Multiple Lithologies Research Articles

Logging Lithology Discrimination with Enhanced Sampling Methods for Imbalance Sample Conditions

In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments.

Lithological mapping of geological remote sensing via adversarial semi-supervised segmentation network

Geological remote sensing interpretation (GRSI), which aims to recognize multiple geological elements based on their characteristics on satellite remote sensing images, is vital in large-scale regional lithological mapping. However, due to the influence of long-term geological movements, the spatial distribution of geological elements (such as lithology, glaciers, and soils) on the image is often complex and highly fragmented. In addition, the characteristics of high inter-class similarity and severe homogenization make the annotation of geological element samples require significant cost and expert knowledge. These lead to insufficient interpretation accuracy and limited annotation samples in GRSI. To alleviate the dependence of labeled samples and promote the performance of GRSI, we propose an adversarial semi-supervised segmentation network with object-context and global-attention (AdvSemi-OCGNet) for the GRSI, which achieves effective segmentation results in the case of limited labeled samples. Under the architecture of adversarial learning, a proposed baseline network OCGNet and a full convolution discriminator (FCD) are integrated to conduct semi-supervised segmentation. OCGNet, as the generator, aims to confuse FCD by predicting probability maps. Then FCD selects trustworthy regions with high-confidence of unlabeled sample to generate pseudo-labels for semi-supervised segmentation of OCGNet. Iterative adversarial learning is employed to simulate the process of expert interpretation of geological elements through continuous discrimination and correction. Finally, a fully connected conditional random field eliminates holes and isolated areas of segmented results caused by misclassification. Two study areas are selected, which include various geological elements such as multiple lithologies, soils, rock glaciers, and surface water. Numerous experiments have revealed the superiority of the proposed model in GRSI with limited annotation samples.

Periods and Processes of Oil and Gas Accumulation in the HZ-A Structure Double Paleogene Field, Pearl River Mouth Basin

The source of oil and gas and the stages of oil and gas accumulation in the “double-Paleo” field of the HZ-A structure in the Pearl River Mouth Basin are analyzed, and the spatiotemporal coupling relationship of the key conditions of oil and gas accumulation are discussed to reconstruct the process of oil and gas accumulation. Based on previous research results, which are based the characteristics of biomarker compounds, the oil and gas in the HZ-A structure double Paleogene field came from the Paleogene Wenchang Formation hydrocarbon source rocks in the HZ26 sub-sag. By means of the casting thin section identification and inclusion homogenization temperature measurement, this paper reveals the three major hydrocarbon accumulation periods and corresponding fluid charging types in the “double-Paleo” field of the HZ-A structure in the Pearl River Mouth Basin. The results show that 13.8–10 Ma is the charging period of low mature crude oil, 10–5.3 Ma is the charging period of mature crude oil, and from 5.3 Ma is the natural gas charging period. Based on actual geological, drilling, logging, and seismic data, the key conditions for hydrocarbon accumulation in the HZ-A structure “double-Paleo” field are sorted out; that is, the source conditions are characterized by high-quality lacustrine source rocks generating early oil and late gas and a near-source continuous hydrocarbon supply. The reservoir conditions are characterized by weathering and superposition of a fracture zone that transforms into a reservoir, and a large-scale sandstone rock mass that transforms into a reservoir. The caprock conditions are characterized by the stacking of several thin mudstones that form a seal and the combination of multiple lithologies that block hydrocarbon migration. The trap conditions are characterized by multistage uplift structure traps and fracture-lithology combination control traps. The transport conditions are characterized by multi-stage cross-bed transport of source-connected faults and lateral differential transport of shallow sand in deep fractures. Finally, oil and gas accumulation models of the HZ-A structure double Paleogene field were established, and the accumulation process was reconstructed. The overall process involved three stages, with the first stage being the localized oil-displacing-water mode, the second being the large-scale oil-displacing-water mode, and the third being the late progressive gas-displacing-oil mode.

Are Large Sulfur Isotope Variations Biosignatures in an Ancient, Impact-Induced Hydrothermal Mars Analog?

Discrepancies have emerged concerning the application of sulfur stable isotope ratios as a biosignature in impact crater paleolakes. The first in situ δ34S data from Mars at Gale crater display a ∼75‰ range that has been attributed to an abiotic mechanism. Yet biogeochemical studies of ancient environments on Earth generally interpret δ34S fractionations >21‰ as indicative of a biological origin, and studies of δ34S at analog impact crater lakes on Earth have followed the same approach. We performed analyses (including δ34S, total organic carbon wt%, and scanning electron microscope imaging) on multiple lithologies from the Nördlinger Ries impact crater, focusing on hydrothermally altered impact breccias and associated sedimentary lake-fill sequences to determine whether the δ34S properties define a biosignature. The differences in δ34S between the host lithologies may have resulted from thermochemical sulfate reduction, microbial sulfate reduction, hydrothermal equilibrium fractionation, or any combination thereof. Despite abundant samples and instrumental precision currently exclusive to Earth-bound analyses, assertions of biogenicity from δ34S variations >21‰ at the Miocene Ries impact crater are tenuous. This discourages the use of δ34S as a biosignature in similar environments without independent checks that include the full geologic, biogeochemical, and textural context, as well as a comprehensive acknowledgment of alternative hypotheses.

MeltPT: A Python package for basaltic whole-rock thermobarometric analysis with application to Hawaiʻi

Quantifying the depths and temperatures from which igneous rocks are derived is an important step in understanding volcanic, magmatic and mantle processes. We present meltPT, a Python package that allows users to apply twelve published whole-rock thermobarometers within a consistent framework, as well as combine thermobarometric results and geothermal models to estimate mantle potential temperatures. We apply meltPT to basaltic rocks from mid-ocean ridges and the Hawaiian Islands. We find mid-ocean ridge basalts equilibrate between 1–2 GPa and 1275–1475 ℃, corresponding to an ambient mantle potential temperature of ~1400 ℃. We estimate that the Hawaiian plume has an excess temperature of ~150 ℃. Hawaiian melt-equilibration depths increase from 1–3 GPa to 2.5–5 GPa through each island's life cycle. Our results indicate that multiple lithologies are present within the plume, and that transient plume reconfiguration in response to changing plate velocity is a viable mechanism for generating Hawaiʻi's two geochemically distinct plume tracks.

New Detections of Feldspar‐Bearing Volcanic Rocks in the Walls of Valles Marineris, Mars

AbstractPlagioclase‐bearing rocks were first detected in the vicinity of large impact basins on Mars using visible/near‐infrared (VNIR) data. The geologic context is consistent with excavated plutons or ancient crustal outcrops. Our analyses reveal plagioclase outcrops exposed in a 200 m thick, sub‐horizontal layer in the 8 km deep walls of the Valles Marineris canyon. These plagioclase‐bearing rocks are consistent with either a sill, a volcano‐clastic layer, or a porphyritic lava flow, in contrast with the previous understanding that plagioclase feldspar signatures must be indicative of nearly pure, anorthositic rocks inherited from a primary flotation crust or granitoids from either plutonic activity or ancient continental crust. We present here evidence of possibly effusive, volcanic rocks bearing plagioclase VNIR spectral signatures, expanding the geologic setting of these unique and uncommon martian rocks to include multiple lithologies. This has direct implications for Mars magmatic processes and for the nature of its crust.

Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

In this study, we present a global database of ten parameters, which include measurements of rock index properties, strength stiffness and dynamic properties. Hoek–Brown constant mi, is included, and was estimated, using Hoek and Brown proposed guidelines for determining mi values for different rock types that can be used for preliminary design when triaxial tests are not available. This broad database is compiled from 96 studies and is labelled as “ROCK/10/4025”, to describe the type of geomaterial, the number of the parameters, and the number of the data samples included. It consists of 35.4 % igneous, 54.8 % sedimentary, and 9.2% metamorphic rocks. The purpose of this paper is to propose a generic soft computing model applicable to multiple lithologies, that can become more reliable and perhaps more suitable for a specific site study when used in order to densify often limited similar site-specific data. To this end four broad samples of data were selected, and served as training data sets for developed machine learning models, to develop a generic compression strength prediction model applicable to multiple lithologies. The suggested algorithms in this study are Back-Propagation Artificial Neural Networks, Artificial Neuro-Fuzzy Inference Systems, Support Vector Machines, Nearest Neighbour classifiers and Ensemble Bagged Trees. According to the findings of this study, Artificial Neuro-Fuzzy Inference Systems model performance was found to be marginally superior, while Back Propagation Artificial Neural Networks, Support Vector Machines and Ensemble Bagged Trees models were found to have good performance. Constant mi seems to be an important training parameter when training predictive models centred on data from multiple lithologies. As a result, we can suggest that these models are powerful tools that allow for a reliable estimation of compressive strength, based on the performance indicators. The performance was found to be 70%–82% when the problem of compressive strength prediction was approached as a classification problem (that is successful prediction of class from very weak to very strong), and 80%–96% when solved as a function approximation problem.

Assessment of petrologic subtypes, subgroups, and pairing within CV chondrites in the US Antarctic meteorite collection

AbstractCarbonaceous chondrites of the Vigarano group (CV) are primitive (nearly un‐metamorphosed) meteorites that provide a wealth of information about the early solar system, including constraints on chondrule formation, origin of calcium‐aluminum inclusions, stability of organic compounds, and redox conditions. The US Antarctic meteorite collection contains 119 CV samples from 15 dense collection areas (DCAs) from the TransAntarctic Mountains; these samples have been assigned a preliminary classification as CVs, but not to the subgroups oxidized A, oxidized B, and reduced. Furthermore, variation in petrologic grade can be determined non‐destructively using Raman spectroscopy. To update the classification of both subgroups and petrologic types in the collection, we have acquired magnetic susceptibility, metal and sulfide compositions, and Raman spectra. Overall, there are 55 oxidized A samples, 18 oxidized B samples, and 46 reduced samples. Several of the CVs are quite primitive (Lewis Cliffs Ice Tongue and MacAlpine Hills) but are also very small. Multiple pairing groups have been identified in the Miller Range (MIL), Queen Alexandra Range, and Larkman Nunatak DCAs, including all of the subgroups. In MIL 090981, there is evidence for multiple lithologies. We make suggested updates for all the samples, knowing that this information will help to better guide researchers interested in studying the CV chondrites in the US Antarctic meteorite collection.

Lithologic controls on microfracturing from legacy underground nuclear explosions

Detection and verification of underground nuclear explosions (UNEs) can be improved with a better understanding of the nature and extent of explosion-induced damage in rock and the effect of this damage on radionuclide migration. Much of the previous work in this area has focused on centimeter- to meter-scale manifestations of damage, but to predict the effect of damage on permeability for radionuclide migration, observations at smaller scales are needed to determine deformation mechanisms. Based on studies of tectonic deformation in tuff, we expected that the heterogeneous tuff layers would manifest explosion-induced damage differently, with welded tuffs showing more fractures and nonwelded tuffs showing more deformation bands. In comparing post-UNE samples with lithologically matched pre-UNE equivalents, we observed damage in multiple lithologies of tuff through quantitative microfracture densities. We find that the texture (e.g., from deposition, welding, alteration, etc.) affects fracture densities, with stronger units fracturing more than weaker units. While we see no evidence of expected deformation bands in the nonwelded tuffs, we do observe, as expected, much larger microfracture densities at close range (<50 m) to the explosive source. We also observe a subtle increase in microfracture densities in post-UNE samples, relative to pre-UNE equivalents, in all lithologies and depths. The fractures that are interpreted to be UNE-induced are primarily transgranular and grain-boundary microfractures, with intragranular microfracture densities being largely similar to those of pre-UNE samples. This work has implications for models of explosion-induced damage and how that damage may affect flow pathways in the subsurface.

Sedimentation in the adjacencies of a southwestern Atlantic giant carbonate ridge

Although carbonate mounds have been investigated for 100 years, few studies focus on the giant variety. The Alpha Crucis Carbonate Ridge (ACCR), a ~17 x 12-km ring-shaped ridge formed by hundreds of mounded structures, located between the 300 and 800-m isobaths and reaching a maximum height of 340 meters above the adjacent seafloor, is the first giant carbonate mounded feature described for the SW Atlantic margin. This study provides the first multiproxy approach to investigate sediments covering the ACCR and its adjacencies. Most of the area is located under the Intermediate Western Boundary Current (IWBC) flow, which carries the nutrient-rich Antarctic Intermediate Water (AAIW). Radiocarbon aging shows pronounced differences for the shallow layers (MIS3 for the top of the mounds and late Holocene for the adjacencies). Grain size data indicate the prevalence of sandy fractions on top of the mounds and muddy sediments in the adjacent areas. Fe/Ca and Ti/Ca proxies allowed for identifying mainly biogenic sedimentation in the area. However, the input of allochthonous terrigenous sediment is necessary for mound buildup, and values of Fe and Ti collected on the top of the mounds are significant. End-Members distributions and metal concentrations also allowed for recognition of distinct sources of sediment. εNd and Ln(Fe/K) indicated two primary terrigenous sources, the Precambrian rocks of the Brazilian shield (Cabo Frio end-member) and the multiple lithologies drained by the Rio de la Plata basin. Redox condition proxies indicated that the area is submitted to oxic conditions, probably reflecting the action of the IWBC. This work provides the first insight into an integrated grain-size and geochemical characterization of the Alpha Crucis Carbonate Ridge (southwestern Atlantic margin).

Quantifying Uncertainty in Downscaling of Seismic Data to High-Resolution 3-D Lithological Models

Building high-resolution lithological models using seismic data can facilitate decision-makings for earth resources development, but they are subject to considerable uncertainties due to the limited seismic resolution. Traditionally, high-resolution lithological models are built deterministically or stochastic simulation is performed using seismic as “soft data” in geostatistical contexts. Many approaches have been developed to do the above, but in this work, we explicitly account for the uncertain relationship between seismic data and lithological models. We introduce a data-driven Bayesian approach to first quantify soft data uncertainty by generating multiple lithology proportions from seismic. The spatial lithology proportion uncertainty arises from the uncertain relationship between low-resolution seismic and lithology proportions. We quantify this proportion uncertainty by learning the statistical relationships with seismic using high-resolution borehole data. Once conditioning the statistical relationships to observed seismic, we can generate multiple realizations of spatial proportions. With the generated proportions as volume averages, a sequential indicator simulation (SISim) is then performed to build high-resolution lithofacies models. This approach is applied to a real channelized turbidite system with four lithofacies. We demonstrate that the generated high-resolution lithological models can preserve the global uncertainties captured by proportion trends while locally matching borehole observations. When compared with the conventional approaches that use deterministic soft data, our statistical learning approach avoids the problem of underestimating reservoir model uncertainty. More importantly, the lithofacies models from the proposed method are less likely to be falsified by observed data. Additionally, an open-source Python library for the uncertainty quantification is provided.

Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs

Derivative and volatility attributes can be usefully calculated from recorded gamma ray (GR) data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such attributes extract information about the log curve shape that cannot be readily discerned from the recorded well log data. A logged wellbore section for which 8911 data records are available for the three recorded logs (GR, sonic (DT) and bulk density (PB)) is evaluated. That section demonstrates the value of the GR attributes for machine learning (ML) lithofacies predictions. Five feature selection configurations are considered. The 9-var configuration including GR, DT, PB and six GR attributes, and the 7-var configuration of GR and the six GR attributes, provide the most accurate and reproducible lithofacies predictions. The other three feature configurations evaluated do not include the GR attributes but just one to three of the recorded log features. The results of seven ML models and two regression models reveal that K-nearest neighbor (KNN), random forest (RF) and extreme gradient boosting (XGB) are the best performing models. They generate between 14 and 23 misclassification from 8911 data records for the 9-var model. Multi-layer perceptron (MLP) and support vector classification (SVC) do not perform well with the 7-var model which lacks the PB feature displaying the highest correlation with facies class. Annotated confusion matrices reveal that KNN, RF and XGB models can effectively distinguish all facies classes for the 9-var and 7-var configurations (that includes the GR attributes), whereas none of the models can achieve that outcome for the 3-var configuration (that excludes the GR attributes). Accurately distinguishing lithofacies using well-log data in sedimentary sections is an important objective in applied geoscience. The straightforward, GR-attribute method proposed works to improve confidence in ML-lithofacies classifications based on limited recorded well-log data.

Cosmogenic nuclide exposure age scatter records glacial history and processes in McMurdo Sound, Antarctica

Abstract. The preservation of cosmogenic nuclides that accumulated during periods of prior exposure but were not subsequently removed by erosion or radioactive decay complicates interpretation of exposure, erosion, and burial ages used for a variety of geomorphological applications. In glacial settings, cold-based, non-erosive glacier ice may fail to remove inventories of inherited nuclides in glacially transported material. As a result, individual exposure ages can vary widely across a single landform (e.g., moraine) and exceed the expected or true depositional age. The surface processes that contribute to inheritance remain poorly understood, thus limiting interpretations of cosmogenic nuclide datasets in glacial environments. Here, we present a compilation of new and previously published exposure ages of multiple lithologies in local Last Glacial Maximum (LGM) and older Pleistocene glacial sediments in the McMurdo Sound region of Antarctica. Unlike most Antarctic exposure chronologies, we are able to compare exposure ages of local LGM sediments directly against an independent radiocarbon chronology of fossil algae from the same sedimentary unit that brackets the age of the local LGM between 12.3 and 19.6 ka. Cosmogenic exposure ages vary by lithology, suggesting that bedrock source and surface processes prior to, during, and after glacial entrainment explain scatter. 10Be exposure ages of quartz in granite, sourced from the base of the stratigraphic section in the Transantarctic Mountains, are scattered but young, suggesting that clasts entrained by sub-glacial plucking can generate reasonable apparent exposure ages. 3He exposure ages of pyroxene in Ferrar Dolerite, which crops out above outlet glaciers in the Transantarctic Mountains, are older, which suggests that clasts initially exposed on cliff faces and glacially entrained by rock fall carry inherited nuclides. 3He exposure ages of olivine in basalt from local volcanic bedrock in the McMurdo Sound region contain many excessively old ages but also have a bimodal distribution with peak probabilities that slightly pre-date and post-date the local LGM; this suggests that glacial clasts from local bedrock record local landscape exposure. With the magnitude and geological processes contributing to age scatter in mind, we examine exposure ages of older glacial sediments deposited by the most extensive ice sheet to inundate McMurdo Sound during the Pleistocene. These results underscore how surface processes operating in the Transantarctic Mountains are expressed in the cosmogenic nuclide inventories held in Antarctic glacial sediments.

Artificial Intelligence Models for Real-Time Bulk Density Prediction of Vertical Complex Lithology Using the Drilling Parameters

Practically, the rock bulk density is measured either through logging while drilling tools or wireline logging techniques. However, these measurements are not always available, which necessitates using different empirical correlations. But these correlations have considerable limitations, which restricted their reliability and accuracy. This work aims to develop several artificial intelligence models for real-time bulk density prediction of complex lithology while drilling. The support vector machine (SVM), functional networks (FN), and random forest (RF) techniques were applied using the drilling parameters as inputs. A vertical well of 2912 data points of complex lithology containing sand, shale, and carbonate was used for model development. The developed models were validated using different dataset from another well. The results demonstrated that the three developed models predicted the bulk density with high matching accuracy. The SVM approach gives correlation coefficient (R) values of 0.999 and 0.984 and average absolute percentage error (AAPE) values of 0.113 and 0.512% in training and testing processes. The FN technique has R values of 0.980 and 0.979 and AAPE of 1.115 and 1.163%, while the RF-based model results in R values of 0.996 and 0.992 and AAPE values of 0.453 and 0.635% for training and testing, respectively. The validation process indicated the reliability and robustness of the constructed models with R values of 0.992, 0.977, and 0.990 and AAPE of 0.366, 1.224, and 0.733% for SVM, FN, and RF approaches, respectively. Each developed model can predict inexpensively the bulk density for multiple lithology types in real-time at high matching accuracy.

Discussion on the applicability of 90° phase conversion technique in seismic sedimentology

Since 1998, domestic and foreign scholars have conducted more systematic research on the theory and application technology of seismic sedimentology, and 90° phase conversion has been proposed as one of the core technologies of seismic sedimentology. But in actual research and application, the technology has not achieved the expected results. The 90° phase conversion intends to give the meaning of the seismic reflection event representing the stratum interface to the lithologic strata by means of phase shift. Due to the complexity and change of the underground strata, there are usually multiple lithology frequent thin interbeds. Therefore, the event axis in the seismic profile is the combined effect of multiple sets of thin interbeds. The seismic data after 90° phase conversion cannot convert the original seismic data from interface seismic profile to lithologic stratigraphic profile, and lacks clear geological meaning. Its application ability in seismic structure interpretation and seismic lithology interpretation is very limited. Therefore, it is difficult to become an applied technology of seismic sedimentology. Therefore, it is not appropriate to use the 90° phase conversion technology as the core technology of seismic sedimentology. This technology neither improves the seismic data resolution capability nor accurately converts the seismic profile into a lithological profile.

Real-time static Poisson’s ratio prediction of vertical complex lithology from drilling parameters using artificial intelligence models

The experimental Poisson’s ratio prediction is time-consuming and expensive and resulted in discontinuous profile. Besides, the limited applicability of the existing empirical correlations highlights the application of artificial intelligence with its booming utilization in petroleum industry. The purpose of this work is to develop several artificial intelligence models for predicting real-time static Poisson’s ratio of complex lithology while drilling. The artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) techniques were utilized using the drilling parameters as inputs. Data points (1775) from a vertical well, containing sand, shale, and carbonate lithologies, were used to develop the models. The models were validated using different dataset from another well. New empirical correlation was extracted based on the optimized ANN approach. The three developed models predicted the static Poisson’s ratio at good matching accuracy. The correlation coefficient (R) and average absolute percentage error (AAPE) of the developed models range from 0.95 to 0.96 and 2.18 to 5.79% for training process, respectively, while in testing process, the R and AAPE values range from 0.92 to 0.93 and 5.81 to 6.74%. The validation process confirmed the reliability of the developed models with R values of 0.90, 0.91, and 0.90 and AAPE of 6.57, 7.25, and 8.12% for SVM, ANFIS, and ANN approaches, respectively. The developed ANN-based model was switched into a white box model with new empirical correlation, which is applicable with the extracted weights and biases. The constructed models can predict inexpensively the static Poisson’s ratio for multiple lithologies in real-time at reasonable accuracy.

The growth of faults and fracture networks in a mechanically evolving, mechanically stratified rock mass: a case study from Spireslack Surface Coal Mine, Scotland

Abstract. Fault architecture and fracture network evolution (and resulting bulk hydraulic properties) are highly dependent on the mechanical properties of the rocks at the time the structures developed. This paper investigates the role of mechanical layering and pre-existing structures on the evolution of strike–slip faults and fracture networks. Detailed mapping of exceptionally well exposed fluvial–deltaic lithologies at Spireslack Surface Coal Mine, Scotland, reveals two phases of faulting with an initial sinistral and later dextral sense of shear with ongoing pre-faulting, syn-faulting, and post-faulting joint sets. We find fault zone internal structure depends on whether the fault is self-juxtaposing or cuts multiple lithologies, the presence of shale layers that promote bed-rotation and fault-core lens formation, and the orientation of joints and coal cleats at the time of faulting. During ongoing deformation, cementation of fractures is concentrated where the fracture network is most connected. This leads to the counter-intuitive result that the highest-fracture-density part of the network often has the lowest open fracture connectivity. To evaluate the final bulk hydraulic properties of a deformed rock mass, it is crucial to appreciate the relative timing of deformation events, concurrent or subsequent cementation, and the interlinked effects on overall network connectivity.

Biconical reflectance, micro‐Raman, and nano‐FTIR spectroscopy of the Didim (H3‐5) meteorite: Chemical content and molecular variations

AbstractThe Didim meteorite contains multiple lithologies and clasts of different petrologic types in a single stone. A mixture of H5 clasts in an unequilibrated H3 host was previously observed in Didim, according to the initial characterization reported in the Meteoritical Bulletin Database, providing an opportunity to investigate molecular composition that contains varying degree of equilibrium with varying mineralogy. We have taken a “from large scale to small scale” approach to spectroscopically investigate the chemical content of Didim. Centimeter‐scale biconical reflectance spectra show that Didim contains abundant olivine, pyroxene, and other optically active minerals, evident from a strong Band I near 0.93 µm and a weak Band II near 1.75 µm. Micrometer‐scale Raman spectroscopic investigations reveal the presence of carbonaceous material in addition to forsteritic olivine, pyroxene (augite and enstatite), feldspars, and opaque phases such as chromite and hematite. 3‐D Raman tomographic imaging shows that the carbonaceous material near chondrules extends underneath a large olivine grain, going further down toward the interior, indicating that the observed carbonaceous matter is likely indigenous. Nano‐scale infrared measurements reveal that the observed chemical materials in Didim contain spectral, and therefore, molecular, variations at the ~20 nm spatial scale. These chemical variations are normally not accessible via conventional infrared techniques, and indicate the presence of different cations in the molecular composition of observed minerals. By taking the “large scale to small scale” approach, we show that these compositional variations can be captured and investigated nondestructively in meteorites to understand formation/evolution of chemical components in the parent body.

Comparative Study on Dynamic Mechanical Properties and Energy Dissipation of Rocks under Impact Loads

Geological conditions are often complex during rock engineering construction (such as blasting and drilling) and usually involve rocks of several or multiple lithologies. However, comparative studies on the dynamic mechanical response of rocks of different lithologies are relatively few. To study differences in dynamic mechanical properties and energy dissipation of rocks of different lithologies, three kinds of rock that are common in rock engineering and have large differences in wave impedance were selected: red sandstone, gray sandstone, and granite. Samples of these three rocks were subjected to impact compression tests at different impact speeds, and the rock fragments after impact compression were screened to quantify the degree of fragmentation. The characteristics of stress wave propagation, dynamic stress-strain relationships, degree of fragmentation, and energy dissipation laws of the three rock types were compared and studied. The results show the following: (1) affected by the wave impedance matching relationship, the reflected waves, strain rates, and reflected energy of the three kinds of rock showed significant differences under the same incident stress wave. (2) The dynamic mechanical characteristics and energy dissipation laws of the rocks all had obvious strain rate effects, but the dynamic uniaxial compressive strength and energy dissipation density of the three rock types had different sensitivities to the strain rate. (3) The change trend of the energy utilization efficiency of the gray sandstone with incident energy was different from that of the red sandstone and granite; there was no obvious extreme point in the incident energy range. The three kinds of rock required different incident energies to reach maximum energy utilization efficiency. (4) The equivalent average fragment size of the three rock types decreased as a function of power with increasing impact velocity and energy dissipation density. (5) Under the same incident wave, although the reflection energy, transmission energy, and degree of fragmentation of the three kinds of rock showed significant differences, the differences in dissipation energy were small.

Weak impact of landscape parameters and rock lithology on Mg isotope composition of the Yenisey River and its tributaries

Constraining the mechanisms controlling the riverine flux of major cations and their isotopes including that of Mg to the World Ocean is one of the challenges in Earth surface isotope geochemistry. In an attempt to identify the main factors affecting the Mg isotopic composition of large rivers including vegetation, climate and lithology of the watershed, we studied the largest, in terms of discharge, Siberian river, Yenisey, and 20 of its main tributaries, during spring flood, summer flow and winter. The working hypothesis was that the influence of bedrock composition is most pronounced in winter, when the soils are frozen and the rivers are fed by deep underground waters. Thus, we anticipated that the presence of permafrost will help to distinguish the impact of surface processes, linked to biological uptake and release, and deep soil/underground transport of Mg from mineral sources. In contrast to these expectations, no sizable differences in the Mg isotope composition of the river water (±0.1‰) for both the Yenisey tributaries and its main channel has been observed between the spring flood (May) and the winter (March) period. Those two periods are characterized by the differences of discharge and degree of lithological impact on element source in the river water. Regardless of the season, there was no straightforward control of lithology (relative abundance of carbonates, basalts, granites and sedimentary rocks) on δ26Mg in the main tributaries of the Yenisey river. Our findings suggest that the use of riverine Mg isotope signature for tracing weathering mechanisms and dominant lithological impact is not straightforward at the scale of large rivers whose watersheds present multiple lithologies, variable climatic conditions and vegetation types.