The continuous wavelet transform in processing data of high frequency electromagnetic prospecting

A data-driven multidimensional signal-noise decomposition approach for GPR data processing

In this paper practical results of the long-term (1999-2007) application of Ground Penetrating Radar (GPR) technique for estimation of electrical parameters of contaminated ground layers are presented. Contaminations were caused by oil products. GPR technique includes the procedures of profiling, taking hodographs according to the Common-Midpoint (CMP) method and data optimal processing. GPR data processing was applied to acquire the estimations of values of ground layers thickness and their relative dielectric permittivity e by solving the GPR inverse problem. For data processing were used the methods of weighted least squares, the recursive Kalman filter, the maximum likelihood and the Nelder-Mead optimization. The comparative analysis of these methods allows to define the most expedient technique for the practical estimation of the ground electrical parameters using GPR data. The acquired values of e were connected with the degree of ground contamination which was attested by measurements of a floating layer thickness of oil products in the observation wells and their concentration in the ground. The monitoring data acquired in 2007 shows in comparison with the year 2006 the values of ground contamination degree estimated by GPR technique were decreased from 2.7% up to 58.8% for different observation wells. These decreases were confirmed by measurements in the observation wells.

Presently there are no suitable non-invasive methods for precisely detecting the subsurface defects in logs in real time. Internal defects such as knots, decays, and embedded metals are of greatest concern for lumber production. While defects such as knots and decays (rots) are of major concern related to productivity and yield of high value wood products, embedded metals can damage the saw blade and significantly increase the down time and maintenance costs of saw mills. Currently, a large number of logs end up being discarded by saw mills, or result in low value wood products since they include defects. Nondestructive scanning of logs using techniques such as Ground Penetrating Radar (GPR) prior to sawing can greatly increase the productivity and yield of high value lumber. In this research, the GPR scanned data has been analyzed to differentiate the defective part of the wooden log from the good part. The location and size of the defect has been found in the GPR scanned data using the MATLAB algorithm. The output of this algorithm can be used as an input for generating operating instructions for a CNC sawing machine. This paper explains the advantages of the GPR technique, experimental setup and parameters used, data processing using RADAN software for detection of subsurface defects in logs, GPR data processing and analysis using MATLAB algorithm for automated defect detection, and comparison of results between the two processing methods. The results show that GPR in conjunction with the proposed algorithm provides a very promising technique for future on-line implementation in saw mills.

<p>The near-surface geophysical methods have been widely applied to investigations of shallow targets for scientific and engineering research. Various data processing algorithms are available to help visualize targets, data interpretation, and finally, achieve research goals.</p><p>Most of the available algorithms are Fourier-based with linear stationary assumptions. However, the real data are rarely the case and should be treated as nonlinear and non-stationary. In recent decades, a few newer algorithms are proposed for processing non-stationary, or nonlinear and non-stationary data, for instance, wavelet transform, curvelet transform, full-waveform inversion, Hilbert-Huang transform, etc. This progress is encouraging, but conventional algorithms still have many advantages, like strong theoretical bases, fast, and easy to apply, which the newer algorithms are short of.</p><p>In this study, we try to fuse both conventional and contemporary algorithms in near-surface geophysical methods. A cost-effective ground-penetrating radar (GPR) data processing scheme is introduced in shallow depth structure mapping as an example. The method integrates a nonlinear filtering technique, natural logarithmic transformed ensemble empirical mode decomposition (NLT EEMD), with the conventional pseudo-3D GPR data processing methods including background removal and migration to map the subsurface targets in 2D profile. The finalized pseudo-3D data volume is constructed by conventional linear interpolation. This study shows that the proposed technique could be successfully employed to locate the buried targets with minimal survey effort and affordable computation cost. Furthermore, the application of the proposed method is not limited to GPR data processing, any geophysical/engineering data with the similar data structure are applicable.</p>

<p>The most frequently used survey mode for acquiring Ground Penetrating Radar (GPR) data is common offset (CO) – where a single transmitter and receiver pair move along a survey line at a constant (offset) separation distance. This allows rapid and dense data acquisition, and therefore high-resolution large-scale investigations, to be carried out with relative ease, and at relatively low cost. However, it has long been known that multi-offset survey methods, such as common mid-point (CMP) and wide-angle reflection-refraction (WARR), can offer many benefits over CO: detailed subsurface EM wave velocity models; enhanced reflection sections with higher signal-to-noise ratio (SNR); the potential to adapt well-established advanced seismic processing schemes for GPR data [1-2].</p><p>Despite the advantages of multi-offset GPR data, these methods have seen limited adoption as, in the past, they required significantly more time, effort, and hence cost, to collect. However, recent advances in GPR hardware, particularly in timing and control technology, have enabled the development of multi-concurrent sampling receiver GPR systems such as the “WARR Machine” manufactured by Sensors & Software Inc. [3-4]. These newly developed GPR systems have the potential to provide all the aforementioned benefits with considerably less effort and therefore reduced survey cost, as they allow for the fast acquisition of multi-offset WARR soundings.</p><p>In this work, we look at the challenges and opportunities from collecting and processing multi-offset GPR data. We demonstrate a processing workflow that combines standard GPR processing approaches, with methods adapted from seismic processing, as well as our own algorithms. This processing framework has been implemented into a GUI-based software written in MATLAB [5], and has been tested using both synthetic [6] and real multi-offset GPR data. Some of the specific challenges with multi-offset GPR that we investigate are time zero misalignments, CMP balancing, velocity analysis, and automated velocity picking. We show how addressing these issues can result in improved velocity analysis, and ultimately in improved subsurface velocity models, and stacked sections.</p><p><strong>References</strong></p><p>[1] Ursin, B., 1983. Review of elastic and electromagnetic wave propagation in horizontally layered media. Geophysics, 48(8), pp.1063-1081.</p><p>[2] Carcione, J. and Cavallini, F., 1995. On the acoustic-electromagnetic analogy. Wave Motion, 21(2), 149-162.</p><p>[3] Annan, A. P., and Jackson, S., 2017. The WARR machine. 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).</p><p>[4] Diamanti, N., Elliott, J., Jackson, R. and Annan, A. P., 2018, The WARR Machine: System Design, Implementation and Data: Journal of Environmental & Engineering Geophysics, 23, pp.469-487.</p><p>[5] Angelis, D., Warren, C. and Diamanti, N., 2020. A software toolset for processing and visualization of single and multi-offset GPR data. 18th International Conference on Ground Penetrating Radar.</p><p>[6] Warren, C., Giannopoulos, A. and Giannakis, I., 2016. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications, 209, pp.163-170.</p>

Because of diffraction wave exists in the reflected signal of ground penetrating radar (GPR), which makes the graphics data processing difficult and the data calculation precision inaccurate of ground penetrating radar. A method based on the wave equation migration with finite difference to eliminate the diffraction waves in GPR data processing is proposed in this paper. The research work includes algorithm analysis, practical sample modeling and test. The measurement results prove that this algorithm can meet the test range and precision requirements of GPR.

As ground-penetrating radar (GPR) is increasingly used for assessing the condition of bridge decks, quantifying and controlling the quality of GPR measures becomes an important challenge. A methodology developed to assess the accuracy of deck condition measures is presented, and its use in a case study involving real data is demonstrated. The latter are generated during GPR applications on a large bridge deck and are processed with a commercial image-processing algorithm. The measures extracted from the processed GPR data are the rebar reflection amplitude and the dielectric constant of the deck material. The accuracy of the GPR assessments is evaluated by comparing core data (ground truth) with the GPR measures. The methodology uses appropriate statistical characteristic curves for quality control. It is based on a use of data to plot the probabilities of true detection versus false detection. Image interpretation requires using a threshold value (typically established from experience) selected to optimize true and false detection rates. The results of the case study indicate that rebar reflection data detect defects of the bridge decks at a 75% true detection rate with a 15% false detection rate. The dielectric data generated during field testing appear not to adequately represent the condition of the bridge deck because of the presence of latex-modified concrete overlay. The details of this finding and important conclusions are presented and discussed.

Soil piping, an insidious form of internal erosion within soil structures, presents substantial challenges in land stability and infrastructure integrity. Detecting and pre- emptively managing soil piping require robust and non- destructive methodologies to identify vulnerable areas. Ground- penetrating radar (GPR) has emerged as a valuable tool in this context, offering promising insights into subsurface conditions and potential erosion pathways. This paper presents an overview of recent advancements in the integration of artificial intelligence (AI) techniques with GPR data processing and interpretation. The recent speedy development of AI technologies (machine learning, deep learning, etc.) provides a great opportunity to develop reliable, accurate and time- effective processing solutions to advance most of the current and emerging Earth observation and remote sensing technologies. By combining the prowess of GPR's non-destructive subsurface imaging with the intelligence of AI-driven data interpretation, we can better understand the underlying complexities of different materials and develop more efficient, accurate, and reliable solutions for piping.

Nowadays, benefiting from its strong capability of non-destructive detection, the ground penetrating radar (GPR) has been applied to detect and reconstruct underground targets and has drawn lots of attention both in military and civilian fields. However, in the processing of GPR imaging, due to the dispersion errors caused by random distribution of various particles in soil, conventional imaging methods have disadvantages of low signal-noise ratio (SNR), low resolution and unbalanced amplitude. In this paper, in order to achieve high resolution and high veracity on underground targets 3-D reconstruction, we improved the conventional reverse time migration (RTM) algorithm in perspective of medium constitutive relationship. Besides, we extended the improved RTM method in 3-D environments and reconstructed the 3-D structure of several underground targets. To make RTM algorithm suitable for stepped frequency continuous wave (SFCW) GPR system, we generated three excitation signal models and analyzed the effect of different excitation signals on imaging performance. Finally, through the quantitative analysis of simulation and on-vehicle experimental results, we found that the 3-D images generated by improved RTM method had higher resolution, smaller measurement error, and higher veracity than those of conventional RTM method.

Ground Penetrating Radar (GPR) is a high frequency (from 10 MHz to 1 GHz) electromagnetic (EM) method which provides high-resolution images of near-surface structure (Smith and Jol, 1995). The depth of investigation depends on the frequency used and on the medium crossed by EM waves, and for low-loss geological materials it does not exceed 50 m (Davis and Annan, 1989). In some cases, GPR data show strong diffraction hyperbolae due to objects on the ground. EM waves propagate with little attenuation in air and conductive surface objects (power lines, metallic fences) and trees are strong reflectors, so reflections from above ground features are often present in GPR data (Sun and Young, 1995). In July 1997 a series of GPR lines were acquired in the seismically active Rhine Valley. We try to image by GPR the geometry of Quaternary sedimentary deposits and identify possible recent faults hidden under a vegetal cover. Some GPR profiles show strong diffraction hyperbolae due to the surface objects on the ground. The purpose of this article is to show the methodology of the acquisition and some preliminary results of GPR processing and interpretation.

A novel classification method for GPR B-scan images based on weak-shot learning

Near‐surface investigations often require characterization of vadose zone hydraulic parameters. Conventional sampling or borehole techniques for estimating these parameters are costly, time consuming, and invasive, all of which limit collection of hydrogeological data at a spacing needed for detailed site characterization. Incorporation of two‐ or three‐dimensional densely sampled geophysical data with conventional hydrological data increases the amount of data available for the characterization and thus has the potential to significantly improve the hydraulic parameter estimates over those obtained from borehole data alone. The hydraulic estimation procedure can be greatly improved by incorporating dielectric information potentially available from ground penetrating radar (GPR), a noninvasive, high‐resolution geophysical method. The procedures for collecting and processing GPR data in the format needed for the proposed estimation technique are relatively new and still a topic of research; our method requires as a starting point the ability to estimate dielectric constants from GPR data. Numerical experiments were performed to investigate the general utility of the GPR‐assisted estimation technique under a range of conditions. Three bimodal systems were investigated, each system being composed of a sand facies together with another facies with a larger clay volume fraction; each facies was defined using characteristic values of clay content, porosity, and permeability. Using dielectric information and petrophysical relations, degree of saturation and intrinsic permeability values at each location within the three systems were identified. For bimodal systems, a dielectric constant measurement corresponds to two possible values of saturation and intrinsic permeability at each location; single values of saturation and intrinsic permeability were estimated from these values using the principle of maximum likelihood. Results from case studies demonstrate that a combination of GPR data with conventional borehole data significantly improves the estimates of saturation and has the potential to improve the estimates of permeability over those obtained from well bore data alone. The proposed method should be especially advantageous for vadose zone characterization in areas favorable for GPR data acquisition, where detailed hydraulic parameter information is required but the drilling of numerous boreholes is prohibited.

In order to improve the accuracy of Ground Penetrating Radar image analysis and interpretation, to realize the effective detection of tunnel structure, as well as to improve the performance of its application in engineering inspection, this study adopts the intrinsic time-scale decomposition method to process the Ground Penetrating Radar data. By analyzing the processed data, we find that this method has significant effect in improving the accuracy and stability of Ground Penetrating Radar data processing, and this study is of great significance for the practical application of Ground Penetrating Radar.

This paper presents ground-coupled ground penetrating radar (GPR) data-processing procedures for concrete bridge deck evaluation. GPR signals are analyzed in the depth range from the concrete surface to top reinforcement mat. Although most ground-coupled GPR analysis methods focus on the attenuation of signals from rebar reflections only, the proposed algorithms extract three types of parameters from GPR scans: Direct coupling amplitude, wave velocity in concrete cover depth, and normalized, depth-corrected GPR signal amplitude from rebar reflections. These parameters provide information about bridge deck deterioration conditions at different depths. First, the signal amplitude of the direct coupling wave on the concrete surface is analyzed over the entire bridge. Second, the wave velocity in cover concrete is obtained through migration of rebar reflections, and then the rebar depth can be calculated. Third, signal attenuation in concrete is calculated from the rebar reflection and further corrected by the rebar depth. In addition, the true time zero for GPR signal analysis is validated by numerical simulation and experimental data. Finally, this algorithm is demonstrated on field testing data, and the final results are presented in the forms of direct coupling wave amplitude, wave velocity, and attenuation maps.

At the scale of geomorphological units (riffles, pools, and gravel bars), the contribution of the hyporheic zone to the functioning of streams and rivers depends on the hydrological exchanges between surface water and groundwater. These exchanges are largely controlled by sediment structure and texture, which are difficult to assess with classical methods (shovelling and freeze coring). We aimed to evaluate the ability of a non‐destructive method, ground‐penetrating radar (GPR), to detect sediment structures associated with subsurface biologically active zones in a gravel‐bed river. After GPR data acquisition and processing, a three‐dimensional reconstruction of a gravel bar permitted the identification of two sediment facies: a cobble/gravel lithofacies denoted as ‘coarse’ and a sand/gravel lithofacies denoted as ‘fine’. We installed piezometers along two longitudinal profiles (each corresponding to a lithofacies identified by GPR) and monitored hydraulic head and temperature for 20 days. Water and sediments were sampled along the two profiles to measure water physicochemistry, sediment characteristics, bacterial abundance and activity, and interstitial invertebrate assemblages. These measurements confirmed that the two profiles were characterized by distinct hydrological flow rates and associated biological activities. Rapid water transport in the coarse profile fuelled the hyporheic zone with organic matter, whereas water and organic matter supplies were lower in the fine profile. Consequently, the higher supply of organic matter in the coarse profile was associated with higher microbial activities and invertebrate density and diversity. Therefore, GPR could be an efficient tool to detect the sediment features playing a key role in hyporheic zone functioning. Copyright © 2014 John Wiley & Sons, Ltd.