Due to the combined influences such as ore-forming temperature, fluid and metal sources, sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc (Pb-Zn) deposits. Therefore, trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types. However, previous discriminant diagrams usually contain two or three dimensions, which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits. In this study, we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can discriminate Pb-Zn deposit types using machine learning algorithms. A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications, containing 12 elements (Mn, Fe, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Pb) from 5 types, including Sedimentary Exhalative (SEDEX), Mississippi Valley Type (MVT), Volcanic Massive Sulfide (VMS), skarn, and epithermal deposits. Random Forests (RF) is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits, most of which are falsely distinguished as skarn and epithermal types. To further discriminate VMS deposits, future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when constructing the classification model. RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification. Besides, a visualized tool, t-distributed stochastic neighbor embedding (t-SNE), was used to verify the results of both classification and evaluation. The results presented here show that Mn, Co, and Ge display significant impacts on classification of Pb-Zn deposits and In, Ga, Sn, Cd, and Fe also have relatively important effects compared to the rest elements, confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in sphalerite. Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses, inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data. 

Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm

Abstract Textures are important features of sandstones; however, their controlling factors are not fully understood. We present a detailed textural analysis of fluvio-lacustrine sandstones and discuss the influences of provenance and depositional environments on sandstone textures. The upper Permian – lowermost Triassic Wutonggou sandstones in the Bogda Mountains, NW China, are the focus of this study. Sandstone thin-sections were studied by point counting and their textures were analysed using statistical and principal component analysis. Fluvial lithic, fluvial feldspathic, deltaic lithic, deltaic feldspathic, littoral lithic and littoral feldspathic sandstone were classified and compared. These comparisons indicate that lithic and feldspathic sandstones from the same depositional settings have significant differences in graphic mean, graphic standard deviation and roundness; in contrast, sandstones from different depositional settings but with similar compositions have limited differences in textures. Moreover, three principal components (PCs) are recognized to explain 75% of the total variance, of which the first principal component (PC1) can explain 44%. In bivariate plots of the PCs, sandstones can be distinguished by composition where lithic and feldspathic sandstones are placed in different fields of the plots along the axis of PC1. However, sandstones from different depositional settings overlap and show no clear division. These results indicate that provenance, mainly the source lithology, is the most significant controlling factor on sandstone texture, whereas the depositional environment has limited influence. This study improves our understanding of textural characteristics of fluvio-lacustrine sandstones and their controlling factors, and shows the potentiality of principal component analysis in sandstone studies.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C5258EC4196ADD4D744AD74FBF31DFEC/S0016756821000418a.pdf/div-class-title-principal-component-analysis-of-textural-characteristics-of-fluvio-lacustrine-sandstones-and-controlling-factors-of-sandstone-textures-div.pdf

Principal component analysis of textural characteristics of fluvio-lacustrine sandstones and controlling factors of sandstone textures

Identification of tight sandstone reservoir lithofacies based on CNN image recognition technology: A case study of Fuyu reservoir of Sanzhao Sag in Songliao Basin

The identification of shale lithofacies is the basic work of shale gas exploration and development. Accurate quantitative characterization of different mineral components in fine-grained mixed shale is of great significance for the identification and classification of lithofacies types, the enrichment conditions of shale oil and gas (hydrocarbon generation, reservoir, occurrence and preservation), and the evaluation of shale oil potential (reservoir, oil bearing, fracturability and oil mobility). When organic-rich argillaceous laminae are moderately mixed with brittle laminae, the laminated shale is not only the favorable interval of shale oil and gas enrichment for hydrocarbon generation (organic-rich argillaceous laminae) and storage (brittle laminae), but also the excellent “sweet spots” for continental shale oil and gas optimization. Different shale lithofacies have distinct rock texture, fabric and composition, leading to different brittleness and rock physical properties. For the identification of shale lithofacies containing various components, the overlaps of different wire-line logging responses and the vague boundaries between various logging data cause the large deviation in the logging prediction of lithofacies by traditional methods. In this study, shale samples from the lower part of the third member of Shahejie Formation in Bozhong Sag of the Bohai Bay Basin are selected to carry out the reservoir characterization and then the data mining of logging information by a Back Propagation (BP) neural network coupled with Atomic Search Optimization (ASO) algorithm. The BP algorithm based on the identified shale lithofacies (expected output) and logging data index (input) is used to train the neural network. The complex and unrecognized nonlinear relationship between shale lithofacies and logging data is mapped onto the high-dimensional identifiable nonlinear quantitative relationship to establish the prediction model of the relative content of clay minerals, silicate minerals and carbonate minerals. This study reveals the main lithologic characteristics of lacustrine shale lithofacies from Shahejie Formation in Bozhong Sag, and the main controlling factors for shale lithofacies prediction based on logging data. Our results show that the main mineral composition of shale lithofacies associations can be effectively predicted through the whole rock X-ray diffraction data, wire-line log data and neural network analysis, which provides the basis for lithofacies identification shale interval in well locations lacking core and test data. • Based on the optimized algorithm, a neural network prediction model of mineral composition is established to mine the hidden information in conventional logging data to predict lithofacies, and the accuracy is improved compared with the conventional neural network model. • The prediction of lithofacies mineral composition data for a wider range of non-coring wells by neural network provides more geological basis for the selection and prediction of favorable lithofacies zone - dessert area in subsequent exploration. 

Identification of lithofacies and prediction of mineral composition in shales – A case study of the Shahejie Formation in the Bozhong Sag

Lithofacies identification and classification are critical for characterizing the hydrocarbon potential of unconventional resources. Although extensive applications of machine learning models in predicting lithofacies have been applied to conventional reservoir systems, the effectiveness of machine learning models in predicting clay-rich, lacustrine shale lithofacies has yet to be tackled. Here, we apply machine learning models to conventional well log data to automatically identify the shale lithofacies of Gulong Shale in the Songliao Basin. The shale lithofacies were classified into six types based on total organic carbon and mineral composition data from core analysis and geochemical logs. We compared the accuracy of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest models. We mitigated the bias of imbalanced data by applying oversampling algorithms. Our results show that ensemble methods (XGBoost and Random Forest) have a better performance in shale lithofacies identification than the other models do, with accuracies of 0.868 and 0.884, respectively. The organic siliceous shale proposed to have the best hydrocarbon potential in Gulong Shale can be identified with F1 scores of 0.853 by XGBoost and 0.877 by Random Forest. Our study suggests that ensemble machine learning models can effectively identify the lithofacies of clay-rich shale from conventional well logs, providing insight into the sweet spot prediction of unconventional reservoirs. Further improvements in model performances can be achieved by adding domain knowledge and employing advanced well log data.

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Machine Learning Algorithms for Lithofacies Classification of the Gulong Shale from the Songliao Basin, China

Application of machine learning in the identification of fluvial-lacustrine lithofacies from well logs: A case study from Sichuan Basin, China

• An automated zircon classification from cathodoluminescence images. • Deep learning models using over 4000 cathodoluminescence images of igneous, metamorphic, and hydrothermal zircons. • The model facilitates quick and nearly error-free zircon type distributions. Zircon is a widely-used heavy mineral in geochronological and geochemical research because it can extract important information to understand the history and genesis of rocks. Zircon has various types, and an accurate examination of zircon type is a prerequisite procedure before further analysis. Cathodoluminescence (CL) imaging is one of the most reliable ways to classify zircons. However, current CL image examination is conducted by manual work, which is time-consuming, bias-prone, and requires expertise. An automated and bias-free method for zircon classification is absent but necessary. To this end, deep convolutional neural networks (DCNNs) and transfer learning are applied in this study to classify the common types of zircons, i.e., igneous, metamorphic, and hydrothermal zircons. An atlas with over 4000 CL images of these three types of zircons is created, and three DCNNs are trained using these images. The results of this study indicate that the DCNNs can distinguish hydrothermal zircons from other zircons, as indicated by the highest accuracy of 100%. Although similar textures in igneous and metamorphic zircons pose great challenges for zircon classification, the DCNNs successfully classify 95% igneous and 92% metamorphic zircons. This study demonstrates the high accuracy of DCNNs in zircon classification and presents the great potentiality of deep learning techniques in numerous geoscientific disciplines. 

Zircon classification from cathodoluminescence images using deep learning

The drift history of the Lhasa terrane from Gondwana to Asia plays a crucial role in understanding the Tethys evolution and true polar wander (TPW). However, few reliable paleomagnetic results from Jurassic strata are currently available for reconstructing its northward journey. We performed a combined paleomagnetic and geochronological study on Bima Formation strata in the Xigaze area. Combined with previous results from the Sangri area, our results reveal a paleolatitude of 8 ± 4°S at ∼180 Ma for the reference point (29.3°N, 90.3°E). Along with other paleomagnetic results from the Triassic to Cretaceous, our new results suggest that the Lhasa terrane motion accelerated from ∼2 cm/yr during ∼220–180 Ma to ∼17 cm/yr during ∼180–170 Ma. Paleolatitude information of the North Qiangtang terrane and Tethyan Himalaya is calculated from paleopoles that meet five criteria, which include (a) structural control, (b) well‐determined rock age, (c) stepwise demagnetizations, (d) a minimum of 25 specimens or 8 sites are contained, and (e) robust field or reversal tests are provided. Both terranes also show significant acceleration during their northward motion, which may be related to oceanic slab subduction. Thus, all Gondwana‐derived microcontinents seem to share a significant acceleration during their northward motion. In addition, recent paleomagnetic results from volcanic rocks dated at ∼155 Ma subdivide the overall northward motion during ∼170–130 Ma into two stages, which include a southward drift during ∼170–155 Ma followed by northward motion during ∼155–130 Ma. These results support the fast Late Jurassic TPW during a ∼10 Myr time span.

Jurassic Paleomagnetism of the Lhasa Terrane—Implications for Tethys Evolution and True Polar Wander

The reconstruction of high-resolution sea-level variation curves in deep time based on the standard carbonate microfacies knowledge graph (SMFKG) is of great scientific significance for exploring the Earth system evolution and predicting future sea-level and climate changes. In this study, the concepts, attributes, and relationships among standard carbonate microfacies (SMF) are comprehensively analyzed; an ontology layer is established and its data layer is constructed using thin-section descriptions; and finally, the SMFKG is established. Additionally, based on the knowledge graph, an application for automatically identifying SMF using identification markers and reconstructing the high-resolution relative sea-level variation curve using the SMF and facies zones is compiled. Then, all thin sections of the late Ediacaran Dengying Formation in the western margin of the Yangtze Platform are observed and described in detail, the SMF and facies zones are identified automatically, and the relative sea-level curve is reconstructed automatically using the SMFKG. The reconstruction results show that the Yangtze Platform experienced four sea-level rise and fall cycles in the late Ediacaran, of which two intense regressions led to subaerial-exposed unconformities in the interior and top of the Dengying Formation, which is highly consistent with previous research results. This shows that the high-resolution relative sea-level variation curve in deep time can be reconstructed efficiently and intelligently using the SMFKG. Additionally, in the near future, the combination of an automatic digital slide-scanning system, machine-learning techniques, and the SMFKG can achieve one-stop fully automatic SMF recognition and reconstruction of high-resolution relative sea-level variation curves in deep time, which has a high application value. 

A knowledge graph for standard carbonate microfacies and its application in the automatical reconstruction of the relative sea-level curve

Provenance and depositional environments of the upper Permian-lowermost Triassic fluvial and lacustrine sandstones, Wutonggou low-order cycle, Bogda Mountains, NW China

Dongyu Zheng

Papers

Application of machine learning in the identification of fluvial-lacustrine lithofacies from well logs: A case study from Sichuan Basin, China

Zircon classification from cathodoluminescence images using deep learning

Jurassic Paleomagnetism of the Lhasa Terrane—Implications for Tethys Evolution and True Polar Wander

A knowledge graph for standard carbonate microfacies and its application in the automatical reconstruction of the relative sea-level curve

Provenance and depositional environments of the upper Permian-lowermost Triassic fluvial and lacustrine sandstones, Wutonggou low-order cycle, Bogda Mountains, NW China