An automatic peak deconvolution method for Raman spectra of terrestrial carbonaceous material for application to the geothermometers of Kouketsu et al. (2014)

Abstract

Carbonaceous material undergoes changes in its maturity mainly in response to its thermal history. Among the various geothermometers based on Raman spectra of carbonaceous material, those proposed by Kouketsu et al. (2014) have been employed to investigate the temperatures experienced by terrestrial rocks that undergo low‐ to medium‐grade metamorphism. Although their geothermometers use spectral parameters that can be determined only by peak deconvolution, criteria for setting the initial conditions for nonlinear least‐squares fitting have not been specified, which may generate unnecessary errors in the estimated temperatures owing to differences in fitting results among data analysts. To address this issue, we developed a code that automatically calculates the parameters required for the geothermometers of Kouketsu et al. (2014); that is, the widths of the D1‐ and D2‐bands. Our code yields parameter values consistent with those of Kouketsu et al. (2014) for the same Raman data and is applicable to datasets other than those used to develop it. The geothermometers based on the obtained results show a comparable performance to Kouketsu et al. (2014). Errors in the estimated temperatures caused by variation in Raman systems and the subjective selection of the fitting method are smaller than the uncertainties of the geothermometer itself, so long as the temperature is calculated by following the procedures of the present study. Although this study focused only on geothermometers that require peak deconvolution, the development of those based on fitting‐free Raman parameters and on the reaction kinetics of carbonaceous material should be investigated in future studies.

An automatic peak deconvolution code for Raman spectra of carbonaceous material and a revised geothermometer for intermediate- to moderately high-grade metamorphism

“…Since a combination of the geothermometers of Kaneki and Kouketsu (2022) and the present study is incapable of estimating T of samples with maturity in a particular range, further refinement or a slight extrapolation of the present geothermometer is required. In the latter case, we propose R2 = 0.57 as a possible boundary where users should switch from the geothermometer presented here to the one proposed by Kaneki and Kouketsu (2022).…”

“…An alternative approach is to simply extrapolate the applicable range of Eq. ( 10), as implicitly done in some previous studies (e.g., Kaneki and Kouketsu 2022;Kouketsu et al 2014;Rahl et al 2005). In this case, we need to consider an appropriate extent for the extrapolation of our geothermometer.…”

“…K2014 and KK2022 demonstrated that for the Raman data of a specific sample measured under different experimental conditions (i.e., spectrometer, laser wavelength, pinhole size, and grating), some representative Raman parameters (i.e., Ŵ , S2. R2 were calculated using our code, while Ŵ D1 were determined using the code for Fitting E of Kaneki and Kouketsu (2022). A close-up of the gray area in a is shown in b.…”

“…Although both approaches require peak deconvolution of the measured Raman spectra, the criteria for setting the initial conditions for nonlinear least-squares fitting are not clearly presented in the original papers, meaning temperature estimates could vary depending on the skill and experience of the analysts. Kaneki and Kouketsu (2022) (hereafter, referred to as KK2022) addressed this issue by developing a Python code that automatically performs peak deconvolution to calculate the widths of the D1-and D2-bands. The main objective of the present study is to develop a similar code to calculate R2.…”

分光学と地質学の融合：光を通じて視る地球内部環境

“…定性分析に活用されている(e.g., Griffith, 1969;Andò and Garzanti, 2014;Nasdala and Schmidt, 2020) ．筆者らは分光法で得られたスペクトルからより多くの定量的な地質 学的情報を引き出すため，地質温度圧力計の開発・較正に取り組んできた (Kouketsu et al, 2014a(Kouketsu et al, , 2014b) ．また，ラマン分光法や赤外分光法は 1 点あたり数秒から数分で 分析できることやマップ機能も向上していることから，大量のスペクトルデータを取 得することが可能であるが，データ処理方法が確立されておらず，得られたデータを 生かしきれない事が多い．そこで，筆者らはプログラミングを用いることで，スペク トルの自動解析を行い，岩石からより多くの情報を短時間で抽出できる手法開発も行 っている (Sakaguchi et al, 2020;Kaneki and Kouketsu, 2022) Angel, R.J., Mazzucchelli, M.L., Alvaro, M. and Nestola, F. (2017b): EosFit-Pinc: A simple GUI for host-inclusion elastic thermobarometry. Amer.…”