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A highly efficient and interpretable framework for high-precision lithological identification integrating SERS and

Peng An¹, Mengtian Li¹, Yang Yang¹

¹State Key Laboratory for Tunnel Engineering, Shandong University, No. 17923 Jingshi Road, Jinan, 250061, Shandong Province, China; School of Future Technology, Shandong University, No. 17923 Jingshi Road, Jinan, 250061, Shandong Province, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|June 29, 2026

Summary

This study introduces a new method for identifying rock types using Surface-Enhanced Raman Scattering (SERS) and artificial intelligence (AI). The Extreme Gradient Boosting (XGBoost) model accurately classifies rocks, offering a fast and reliable geological exploration tool.

Keywords:

Lithological identification SERS SHAP XGBoost

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Area of Science:

Geochemistry
Spectroscopy
Artificial Intelligence

Background:

Traditional lithological identification methods are slow, destructive, and subjective.
Accurate rock identification is crucial for geological exploration and engineering.

Purpose of the Study:

To develop a high-precision lithological classification framework using SERS and AI.
To evaluate and compare the performance of different AI models for rock classification.
To provide an interpretable and computationally efficient method for real-time geological monitoring.

Main Methods:

Synthesized silver nanoparticles (AgNPs) for SERS substrate to enhance Raman signals.
Utilized Finite-Difference Time-Domain (FDTD) simulations to understand electromagnetic enhancement.
Trained and evaluated six AI models on a self-constructed SERS dataset of eight rock types.
Employed SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

The Extreme Gradient Boosting (XGBoost) model demonstrated superior performance in lithological classification.
XGBoost mitigated issues of computational latency and training instability common in deep learning.
The AI model successfully distinguished between rocks with similar spectra by analyzing lattice vibrations and trace signals.
SHAP analysis confirmed the model's ability to extract intrinsic physical features.

Conclusions:

The integrated SERS-AI framework offers a reliable and interpretable approach for lithological identification.
The XGBoost model provides a computationally efficient solution for real-time geological monitoring.
This method overcomes limitations of traditional techniques in complex construction environments.