Convolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition
Convolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition
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DOI：
10.1016/j.sab.2020.105801
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发表时间：
2020-04-01
影响因子：
3.3
通讯作者：
Duan, Yixiang
中科院分区：
文献类型：
--
作者：
Chen, Junxi;Pisonero, Jorge;Duan, Yixiang
利用光谱技术快速、准确地识别多种类型的岩石具有广阔的市场应用前景，但由于岩石化学成分相似、基质效应复杂等原因，一直是一个挑战。近年来，激光诱导击穿光谱（LIBS）结合监督机器学习和化学计量学方法，（例如，k-最近邻（kNN）、支持向量机（SVM）、偏最小二乘（PLS）、人工神经网络（ANN））并与特征工程技术相结合（例如，主成分分析（PCA））已经证明了有效识别具有相似化学组成的材料的巨大能力。为了进一步提高分类精度，LIBS与具有二维输入的卷积神经网络（2D CNN）相结合，用于识别岩石样本，包括花岗岩，花岗岩，石灰岩，泥岩和页岩。首先，根据验证数据集的性能，设计了一个正则化的网络结构，使岩石标本的最可靠的判别。然后通过确定的模型评估测试数据集的准确性。结果表明，2D CNN的验证集和测试集分别能够达到0.9877和1的准确度。最后，将该方法与其他识别方法的性能进行了比较，包括：一维卷积神经网络（1D CNN），kNN，PCA-kNN，SVM，PCA-SVM，PLS-DA和人工辅助ANN（HA-ANN）。实验结果表明，CNN在岩性识别方面具有很大的潜力，可以作为LIBS光谱数据处理的一种有效工具。
Rapid and accurate identification of multiple types of rocks using spectroscopic techniques has a wide market application prospect and is always challenging due to similar chemical composition and complex matrix effects. In recent years, laser induced breakdown spectroscopy (LIBS) coupled with supervised machine learning and chemometrics methods (e.g. k-nearest neighbor (kNN), support vector machine (SVM), partial least squares (PLS), artificial neural network (ANN)) and combined with feature engineering techniques (e.g. principal component analysis (PCA)), has demonstrated great capabilities for efficient identification of materials with similar chemical composition. To further increase the classification accuracy, LIBS coupled with a convolutional neural network with two-dimensional input (2D CNN) is here investigated for the identification of rock samples, including dolomites, granites, limestones, mudstones and shales. A regularized network structure was first designed, according to the performance of validation dataset, to enable the most reliable discrimination of the rock specimens. The accuracy of test dataset was then evaluated by the determined model. Results indicated that validation and test set of the 2D CNN was able to reach an accuracy of 0.9877 and 1, respectively. Finally, the performance was compared with other identification methods, including: one-dimensional convolutional neural network (1D CNN), kNN, PCA-kNN, SVM, PCA-SVM, PLS-DA, and Human-Assisted ANN (HA-ANN). The proposed approach has demonstrated that CNN has a great potential for the lithological identification and could be a feasible and useful tool for LIBS spectral data processing.