Imaging spontaneous imbibition in full Darcy‐scale samples at pore‐scale resolution by fast X‐ray tomography

Spontaneous imbibition is a process occurring in a porous medium which describes wetting phase replacing nonwetting phase spontaneously due to capillary forces. This process is conventionally investigated by standardized, well-established spontaneous imbibition tests. In these tests, for instance, a rock sample is surrounded by wetting fluid. The following cumulative production of nonwetting phase versus time is used as a qualitative measure for wettability. However, these test results are difficult to interpret, because many rocks do not show a homogeneous but a mixed wettability in which the wetting preference of a rock varies from location to location. Moreover, during the test the flow regime typically changes from countercurrent to cocurrent flow and no phase pressure or pressure drop can be recorded. To help interpretation, we complement Darcy-scale production curves with X-ray imaging to describe the differences in imbibition processes between water-wet and mixed-wet systems. We found that the formation of a spontaneous imbibition front occurs only for water-wet systems; mixed-wet systems show localized imbibition events only. The asymmetry of the front depends on the occurrence of preferred production sites, which influences interpretation. Fluid layers on the outside of mixed-wet samples increase connectivity of the drained phase and the effect of buoyancy on spontaneous imbibition. The wider implication of our study is the demonstration of the capability of benchtop laboratory equipment to image a full Darcy-scale experiment while at the same time obtaining pore-scale information, resolving the natural length and time scale of the underlying processes

NaCl-related weathering of stone : the importance of kinetics and salt mixtures in environmental risk assessment

Salt weathering is one of the most important causes of deterioration in the built environment. Two crucial aspects need further investigation to understand the processes and find suitable measures: the impact of different climatic environments and the properties of salt mixture crystallization. We demonstrate the importance of kinetics in quantifying crystallization and dissolution cycles by combining droplet and capillary laboratory experiments with climate data analysis. The results proved that dissolution times for pure NaCl were much slower than crystallization, while thermodynamic modelling showed a lower RHeq of NaCl (65.5%) in a salt mixture (commonly found in the built heritage) compared to its RHeq as a single salt (75.5%). Following the results, a minimum time of 0.5 hour is considered for dissolution and the two main RHeq thresholds could be applied to climate data analysis. The predicted number of dissolution/crystallization cycles was significantly dependent on the measurement frequency (or equivalent averaging period) of the climatic data. An analysis of corresponding rural and urban climate demonstrated the impact of spatial phenomena (such as the urban heat island) on the predicted frequency cycles. The findings are fundamental to improve appropriate timescale windows and to illustrate a methodology with specific points of interest to quantify salt crystallization cycles in realistic environments as a risk assessment procedure that can be applied to climate data. The results are the basis for future work to improve the accuracy of salt risk assessment by including the kinetics of salt mixtures. This will improve the understanding of past and future salt weathering mechanisms and enable scientifically informed conservation strategies

The dissolution and microbial degradation of mobile aromatic hydrocarbons from a Pintsch gas tar DNAPL source zone

Source zones containing tar, a dense non-aqueous phase liquid (DNAPL), can contaminate groundwater for centuries. A common occurrence of tar is at former Pintsch gas factories. Little is known about the composition and fate of contaminants dissolving from Pintsch gas tar DNAPL. In this study, we determined the composition and water-soluble characteristics of mobile aromatic hydrocarbons and their biodegradation metabolites in the DNAPL contaminated groundwater at a former Pintsch gas tar plant. We assessed the factors that determine the fate of observed groundwater contaminants. Measured values of density (1.03–1.06 kg/m3) and viscosity (18.6–39.4 cP) were found to be relatively low compared to common coal tars. Analysis showed that unlike common coal tars phenanthrene is the primary component rather than naphthalene. Moreover, it was found that Pintsch gas tar contains a relatively high amount of light molecular aromatic hydrocarbon compounds, such as benzene, toluene, ethylbenzene and xylenes (BTEX). Less commonly reported components, such as styrene, ethyltoluenes, di-ethylbenzene, 1,2,4,5-tetramethylbenzene, were also detected in water extracts from Pintsch gas tar. Moreover, 46 relatively hydrophilic metabolites were found within the tar samples. Metabolites present within the tar suggest biodegradation of mobile aromatic Pintsch gas tar compounds occurred near the DNAPL. Based on eleven detected suspect metabolites, a novel anaerobic biodegradation pathway is proposed for indene. Overall, our findings indicate that Pintsch gas tar has higher invasive and higher flux properties than most coal tars due to its relatively low density, low viscosity and, high content of water-soluble compounds. The partitioning of contaminants from multi-component DNAPL into the aqueous phase and re-dissolution of their slightly less hydrophobic metabolites back from the aqueous phase into the DNAPL is feasible and demonstrates the complexity of assessing degradation processes within a source zone

Quantitative characterisation of contourite deposits using medical CT

Five sediment cores, retrieved from four different depositional contouritic morphological settings (a sheeted drift, a confined mounded drift, a mounded elongated drift and a plastered drift) from the Northern Gulf of Cadiz and the Alboran Sea have been analysed using medical X-ray computed tomography (medical CT). A quantitative approach has been used, resulting in a workflow that delineates several radio-density ranges based on the Hounsfield Unit (HU) histogram of each core and tracks these ranges throughout the cores. In order to derive the geological significance, the radio-density ranges of all cores have been compared to non-destructive, continuous chemical and physical proxies as well as grain size measurements. The highest correlations occurred between high HU and proxies indicating elevated bottom currents, such as Zr/Al and sortable silt. Additionally, a continuous increase in average HU and inferred bottom current velocities, needed for the creation of the specific contourite setting, could be observed throughout the five cores. Despite imperfections and the requirement of additional research, promising results have been obtained which could improve the detection of diagnostic criteria for contourites. Moreover, the CT data can give more conclusive evidence on the nature of the (contourite) sedimentary sequence boundaries

Characterization of Open-Cell Sponges via Magnetic Resonance and X-ray Tomography

The applications of polymeric sponges are varied, ranging from cleaning and filtration to medical applications. The specific properties of polymeric foams, such as pore size and connectivity, are dependent on their constituent materials and production methods. Nuclear magnetic resonance imaging (MRI) and X-ray micro-computed tomography (mu CT) offer complementary information about the structure and properties of porous media. In this study, we employed MRI, in combination with mu CT, to characterize the structure of polymeric open-cell foam, and to determine how it changes upon compression, mu CT was used to identify the morphology of the pores within sponge plugs, extracted from polyurethane open-cell sponges. MRI T-2 relaxation maps and bulk T-2 relaxation times measurements were performed for 7 degrees dH water contained within the same polyurethane foams used for mu CT. Magnetic resonance and mu CT measurements were conducted on both uncompressed and 60% compressed sponge plugs. Compression was achieved using a graduated sample holder with plunger. A relationship between the average T-2 relaxation time and maximum opening was observed, where smaller maximum openings were found to have a shorter T-2 relaxation times. It was also found that upon compression, the average maximum opening of pores decreased. Average pore size ranges of 375-632 +/- 1 mu m, for uncompressed plugs, and 301-473 +/- 1 mu m, for compressed plugs, were observed. By determining maximum opening values and T-2 relaxation times, it was observed that the pore structure varies between sponges within the same production batch, as well as even with a single sponge

Spectral X‐ray computed micro tomography : 3‐dimensional chemical imaging

We present a new approach to 3-dimensional chemical imaging based on X-ray computed micro tomography (CT), which enables the analysis of the internal elemental chemistry. The method uses a conventional laboratory-based CT scanner equipped with a semiconductor detector (CdTe). Based on the X-ray absorption spectra, elements in a sample can be distinguished by their specific K-edge energy. The capabilities and performance of this new approach are illustrated with different experiments, i.e. single pure element particle measurements, element differentiation in mixtures, and mineral differentiation in a natural rock sample. The results show that the method can distinguish elements with K-edges in the range of 20 to 160 keV, this corresponds to an element range from Ag to U. Furthermore, the spectral information allows a distinction between materials, which show little variation in contrast in the reconstructed CT image

Support Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestone

Featured Application: AI application in UCS prediction for limestones of Maragheh. The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R2) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves

Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis

Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (β), dry density (γd), cohesion (c), and internal friction angle (φ), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470

Paleostress Analysis in the Northern Birjand, East of Iran: Insights from Inversion of Fault-Slip Data

This research assessed stress regimes and fields in eastern Iran using fault-slip data and the tectonic events associated with these changes. Our stress analysis of the brittle structures in the Shekarab Mountains revealed significant changes in stress regimes from the late Cretaceous to the Quaternary. Reconstructing stress fields using the age and sense of fault movements showed that during the late Cretaceous, the direction of the maximum horizontal stress axes (σ1) under a compressional stress regime was ~N290°. This stress regime led to the uplifting of ophiolites and peridotites in eastern Iran. During the Eocene, the σ1 direction was NE-SW. The late Eocene and Oligocene stress states showed two distinct transpression and transtension stress regimes. This transition from transpression to transtension in the eastern Shekarab Mountains was the consequence of regional variations in stress regimes. The Quaternary stress state indicates that the tectonic regime in the Quaternary is strike-slip and the σ1 direction is ~N046°, which coincides with the current convergence direction of the Arabia–Eurasia plates. Our paleostress analysis revealed that four distinct stress regimes have been recognized in the area, including compressional, transtensional, transpressional, and strike-slip regimes. Our findings indicated that the diversity of the tectonic regimes was responsible for the formation of a variety of geological structures, including folds with different axes, faults with different mechanisms, and the current configuration of the Sistan suture zone

Comparative Analysis for Slope Stability by Using Machine Learning Methods

Featured Application: The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes. Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments