Issue 3, 2022

Simultaneous determination of lithology and major elements in rocks using laser-induced breakdown spectroscopy (LIBS) coupled with a deep convolutional neural network

Abstract

Accurate lithological recognition and quantitative determination of multiple chemical elements have a wide market application prospect in geological and geochemical exploration. In recent years, with the development of machine learning, laser induced breakdown spectroscopy (LIBS) coupled with various chemometric methods has demonstrated great advantages to solve both classification and quantitative problems in rock analysis. However, these methods are usually applied for single-task analysis. To further increase the analysis efficiency, LIBS combined with a deep convolutional neural network with a two-dimensional (2D-CNN) algorithm is proposed here for simultaneous determination of lithology and seven major chemical elements in rock samples from six different types. The structure of our CNN model was designed with two different outputs, which could complete classification and regression tasks at the same time. Based on the prediction results of the test set, the experimental results show that the classification accuracy of the CNN model could reach 99.26% the coefficient of determination (R2) of target elements could reach a good level (RSi2 = 0.9917, RAl2 = 0.9935, RCa2 = 0.9906, RMg2 = 0.9926, RFe2 = 0.9716, RNa2 = 0.9693, RK2 = 0.8781), and both the mean absolute error (MAE) of each test sample and prediction relative error of each element are improved. To verify the superior performance of this CNN model, the CNN algorithm was compared with other models, including k-nearest neighbor (kNN), support vector machine (SVM), partial least squares discrimination analysis (PLS-DA), back propagation artificial neural network (BP-ANN) and partial least squares regression (PLSR). The results indicate that the CNN has great potential in the field of rock quantitative and qualitative analyses and provides a reliable data processing method for LIBS to complete multi-task analysis with high efficiency and good accuracy.

Graphical abstract: Simultaneous determination of lithology and major elements in rocks using laser-induced breakdown spectroscopy (LIBS) coupled with a deep convolutional neural network

Supplementary files

Article information

Article type
Paper
Submitted
24 Nov. 2021
Accepted
12 Janv. 2022
First published
20 Janv. 2022

J. Anal. At. Spectrom., 2022,37, 508-516

Simultaneous determination of lithology and major elements in rocks using laser-induced breakdown spectroscopy (LIBS) coupled with a deep convolutional neural network

S. Chen, H. Pei, J. Pisonero, S. Yang, Q. Fan, X. Wang and Y. Duan, J. Anal. At. Spectrom., 2022, 37, 508 DOI: 10.1039/D1JA00406A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements