A study on geological structure prediction based on random forest method

Chen, Zhen; Wu, Qingsong; Han, Sipeng; Zhang, Jungui; Yang, Peng; Liu, Xingwu

doi:10.1016/j.aiig.2023.01.004

Cited by 4 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the quality of the data, preprocessing operations were conducted on the raw data with the following main steps: (1) Data Integration: the consolidation and merging of two databases with different formats were performed, encompassing 37 fields, including major elements, trace elements, latitude, and longitude. (2) Transforming Fe 2 O 3 and FeO into FeOt content (Chen et al, 2022). (3) Removing samples with fewer than 20 non-null values.…”

Section: Data Descriptions and Pre-processingmentioning

confidence: 99%

“…In most of the literature on tectonic environment discrimination, the SVM method is commonly used (Liu & Shi, 2022;Ueki et al, 2018). However, considering the development of machine learning algorithms, ensemble algorithms based on tree models demonstrate better performance in certain scenarios (Chen et al, 2022;Zhang et al, 2023). Combining the results of a preliminary trial comparing various popular machine learning algorithms, this study adopts SVM, RF based on Bagging ensemble, and XGBoost based on Boosting ensemble for classification.…”

Section: Classification Modelmentioning

confidence: 99%

See 1 more Smart Citation

A practical approach for tectonic discrimination of basalts using geochemical data through machine learning

Gao,

Zhang,

et al. 2024

Preprint

View full text Add to dashboard Cite

Identifying the tectonic setting of formation of rocks is an essential component in the field of geosciences. The conventional approach is to employ standard tectonic discrimination diagrams based on elemental correlations and ratios, which sometimes are plagued with uncertainties and limitations. The application of machine learning algorithms based on big data can effectively overcome these problems. In this study, three machine learning algorithms, namely Support Vector Machine, Random Forest, and XGBoost, were employed to classify the various types of basalts from diverse settings such as intraplate basalts, island arc basalts, ocean island basalts, mid-ocean ridge basalts, back-arc basin basalts, oceanic flood basalts, and continental flood basalts into seven tectonic environments. For the altered basalts and fresh basalt, we use 22 relatively immobile elements (TiO2, P2O5, Nb, Ta, Zr, Hf, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Ho, Er, Yb, Lu, Dy, Tb, Cr, Ni) and 35 major plus trace elements to build discrimination models for seven types of tectonic settings of basalt, respectively. The results indicate that XGBoost demonstrates the best performance in discriminating basalts into seven tectonic settings, achieving an accuracy of 85% and 89% respectively. Compared to previous models, our new method presented in this study is expected to have better practical applications.

show abstract

Section: Data Descriptions and Pre-processingmentioning

confidence: 99%

Section: Classification Modelmentioning

confidence: 99%

A practical approach for tectonic discrimination of basalts using geochemical data through machine learning

Gao,

Zhang,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Refined implicit characterization of engineering geology with uncertainties: a divide-and-conquer tactic-based approach

Li,

Chen,

Liang

et al. 2024

Bull Eng Geol Environ

View full text Add to dashboard Cite

In engineering geology, a reasonable assessment of the spatial distribution of uncertainty in a region is vital in guiding research, saving money, and shortening the period. However, the traditional modeling process requires a lot of manual interaction, and the uncertainty of the geological model cannot be accurately quantified and utilized. This paper proposes a novel implicit geological modeling and uncertainty analysis approach based on the triangular prism blocks, which is divided into data point acquisition, ensemble model with divide-and-conquer tactic (EMDCT), uncertainty analysis, and post-processing. By employing machine learning algorithms, the EMDCT gives superior results for implicit modeling. The sensitivity analysis of the prediction results is further evaluated via information entropy. According to the distribution of uncertainty, supplementary boreholes are selected as additional knowledge to retrain the local components of the model to enhance their performances. The implicit modeling method is applied to real hydraulic engineering problems by employing the EMDCT, and the proposed model has obvious advantages in the implicit geological characterization. The overall accuracy in the working area with sparse boreholes reaches 0.922, which is 0.013 higher than the traditional method. By evaluating the distribution of uncertainty, an accuracy of 0.962 can be achieved, which is equivalent to reducing 10 boreholes.

show abstract

Modeling the feasibility of Se-rich corn cultivation in Se-deficient agricultural fields using random forest algorithm

Zhang,

Huo,

Mao

et al. 2024

Environ Geochem Health

View full text Add to dashboard Cite

A study on geological structure prediction based on random forest method

Cited by 4 publications

References 21 publications

A practical approach for tectonic discrimination of basalts using geochemical data through machine learning

A practical approach for tectonic discrimination of basalts using geochemical data through machine learning

Refined implicit characterization of engineering geology with uncertainties: a divide-and-conquer tactic-based approach

Modeling the feasibility of Se-rich corn cultivation in Se-deficient agricultural fields using random forest algorithm

Contact Info

Product

Resources

About