Antonio M. Álvarez-Valero, Hirochika Sumino, Ray Burgess
et al.
Abstract Cosmogenic nuclei production for dating the Earth surface exposure of rock/mineral samples, especially 3He, is a robust technique in geochronology. We describe its application to constrain the ages of key eruptive episodes of the volcanic history of Deception Island (Antarctica): (i) the volcanic products of the island formed before the caldera collapse (pre-caldera material); and (ii) the caldera-forming event (syn-caldera material). High 3He/4He ratios (up to 910 RA; RA = 1.39 × 10–6) in the crystal structure of olivine phenocrysts measured through total fusion He release are much higher than the magmatic values previously obtained in the inclusions of the same olivines obtained by hydraulic crushing. Such high values indicate a cosmogenic origin and reveal an age of c. 4 Ma for the pre-caldera material, and c. 4.6 ka and 170 ka for the syn-caldera deposits. The result of c. 4.6 ka for the caldera collapse episode is consistent with previous age estimations based on tephrochronology, whereas the c. 170 ka result reveals the presence of pre-caldera olivines embedded in the syn-caldera deposits that experienced less exposure to cosmic rays compared to the samples with ages of 4 Ma. This oldest age estimate represents the first quantitative geochronological approach attempting to date Deception Island formation.
This study proposes an automatic classification approach for seismic events, designed to discriminate
between earthquakes and anthropogenic explosions by employing the Random Forest algorithm. The model
operates exclusively on features extracted from the signal recorded at a single seismic station without considering
the source location or depth. The feature vector included amplitude ratios, along with temporal, spectral, and
fractal parameters of the seismogram. A balanced dataset comprising more than 24 000 seismic records from the
Pacific Northwest Curated Seismic Dataset was utilized for training and validation. The trained classifier achieved
an accuracy of about 94 % on the test dataset. Feature importance analysis indicated that temporal, fractal, and
spectral parameters contributed most to the classification, which is consistent with the underlying differences in
the generation of natural and anthropogenic signals. The obtained results demonstrate that the proposed method
ensures reliable and robust classification performance and can be applied for automatic filtering of anthropogenic
events in seismic monitoring.
Muhammad Tayyab Naseer, Sultan Alshehery, Ilyas Khan
et al.
Abstract Incised valley sandfills are globally renowned for forming excellent stratigraphic traps. These incised valleys are developed during the extensively falling stage of the sea, followed by a negligible rise, which fills the incised valleys with coarse-grained reservoir facies. However, sea-level fluctuations cause fluctuations in the lateral distribution of the reservoir. Hence, it becomes very difficult to simulate the exact thickness, impedances, and lateral phase changes. Hence, these might act as direct hydrocarbon indicators (DHI). Therefore, this study applies the state-of-the-art spectral decomposition and static acoustic impedance reservoir simulations tool to determine the thin-bedded reservoirs within a stratigraphically complex unit for implicating the future well drilling strategies for the known gas field of Indus Onshore, Pakistan. The key emphasis was given to the selection and optimizations of the spectral waveform-based simulations. The outcomes of these simulations were to develop strategies for horizontal well drillings. The stratigraphic traps are NNW-SSE oriented with localized transpressional fault-controlled components. These fault-controlled components have played a vital role in the upward migration of hydrocarbon-bearing reservoir facies. The 21-Hz, 29-Hz, and 41-Hz tuning blocks outline the hydrocarbon-bearing sand-filled reservoir facies inside the Lowstands system tract (LST). The 57-Hz tuning block recognizes the transgressive seal facies at the top of the LST. The bandlimited static reservoir model (SRM) shows some noise events within the sedimentary reflections. The 21-Hz spectral wavelet-based developed SRM has enhanced the signal-to-noise (S/N) ratio for imaging a 34 m thick sand-filled lens. Consequently, this study serves as an analog for global shallow-marine incised valley systems.
Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora
et al.
Abstract Accurate estimation of permeability reduction in clay-rich sandstones during low-salinity water flooding is critical for optimizing enhanced oil recovery (EOR) strategies and ensuring efficient reservoir management. Traditional methods often rely on costly experiments or simplified empirical correlations, which struggle to capture the complex, non-linear interactions governing this phenomenon. This study introduces a novel data-driven approach utilizing a comprehensive suite of machine learning (ML) methods—including random forest, decision tree, adaptive boosting, ensemble learning, K-nearest neighbors, multilayer perceptron artificial neural networks, convolutional neural networks, and support vector machines—to provide robust predictions of permeability reduction. Methodology of current work, applied to 300 meticulously curated experimental observations, involved rigorous data preprocessing (outlier detection, integrity verification) and k-fold cross-validation to ensure generalizability. The results show that random forest and ensemble learning algorithms delivered the highest predictive accuracy, evidenced by the most substantial coefficient of determination (R2) and minimal error metrics. A sensitivity analysis further clarified that while increasing flooding water salinity and ionic strength leads to a reduction in permeability drop, both the flow rate and the sandstone's clay content exhibit a positive correlation with permeability impairment. This work provides a comprehensive, validated, and highly accurate ML framework specifically tailored for predicting complex permeability alterations, offering a superior alternative to conventional approaches and enhancing decision-making in EOR projects.
Basalts and basaltic rocks are the most abundant igneous rocks on the earth and their petrologic and geochemical studies have formed our knowledge base on the thermal structure and composition of the mantle with which we have developed workable models on the chemical differentiation of the earth. All this would not have been possible without innovative and painstaking experimental petrology on mantle peridotite melting, basaltic magma generation and evolution largely done in the period of 1960s -1980s. However, the ~30 year lively debate on the nature of “primary magma” among experimental petrologists and the petrology community during this time had inadvertently shelved the development of consensus models on mantle melting in the context of plate tectonics. Continued experimental petrology in parallel with worldwide sampling and study of mid-ocean ridge basalts (MORB) brought about new insights, culminating with a model in 1980s that mantle potential temperature (TMP) variation controls the extent and pressure of mantle melting and basalt compositions. The tenet of this model is that hotter rising mantle begins to melt deeper and thus has greater decompression depth interval to melt more with the melt having the petrological signature of higher extent and pressure of melting than cooler mantle. This model has gained wide acceptance in MORB studies and has also been invoked in the study of intra-plate basalts in ocean basins and in continental settings. Basalt generation above subduction zones, on the other hand, has been generally accepted as resulting from slab-dehydration induced mantle wedge melting since early 1980s, but recent studies also advocate mantle temperature variation as the primary control on the extent of mantle wedge melting. All these views with laudable merits have formed a paradigm on mantle melting and basaltic magmatism. In this paper, I review the historical developments towards this paradigm and demonstrate in simple clarity that it is the lithosphere thickness, not TMP, that controls the extent of mantle melting, depth of melt extraction and basalt compositions, i.e., the lid effect. The lithospheric lid caps the rising melting mantle, thus limiting the extent of decompression melting and equilibrium pressure/depth of melt extraction, which is well registered in the compositions of MORB, intra-plate ocean island basalts (OIB), volcanic arc basalts above subduction zones (VAB) and basalts in continental interiors (CIB). Hence, lithosphere thickness is the governing variable that controls mantle melt compositions in all tectonic settings on earth. Major element compositions (e.g., Si-Mg-Fe) of erupted basalts have no memory of initial depth of melting because of effective and efficient melt-solid (e.g., olivine [Mg,Fe]2SiO4) equilibration in the rising meting mantle. Therefore, basaltolivine based thermobarometry, albeit useful, supplies no information on TMP. It is also the lithosphere thickness that controls whether “mantle plumes” can surface or not and the large igneous provinces (LIPs) serve as effective manifestations for thin or thinned lithosphere at the time of emplacement. This new understanding based on global observations, well-understood experimental petrology and rigorous analysis is fundamental and requires a major change to the current paradigm. * Corresponding author at: Durham University, UK. E-mail address: yaoling.niu@durham.ac.uk.
Zircon geochemistry provides a sensitive monitor of its parental magma composition. However, due to the complexity of the uptake of trace elements during zircon growth, identifying source magmas remains challenging, particularly for detrital grains whose petrological context is lost. We use a machine learning-based approach to explore the classifiers for zircon provenance, based on 3794 published, high-quality zircon trace element analyses compiled from I-, S-, and A-type granites. Three supervised machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) were used and trained with 11 features, including 7 trace elements (Ce, Eu, Ho, Nb, Ta, Th, and U) and 4 derived trace element ratios (Th/U, U/Yb, Ce/Ce*, and Eu/Eu*). Our results show that all three trained machine learning methods perform very well with accuracy varying from 0.86 to 0.89, and that input–output relationships captured by different ML methods are nearly consistent and can be explained by the known petrological processes. The application of our trained machine learning classifiers to detrital zircon studies will enhance the interpretability of zircon assemblages of different origins. It also helps develop interpretations, approaches, and tools that will benefit, for example, the study of continental crust evolution and mineral exploration.
Abstract This work presents a numerical study incorporating the impact of temperature variations along the fracture on the viscosity of fracturing fluids and consequently on proppant distribution in hydraulic fracturing. Traditional models have not considered non-uniform temperature distributions, resulting in less accurate predictions of proppant migration and distribution. The proposed model integrates the thermal variations to enhance the understanding of proppant dynamics under realistic field conditions. The proposed model is validated through physical experiments, demonstrating significant differences in proppant placement due to temperature- induced viscosity changes. Our results show that proppant distribution is substantially affected by lower temperatures at the fracture opening and higher temperatures at the distal end, contrasting sharply with distribution patterns observed under uniform viscosity conditions. As the temperature at the fracture opening decreases, the viscosity of the fracturing fluid increases, enhancing its capacity to transport proppant. The increased viscosity facilitates the transport of proppant deeper into the fracture, resulting in a reduction of the total amount of proppant near the fracture opening and a smaller stacking angle compared to those observed at fixed viscosities of 10, 100, and 200 mPa sThe findings offer critical insights into the mechanics of proppant flow, holding substantial theoretical and practical implications for optimizing hydraulic fracturing treatments.
Abstract The intake plugging of an electrical submersible pump (ESP) has presented a formidable challenge to conventional ESP wells. Attention to cumulative solid deposition is essential since it intensifies the intake plugging severity and impedes ESP performance. We present a new approach to evaluate the ESP performance degradation during increased intake plugging severity. In particular, we employ the intake plugging factor, rate-derating factor, and affinity law to calculate the new ESP speed at different plugging conditions. We used Schlumberger PIPESIM software to perform nodal analysis of the newly calculated ESP speed. The result was validated using the actual field data and compared to the field cases that reported the intake plugging issue. The nodal analysis showed a steady maximum ESP head with zero rate derating at the shut-in point. The intake plugging factor caused a significant reduction in the ESP operating rate and increased pump intake pressure and annulus liquid level. Based on the existing intake plugging field data, we established the quantitative standard for the normal and abnormal intake plugging factor range. The observed results agreed with the field downhole data recorded during the intake plugging problem. We identified that regulating the ESP speed to the reduced operating rate could minimize unexpected pump stoppage. It is also possible to carefully monitor the intake plugging problem by combining the annulus liquid level, the signature of pump intake pressure, and a deadhead test.
This review places emphasis on ancient lunar nonmare igneous samples alkali-suite and Mg-suite. The material on mineralogy and petrology of the Mg-suite presented here draws from “Lunar samples” by Papike et al. and “Origin of the lunar highlands Mg-suite: An integrated petrology, geochemistry, chronology, and remote sensing perspective” by Shearer et al. Readers can refer to these comprehensive studies for more information. The in-depth studies on Chang'e-5 (CE-5) lunar basaltic samples put new constraints on the thermal state of lunar interior, although the discussions on their formation mechanism heavily rely on the lunar magma ocean (LMO) models. Yet, chemical composition and structure of the lunar mantle are modified by migration of lunar materials during or after LMO fractionation. Alkali-suite rocks and Mg-suites are two important ancient lunar sample suites that represent early lunar magmatic activities. Studies on these samples are crucial to inverse modeling the pattern and scale of material migration inside the Moon during the “post-LMO” period. However, some Mg-suite samples and granitic samples are found outside the Procellarum KREEP (K, REE [rare earth element], and P) Terrane, bringing difficulties to explain their chemical characteristics and formation mechanisms. More work is needed to constrain the primitive melt formed deeply inside the Moon based on the updated LMO models, and to quantify the scale and effect of lunar mantle overturn. In addition, analysis on more lunar samples and lunar exploration missions with emphasis on ancient samples will also help to constrain their formation mechanisms, to refine the LMO differentiation models, and to depict the constitution of the lunar interior.
Motor vehicles. Aeronautics. Astronautics, Astronomy
Luciano Galone, Federico Feliziani, Emanuele Colica
et al.
The Maltese archipelago is renowned for its spectacular coasts, characterized by vertical cliffs and scree slopes. In the western sector of Malta and the eastern region of Gozo, a marly clay formation with ductile properties underlying a stiff limestone unit has led to relevant lateral spreading. Utilizing drone aerial photogrammetry, digital elevation models, and satellite imagery, we analyzed the ongoing geomorphological processes across five promontories, selected as case studies. Our analysis reveals a complex interaction between geological structures, Quaternary sea level fluctuations, and lateral spreading processes. Photogrammetric models show that once detached, blocks from the plateaus tend to topple and fall or experience subsidence and backtilting. At Rdum il-Qammieħ, fractures up to 250 m long and openings of up to 2 m were observed, while at Sopu, detached blocks exhibit subsidence of up to 50% and rotations nearing 60°. In all the studied promontories, rotational slides predominantly occur at the frontal sectors, while toppling mechanisms are more common along scarp-edged plateaus. The thickness ratio between the stiff and the ductile formation, ranging from 0.13 to 1.12, along with slope gradients between 10° and 41°, further influence the stability of these coastal features. We discuss the structural and sea level influences on Maltese coastal cliff development over the last 125 ky. We propose a conceptual model outlining the evolution of the Malta Graben promontories through a three-stage evolutionary model: proto-promontories, cliff demolition, and isolation. This model emphasizes the significant role of predisposing, preparatory, and triggering factors in the geomorphological evolution of the Maltese coastline. Our findings provide essential insights into the landscape changes in the Maltese archipelago and represent a useful tool for coastal management and hazard mitigation strategies.
Abstract Sedimentary petrology is the basis for most mineral and textural identification in sandstones. Automating mineralogical interpretation of an entire thin section image has many practical applications, including improved geological understanding, input of spatial distribution of mineralogy and grain size for petrophysical evaluations, and integration with 3D imaging modalities (micro-CT, nano-CT). We investigate the application of Convolutional Neural Network (CNN) based supervised semantic segmentation methods for predicting pixel-scale mineralogy using 2D RGB images of sandstones acquired by transmission light microscopy. Models were trained to interpret a simple binary pore-mineral (grain) segmentation and a 10-class segmentation (porosity, quartz, feldspar, rock fragments, carbonate grains, opaque grains, quartz cement, carbonate cement, clay cement, and hydrocarbons filling pores). For the 2-class classification framework to distinguish between pores and minerals, most models lead to satisfactory results with acceptable accuracy. For the 10-class classification framework, models trained with Deeplab V3+ Resnet-18 network yield more continuous results compared to those based on VGG networks. We conclude that the effectiveness of the models, in predicting a petrology class in a thin section, strongly correlates with the amount of labeled data available to train the model to interpret the class in question. Semantic segmentation models, based on CNNs, can produce encouraging results for a 10-class petrological classification framework of an entire thin section image and thus provide a complete scene understanding which is difficult to produce manually.
Over the last few decades, significant advances in using geophysical techniques to image the structure of magma plumbing systems have enabled the identification of zones of melt accumulation, crystal mush development, and magma migration. Combining advanced geophysical observations with petrological and geochemical data has arguably revolutionised our understanding of, and afforded exciting new insights into, the development of entire magma plumbing systems. However, divisions between the scales and physical settings over which these geophysical, petrological, and geochemical methods are applied still remain. To characterise some of these differences and promote the benefits of further integration between these methodologies, we provide a review of geophysical techniques and discuss how they can be utilised to provide a structural context for and place physical limits on the chemical evolution of magma plumbing systems. For example, we examine how Interferometric Synthetic Aperture Radar (InSAR), coupled with Global Positioning System (GPS) and Global Navigation Satellite System (GNSS) data, and seismicity may be used to track magma migration in near real-time. We also discuss how seismic imaging, gravimetry and electromagnetic data can identify contemporary melt zones, magma reservoirs and/or crystal mushes. These techniques complement seismic reflection data and rock magnetic analyses that delimit the structure and emplacement of ancient magma plumbing systems. For each of these techniques, with the addition of full-waveform inversion (FWI), the use of Unmanned Aerial Vehicles (UAVs) and the integration of geophysics with numerical modelling, we discuss potential future directions. We show that approaching problems concerning magma plumbing systems from an integrated petrological, geochemical, and geophysical perspective will undoubtedly yield important scientific advances, providing exciting future opportunities for the volcanological community.
Abstract Granites (sensu lato) come in many types and flavours, defining distinct magmatic series/suites/types. A good classification not only gives generally accepted and understandable names to similar rocks but also links the bulk chemical composition to the stoichiometry of the constituent minerals and, potentially, also to the likely source, magmatic evolution and tectonic setting. The ‘ideal’ granitoid classification should be based on chemical criteria amenable to an objective treatment. Statistical analysis helps to identify the most discriminant variables. The key properties are (1) acidity/maficity, (2) alkalinity (balance of Na + K v. Ca), (3) aluminosity (balance of Al v. Ca, Na and K), (4) Fe/Mg balance and (5) Na/K balance and K contents at the given SiO2 level. These are used by successful classifications, e.g. the I/S dichotomy is based mainly on aluminosity, while the Frost et al. (2001; ‘A geochemical classification for granitic rocks', Journal of Petrology, 42, 2033–2048, https://doi.org/10.1093/petrology/42.11.2033) classification includes all but Na/K. Even though it is commonplace to use weight percentages of oxides, we suggest that a better strategy is to employ simple atomic parameters (e.g. millications-based) that can be directly linked to modal proportions and compositions/crystal structure of individual rock-forming minerals. This facilitates a petrological interpretation, which, in turn, can be related to petrogenesis and, ultimately, to likely tectonic setting(s).