Abstract Ground penetrating radar (GPR) is a commonly used tool for railway trackbed inspection due to its ability to collect information about subsurface materials at high resolution and high speed. Although GPR recording systems allow for the collection of vast quantities of data (hundreds of kilometres per day), accurate ground truth information is difficult to obtain. Models of trackbed can be used to generate synthetic radargrams to provide a better understanding and predictability of GPR responses to a wide range of trackbed conditions. In this research, we produced models of ballast using randomly shaped 3D particles, with a range of particle size distributions to represent various stages of ballast breakdown. Additionally, void spaces are partially filled with a constant dielectric material to represent ballast contamination. We used gprMax to simulate the GPR response for a 2 GHz horn antenna over the trackbed models. These simulations resulted in radargrams that are visually indistinct from real recorded data in known conditions. These radargrams, along with their formative models, have provided valuable insights into how variations in trackbed conditions can impact GPR data.

Abstract Rice is a staple crop in the Vietnam Mekong Delta (VMD) in which more than half of Vietnam's rice is produced. However, rice production in the VMD is threatened by increasing saltwater intrusion due to land subsidence and climate change induced sea level rise. Saltwater intrusion into lowland areas through the canal system or capillary rise of saline water from near surface saline water tables may result in salt accumulation in the topsoil. Therefore, it is important to disentangle the two effects and their relative importance to implement appropriate strategies for water and salinity management for adapting rice production systems of the VMD to climate change. Here, we report on the possibility of using geoelectrical methods to evaluate the potential threat of subsoil salinity to rice production. To evaluate the level of subsoil salinity, we measured soil electrical resistivity using an ARES II to a depth of 40 m in a case study comprising five locations in the VMD. Electrical resistivity measurements were calibrated to soil types, which were identified through evaluating 1 m core sections obtained by drilling down to 40 m depth. The relationship between drilling data and soil resistivity was determined by applying clustering and principal component analysis. Resistivity values smaller than 3 Ω m were clearly identified as indicative for a saline water table. The results show a direct link between the depth of the saline water table and the proximity to the sea, but not to the rice production system (single, double, or triple cropping). This study proved for the first time the applicability of the electrical resistivity tomography method for identifying groundwater tables and evaluating subsoil salinity on an agricultural field scale in the VMD.

Abstract Many coastal areas are affected by groundwater salinization due to the unsustainable use of groundwater resources. For a cost‐effective quantitative assessment of groundwater resources, electrical resistivity (ER) tomography is often used as a standalone geophysical technique. In this paper, we present an application of the integration of direct‐current ER and full‐decay‐induced polarization (IP) method at the Pontina Plain (Central Italy). The case study is a coastal area in Central Italy prone to salinization due to both geological and anthropogenic factors. To achieve these goals, we inverted full‐decay time‐domain electrical data for Cole–Cole parameters. The resulting multi‐parameter model provides a first approximation prediction of the permeability, employing well‐established empirical relationships with the electrical parameters. We demonstrated that our approach: (i) can locate highly conductive zones directly related to saline intrusion inland using the resistivity as a fast proxy; (ii) can remove the ambiguity in the detection of clay/silt layers in the near‐surface; and (iii) permit a prediction of the permeability, employing full‐decay inversion of time‐domain electrical data. However, the extremely conductive environment prevents the use of IP data for the reconstruction of deep layers or detection of the salt wedge front. Therefore, this approach can be used for hydro‐geophysical screening and monitoring of salinization‐prone sites, where strong limitations to direct inspection exist due to external constraints (e.g., protected lands).

Abstract We interpreted and modelled aeromagnetic data from helicopter surveys of the Morobe goldfield, Papua New Guinea, by comparing gridded magnetic data with mapped surface geology. In some cases, geologic units may be recognized and distinguished based on contrasts in magnetic intensity and texture, including the Morobe Granodiorite, Kaindi Metamorphics, Bulolo Volcanics, and Langimar Beds. Magnetic anomalies occur due to intrusions of Edie Porphyry and related igneous rocks that are associated with gold mineralization. Numerous faults control the size and shape of the intrusions. Magnetic anomalies of interest were identified in gridded data from high‐resolution surveys and modelled using flight line data. Two‐dimensional magnetic models included dipping slabs with thicknesses of 100–400 m. Three‐dimensional models were prepared, including a dipping tabular body and a dipping cylinder. A 2008 survey of the Papuan peninsula sponsored by the European Union (EU) provided continuous coverage of the Morobe goldfield area and helped resolve regional features that were only partially covered by previous smaller surveys. In particular, the EU survey covers the full length of the Sunshine Fault and greatly extends coverage of outcrops of the Morobe Granodiorite and Langimar Beds. The EU survey contains isolated anomalies south‐west of Wau that may correspond to intrusions of Edie Porphyry, Morobe Granodiorite.

Abstract The echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

ABSTRACTIn karst topography, hidden caves or underground rivers may imperil the safety of tunnels. It is critical to accurately detect the location and geometric shape of hidden karst caves for assessing the safety of tunnels. This paper reports a case where a large karst cave was exposed during the construction of the Nongmo tunnel in Duan County, Guangxi, China. A 2D ground‐penetrating radar survey was employed to preliminarily detect the distribution of hidden karst caves along the tunnel axis. Advanced drilling was used to obtain the velocity of electromagnetic waves and verify the 2D ground‐penetrating radar detection. Ground‐penetrating radar 3D grid detection was carried out for further detecting the geometric shape of the hidden karst caves. Rotary thrust power ratio, which is a function of drilling speed, rotating speed, propulsion pressure and torque, was proposed to reflect the integrity of the surrounding rocks. It was found that the depth of the peak value in the rotary thrust power ratio curve is consistent with that of the amplitude jump in ground‐penetrating radar A‐scans and in time–frequency distributions. The results of 3D attributes analysis indicate that due to the variation of highlights in different ground‐penetrating radar signal attributes, the obtained karst cave shapes using different attributes would be various. A multi‐attributes fusion method was proposed to generate a data volume considering various attributes. The 2D geometric shapes at different depths were obtained using the anomalous body lineament in the generated date volume slices. The 3D geometric shapes were reproduced by combining the obtained 2D geometric shapes at various depths. Based on different detection and analysis methods, the outline shape of karst caves at YK364 + 199 ∼ YK364 + 184 of the Nongmo tunnel was revealed in detail.

ABSTRACTSeismic ray tomography is a popular tool for reconstructing seismic velocity models from traveltime data. Here we study how the model parameterization affects the resolution and accuracy of the tomographic inversion for the near‐surface model building. In particular, we consider the weighting of the elements of the model perturbation vector based on the values of the initial velocity model. When the model parameters are defined in terms of velocities, then the tomographic‐inversion resolution is better for the shallow part but degrades for the deeper part of the model. The opposite is true when the model parameters are defined in terms of slowness values. This effect is associated with the method of forming the tomographic matrix. When linearizing the tomography problem for different model parameters, the matrix elements have different weight coefficients. This affects the inversion results and can lead to large errors. We suggest a new parameterization (in‐between the velocity and the slowness) that provides better quality of the tomographic inversion and balanced resolution between the shallow and deeper part of the model. The good performance of this new parameterization is confirmed by a series of synthetic tests and one real‐data example.

ABSTRACTSensitive glaciomarine clays, often referred to as ‘quick clay’, commonly occur in many countries at high, northerly latitudes, causing frequent and occasionally devastating landslides. The salt content of quick clay is strongly correlated to both its shear strength and electrical resistivity. Hence, it can be mapped using electromagnetic methods more efficiently than traditional intrusive methods, the latter of which can often be slow and costly. However, the resistivity signature of quick clay is non‐unique, leading to ambiguous, imprecise interpretations of geophysical models. In this study, we present an improved method for predicting the probability of quick clay using airborne electromagnetics. Using machine learning algorithms, we combine geophysical models with geotechnical data to address the issue of their non‐unique resistivity signature. Beyond resistivity values, the machine learning algorithms use spatial derivatives of resistivity and spatial attributes. We evaluate the performance of this method using data collected from a road construction project in central Norway. Results show that this method is able to make plausible and accurate predictions of quick clay occurrence using as few as 10 boreholes across an area of 14.8 km2, and that it outperforms a simple interpretation based on resistivity intervals alone. In addition to a ‘best guess’ categorical classification, these algorithms output probability estimates, and we demonstrate that they are a reliable indication of uncertainty. The accuracy of these predictions also tends to increase as more geotechnical data are included as training data, helping compensate for the limited resolution of the airborne electromagnetics data. Given that the petrophysics of the clays at this test site are consistent with observations in other regions, we expect this method has the potential to make quick clay hazard mapping more efficient by offering valuable early‐phase insights, leading to large time and cost savings for both infrastructure planning and regional hazard mapping.