Mineral Element Abundance Identification Based on LIBS Emission Line Selection by Loading Space Distance of Principal Component Analysis

Kai-chen, 郭恺琛 Guo; Zhong-chen, 武中臣 Wu; Xiang-ping, 朱香平 Zhu; Zong-cheng, 凌宗成 Ling; Jiang, 张江 Zhang; Yun, 李芸 Li; Mao-cheng, 钱茂程 Qian

doi:10.3788/gzxb20194810.1030002

Cited by 3 publications

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“…The ChemCam engineering model is primarily composed of three spectrometers, a remote micro-imager, a telescope, a laser, a demultiplexer, and associated digital and electronic devices. The laser source of the instrument is Nd: KGW 1067 nm, whose frequency range is 3-10 Hz, and pulsed laser energy can be up to 14 m with a 5-ns duration when the temperature is below 0 • C. The light from the generated plasma is captured through a 110 mm diameter telescope and transmitted via optical fiber to a group of three Czerny-Turner spectrometers (covering 240-850 nm) [3]. The primary purpose of Martian LIBS is to determine the chemical compositions of rocks and soil.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the major elements Si, Al, and K were analyzed because they cover three representative ranges, and their concentration differences are important characteristic indexes that reflect the variation of sedimentary conditions [4]. They are also widely chosen as examples for mineral identification, emission line selection, element abundance identification, and other LIBS tasks [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

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When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

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Modeling the quantitative relationship between target components and measured spectral information is an essential part of laser-induced breakdown spectroscopy (LIBS) analysis. However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.

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Section: Introductionmentioning

confidence: 99%