Geological Applications Research Articles

Comparison of analogue and digital silicon drift detectors in PIXE analysis

The AXAS-A silicon drift detector that has been a part of the University of Guelph PIXE system for over a decade has recently been replaced by an AXAS-D unit. The calibration process previously performed on the AXAS-A, which uses a suite of high purity, “thick-target” single element and simple compound standards, was repeated for the AXAS-D. This allows for comparison between the analogue and digital approach to measuring PIXE spectra. The similarities between the results of the two detectors and the high accuracy found in geological applications affirm the validity of the use of an energy-dependent calibration factor within the GUPIX software. The upgrade to a digital detector offers multiple advantages. Unlike the AXAS-A, the AXAS-D contains a pre-amplifier and digital pulse processing circuitry and therefore requires significantly less supporting equipment. The improved count rate of the AXAS-D decreases the time required to complete projects.

Molecular simulation of the impact of surface roughness on carbon dioxide adsorption in organic-rich shales

This study investigates the adsorption behavior of carbon dioxide in organic nanopores with different surface roughness. The nanopores are constructed by sinusoidally corrugating the graphite slit pore walls. By computing the density distributions, adsorption quantities and orientation of carbon dioxide under various pressure and roughness conditions, we elucidate the impacts of surface roughness on carbon dioxide adsorption in organic nanopores. The Langmuir-Freundlich adsorption model is utilized to fit the isotherms of CO2 adsorption under three different roughness conditions. the results show that increasing surface roughness led to the increase in the adsorption of carbon dioxide, as the relative roughness increased from 0% to 12.92%, the average CO2 adsorption capacity increased by 0.003 mmol/m2. Both the adsorbed layer density and monolayer maximum adsorption capacity increased concurrently with escalating roughness. Moreover, carbon dioxide molecules preferentially aligned parallel to the rough organic surface within the adsorption layer, consistent with the smooth graphitic wall configuration. All simulations, observations, and calculations were performed through grand canonical Monte Carlo (GCMC) simulations. These findings provide insights into the influence of surface roughness on CO2 adsorption, especially in organic nanopores, which has substantial implications for carbon capture and geological sequestration applications. The results could facilitate optimization of strategies for efficient, secure geological CO2 storage.

The Extended Log-Logistic Distribution: Properties, Inference, and Applications in Medicine and Geology

The Extended Log-Logistic Distribution: Properties, Inference, and Applications in Medicine and Geology

Data Management and Application System of Geological Gisaster Monitoring Data

The geological disaster monitoring data management and application system adopts B/S technology structure, based on data pre-processing technology and comprehensive analysis technology such as statistic calculation of the monitoring data, displacement vector map and comprehensive analysis map of displacement and rainfall, etc., this paper expounds the management and application of geological disaster monitoring data, and solves the problems of the lack of data, the poor quality of data and the lack of analytical models in geological disaster monitoring work. The ability of comprehensive processing and scientific analysis of geological disaster monitoring data has been further improved.

Using Machine Learning to Predict Lithostratigraphic Facies

This paper aims to use rapidly growing machine learning applications in geology to predict vertical layers in rock based on properties. These layers in rock with similar chemical and physical properties are referred to as facies. Understanding the underlying strata and various facies informs geologists about the nature of a particular area. The order and nature of the layers in the ground can represent both how the location formed, as well as its evolution over time. This paper takes commonly analyzed wells from a block in the Dutch sector of the North Sea and shows methodology in selected particular models and parameters for prediction. Visual representation of the parameters allows for influence on the facies to be determined. My approach filters through extraneous properties and applies a Butterworth low-pass filter. Because depth is a continuous data parameter that cannot be pieced apart for training data, splitting the training data was an obstacle. However, this problem was circumvented by using a stratified k-fold split. Six different models of supervised learning were directly compared both visually and analytically. Results from these comparisons from the F02-1 well indicate that a K-Nearest-Neighbors model is most accurate and should be used by lithostratigraphic drillers. Results on the test data yielded a prediction accuracy of 99%, but prediction accuracy is yet to be extensively applied to other wells. Finally, a visual reconstruction of the facies of a nearby F02-3 well presents the results of the application and reveals the geographic history of the North Sea.

Micro FT-IR spectroscopic measurements of CH4: Experimental calibration with high-pressure and high temperature optical cell (HPOC)

Fourier transform infrared spectroscopy (FTIR) is a prevalent nondestructive in situ analytical technique widely employed for the qualitative characterization of natural fluid inclusions. Presently, the quantitative determination of the temperature, pressure, and composition of natural inclusions stands as a pivotal challenge in the geological application of laser spectroscopy. Through the integration of the capillary high-pressure optical cell (HPOC) technique and Fourier transform infrared spectroscopy, infrared quantitative models for the C–H symmetric stretching band (v3) in the vapor phase of CH4 under varying temperature (40–200 °C) and pressure (20–500 bar) conditions are established for the first time. This accomplishment is achieved through fitting and calculation of infrared spectra. A linear quantitative relationship and the dynamic evolution of infrared spectral parameters (peak area, full width at half maximum, peak height ratio, and peak shifts) concerning temperature, pressure, and density are meticulously investigated. The findings reveal that the peak area and full width at half maximum exhibit an upward trend with increasing pressure and density, and are inversely proportional to temperature. The height ratio (P band/Q band, R band/Q band) experiences an increase as density escalates. Moreover, the CH4v3 band position shows a higher wavenumber shift with increasing temperature, while its position correlates negatively with pressure and density. Thus, this method demonstrates its applicability in deducing the PVT-x properties of CH4-bearing systems, including CH4-rich fluid inclusions, CH4 content within hydrocarbon inclusions, and experimental fluid investigations. In contrast to the Raman quantitative approach, the infrared quantitative formula can be universally adopted in any infrared laboratory owing to the quantitative theory (Beer–Lambert law) governing infrared absorption spectra. By collecting Raman spectra and infrared spectra of pure methane fluid inclusions from the central region of the Alps and subsequently calculating the results using two distinct quantitative methods for fluid inclusions, we have demonstrated the feasibility of applying infrared spectroscopy in natural fluid inclusion studies.

Adsorption-induced swelling impact on CO[formula omitted] transport in kerogen microporosity described by free volume theory

In order to examine the impact of the flexibility of kerogens’ microstructure on the transport of adsorbed CO2, we develop a free volume model that notably accounts for adsorption-induced swelling exhibited by flexible kerogen structures. We first show that, for CO2 compared to CH4 at constant fluid molar loading and in the same temperature and confining pressure conditions, an atomistic model of microporous type I kerogen in the middle of the oil formation window exhibits slightly less swelling upon adsorption. This swelling comes with an increase in the free volume accessible to the CO2 molecules, which can then diffuse faster when the fluid loading increases, without introducing significant collective effects on the transport properties. Consequently, transport can be considered diffusive, and the self-diffusion coefficient can be used as a proxy for the collective diffusion (or transport) coefficient. Finally, as previously demonstrated for CH4, a free volume model based on the Fujita–Kishimoto theory describes the transport enhancement with CO2 fluid loading. Although CO2 interacts more strongly than CH4, the parameters of the free volume models for both fluids are shown to be similar. Together with the diffusive nature of transport, it suggests an even broader applicability of the model to the transport of light alkanes and mixtures, thus greatly simplifying transport modeling for geological applications such as carbon sequestration in shales.

Rectangular harmonic analysis for gravity field representation by fusing ground gravity and airborne gravity gradient tensor data: A case study in Vinton salt dome, USA

Gravity and Gravity Gradient Tensor (GGT) surveys are commonly used to investigate mineral deposits, oil and gas reservoirs, etc. However, the different kinds of discrete observations with unknown noises are usually irregularly and unevenly spaced. Therefore, the practical observations need to be self-consistently and reliably fused and then the regularly distributed grid data of desired physical quantity can be predicted by the data fusion method. Rectangular Harmonic Analysis (RHA) is one of efficient and easy-to-implement methods for data processing, gravity field representation and so on. Combining ground gravity and airborne GGT data to determine RHA coefficients is beneficial for improving the reliability of complete wavelength components of the gravity field. In this study, we employ the modified RHA method to recover the gravity field by fusing ground gravity and airborne GGT data sets. Through a series of tests with synthetic data, we discuss the option of maximum RHA order, data sampling spaces and performances by using different simulated data sets assemble to represent the gravity field under different practical conditions. The synthetic tests show that the RHA can effectively extract the coherent signals as far as possible from the different data sets and guarantees better fitness between the predicted gravity and GGT data to observed data. Finally, we apply the RHA method to practical ground gravity and airborne GGT data sets over the Vinton salt dome, Louisiana, USA, to represent and predict the gravity and GGT data sets at different altitudes over the study region. As an example of geological application, the predicted GGT data sets are employed to interpret the lithological boundaries with density contrasts beneath the study area.

Extraction, Mapping, and Analysis of Structural Lineaments using Geospatial Azimuth-Altitude Ratio Technique on a DEM of a Sub-Catchment of the Upper Benue River Basin, Nigeria

Lineaments have a wide range of implications and applications in geology, hydrogeology and geomorphology. The focus of this study was on structural or geological lineaments and their implication on hydrogeology and hydrogeomorphology. Hence, this study aimed at the extraction, mapping, and analysis of structural lineaments using a DEM in the Adamawa sub-catchment of the Upper Benue River basin. ArcGIS 10.8 was used for the geospatial technique of Azimuth-Altitude ratio (315:45, 200:50, 150:65, and 100:60) variation, manual digitization for the extraction, mapping and analysis of lineaments. The choice of manual or visual digitization was to apply expert experience to extract only structural and exclude geomorphological and pseudo lineaments. The Azimuth-Altitude ratio variation approach was to improve clarity and identification to aid the manual extraction. The lineament map generated after extraction was used to analyze for lineament density, total number, and total length. A further analysis applied the rose diagram to depict the directional orientation or trends of these lineaments. The result revealed that at various Azimuth and Altitude ratios, the features on the image become more distinguishable for extraction. At 315:45 Azimuth and Altitude ratio, the hill-shade produced revealed the major lineaments more clearly compared to other ratios but minor lineaments were undermined. Also, a total number of 343 lineaments were found in the Adamawa sub-catchment of the Upper Benue River Basin with a total length of 2897.81km. The lineament density was found to range between 1km/km2 and 5km/km2. The result of the lineament statistical analysis showed that the extracted lineaments have trends in the direction of N-S, NNE-SSW, NE-SW, E-W, SE-NW, and SSE-NNW. This therefore concluded that the Adamawa sub-catchment of the Upper Benue River Basin has high lineament density and lineament extraction from DEM at various Azimuth and Altitude ratios enhances lineament extraction. The implications of the findings from the results suggest that areas with a high density of lineaments might be affected by higher structural deformation, which signifies higher groundwater infiltration potential zones and initiation of first-order drainage networks. This information is vital for water resources management in the sub-catchment.

GeoFault: A well-founded fault ontology for interoperability in geological modeling

Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general level. Therefore, domain ontologies that describe geological knowledge must be based on a sound ontological background to ensure this knowledge is integrable. Faults are essential for understanding and solving structural problems but are complex to model because the concept of fault includes a group of geological entities with a distinct ontological nature. A fault can correspond to thin, deformed rock volumes or spatial arrangements resulting from the displacement of geological blocks, but at a broader scale, geologists describe faults as surfaces or components of complex fault arrays. Our work intends to harmonize these views by presenting a domain ontology, GeoFault, resting on the Basic Formal Ontology (BFO) and the GeoCore ontology. GeoCore and GeoFault support the parametric description of geological sites as a preliminary step for quantitative and qualitative analysis. We have proposed GeoFault after systematically revising the literature and several knowledge-acquisition sessions with expert structural geologists. The ontology formalizes a vocabulary for fault “sensu stricto,” excluding ductile shear deformations. It covers the regional to outcrop scales, excluding structures at the microscopic, orogenic, and tectonics scales, and it avoids interpretive language associated with geological processes as far as possible. Extending the BFO and GeoCore ontologies allows the fault concept to be related to formal ontological classes in a consistent semantic-rich framework. The ontology artifact is implemented in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The GeoFault ontology is publicly available and provides a solid framework for clarifying fault knowledge and a foundation for many applications.

Rate-dependence of the compressive and tensile strength of granites

Abstract. The strength and rupture of geomaterials are integral to subsurface engineering practices, such as those required to optimise geothermal energy extraction. Of particular importance is the time- and strain-rate-dependence of material strength, which dictates the energy released upon failure, and impacts the magnitude of induced seismicity, fracture architecture and thus hydraulic conductivity and system permeability. Here, we performed a series of uniaxial compression and Brazilian tensile strength measurements at a range of deformation rates in order to constrain the impact of strain rate on the strength of G603 granite. The dense, low permeability, medium-grained granites were mechanically tested at 4 strain rates (or diametric equivalent strain rates in the case of Brazilian tests) from 10−5 to 10−2 s−1, such that sample failure was achieved in anything from below 1s at the fastest rate in tension, to over 1000s at the slowest rate in compression. The applied rates encompassed those recommended by ISRM and ASTM material testing standards for compressive and Brazilian tensile testing. We found a significant rate strengthening effect, whereby compressive and tensile strength both increased by approximately 35 % across the 4 orders of magnitude of strain rate tested. We found that the static Young's modulus remained relatively constant across this range of deformation rates, however variability was reduced at faster rates, owing to the reduced time for equilibration of the system to imposed stresses. The lower strength at slower strain rates causes smaller stress drops, indicating that rocks driven to compressive and tensile failure at slower rates release less energy upon failure. Such constraints of the strain-rate-dependence of material strength, in contrast to the use of standardised material characteristics conventionally used in Engineering Geology applications, will prove useful as we develop increasingly sophisticated strategies such as cyclic soft stimulation to access resources using less energy, whilst reducing environmental risk and producing less waste.

Natural ilmenite reference material for trace elemental in situ microanalysis

Ilmenite is a commonly occurring mineral in both igneous and metamorphic rocks and its major and trace elements have various applications in geology, including as a geothermometer, oxygen barometer, and geochemical tracer. In this study, a 500 g ilmenite megacryst sample (LY) was developed as a reference material for in situ element microanalysis. Twenty-two ilmenite fragments, with grain sizes ranging from 1 to 8 mm, were subjected to major and trace element analysis using electron microprobe analysis (EMPA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) in four different laboratories. The results indicate that the ilmenite sample is homogeneous with respect to TiO2, FeO, Mg, Sc, V, Cr and Mn, whereas Co and Nb are minor inhomogeneous. Consequently, this sample can be used as a reliable reference material for in situ microanalysis.

An assisted multi-frame approach for super-resolution in hyperspectral images of rock samples

Imaging spectroscopy is decisive for accurately characterizing rock samples for geological and mineral applications, since slight differences in mineral composition can be easily recognized in spectral signatures. Although, instrument limitations prevent high spatial resolution pixels scanned along hundreds of spectral channels, which considerably reduces the data capabilities. Multi frame Super-Resolution (SR) techniques can overcome this issue by retrieving high-quality images using purely computational methods. However, these approaches still have important drawbacks because of inherent uncertainties when estimating the spatial variation between frames, which strongly penalize the spectral information, essential for lithology. In this paper we propose an assisted framework to provide fully controlled motion parameters of the imaging process, skipping the intricate registration task. Essentially, we use a stepping device to successively change the position of rock samples for each new frame acquisition. With accurate knowledge about motion parameters, we were able to overcome the uncertainties regarding spatial registration between frames. Therefore, a reasonable number of equations connecting the low-resolution frames and the super resolved image could be used to solve the optimization problem. Extensive experiments proved that the proposed assisted method achieved best performances (both qualitatively and quantitatively) compared with similar approaches. Moreover, detailed spectral analysis showed almost absolute consistency between the original low-resolution image and the super-resolved result, which is crucial to guarantee reliable mineral characterization.

Improvement of in situ LA-ICP-MS U-Pb dating method for carbonate minerals and its application in petroleum geology

Improvement of in situ LA-ICP-MS U-Pb dating method for carbonate minerals and its application in petroleum geology

Mineral prospectivity mapping integrated with geological map knowledge graph and geochemical data: A case study of gold deposits at Raofeng area, Shaanxi Province

The knowledge graph in geological applications is highly regarded, and its usefulness requires the support of geological survey data. Therefore, this paper proposes a mineral prospectivity mapping (MPM) method that integrates geological map knowledge graphs with geochemical data. The geological map knowledge graph is used in the vectorized form as knowledge features in the training of the mineral prediction model. It is also used for knowledge-based ore-forming interpretation of prospective areas, achieving bidirectional application of knowledge in prediction and interpretation of prediction results. Unlike most knowledge graphs, the geological map knowledge graph represents spatial correlations and geological features in the form of “entity-relation-entity” triplets, with geological map units as entities and assigned geological attributes. Based on this, an MPM process driven by knowledge graph and data is proposed: (1) Convert the geological map knowledge graph into low-dimensional vectors using node2VEC. (2) Align geological knowledge with geochemical data spatially through knowledge embedding gridization, achieving a forward fusion of knowledge and data. (3) Collect the integrated data using window sampling as the training dataset for DenseNet to train the mineral prospection model. A case study was conducted to demonstrate the advantages of the method for producing a potential map linked to gold mineralization in the Rao Feng area, Shaanxi Province, China. The results show that the prospective areas with knowledge graph embedding and geochemical data are better and delineate six unmineralized prospective areas. Ore-forming interpretations based on knowledge graph information were conducted on the prospective areas, demonstrating strong consistency between the ore-forming geological features of the prediction areas and known mineral deposits.

Anomalous patterns of clay minerals in fault zones

The anomalous enrichment of clay minerals in fault zones is considered a key factor affecting the seismic behavior of faults, fault permeability, and mineralization. This process also weakens the rock strength and triggers deformation, instability, and geological disasters. Nevertheless, the geological factors affecting the growth and anomalous enrichment patterns of clay minerals in fault zones are not systematically understood. In this study, the anomalous enrichment patterns of clay minerals within 220 natural faults are investigated (detailed information can be found in Appendix A). The main geological factors and processes affecting the anomalous enrichment of clay minerals in fault zones are revealed. The main formation modes and occurrence forms of the clay minerals in fault zones are investigated. The anomalous patterns of clay minerals in fault zones are classified into four categories and eight types, i.e., the felsic anomaly category (illite type and kaolinite type), mafic anomaly category (chlorite type), ultramafic anomaly category (smectite type, serpentine + talc type, vermiculite type, and palygorskite + sepiolite type), and argillaceous anomaly category (mixed-layer type). The 220 case studies include different types of faults and fault rocks. Thus, the results are promising for a wide range of engineering geological applications, e.g., analysis of fault activity and permeability changes and studying the genetic mechanisms of geological disasters near faults.

Review on detection method, main source and geological application of diamondoids in crude oil

Diamondoids are alkanes with cage-like structure. Their diamond-like structure makes them have high stability and anti-biodegradability, and can be preserved and enriched in complex and long geological processes. Therefore, the continuous development of quantitative detection methods for diamondoids in crude oil has deepened the research of these compounds and made them more widely used in crude oil cracking evaluation, maturity evaluation, biodegradation evaluation and other aspects.

Solubility models of CH4, CO2, and noble gases and their geological applications

Solubility models for CH4, CO2, and noble gases are widely used in Earth Sciences, playing pivotal roles in the study of homogenization pressure of inclusions, paleoclimate variation, gas migration and accumulation, formation of helium-rich gas plays, and the volumetric ratio of gas to water in reservoirs. This paper reviews solubility models of CH4, CO2, and noble gases in pure water and aqueous NaCl solutions. Specifically, the models with high accuracy and wide applicability are introduced in detail: (1) CH4 solubility model in aqueous solutions within the range of 0–250 °C, 0.1–200 MPa, and 0–6.0 mol/kg NaCl; (2) CO2 solubility model in aqueous solutions within the range of 0–450 °C, 0.1–150 MPa, and 0–4.5 mol/kg NaCl; (3) Models for calculating the solubility and Henry's constant of atmospheric noble gases within 0–80 °C range; (4) Models for calculating the Henry's constant of noble gases in pure water; (5) Solubility models of noble gases in aqueous solutions within the range of 0–65 °C, 0.1 MPa, and 0–5.8 mol/kg NaCl. The paper also presents some calculated results obtained using these models. The solubility models of CH4 and CO2 are complex yet highly accurate, with a broad range of applications. In contrast, the solubility models of noble gases exhibit relatively lower accuracy and a narrower application range, necessitating corrections. In the noble gases-CO2-H2O system, low-density CO2 has little effect on the solubility of noble gases, whereas high-density CO2 significantly influences their solubilities. Currently, accurately evaluating the solubility of CH4, CO2, and noble gases in their mixtures proves challenging, warranting further research into solubility models for gas mixtures.

SRTM DEM Correction Using Ensemble Machine Learning Algorithm

The Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) is a widely utilized product for geological, climatic, oceanic, and ecological applications. However, the accuracy of the SRTM DEM is constrained by topography and vegetation. Using machine learning models to correct SRTM DEM with high-accuracy reference elevation observations has been proven to be useful. However, most of the reference observation-aided approaches rely on either parametric or non-parametric regression (e.g., a single machine learning model), which may lead to overfitting or underfitting and limit improvements in the accuracy of SRTM DEM products. In this study, we presented an algorithm for correcting SRTM DEM using a stacking ensemble machine learning algorithm. The proposed algorithm is capable of learning how to optimally combine the predictions from multiple well-performing machine learning models, resulting in superior performance compared to any individual model within the ensemble. The proposed approach was tested under varying relief and vegetation conditions in Hunan Province, China. The results indicate that the accuracy of the SRTM DEM productions improved by approximately 46% using the presented algorithm with respect to the original SRTM DEM. In comparison to two conventional algorithms, namely linear regression and artificial neural network models, the presented algorithm demonstrated a reduction in root-mean-square errors of SRTM DEM by 28% and 12%, respectively. The approach provides a more robust tool for correcting SRTM DEM or other similar DEM products over a wide area.

Soret-induced convection of ternary fluid in a horizontal porous layer heated from below

The present paper deals with the investigation of the Soret-induced convection of a three-component mixture of hydrocarbons in a horizontal porous layer. This problem is important for geological applications. The first part of the paper is devoted to the linear stability of the conductive state. The longwave instability is studied analytically by the expansion into the power series with respect to the wave number. A new long-wave oscillatory instability mode existing at negative separation ratios is found out. It is shown that this mode is more dangerous than the long-wave monotonic instability mode in the entire range of its existence. The instability to the perturbations with nonzero wave numbers is studied numerically by the shooting method. Stability map is obtained. The results confirm the predictions of the longwave analysis. In the second part of the paper, the nonlinear convection regimes are studied by the finite difference method. The calculations give the results consistent with the linear stability analysis. It is found that the primary bifurcation as a result of which the conductive state losses its stability is supercritical. The transformations of the convective flow structure with the change in the Rayleigh–Darcy number are accompanied by the hysteresis phenomena.