Spectral balancing GPR data using time-variant bandwidth in the t-f domain

Ground-penetrating radar (GPR) sections encounter a resolution reduction with depth because, for electromagnetic (EM) waves propagating in the subsurface, attenuation is typically more pronounced at higher frequencies. To correct for these effects, we have applied a spectral balancing technique, using the S-transform (ST). This signal-processing technique avoids the drawbacks of inverse [Formula: see text] filtering techniques, namely, the need for estimation of the attenuation factor [Formula: see text] from the GPR section and instability caused by scattering effects that result from methods of dominant frequency-dependent estimation of [Formula: see text]. The method designs and applies a gain in the time-frequency ([Formula: see text]) domain and involves the selection of a time-variant bandwidth to reduce high-frequency noise. This method requires a reference amplitude spectrum for spectral shaping. It performs spectral balancing, which works efficiently for GPR data when it is applied in very narrow time windows. Furthermore, we have found that spectral balancing must be applied prior to deconvolution, instead of being an alternative technique. </jats:p

GPR Data Interpretation Approaches in Archaeological Prospection

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.</jats:p

GPR Data Interpretation Approaches in Archaeological Prospection

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed

GPR data processing techniques

Ground penetrating radar (GPR) is a non-destructive geophysical method that uses radar pulses to image the subsurface. Notwithstanding that it is particularly promising for soil studies, GPR is characterised by notoriously difficult automated data analysis. Hence, the focus of this chapter is to provide the reader with a deep understanding of the state of the art and open issues in the field of GPR data processing techniques as well as of the interesting application of GPR in the field of civil engineering. In particular, we present an overview on noise suppression, deconvolution, migration, attribute analysis and classification techniques for GPR data

Fast and efficient void detection in carbonates by combined ERT and borehole data: A case study from Chania Airport in Greece

A typical approach for karstic carbonate risk assessment is to utilize integrated geophysical research and calibrate the data with either pre- or postmeasurement acquisition borehole information. Here, we present a case study of a time-demanding investigation on the detection of subsurface areas prone to new building foundation stability at a site rich in karstic structures belonging to the Trypali Unit (Triassic-Jurassic). During excavation and construction to expand the airport in Chania, Crete, Greece, a large karstic void was revealed, which triggered an alert that led to an immediate work stoppage and the demand for an investigation of the subsurface of the whole area. The scope was to detect weak zones or voids larger than 0.5 m with a fast and timely approach because the costs of a long project stop are high. To meet the time demands with an efficient approach, we utilized the electrical resistivity tomography method, which guided a fast postacquisition borehole program and was supplemented by in-borehole video recordings, aiding in the direct detection of karstic structures. The 3D inversion of the electrical data provided electrical resistivity tomography images of the subsurface, which characterized the area as highly karstified and fractured and detected voids with sizes ranging from 0.5 to 6 m. </jats:p

A New Approach for Adaptive GPR Diffraction Focusing

Several researchers have utilized multipath summation to manage the common problem of scattered energy within GPR sections. Such energy results in degrading the lateral resolution and continuity of reflectors. If detailed velocity models are known, then it is fairly easy to focus the scattered energy by means of conventional migration methods. However, this is rarely the case in GPR sections, as the common-offset antenna array is mostly used, and therefore cannot provide velocity models. This gives an important advantage for the multipath summation method, which has proved to be successful in focusing such diffractions, without the need to build a detailed migration velocity field model. This multipath summation method is based on stacking (summation) of constant velocity migrated sections (weighted or not) over a predefined velocity range. The main drawback of this technique is the high computational cost and the need for user interference to select the appropriate stacking weights. We developed an improved implementation of the weighted multipath summation method that reduces both the computational cost, and the user interference in stacking weights selections. This data adaptive methodology can expedite the migration process, suppress the need for a detailed velocity model, and reduce the user subjectivity. Moreover, a data adaptive spectral scaling scheme was developed. This is applied on the output of the multipath summation process to reduce the expected blurriness in the resulting GPR sections