground-penetrating radar includes four decomposition methods, namely: (1) Pauli decomposition method, (2) H-α decomposition method, (3) Freeman decomposition method and (4) polarimetric anisotropy analysis method .This paper mainly applys Freeman decomposition method to recognition of metal surface plate, dihedral and metal ball. The potential of polarimetric target decomposition techniques to metal surface plate, dihedral and metal ball characterization and classification is shown which provides valuable information.

IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method

Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing and imaging. With respect to signal processing, GPR data are typically non-stationary, which limits the applications of these methods moving forward. Empirical mode decomposition (EMD) provides alternative solutions with a fresh perspective. With EMD, GPR data are decomposed into a set of sub-components, i.e., the intrinsic mode functions (IMFs). However, the mode-mixing effect may also bring some negatives. To utilize the IMFs’ benefits, and avoid the negatives of the EMD, we introduce a new decomposition scheme termed variational mode decomposition (VMD) for GPR data processing for imaging. Based on the decomposition results of the VMD, we propose a new method which we refer as “the IMF-slice”. In the proposed method, the IMFs are generated by the VMD trace by trace, and then each IMF is sorted and recorded into different profiles (i.e., the IMF-slices) according to its center frequency. Using IMF-slices, the GPR data can be divided into several IMF-slices, each of which delineates a main vibration mode, and some subsurface layers and geophysical events can be identified more clearly. The effectiveness of the proposed method is tested using synthetic benchmark signals, laboratory data and the field dataset.

Full-polarimetric GPR system for underground targets measurement

A conventional GPR system includes PC, network analyzer, rectangular coordinates robot and a single antenna for transmission and reception, resulting in a response only to Co-Polarization signal. However, we expect that more polarimetric information can be obtained. So we developed a full-polarimetric GPR system including PC, network analyzer, position controller, switch driver and polarimetric antenna array. This antenna array can obtain CMP multi-offset data gather directly. At every measurement position, the total of three signals was collected not only in Co-Polarimetric mode but also in Cross-Polarimetric mode. Two groups of experiments have been presented. The first group is concerned with a metal dihedral which is made of two orthogonal conducting plates and a metal trihedral as the targets. The result of this experiment shows that the surface morphology of the target characteristics, the relative position and attitude have a certain influence on the measurement results. The second group is using a metal trihedral as the target. The results of experiment are shown in this presentation are consistent with the theoretical values and helps us to identify target attributes such as direction.

Radar polarimetry analysis applied to fully polarimetric Ground Penetrating Radar

Polarimetric decomposition techniques have been applied in remote sensing in the area of air-space-borne radar and have achieved much progress in recent years. However, very few apply these polarimetric decomposition techniques to the Ground Penetrating Radar (GPR).We currently apply GPR data sets to characterize and classify the subsurface targets using Pauli decomposition method. The Pauli decomposition method provided important radar polarimetry information of subsurface targets, and the Pauli decomposition method made a significant contribution to understanding the scattering mechanisms from different subsurface targets with different properties. Analyzing polarimetric attributes of subsurface targets provides facilitates for classifying subsurface targets. Because some methods only can identify the approximate outline of subsurface targets, but can not classify the targets such as imaging technique that can only identify the outline of subsurface targets, but can not classify targets. So we apply Pauli decomposition for classifying subsurface targets and this analysis result is relatively good. The decomposition technique plays a key role for classifying subsurface targets such as metal surface plate, dihedral, metal ball and other subsurface targets. This paper mainly applies Pauli decomposition method to recognize subsurface metal surface plate, subsurface dihedral, subsurface metal ball, subsurface metal bucket, and subsurface chaotic scattering target. This decomposition technique provides valuable information for the study of properties of the subsurface targets.

Developing calibration technology for full-polarimetric GPR

Polarimetric GPR requires accurate calibration of channel imbalance and crosstalk not only in the amplitude term but also in the phase term. Currently, there have some calibration techniques. Though these techniques are very easy to perform, they provide less accurate calibration results for the crosstalk. To improve on the accuracy of calibration, we have developed a mathematical formulation to calibrate polarimetric GPR data. We measured several scattering matrices to obtain the necessary calibration parameters. The calibration technique was tested from measurements conducted on dihedral corner reflector.

Design and performance of Full-polarimetric airborne GPR testing system

Airborne ground penetrating radar (GPR) is a suitable tool to perform cost-effective surveys of the underground of a large possibly non-accessible areas. And It is concluded that airborne GPR will receive more attention in the future. So we have developed a L-band Full-polarimetric Step-Frequency GPR acquisition system, which consists of a GPS receiver, the Vivaldi antenna, a signal amplifier and a vector network analyzer (VNA) under the control of a PC unit. The main objective of our work is to conduct some experiments to test the feasibility of this airborne testing system.

Subsurface imaging by modified migration for irregular GPR data

Handheld ground-penetrating radar (GPR) system is one of a number of technologies that has been researched as a means of improving landmine detection efficiency. However, as the measurement points are random and data are irregular for the human operator, it is difficult to display subsurface visualization imaging. Also detection of buried landmines by GPR normally suffers from very strong clutter that will decrease the image quality. To solve the problem, a modified migration algorithm was proposed to process irregular GPR data, which has both the advantage of migration that can improve signal-clutter ratio and the advantage of interpolation that produces the grid data set for visualization. An application to field data acquired in Afghanistan shows clear landmine image in both vertical profile and horizontal slice.

Improving iamging achieved by GPR offset from the air-ground interface

There have hand-held GPR systems and vehicle-mounted GPR systems, which are offset from the air-ground interface by a nonnegligible distance, developed for landmine detection. Vehicle-mounted systems have exclusive advantage that can show subsurface imaging in horizontal slices based on grid GPR data set. Hand-held GPR system is one of advantageous technologies in mountain district etc. But handheld GPR system usually cannot display subsurface imaging in horizontal slices because human being operator cannot precisely control GPR sensor to scan the measurement area along the regular survey line and observation positions are random. For both hand-held GPR systems and vehicle-mounted GPR systems, clutter reduction is very challenging due to these physical limitations. We used the technique of CMP antenna array and migration processing to improve the imaging quality for vehicle-mounted GPR systems, and used the modified migration algorithm to perform both interpolation and migration for hand-held GPR systems.