Near Surface Geophysics - Volume 22, Issue 5, 2024

Understanding near‐surface groundwater storage, flow patterns, surface and groundwater interactions in mining areas can assist in making mining more efficient and profitable. This is especially important in opencast mines affected by water inflows that may negatively affect production and increase mining costs. We map and characterize the near‐surface aquifer zones at the opencast site of Tharisa Minerals, located in the southwestern region of the Bushveld Complex (South Africa). The main goal is to infer pit water inflow at the mine site and determine how it may be better controlled. The Bushveld Complex hosts partially connected and unconfined alluvial, shallow‐weathered and crystalline bedrock aquifers, which are often connected by small‐scale permeable zones. Seismic refraction tomography, multichannel analysis of surface waves, electrical resistivity tomography and borehole data are used to map and understand the different aquifer zones in the vicinity of the mine, as well as infer their relation to water inflow in the mine pits. The geophysical surveys map the overburden, weathered bedrock aquifer zone, and the top of the crystalline aquifer rock zone reasonably well. They reveal extensive and deep weathering, and possible high hydraulic conductivity in the vicinity of the mine. The results provide a better understanding of the mine's near‐surface environment, which could be used to implement effective and targeted dewatering techniques, thus enabling better pit inflow water control to improve mine working conditions and production.

First‐break picking is an essential step in seismic data processing. For reliable results, first arrivals should be picked by an expert. This is a time‐consuming procedure and subjective to a certain degree, leading to different results for different operators. In this study, we have used a U‐Net architecture with residual blocks to perform automatic first‐break picking based on deep learning. Focusing on the effects of weight initialization on first‐break picking, we conduct this research by using the weights of a pre‐trained network that is used for object detection on the ImageNet dataset. The efficiency of the proposed method is tested on two real datasets. For both datasets, we pick manually the first breaks for less than 10 of the seismic shots. The pre‐trained network is fine‐tuned on the picked shots, and the rest of the shots are automatically picked by the neural network. It is shown that this strategy allows to reduce the size of the training set, requiring fine‐tuning with only a few picked shots per survey. Using random weights and more training epochs can lead to a lower training loss, but such a strategy leads to overfitting as the test error is higher than the one of the pre‐trained network. We also assess the possibility of using a general dataset by training a network with data from three different projects that are acquired with different equipment and at different locations. This study shows that if the general dataset is created carefully it can lead to more accurate first‐break picking; otherwise, the general dataset can decrease the accuracy. Focusing on near‐surface geophysics, we perform traveltime tomography and compare the inverted velocity models based on different first‐break picking methodologies. The results of the inversion show that the first breaks obtained by the pre‐trained network lead to a velocity model that is closer to the one obtained from the inversion of expert‐picked first breaks.

This study demonstrates the application of the cross‐gradient joint inversion method to investigate iron mineralization zones within a volcano‐sedimentary environment. The presence of minerals with intense contrasts in density or magnetic susceptibility, such as hematite or magnetite, facilitates modelling the distribution of ore bodies with depth. Our approach involves establishing a unified interpretation of reconstructed density and susceptibility models through both independent and joint inversion with sparsity regularization in conjunction with a petrophysical model resulting from core data. This approach provides an ideal strategy to uncover the realistic geologic setting of iron ore deposits. We initially simulated a synthetic model closely resembling real‐case scenarios to assess the efficacy of the cross‐gradient joint inversion algorithm in comparison to independent inversion. Subsequently, the inversion algorithms were implemented on gravity and magnetic data, collected over an area of 500 × 600 m² in Shavaz iron‐bearing deposits located in the central Iranian block. The primary iron oxide–apatite type mineralization in the study area is associated with the Nain–Dehshir–Baft fault as a NW–SE trending strike‐slip fault. Although both inversion methods yield satisfactory models, incorporating the cross‐gradient constraint in joint inversion resulted in a more constrained delineation of iron–oxide ore deposits in the fault system. This improvement facilitates the differentiation between hematite and a small percentage of magnetite, providing a more accurate estimation of ore depth. Inversion results suggest that the magnetite mineralization is coated with extensive hematite mineralization and both are positioned relatively within the same depth interval, covered by approximately a 15–25 m sequence of sediments.

The ultrasonic echo technique is broadly applied in non‐destructive testing (NDT) of concrete structures involving tasks such as measuring thickness, determining geometry and locating built‐in elements. To address the challenge of enhancing ultrasonic imaging for complex concrete constructions, we adapted a seismic imaging algorithm – reverse time migration (RTM) – for NDT in civil engineering. Unlike the traditionally applied synthetic aperture focusing technique (SAFT), RTM takes into account the full wavefield including primary and reflected arrivals as well as multiples. This capability enables RTM to effectively handle all wave phenomena, unlimited by changes in velocity and reflector inclinations. This paper concentrates on applying and evaluating a two‐dimensional elastic RTM algorithm that specifically addresses horizontally polarized shear (SH) waves only, as these are predominantly used in ultrasonic NDT of concrete structures. The elastic SH RTM algorithm was deployed for imaging real ultrasonic echo SH‐wave data obtained at a concrete specimen exhibiting a complex back wall geometry and containing four tendon ducts. As these features are frequently encountered in practical NDT scenarios, their precise imaging holds significant importance. By applying the elastic SH RTM algorithm, we successfully reproduced nearly all reflectors within the concrete specimen. In particular, we were capable of accurately reconstructing all vertically oriented reflectors as well as the circular cross sections of three tendon ducts, which was not achievable with traditional SAFT imaging. These findings demonstrate that elastic SH RTM holds the ability to considerably improve the imaging of complex concrete geometries, marking a crucial advancement for accurate, high‐quality ultrasonic NDT in civil engineering.