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<p>Semi-quantitative analysis is performed for clay minerals based on the area ratios of individual peaks from X-ray diffraction (XRD), after over 16&#xa0;h of organic matter pretreatment to separate intensity peaks. However, the analysis is time-intensive and subject to expert interpretation. This study developed machine-learning-based analysis models using the intensity profiles from air-dried or ethylene glycol-treated samples, Cases 1 and 2, respectively, from the Onsan–Busan coastal area. Additionally, Case 3 used only the main peaks selected by the Biscaye method. After developing a convolutional neural network-based model using 174 training and 75 validation samples, their performances were evaluated on 28 test samples. The output of the model is the compositional proportions of smectite, illite, kaolinite, and chlorite. Case 2 showed higher reliability, resulting in a 33% lower mean absolute error (MAE), compared to Case 1, because it used clearly separated smectite and chlorite peaks through pretreatment. Nevertheless, Case 1 maintained an acceptable MAE within 2 wt%, offering an efficient alternative without pretreatment. However, Case 3 showed superior performance, with an average MAE of 0.83 wt% and a coefficient of determination (R²) of 0.94, despite using approximately 30% of the original 1,337 input values, or 492 intensities, by focusing on the main peaks. Furthermore, applying Case 3 to 12 unseen samples from the Pohang area with transfer learning yielded an MAE of 3.28 wt% and R² of 0.70, confirming the scalability of the model to new regions. In conclusion, Case 3 provides useful guidance for expert analysis, enhancing analytical efficiency.</p>

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Machine-learning-based semi-quantitative analysis model of clay minerals in Onsan–Busan coastal sediments, South Korea

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Ju Young Park,
  • Yesung Jo,
  • Hyo Jin Koo,
  • In Kwon Um,
  • Kyungbook Lee

摘要

Semi-quantitative analysis is performed for clay minerals based on the area ratios of individual peaks from X-ray diffraction (XRD), after over 16 h of organic matter pretreatment to separate intensity peaks. However, the analysis is time-intensive and subject to expert interpretation. This study developed machine-learning-based analysis models using the intensity profiles from air-dried or ethylene glycol-treated samples, Cases 1 and 2, respectively, from the Onsan–Busan coastal area. Additionally, Case 3 used only the main peaks selected by the Biscaye method. After developing a convolutional neural network-based model using 174 training and 75 validation samples, their performances were evaluated on 28 test samples. The output of the model is the compositional proportions of smectite, illite, kaolinite, and chlorite. Case 2 showed higher reliability, resulting in a 33% lower mean absolute error (MAE), compared to Case 1, because it used clearly separated smectite and chlorite peaks through pretreatment. Nevertheless, Case 1 maintained an acceptable MAE within 2 wt%, offering an efficient alternative without pretreatment. However, Case 3 showed superior performance, with an average MAE of 0.83 wt% and a coefficient of determination (R²) of 0.94, despite using approximately 30% of the original 1,337 input values, or 492 intensities, by focusing on the main peaks. Furthermore, applying Case 3 to 12 unseen samples from the Pohang area with transfer learning yielded an MAE of 3.28 wt% and R² of 0.70, confirming the scalability of the model to new regions. In conclusion, Case 3 provides useful guidance for expert analysis, enhancing analytical efficiency.