基于SMOTE-LSTM算法的测井岩性识别研究

doi:10.11720/wtyht.2025.1492

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Abstract

Artificial intelligence algorithms have been developed to automatically identify the spatial structures of formation lithologies from multivariate log data. They represent a promising approach to reducing lithology logging costs and mitigating the subjectivity inherent in lithology identification. Considering the imbalanced distribution of lithology sample data and the spatialtemporal variability in the relationships between log attributes and lithologies, this study constructed a synthetic minority oversampling technique (SMOTE)-long short-term memory (LSTM) hybrid model. The SMOTE algorithm effectively balances the sample distributions of different lithologies, while the LSTM algorithm, using its deep learning architecture, extracts lithological characteristics from the log sequence data. With the borehole log data and lithology records from a sandstone uranium deposit as training data, the SMOTE-LSTM hybrid model achieved a prediction accuracy exceeding 85% in lithology classification. Compared to several other machine learning methods, the SMOTE-LSTM hybrid model demonstrated significantly improved accuracy and reliability in lithology identification.

Key words： lithology identification long short-term memory (LSTM) neural network log

Received: 26 December 2024 Published: 30 December 2025

ZTFLH:

P631

Corresponding Authors: JIANG Zhen-Jiao E-mail: h_liangn@163.com;zjjiang@jlu.edu.cn

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	Articles by authors
	Liang HUANG
	Xuan-Yi CHEN
	Zhen-Jiao JIANG
	Jin-Xin WANG
	Chen-Yu ZHANG
	Gen-Fa SONG

Cite this article:

Liang HUANG,Xuan-Yi CHEN,Zhen-Jiao JIANG, et al. Log-based lithology identification using the SMOTE-LSTM hybrid model[J]. Geophysical and Geochemical Exploration, 2025, 49(6): 1402-1410.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.1492 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I6/1402

Distribution of logging positions in the study area (a), distribution of logging data frequency (b) and correlation coefficient between logging (c)

Comparison between SMOTE-LSTM algorithm flow chart and main machine learning algorithms

The accuracy of SMOTE-LSTM model training set and verification set varies with (a) the number of LSTM network layers, (b) the type of optimizer, (c) the number of neural units in hidden layer, and (d) Batch size parameters

The trend chart of accuracy of model training set and verification set (a) and classification result of model confusion matrix (b)

Comparison of accuracy of test wells before and after SMOTE algorithm processing

Comparison of accuracy and accuracy of lithologic identification algorithm model (a) and ten-fold cross-validation results of comparison model (b)

Comparison between predicted lithology and real lithology of BC-10 test well

Parameter characteristics of prediction results of Smote-LSTM joint model

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