Deep spectral CNN for laser induced breakdown spectroscopy

Castorena, Juan Enrique; Oyen, Diane Adele; Ollila, Ann Martha; Lanza, Nina Louise; Legett, Carey IV

doi:10.1016/j.sab.2021.106125

Deep spectral CNN for laser induced breakdown spectroscopy

Journal Article · Thu Apr 01 00:00:00 EDT 2021 · Spectrochimica Acta. Part B, Atomic Spectroscopy

DOI:https://doi.org/10.1016/j.sab.2021.106125· OSTI ID:1770112

^[1]; ^[1]; ^[1]; ^[1]; ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1770112

Alternate ID(s):: OSTI ID: 1815219

Report Number(s):: LA-UR--20-28315

Journal Information:: Spectrochimica Acta. Part B, Atomic Spectroscopy, Journal Name: Spectrochimica Acta. Part B, Atomic Spectroscopy Vol. 178; ISSN 0584-8547

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (13)

Multiway calibration. Multilinear PLS Bro, Rasmus Journal of Chemometrics, Vol. 10, Issue 1 https://doi.org/10.1002/(SICI)1099-128X(199601)10:1<47::AID-CEM400>3.0.CO;2-C	journal	January 1996
The ChemCam Instrument Suite on the Mars Science Laboratory (MSL) Rover: Body Unit and Combined System Tests Wiens, Roger C.; Maurice, Sylvestre; Barraclough, Bruce Space Science Reviews, Vol. 170, Issue 1-4 https://doi.org/10.1007/s11214-012-9902-4	journal	June 2012
Universality of deep convolutional neural networks Zhou, Ding-Xuan Applied and Computational Harmonic Analysis, Vol. 48, Issue 2 https://doi.org/10.1016/j.acha.2019.06.004	journal	March 2020
Pre-flight calibration and initial data processing for the ChemCam laser-induced breakdown spectroscopy instrument on the Mars Science Laboratory rover Wiens, R. C.; Maurice, S.; Lasue, J. Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 82 https://doi.org/10.1016/j.sab.2013.02.003	journal	April 2013
Independent component analysis classification of laser induced breakdown spectroscopy spectra Forni, Olivier; Maurice, Sylvestre; Gasnault, Olivier Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 86 https://doi.org/10.1016/j.sab.2013.05.003	journal	August 2013
Correcting for variable laser-target distances of laser-induced breakdown spectroscopy measurements with ChemCam using emission lines of Martian dust spectra Melikechi, N.; Mezzacappa, A.; Cousin, A. Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 96 https://doi.org/10.1016/j.sab.2014.04.004	journal	June 2014
Automatic correction of continuum background in Laser-induced Breakdown Spectroscopy using a model-free algorithm Yaroshchyk, Pavel; Eberhardt, John E. Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 99 https://doi.org/10.1016/j.sab.2014.06.020	journal	September 2014
Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 129 https://doi.org/10.1016/j.sab.2016.12.002	journal	March 2017
Recalibration of the Mars Science Laboratory ChemCam instrument with an expanded geochemical database Clegg, Samuel M.; Wiens, Roger C.; Anderson, Ryan Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 129 https://doi.org/10.1016/j.sab.2016.12.003	journal	March 2017
Automatic preprocessing of laser-induced breakdown spectra using partial least squares regression and feed-forward artificial neural network: Applications to Earth and Mars data Ewusi-Annan, Ebo; Delapp, Dorothea M.; Wiens, Roger C. Spectrochimica Acta Part B: Atomic Spectroscopy, Vol. 171 https://doi.org/10.1016/j.sab.2020.105930	journal	September 2020
De-noising by soft-thresholding Donoho, D. L. IEEE Transactions on Information Theory, Vol. 41, Issue 3 https://doi.org/10.1109/18.382009	journal	May 1995
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin IEEE Transactions on Image Processing, Vol. 26, Issue 7 https://doi.org/10.1109/TIP.2017.2662206	journal	July 2017
Deep Learning: Methods and Applications Deng, Li Foundations and Trends® in Signal Processing, Vol. 7, Issue 3-4 https://doi.org/10.1561/2000000039	journal	January 2013

Similar Records

Quantification of water content by laser induced breakdown spectroscopy on Mars

Journal Article · Sat Feb 11 19:00:00 EST 2017 · Spectrochimica Acta. Part B, Atomic Spectroscopy · OSTI ID:1356155

Exploring Mars with Curiosity and its Laser

Technical Report · Mon Jan 14 23:00:00 EST 2019 · OSTI ID:1492625

In Situ Analysis of Opal in Gale Crater, Mars

Journal Article · Mon Jul 02 20:00:00 EDT 2018 · Journal of Geophysical Research. Planets · OSTI ID:1508550

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Calibration
Deep learning
LIBS
Planetary Sciences
Processing
Residual learning

Deep spectral CNN for laser induced breakdown spectroscopy

Citation Formats

References (13)

Similar Records

Related Subjects