基于非均衡数据的ADASYN-CatBoost测井岩性智能识别——以胶西北招贤金矿床为例

doi:10.11872/j.issn.1005-2518.2023.05.063

Abstract

Abstract:

Logging lithology identification is helpful to quickly and accurately identify the underlying strata and rock mass in the overburden area，which is of great significance to the geological prospecting exploration of metal mines. Based on the actual logging data of the Zhaoxian gold deposit in the northwest of Jiaodong Peninsula，this paper combined machine learning methods to research on intelligent identification of lithology. In view of the diversity and non-equilibrium of lithology distribution of complex rock formations in the deposit，considering the strong non-linear relationship between logging response and lithology，this paper proposed an intelligent identification method for logging lithology based on ADASYN imbalanced data processing and CatBoost machine learning.Firstly，the ADASYN algorithm was used to process the unbalanced logging sample data and generate synthetic samples according to the weighted distribution of small class samples. Then，the CatBoost algorithm was used to construct a machine learning model between logging characteristic and lithology. The validation curve was used to determine the hyperparametric grid search range of the model. Parameters were optimized by combining grid search with grid search and 10-fold cross validation to establish the optimal lithology classification model.Finally，the performance of the model was evaluated by indices such as accuracy，recall and F1 score on the test set，while the results of the lithology classification were interpreted by the model output of the feature importance and the partial dependence map.An example was given on the logging data from the Zhaoxian gold deposit in northwest Jiaodong peninsula，the lithology identification and interpretation analysis were conducted on 10 types of lithologies based on sample data equalisation. The model evaluation results show that the accuracy，recall and F1 score on the test set reached 98.21%，98.20% and 98.20%，respectively.CatBoost lithology classification was compared with GBDT and LightGBM algorithms，and the results show that CatBoost classifier has the best performance and is superior to the lithology recognition effect of sample data without equalization processing.The comparison with the lithology of example logging section cores verifies the validity of the model classification results.The results of the feature importance of the model output indicate that the logging features contribute to lithology classification are resistivity，natural potential and natural gamma.The strong correlation between these logging features and the identification of the lithology is a good indication of further mineralization.

Key words: lithology identification, ADASYN-CatBoost, logging, unbalanced data, machine learning, Zhaoxian gold deposit

CLC Number:

P631.81

Fangying XU, Yanhong ZOU, Zhuowei YI, Fuqiang YANG, Xiancheng MAO. ADASYN-CatBoost Method for Intelligent Identification of Logging Lithology Considering Unbalanced Data：A Case Study of Zhaoxian Gold Deposit in Northwestern Jiaodong Peninsula[J].Gold Science and Technology, 2023, 31(5): 721-735.

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电阻率/（Ω·m）	自然伽马/API	自然电位/mV	岩性	编码
84.6	30.8	23.04	钾化绢英岩化花岗质碎裂岩	10
79.2	54.6	23.10	黄铁绢英岩化碎裂岩	9
90	53.2	6.09	绢英岩化花岗质碎裂岩	4
76.5	40.6	13.94	绢英岩化花岗岩	6
99	23.8	27.70	中粒含黑云二长花岗岩	3
?	?	?	?	?
94.5	43.4	19.03	含黑云二长花岗岩	7
83.7	32.2	16.97	钾化绢英岩化花岗质碎裂岩	10
1 584.9	39.2	4.74	钾化花岗质碎裂岩	2
75.6	39.2	12.01	绢英岩化花岗岩	6
93.6	54.6	21.97	中粒含黑云二长花岗岩	3

岩性	类别	样本数/个
岩性	类别	处理前	ADASYN处理后
总计		2 609	10 540
含角闪黑云英云闪长岩质片麻岩	1	83	1 047
钾化花岗质碎裂岩	2	173	1 038
中粒含黑云二长花岗岩	3	1 045	1 045
绢英岩化花岗质碎裂岩	4	570	1 094
钾化含黑云二长花岗岩	5	27	1 047
绢英岩化花岗岩	6	217	1 038
含黑云二长花岗岩	7	140	1 072
黄铁绢英岩化花岗质碎裂岩	8	149	1 073
黄铁绢英岩化碎裂岩	9	80	1 050
钾化绢英岩化花岗质碎裂岩	10	125	1 036

分类器	超参数	搜索范围	最优参数
GBDT	学习率	0.000001~0.5	0.1
	弱学习器个数	50~130	119
	叶子节点最小样本数	5~50	10
	树的最大深度	2~30	25
LightGBM	学习率	0.001~0.800	0.2
	弱学习器个数	50~130	102
	树的最大深度	1~50	24
	树的叶子节点个数	15~60	46
	叶子节点最小数据量	5~55	30
CatBoost	学习率	0.001~0.800	0.1
	树的深度	3~17	10
	最大迭代次数	50~500	300
	L2正则化参数	1~20	1

分类器	精确率	召回率	F1分数
GBDT	0.9355	0.9349	0.9327
LightGBM	0.9561	0.9554	0.9552
CatBoost	0.9503	0.9600	0.9600
ADASYN-GBDT	0.9472	0.9469	0.9466
ADASYN-LightGBM	0.9695	0.9695	0.9695
ADASYN-CatBoost	0.9821	0.9820	0.9820

排序	特征	CatBoost
1	电阻率测井	52.4%
2	自然电位测井	28.9%
3	自然伽马测井	18.7%

ADASYN-CatBoost Method for Intelligent Identification of Logging Lithology Considering Unbalanced Data：A Case Study of Zhaoxian Gold Deposit in Northwestern Jiaodong Peninsula

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