A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features
Lithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-R...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/14/2434 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850071566671413248 |
|---|---|
| author | Yanlin Shao Peijin Li Ran Jing Yaxiong Shao Lang Liu Kunpeng Zhao Binqing Gan Xiaolei Duan Longfan Li |
| author_facet | Yanlin Shao Peijin Li Ran Jing Yaxiong Shao Lang Liu Kunpeng Zhao Binqing Gan Xiaolei Duan Longfan Li |
| author_sort | Yanlin Shao |
| collection | DOAJ |
| description | Lithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-RFGeo, for terrestrial laser scanning (TLS) outcrop point clouds, which integrates spectral and geometric features. The workflow involves several key steps. First, lithological recognition units are created through regular grid segmentation. From these units, spectral reflectance statistics (e.g., mean, standard deviation, kurtosis, and other related metrics), and geometric morphological features (e.g., surface variation rate, curvature, planarity, among others) are extracted. Next, a double-layer random forest model is employed for lithology identification. In the shallow layer, the Gini index is used to select relevant features for a coarse classification of vegetation, conglomerate, and mud–sandstone. The deep-layer module applies an optimized feature set to further classify thinly interbedded sandstone and mudstone. Geological prior knowledge, such as stratigraphic attitudes, is incorporated to spatially constrain and post-process the classification results, enhancing their geological plausibility. The method was tested on a TLS dataset from the Yueyawan outcrop of the Qingshuihe Formation, located on the southern margin of the Junggar Basin in China. Results demonstrate that the integration of spectral and geometric features significantly improves classification performance, with the Macro F1-score increasing from 0.65 (with single-feature input) to 0.82. Further, post-processing with stratigraphic constraints boosts the overall classification accuracy to 93%, outperforming SVM (59.2%), XGBoost (67.8%), and PointNet (75.3%). These findings demonstrate that integrating multi-source features and geological prior constraints effectively addresses the challenges of lithological identification in complex outcrops, providing a novel approach for high-precision geological modeling and exploration. |
| format | Article |
| id | doaj-art-36b367488ddc4f6eb8b12cc1e02ea998 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-36b367488ddc4f6eb8b12cc1e02ea9982025-08-20T02:47:17ZengMDPI AGRemote Sensing2072-42922025-07-011714243410.3390/rs17142434A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric FeaturesYanlin Shao0Peijin Li1Ran Jing2Yaxiong Shao3Lang Liu4Kunpeng Zhao5Binqing Gan6Xiaolei Duan7Longfan Li8School of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaCenter for Governmental Studies, Northern Illinois University, DeKalb, IL 60115, USASchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaSchool of Geosciences, Yangtze University, Wuhan 430100, ChinaLithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-RFGeo, for terrestrial laser scanning (TLS) outcrop point clouds, which integrates spectral and geometric features. The workflow involves several key steps. First, lithological recognition units are created through regular grid segmentation. From these units, spectral reflectance statistics (e.g., mean, standard deviation, kurtosis, and other related metrics), and geometric morphological features (e.g., surface variation rate, curvature, planarity, among others) are extracted. Next, a double-layer random forest model is employed for lithology identification. In the shallow layer, the Gini index is used to select relevant features for a coarse classification of vegetation, conglomerate, and mud–sandstone. The deep-layer module applies an optimized feature set to further classify thinly interbedded sandstone and mudstone. Geological prior knowledge, such as stratigraphic attitudes, is incorporated to spatially constrain and post-process the classification results, enhancing their geological plausibility. The method was tested on a TLS dataset from the Yueyawan outcrop of the Qingshuihe Formation, located on the southern margin of the Junggar Basin in China. Results demonstrate that the integration of spectral and geometric features significantly improves classification performance, with the Macro F1-score increasing from 0.65 (with single-feature input) to 0.82. Further, post-processing with stratigraphic constraints boosts the overall classification accuracy to 93%, outperforming SVM (59.2%), XGBoost (67.8%), and PointNet (75.3%). These findings demonstrate that integrating multi-source features and geological prior constraints effectively addresses the challenges of lithological identification in complex outcrops, providing a novel approach for high-precision geological modeling and exploration.https://www.mdpi.com/2072-4292/17/14/2434outcrop lithology identificationpoint cloud analysisspectral–geometric feature fusionrandom forest classificationstratigraphic constraints |
| spellingShingle | Yanlin Shao Peijin Li Ran Jing Yaxiong Shao Lang Liu Kunpeng Zhao Binqing Gan Xiaolei Duan Longfan Li A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features Remote Sensing outcrop lithology identification point cloud analysis spectral–geometric feature fusion random forest classification stratigraphic constraints |
| title | A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features |
| title_full | A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features |
| title_fullStr | A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features |
| title_full_unstemmed | A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features |
| title_short | A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features |
| title_sort | machine learning based method for lithology identification of outcrops using tls derived spectral and geometric features |
| topic | outcrop lithology identification point cloud analysis spectral–geometric feature fusion random forest classification stratigraphic constraints |
| url | https://www.mdpi.com/2072-4292/17/14/2434 |
| work_keys_str_mv | AT yanlinshao amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT peijinli amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT ranjing amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT yaxiongshao amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT langliu amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT kunpengzhao amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT binqinggan amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT xiaoleiduan amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT longfanli amachinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT yanlinshao machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT peijinli machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT ranjing machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT yaxiongshao machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT langliu machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT kunpengzhao machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT binqinggan machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT xiaoleiduan machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures AT longfanli machinelearningbasedmethodforlithologyidentificationofoutcropsusingtlsderivedspectralandgeometricfeatures |