Subsurface karst features are significantly developed in Guangxi Province, China. This area mainly contains fractured subsurface rock, abundant karst channels, and widely distributed underground fissure networks. Such adverse geological conditions could potentially create hydrogeological hazards such as collapses, water inrush, and mud inrush during infrastructure construction. The Hejing limestone mine is an opencast mine in Pingnan County, Guangxi, that produces cement. Mining activities have altered the seepage fields in this area, causing large amounts of groundwater to flood into the mining pit; this has caused many ground collapses while severely reducing limestone production. More than 24 km of surface electrical resistivity tomography (ERT) profiles have been previously recorded in the region to identify potential karst positions and explore groundwater inrush paths. In this study. we employed surface and cross-borehole ERT surveys to delineate specific groundwater inrush paths on the eastern side of the mine and characterise karst distribution in the study area. Resistivity imaging results revealed some low-resistivity anomaly distributions and provided reliable geological information about the distribution of subsurface karst for future grouting work.

Airborne spectral gamma-ray survey data were processed using Th-normalization technique for oil and gas exploration in the Qaret El-Soda area, Western Desert of Egypt. This technique was applied to suppress the effects of surface lithology, which are the main factors influencing the variation of radioelement content in rocks. Normalization of K and U by thorium yielded residual potassium and residual uranium estimates. Possible occurrences of new hydrocarbon microseepages were determined by mapping low values of residual potassium and high values of residual uranium relative to potassium, which are indicated as DRAD values, which were obtained by subtracting residual potassium from residual uranium values (eUresid – Kresid). Lower residual values of K, which were associated with higher DRAD anomaly values, highlight areas of prospective hydrocarbon accumulations. The obtained results from quantitative analysis and interpretation of aeromagnetic data show sufficiently thick sediments, probably suitable for the accumulation of hydrocarbons. This means that the study area may possess a potential for hydrocarbon exploration if supported by other detailed geophysical and geochemical exploration techniques.

ABSTRACT Many studies have been conducted to analyse dielectric polarisation and the complex electrical conductivity characteristics by the Cole–Cole type relaxation approximating rocks with a uniform material. That method has revealed the correlation between metal contents and chargeability, between the central relaxation time and the metallic particle size, and so on. Based on the model’s success, such models have been extended to those with multiple capacitances. However, these models cannot represent the mechanism of electrical flow in rocks using equivalent circuits and cannot explain the electrical behaviour of rocks that deviates significantly from the assumptions of the models. Therefore, in this study, by re-evaluating Pelton’s equation, we strove to formulate an appropriate multiple-capacitance model expressed by an equivalent circuit, with the aim of ascertaining electrical features more easily and properly than through the convolution of electrical response functions. We achieved theoretical expansion of Pelton-type formulae to multiple capacitances. Our Double-Pelton equivalent circuit model (DPM) was applied to the observed complex conductivity curves of artificial samples including pyrite. The obtained parameters of our DPM were found to have a good correlation to the rock features. We achieved to show that the conductive mechanism of the complex geometrical features of rock samples can be modelled simply and effectively. The continuous efficiency of sulphide particles in the direction of the electric field, and pyrite particles which can act as bottleneck conductors as in percolation theory, are found to be playing an important role in electric conduction.

Soft clayey soils are generally improved by the consolidation process. The different improvement techniques which are in vogue include the use of stone columns, sand drains with and without preload and prefabricated vertical drains. Upon improvement, the soft soil attains a higher strength and better resistance to excessive settlement. The extent of this improvement of the soft soil is measured through different in-situ tests including Standard Penetration Test (SPT), Cone Penetration Test (CPT) and Pressuremeter Test. Most of these tests are invasive and require boreholes for the collection of soil samples. Geophysical tests can scan the entire area within a short time and can accurately interpret the extent of the improvement depending on the expertise of the investigators. Sometimes a combination of two or more geophysical tests is suitable for an accurate prediction of any change in soil properties. The present study is an attempt to measure the effect of ground treatment by implementing two types of geophysical tests, namely, Electrical Resistivity Tomography (ERT) and Multichannel Analysis of Surface Wave (MASW) near a by-pass road in Silchar, Assam. The tests were conducted on a newly constructed road embankment that had undergone excessive settlement and was subsequently treated with stone columns with a preload. Both the geophysical tests showed improvement in soil properties as well as in the bearing capacity of the original soft soil. A bore log survey was also carried out before the improvement work to validate the results of the geophysical tests. Existing correlations among geophysical properties and soil properties were used to obtain the effect of improvement. It has been observed that geophysical tests can easily be carried out to accurately calculate the effect of improvement of soil properties. A combination of two or more geophysical methods yields a better understanding of the post-treatment results.

The pressure dependence of elastic parameters of rocks is mainly controlled by the geometry of the pore space. In general, the compliant-stiff pore structure model can be used to reasonably describe this pressure dependence. However, our experiment measurements revealed that for tight sandstone rock with complex pore structures, the contribution of the compressibility of the stiff pores to the elastic modulus is significant. The dual porosity is not sufficient to explain the variation of ultrasonic velocity with pressure. For this reason, we adopted a triple pore structure to divide the rock pore space into equant pores, intermediate pores and compliant pores. Our laboratory measurement and model results show that this pore space division can better describe the pressure dependence of the elastic moduli of rocks. The low-frequency stress–strain measurements show that the fluid-saturated tight sandstone has obvious dispersion in the seismic frequency band, which is primarily attributed to the squirt flow effect. In order to study the pressure and frequency dependence of the elastic moduli of tight sandstone, we retrieved the geometric parameters of the pore structure from the pressure variation of the ultrasonic velocities under dry conditions. Based on this complex pore structure and the extension of the squirt flow model, we constructed an elaborate rock physics model to explain the pressure and frequency dependence of velocity. The model does not require adjustable parameters, and all parameters are measured and calculated by the laboratory, which improves the accuracy of theoretical modelling. The modified squirt flow model can be used to describe dispersion and attenuation in a wide frequency band, and fit well with the velocity measurements in both the low-frequency range and the ultrasonic frequency range under different pressures. Therefore, this rock physics model could be applied in the extraction of pore microstructure and fluid properties provided elastic moduli or velocities can be estimated accurately.

Adequate knowledge of velocity is required for accurate data imaging and depth conversion, as well as for quantifying the distribution of soil water content. Without complementary borehole information in the form of dielectric permittivity and/or porosity logs along the profile, it is currently impossible to reliably estimate the high-frequency electromagnetic velocity distribution in the probed subsurface region. Here, we present a new method for calculating the precise subsurface velocity structure from ground penetrating radar (GPR) reflection data that does not require boreholes or log data. This study investigates the ability of the pulse_EKKO PRO GPR system to predict a vertical profile for the possible velocity estimation of a layered and contaminated geophysical test site in Hangzhou, China. All data were acquired and saved on the GPR system in various files (projects) before analysis using GPR software to obtain approximated velocity modelling using common midpoint (CMP) gathers. Using the velocity spectrum analysis, a vertical profile of the interval velocities can be derived from each CMP gather. The findings of this study indicate that the proposed method is effective and sustainable. Furthermore, owing to the efficacy of the method in terms of field effort and computational complexity, it can easily be expanded to 3D GPR velocity exploration, increasing its importance in comparison to standard offset-based techniques for estimating velocity using GPR.

Distributed acoustic sensing (DAS) is an emerging technology increasingly employed to monitor changes of formation properties, production noise and micro-seismic activity, and as an array of sensors in active seismic surveys. The data recorded with the DAS systems are very rich; some features observed in DAS records are often not well understood, and thus are underutilised. A systematic analysis of the data recorded passively with a DAS system in a 900-m deep well over a period of 12 weeks in the Perth metropolitan area, Western Australia, reveals the presence of several types of ambient energy in the subsurface, such as earthquakes, ocean swell and urban noise. In particular, over 85 days of the experiment, the analysis detected sixteen earthquakes, with epicentres ranging from 126 km to 900 km (for the local events) and from 2300 km to 6400 km (for the remote events). Signals with frequencies below 0.9 Hz are dominated by the oceanic swell. The recorded urban noise includes mine blasting, machinery and traffic. The experiment shows the ability of DAS to detect these events and as such is potentially useful for subsurface characterisation and monitoring.

Seismic data reconstruction is an inverse problem in the geophysical community. Deep learning-based methods directly learn the projection between undersampled and complete data from large training datasets. However, when the feature difference between the test and training datasets increases, the recovery performance is degraded. Fine-tuning the pretrained network on the undersampled test field data has been introduced to adapt the trained network to new data. However, the pseudo-labels of the fine-tuning process need to be obtained by some traditional method in advance. In this paper, we explore a strategy for interpolating regularly sampled aliased seismic data by incorporating deep external and internal learning, i.e. by combing external pretraining with internal unsupervised fine-tuning. The pretrained network is obtained from an external synthetic dataset. Then, the pretrained network is fine-tuned on the internal dataset to obtain the final trained network adapting the test data. The inputs and labels of the internal dataset are generated solely from the currently regularly undersampled test data. As such, the fine-tuning process is totally unsupervised. Finally, the interpolated result is acquired by feeding the test data into the trained network. The proposed algorithm learns the external priors from large external datasets and it learns the internal features from undersampled test data. Thus, the network has an enhanced capacity to adapt to new data. Poststack and prestack field data are provided to verify the performance of the proposed algorithm.

Full waveform inversion (FWI) makes full use of the seismic waveforms to find high-resolution velocity and density models by minimising residuals between the calculated and recorded data. Q attenuation widely exists in the subsurface media, leading to weak amplitude and misplacement of reflectors. However, the commonly used Q-compensated FWI (QFWI) based on the second-order wave equation has difficulties in simultaneously inverting velocity and density fields. A QFWI method based on new first-order viscoacoustic quasi-differential equations is proposed to simultaneously produce velocity and density fields. Based on the adjoint state inversion theory, Q-compensated forward-propagated operators, adjoint operators, and gradient equations are derived using the newly derived first-order viscoacoustic quasi-differential wave equations. The time-domain multi-scale decomposition method is introduced to update the velocity and density models from a low to a high wavenumber. Numerical examples on an actual work area model and a modified attenuating Marmousi model show that the proposed QFWI method produces higher-accuracy velocity and density models with iterations by correcting the Q attenuation than the conventional acoustic FWI. Even when the Q model is extremely inaccurate, the proposed QFWI obtains acceptable inversion results. Compared to the conventional QFWI, our QFWI better inverts velocity field in the case of an inaccurate density model. Finally, we verify the adaptability of our QFWI to field data.

Regular sampled seismic data is important for seismic data processing. However, seismic data is often missing due to natural or economic reasons. Especially, when encountering big obstacles, the seismic data will be missing in big gaps, which is more difficult to be reconstructed. Conditional generative adversarial networks (cGANs) are deep-learning models learning the characteristics of the seismic data to reconstruct the missing data. In this paper, we use a conditional Wasserstein generative adversarial network (cWGAN) to interpolate the missing seismic data in big gaps. We use the Wasserstein loss function to train the network and use a gradient penalty in the WGAN (cWGAN-GP) to enforce the Lipschitz constraint. We use a pre-stack seismic dataset to assess the performance. The interpolated results and the calculated recovered signal-to-noise ratios indicate that the cWGAN-GP can recover the missing seismic traces in portions or the entire regions, and the cWGAN-GP based interpolation is more accurate than the cGAN.