Geological Applications Research Articles

The effect of long-range interactions on the infrared and Raman spectra of aragonite (CaCO3, Pmcn) up to 25 GPa

Long-range interactions are relevant in the physical description of materials, even for those where other stronger bonds give the leading contributions. In this work, we demonstrate this assertion by simulating the infrared and Raman spectra of aragonite, an important calcium carbonate polymorph (space group Pmcn) in geological, biological and materials science fields. To this aim, we used Density Functional Theory methods and two corrections to include long-range interactions (DFT-D2 and DFT-D3). The results were correlated to IR spectroscopy and confocal Raman spectrometry data, finding a very good agreement between theory and experiments. Furthermore, the evolution of the IR/Raman modes up to 25 GPa was described in terms of mode-Grüneisen’s parameters, which are useful for geological and materials science applications of aragonite. Our findings clearly show that weak interactions are of utmost importance when modelling minerals and materials, even when they are not the predominant forces.

Geochemical survey data cube: A useful tool for lithological classification and geochemical anomaly identification

Geochemical survey data play a critical role in geological studies, mineral exploration, and environmental applications by providing information on geological events and processes such as mineralization and pollution. A typical geochemical survey dataset contains the analysis of multiple elements. For example, the National Geochemical Mapping Project of China comprises 39 major and trace element concentrations. Multiple geochemical maps can be generated by interpolating geochemical samples into raster maps to constitute a geochemical survey data cube in which elements are sorted by their atomic numbers. A geochemical spectrum can be created using these geochemical maps in which each pixel that records geochemical characteristics. In this study, a convolutional neural network (CNN) that considers the geochemical spectrum and spatial pattern of geological objects was employed to mine a geochemical survey data cube, aiming of geological mapping and geochemical anomalies identification associated with mineralization in the eastern part of Hubei Province of China. The results showed that (1) a geochemical survey data cube which built based on various geochemical exploration data provided vital information on mineralization process and the formation of geological features; and (2) a CNN had a strong ability to recognize high-level features in the geochemical survey data cube, and it showed excellent performance in mineral exploration and related geological studies.

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can remotely measure the reflectance properties of the environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene. An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm. A pipeline for unsupervised mapping of spectra on a mine face is proposed which draws from several recent advances in the hyperspectral machine learning literature. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data. The pipeline is evaluated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale. The combined system is shown to produce a superior map to its constituent algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day.

Some applications of Scanning Electron Microscopy to the study of reservoir rock

Scanning electron microscopy (SEM) is a powerful tool for visualising reservoir pore and grain framework systems which (SEM) provides qualitative information about pore geometry through direct observation of a rock or a pore cast of the rock. This aids in understanding reservoir productivity capabilities. The SEM is useful for locating and identifying minerals, particularly clay minerals - an aid when designing drilling and completion programs. SEM unlike conventional light microscopy, produces images by recording various signals resulting from interactions of an electron beam with the sample as it is scanned in a raster pattern across the sample surface. Combined with a backscatter detector (BSE), energy dispersal X-ray spectroscopy (EDS), and EDS - Mapping, SEM can yield multiple types of information about geological samples at the same time, such as superficial microstructure, BSE image, component analysis, and crystal structure features. In addition, new technic of SEM like Qemscan and FIB (Focused ion beam) can automatically minerals mapping and approach to 3-D imaging. In this paper, we use granite rock from a granitic basement reservoir, Bach Ho Oilfield as examples to discuss the geological application of SEM. The most important reservoir properties of granite rock in the oilfield are porosity and fractured pores which are abundant. The fractures have various sizes from mm to micron in width. Some large cracks have 1÷3 cm in lenght and 100÷600 µm in width. The small ones have 0.001÷0.2 mm in length and 10÷100 µm in width. Some large fractures were porefilled by crystals that have blade shape.

The Widespread Use of Remote Sensing in Asbestos, Vegetation, Oil and Gas, and Geology Applications

Remote sensing is the technique of acquiring data from the earth’s surface from sensors installed on satellites or on manned or unmanned aircrafts. Its use is common in dozens of sectors of science and technology, agriculture, atmosphere, soil, water, land surface, oceans and coasts, snow and ice, and natural disasters, among others. This article focuses on an in-depth literature review of some of the most common and promising disciplines, which are asbestos–cement roof identification, vegetation identification, the oil and gas industry, and geology, with the aim of having clarity on the trends in research on these issues at the international level. The most relevant problems in each sector have been highlighted, evidencing the need for future research in the area in light of technological advances in multi- and hyperspectral sensors and the availability of satellite images with more precise spatial resolution. A bibliometric analysis is proposed for each discipline and the network of related keywords is discussed. Finally, the results suggest that policymakers, urban planners, mine, and oil and gas companies should consider remote sensing as primary tool when planning comprehensive development strategies and in field parameter multitemporal analysis.

Surface heat flow measurements in the eastern margin of the Japan Sea using a 15 m long geothermal probe to overcome large bottom-water temperature fluctuations

Accurate surface heat flow data are required for a wide range of geological and geophysical applications. However, sediment temperature measurements beneath the seafloor often involve large uncertainties owing to the influence of bottom-water temperature (BWT) fluctuations. Previous studies reported apparently negative geothermal gradients in the Joetsu Basin of the Japan Sea and suggested that BWT fluctuations disturbed sediment temperatures. To address this problem, we monitored BWTs in the Joetsu Basin over a 2 year period to determine the depth at which the influence of BWT fluctuations on sediment temperature becomes negligible. Combined with sediment thermal diffusivity data, we determined that the BWT fluctuations can disturb sediment temperatures to a depth of 2 m. We obtained heat flow values of 81–88 mW m− 2 by measuring sediment temperatures at depths > 2 m using a 15 m long geothermal probe. The measured heat flow values are inversely correlated with topography owing to the effect of topographic change on the geothermal structure near the seafloor. A two-dimensional geothermal structure model was constructed to account for the topography, yielding an estimated regional background heat flow of 85 ± 6 mW m− 2. This study provides two important guidelines for obtaining accurate surface heat flow data in marine areas with large-amplitude BWT fluctuations: (1) quantitative information regarding BWT fluctuations and sediment thermal diffusivity is required to evaluate the depth range to which BWT fluctuations affect sediment temperature; and (2) information regarding the lithology and consolidation state of seafloor sediments is required for effective penetration using a long probe.

Development towards 53Mn Accelerator Mass Spectrometry capabilities at the University of Notre Dame

Accelerator Mass Spectrometry (AMS) with 53Mn has geological applications as a chronometer for exposure and burial times for discontinuously deposited sediments. It has also been used to search for evidence of recent supernovae events, and proposed as a proxy to monitor the variation in the galactic cosmic ray spectrum over time. The current sensitivity limit amongst active facilities is 53Mn/ 55Mn = 3×10−13 while a sensitivity of 53Mn/ 55Mn = 1×10−13 is necessary to fully exploit 53Mn’s capabilities. At the University of Notre Dame’s Nuclear Science Laboratory (NSL), a 10 MV tandem accelerator and a Browne–Buechner Spectrograph operated as a gas-filled magnet were used to separate 53Mn from 53Cr. Samples covering ranges of 53Mn/ 55Mn = 10−10−10−8 were measured for the first time at the NSL using various settings resulting in a background level of 53Mn/ 55Mn =6.2(3)×10−11. Analysis of the results, descriptions of experimental settings, and further explorations will be presented in this paper.

"Application of geology and exploration technology to make the oil and gas upstream business more resilient and decarbonized"

"Application of geology and exploration technology to make the oil and gas upstream business more resilient and decarbonized"

"Application of geology and exploration technology to make the oil and gas upstream business more resilient and decarbonized"

"Application of geology and exploration technology to make the oil and gas upstream business more resilient and decarbonized"

分光学と地質学の融合：光を通じて視る地球内部環境

Spectroscopy has been widely used in geology since the 1990s because it is non-destructive and easy to analyze. Raman spectroscopy has generally been used to identify mineral phases in geology, but recent studies have proposed new methods to quantitatively estimate the metamorphic pressure (quartz Raman barometry) and peak temperature (Raman carbonaceous material geothermometry). Studies using infrared spectroscopy are also underway to advance our understanding of hydration processes in subduction zones and mantle through the analysis of water in rocks. In addition to the development of new methods using spectroscopy, new tools are also being developed to analyze huge amounts of data through iterative processing, which will enable the extraction of more informative and quantitative results in a shorter time. This paper introduces examples of spectroscopy applications in geology and examines future developments.

Raman Spectroscopic Technique to Distinguish Constituents of Hydrocarbon-Bearing Fluid Inclusions of Kerala-Konkan Basin, Western offshore, India

Fluid inclusion studies have a great diversity of applications in exploration geology and are necessary tools in the determination of palaeotemperature and nature of fluids associated with the rocks in a basin. Using various fluid inclusion techniques such as petrography, microthermometry and Laser Raman Spectroscopy of fluid inclusions with Hydrocarbon fluid inclusions (HCFIs) help us to understand the generation potential of the basin. The representative micron sized fluid inclusions that intruded into the different geological formations of the KK-4C-A1well drilled by Oil and Natural Gas Corporation in Kerala-Konkan Basin, India has been selected for this study. Petrographic analyses confirm the presence of HCFIs with the help of Ultraviolet (UV) light. Raman spectra of HCFIs identified in different formations were examined. The temperature of homogenization (Th) obtained through microthermometric analysis of the fluid inclusions indicate the palaeotemperature of the sedimentary rock units. Coeval-aqueous inclusions associated to HCFIs show Th within the oil window range 60-140oC, indicating a temperature favourable for oil generation in Kerala-Konkan Basin (K-K Basin).Characterisation of hydrocarbon bearing fluid inclusions were carried out using Raman spectroscopy. HCFIs were observed in the annealed micro-cracks of Cannanore (Early Miocene), Calicut (Early Oligocene) formation (Type I) and Kasaragod (Palaeocene to Early Eocene) Formation (Type II), might get trapped along the micro-fracture by re-healing process. Laser Raman study could decipher hydrocarbon species such as Alkanes, SO2, COS, H2S, etc. Keywords: Hydrocarbon Fluid Inclusions, Oil Window Temperature, Raman Spectroscopy, Kerala-Konkan Basin

Land Use Classification of High-Resolution Multispectral Satellite Images With Fine-Grained Multiscale Networks and Superpixel Postprocessing

Land use recognition from multispectral satellite images is fundamentally critical for geological applications, but the results are not satisfied. The scale dimension of current multiscale learning is too coarse to account for rich scales in multispectral images, and pixel-wise classification tends to produce “salt-and-pepper” labels due to possible misclassification in heterogeneous regions. In this paper, these issues are addressed by proposing a new pixel-wise classification model with finer scales for convolutional neural networks. The model is designed to extract multiscale contextual information using multiscale networks at a fine-grained level, addressing the issue of insufficient multiscale learning for classification. Furthermore, a small-scale segmentation-combination method is introduced as a post-processing solution to smooth fragmented classification results. The proposed method is tested on GF-1, GF-2, DEIMOS-2, GeoEye-1, and Sentinel-2 satellite images, and compared with six neural-network-based algorithms. The results demonstrate the effectiveness of the proposed model in finding objects of large scale difference, improving classification accuracy, and reducing classified fragments. The discussion also illustrates that convolutional neural networks and pixel-wise inference are more practical than Transformer and patch-wise recognition.

A study on the cross product of fuzzy numbers

In fuzzy arithmetic, the multiplication operation based on Zadeh’s extension principle owns several unnatural properties both from theoretical and practical points of view. To overcome some of these shortcomings, a new operation called cross product has been introduced recently. We show the main properties of the cross product. We also present a comparative study of the traditional multiplication and the cross product in geological applications, especially for estimating of resources of solid mineral deposits.

A study on the cross product of fuzzy numbers

In fuzzy arithmetic, the multiplication operation based on Zadeh’s extension principle owns several unnatural properties both from theoretical and practical points of view. To overcome some of these shortcomings, a new operation called cross product has been introduced recently. We show the main properties of the cross product. We also present a comparative study of the traditional multiplication and the cross product in geological applications, especially for estimating of resources of solid mineral deposits.

Geological Applications and Limitations of Regional Tephra Layers in Terrestrial Deposits in Korea

Geological Applications and Limitations of Regional Tephra Layers in Terrestrial Deposits in Korea

A deep residual convolutional neural network for mineral classification

In recent years, the deep learning computing paradigm has revolutionized the way of remote sensing data analysis. The emerging hyperspectral remote sensing has paved the way for the efficient and accurate exploration of the minute features of the earth’s surface due to the increased number of contiguous spectral bands. Hence, hyperspectral images can efficiently impart useful information about mineral resources for precise discrimination and identification in lithological studies. Rapid advancements in computing capabilities and deep learning techniques give the research community a new impulse to develop advanced, robust, and efficient hyperspectral remote sensing-based mineral classification frameworks. The present study aims to introduce two novel deep learning-based mineral classification frameworks: mineral-CNN-LSTM and mineral-ResNet. The architecture of mineral-CNN-LSTM is based on 1D-CNN and LSTM model, whereas the architecture of mineral-ResNet is based on 1D-CNN, LSTM model, and residual connections. The frameworks use raw data as input without feature selection or data augmentation preprocessing steps. The widely used early stop method is also utilized to prevent overfitting of the framework during the training process. The experimental evaluation carried out over the AVIRIS hyperspectral image scene of the Cuprite mining area confirms that the mineral-ResNet can effectively identify most of the minerals such as Alunite, Calcite, Halloysite, Kaolinite, Montmorillonite, Muscovite, Chalcedony with the overall accuracy of 92.16%, and kappa value of 0.89 and mineral-CNN-LSTM achieved the overall accuracy of 91.71% and kappa value of 0.88 for these minerals. Furthermore, a comparative evaluation of the proposed frameworks has been performed with widely used Convolutional Neural Network (CNN) based architectures such as VGG19, VGG16, ResNet-50, and AlexNet; and various machine learning based classifiers. The proposed architectures offer better performance with shorter testing and training time than these existing CNN-based architectures. The proposed framework could be useful for other earth observation-related applications in various fields such as agriculture, forestry, geology, hydrology, ecology, urban planning, military and defense applications, etc.

Estimation of density log and sonic log using artificial intelligence: an example from the Perth Basin, Australia

It is well understood that with a large number of data, an excellent interpretation of the subsurface condition can be produced, and also our understandings of the subsurface conditions can be improved significantly. However, having abundant subsurface geological and petrophysical data sometimes may not be possible, mainly due to budget issues. This situation can generate issues during hydrocarbon exploration and/or development activities.
 In this paper, the authors tried to apply artificial intelligence (AI) techniques to estimate outcomes values of particular wireline log data, using available petrophysic data. Two types of AI were selected and these are artificial neural network (ANN), and multiple linear regression (MLR). This research aims to advance our understanding of AI and its application in geology. There are three objectives of this study: (1) to estimate sonic log (DT) and density log (RhoB) using different types of AI (ANN and MLR); (2) to assess the best AI technique that can be used to estimate certain wireline log data; and (3) to compare the estimated wireline log values with the real, recorded values from the subsurface.
 Findings from this study show that ANN consistently provided a better accuracy percentage compared to MLR when estimating density log (RhoB). While using different set of data and technique, estimation of sonic log (DT) produced different accuracy level. Moreover, crossplot validation of the results show that the results from ANN analysis produced higher trendline reliability (R2) and correlation coefficient (R) than the results from MLR analysis. Comparison of the estimated RhoB and DT log data with the original recorded data shows minor mismatch. This is evident that AI technique can be a reliable solution to estimate particular outcomes of wireline log data, due to limited availability of the original recorded subsurface petrophysic data. It is expected that these findings would provide new insights into the application of AI in geology, and encourage the readers to explore and expand the many possibilities of the application of AI in geology.

Comparative analysis of the efficiency of geological exploration for mining company using the example of the deposits of JSC Polymetal

Relevance. Timely and sustainable reproduction of the mineral resource base is a prerequisite for the harmonious growth of mining companies. The increasingly difficult conditions for conducting geological exploration work increase the costs of subsoil users for the study of subsoil. Evaluation of economic efficiency allows us to determine how optimally resources were spent to achieve a geological result. The prupose of the research is the analysis of indicators characterizing the efficiency of geological exploration, their systematization and application in practical activities. Materials and methods. In the process of research, the methods of system analysis, scientific generalization, the method of comparisons, the method of expert assessments were used. Results. The article analyzes the dynamics of financing of geological exploration by Russian subsoil users at their own expense from 2016 to 2021, presents the author’s forecast for 2022. Using the example of OAO Polymetal, the areas of appropriations for subsoil exploration in a number of regions are considered. The indicators characterizing the efficiency of exploration work are presented. The shortcomings of the use of indicators based on the method of discounting cash flows are revealed. In particular, debatable issues related to the use of coefficients that reflect the geological complexity and degree of exploration of deposits, the correctness of the method in long-term operation of deposits, the validity of the accepted discount rate for evaluating geological exploration and mining projects are considered. The practical application of the coefficients for evaluating the efficiency of geological exploration work is demonstrated using the example of two operating fields of OAO Polymetal. The proposed indicators are easy to calculate and allow for a quick assessment. A list of factors influencing the efficiency of exploration work is given. Conclusions. With the growth of exploration costs, the importance of assessing their justification and efficiency increases. For a qualitative assessment, a system of complementary indicators is needed that fully characterizes the efficiency of exploration work. The simplicity of the indicators ensures the correctness of calculations, the high speed of evaluation and the swiftness of making managerial decisions.

High-precision analysis of carbon isotopic composition for individual CO2 inclusions via Raman spectroscopy to reveal the multiple-stages evolution of CO2- bearing fluids and melts

The carbon isotopic composition of CO2 inclusions trapped in minerals reflects the origin and evolution of CO2-bearing fluids and melts, and records the multiple-stages carbon geodynamic cycle, as CO2 took part in various geological processes widely. However, the practical method for determination isotope composition of individual CO2 inclusion is still lacking. Developing a microanalytical technique with spatial resolution in micrometers to precisely determinate the δ13C value of individual CO2 inclusion, will make it possible to analyze a tiny portion of a zoning mineral crystal, distinguish the differences in micro-scale, and possible to find many useful information that could not be obtained with the bulk extraction and analysis techniques. In this study, we systematically collected Raman spectra of CO2 standards with different δ13C values (−34.9 ‰ to 3.58 ‰) at 32.0 °C and from ∼7.0 MPa to 120.0 MPa, and developed a new procedure to precisely determinate the δ13C value of individual CO2 inclusion. We investigated the relationship among the Raman peak intensity ratio, δ13C value, and CO2 density, and established a calibration model with high accuracy (0.5 ‰−1.5 ‰), sufficient for geological application to distinguish different source of CO2 with varying δ13CO2. As a demonstration, we measured the δ13C values and the density of CO2 inclusions in the growth zones of alkali basalt-hosted corundum megacrysts from Changle, Shandong Province. We found the significant differences of density and δ13C between the CO2 inclusions in the core of corundum and those inclusions in the outer growth zones, the δ13C value decreases from core to rim with decreasing density: δ13C values are from −7.5 ‰ to −9.2 ‰ for the inclusions in the core, indicating the corundum core was crystallized from mantle-derived magmas; from −13.5 ‰ to −18.5 ‰ for CO2 inclusions in zone 1 and from −16.5 ‰ to –22.0 ‰ for inclusions in zone 2, indicating the outer zones of corundum grew in a low δ13C value environment, resulted from an infilling of low δ13C value fluid and/or degassing of the ascending basaltic magma.

Estimating rock mechanical properties from microrebound measurements

Understanding rock mechanical properties is essential to many engineering geology applications. Comprehensive rock mechanics testing, however, is often not practical due to limited available sample volume, time constraints, and cost. Therefore, an alternative means for estimating mechanical properties at relatively low cost is desirable. In this investigation, we evaluate microrebound measurement on cut rock surfaces as a substitute for rock mechanical testing. We first explore the influences of rock sample thickness and testing substrate (material upon which samples rest) on microrebound, then perform systematic microrebound analysis of a range of well-characterized rock types with associated rock-mechanics datasets. Our analyses show that microrebound measurements are not sensitive to sample thickness greater than approximately 5 cm. Microrebound appears to be insensitive to the substrate among tested substrates including 2-mm-thick foam rubber, three different thicknesses of polystyrene foam (1.1 cm, 2.2 cm, and 3.6 cm), and standard lab tabletop. Strongest relationships between microrebound and rock properties are for unconfined compressive strength, tensile strength, and cohesion. Moderately strong correlations were found for density, Young's modulus, and porosity. A weak correlation was found for Poisson's ratio, and a very weak correlation was found for friction angle. Microrebound data can be obtained quickly and in a non-destructive manner using cut samples collected from outcrop or core, and at relatively low cost compared to traditional rock mechanics tests. Microrebound measurement can substitute for rock mechanics measurements for a multitude of applications in engineering geology where rock property data are needed, but full rock mechanics testing is not practical.