Igneous rocks lithology identification with deep forest: Case study from eastern sag, Liaohe basin

Han, Ruiyi; Wang, Zhuwen; Wang, Wenhua; Xu, Fengyuan; Xinghua, Qi; Yitong, Cui; Zhang, Zhitao

doi:10.1016/j.jappgeo.2022.104892

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Yang et al [14] integrated the decision tree algorithm with AdaBoost to fuse multiple base classifiers into a strong classifier, which significantly improved the poor generalization of a single model. Another Boosting ensemble algorithm, XGBoost, and the random forest algorithm have been successfully applied to volcanic rock recognition in Liaohe Basin, with high recognition accuracy [15][16][17]. Due to the strong nonlinear relationship between log curves, machine learning algorithms can easily fall into local optimal values when classifying high-latitude spatial samples.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Classification of Volcanic Rocks Based on Honey Badger Optimization Algorithm Enhanced Extreme Gradient Boosting Tree Model: A Case Study of Hongche Fault Zone in Junggar Basin

Chen,

Deng,

Shan

et al. 2024

Processes

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Lithology identification is the fundamental work of oil and gas reservoir exploration and reservoir evaluation. The lithology of volcanic reservoirs is complex and changeable, the longitudinal lithology changes a great deal, and the log response characteristics are similar. The traditional lithology identification methods face difficulties. Therefore, it is necessary to use machine learning methods to deeply explore the corresponding relationship between the conventional log curve and lithology in order to establish a lithology identification model. In order to accurately identify the dominant lithology of volcanic rock, this paper takes the Carboniferous intermediate basic volcanic reservoir in the Hongche fault zone as the research object. Firstly, the Synthetic Minority Over-Sampling Technique–Edited Nearest Neighbours (SMOTEENN) algorithm is used to solve the problem of the uneven data-scale distribution of different dominant lithologies in the data set. Then, based on the extreme gradient boosting tree model (XGBoost), the honey badger optimization algorithm (HBA) is used to optimize the hyperparameters, and the HBA-XGBoost intelligent model is established to carry out volcanic rock lithology identification research. In order to verify the applicability and efficiency of the proposed model in volcanic reservoir lithology identification, the prediction results of six commonly used machine learning models, XGBoost, K-nearest neighbor (KNN), gradient boosting decision tree model (GBDT), adaptive boosting model (AdaBoost), support vector machine (SVM) and convolutional neural network (CNN), are compared and analyzed. The results show that the HBA-XGBoost model proposed in this paper has higher accuracy, precision, recall rate and F1-score than other models, and can be used as an effective means for the lithology identification of volcanic reservoirs.

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Section: Introductionmentioning

confidence: 99%

Intelligent Classification of Volcanic Rocks Based on Honey Badger Optimization Algorithm Enhanced Extreme Gradient Boosting Tree Model: A Case Study of Hongche Fault Zone in Junggar Basin

Chen,

Deng,

Shan

et al. 2024

Processes

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“…Current studies on volcanic oil and gas reservoirs mainly focus on volcanic rock formation environment, volcanic facies, reservoir control factors, lithology identification, oil and gas accumulation, etc. (Han et al, 2023; Tang et al, 2016; Wang, Shi, et al, 2018, Wang, Yang, et al, 2018; Yang et al, 2017; Ye et al, 2022). At present, more studies have been conducted on volcanic lavas in the overflow phase than on volcaniclastic rocks (such as tuff) in the explosive phase.…”

Section: Introductionmentioning

confidence: 99%

Characteristics and controlling factors of tuff reservoirs of Huoshiling Formation in Dehui Fault Depression, Songliao Basin, NE China

Fan,

Wang,

Song

et al. 2023

Geological Journal

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This study takes the tuff reservoir of the Huoshiling Formation in the Dehui Fault Depression, Songliao Basin, as an example to clarify the characteristics and primary controlling factors of tuff reservoirs. 3D seismic, core, image log, thin section, production dynamic and other data were used. Mineral x‐ray diffraction, core computerized tomography scanning, high‐pressure mercury injection, porosity, permeability, scanning electron microscopy, cathodoluminescence thin section, element testing and other experiments were conducted. The volcanic rocks in the study area are divided into andesite, volcanic breccia, lithic tuff and crystal tuff. The experiment results show that the mineral composition of tuff is primarily quartz, feldspar and clay minerals. The reservoir space comprises secondary pores and fractures, whereas primary pores are not developed. Average pore‐throat radius of most samples ranges from 0.017 to 0.131 μm, and the pore‐throat is primarily fine. The average porosity is 12.21% (a typical medium‐high porosity reservoir), and the average permeability is 0.072 mD (an extra‐low permeability reservoir). Tectonic fractures generated by tectonic movement are one of the reservoir spaces of tuff. Faults and tectonic fractures constitute a crucial migration channel of the fluid in the deep stratum. The tuff formed in the underwater environment, and the reservoir is affected by alteration. Devitrification is a crucial cause of medium‐high porosity and ultra‐low permeability in reservoirs. The alteration caused by hydrothermal fluid in deep fluids creates a considerable number of pores and fractures filled with clay minerals. Furthermore, acid fluid dissolves tuff along faults and fractures. In terms of time, the filling action is earlier than the dissolution action. The weathering indexes of CIA, CIW, PIA and WIP indicate that the tuff has suffered intense weathering, and the reservoir's physical properties have been improved to some extent.

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“…As a meta-algorithm framework, XGBoost supports the parallel gradient lifting calculation of feature importance and the base learner, which greatly improves the training speed in the face of large-scale samples. Regularization was introduced to control the complexity of the model, and it can also specify the default direction for missing values, thus significantly improving the generalization ability of the model while preventing the fitting problem [60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lin

Jiang

et al. 2023

Remote Sensing

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.

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Igneous rocks lithology identification with deep forest: Case study from eastern sag, Liaohe basin

Cited by 10 publications

References 28 publications

Intelligent Classification of Volcanic Rocks Based on Honey Badger Optimization Algorithm Enhanced Extreme Gradient Boosting Tree Model: A Case Study of Hongche Fault Zone in Junggar Basin

Intelligent Classification of Volcanic Rocks Based on Honey Badger Optimization Algorithm Enhanced Extreme Gradient Boosting Tree Model: A Case Study of Hongche Fault Zone in Junggar Basin

Characteristics and controlling factors of tuff reservoirs of Huoshiling Formation in Dehui Fault Depression, Songliao Basin, NE China

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

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