Machine learning model for deep exploration: Utilizing short wavelength infrared (SWIR) of hydrothermal alteration minerals in the Qianchen gold deposit, Jiaodong Peninsula, Eastern China

The Jiaodong Peninsula in the eastern North China Craton is renowned globally as a major gold province. Notably, the Jiaojia orefield is an important gold-producing district in this region, and its depths are inferred to offer substantial prospecting potential, underscoring the need for innovative exploration tools to uncover concealed orebodies. The Qianchen gold deposit in the southern part of the Jiaojia orefield is characterized by the disseminated-stockwork-style orebodies at depths of ca. –1000 to –1800 m, with three mineralization stages, namely (I) pre-ore K-feldspar-quartz stage, (II) quartz-sericite-gold-polymetallic sulfide stage (main Au mineralization stage), and (III) post-ore quartz-calcite stage. Systematic short-wavelength infrared (SWIR) spectral analysis reveals that sericite group minerals developing in Stage II are the predominant and wide-distributed alteration minerals at Qianchen. Their spectral characteristics demonstrate a shift in the Al-OH absorption feature wavelength positions (Pos2200) to longer wavelengths (>2205 nm) within the orebodies. This shift is likely due to strong water–rock interactions leading to Fe and Mg substituting for Al in octahedral sites. Orthogonal partial least squares discriminant analysis (OPLS-DA) indicates that the H2O, OH, and FeOH spectral bands (Variable Importance in the Projection (VIP) ≥ 1) of sericite are crucial for distinguishing between gold ores and barren wall rocks. Additionally, Long Short-Term Memory (LSTM) models demonstrate that spectral data of sericite can be used to predict ore samples with an accuracy of 82 %, validated by receiver operating characteristic analysis (84 %), and blind test results demonstrate that the predicted grade closely aligns with the actual grade. Therefore, it is concluded that SWIR spectral characteristics of sericite group minerals can serve as an effective indicator for prospecting the Qianchen concealed gold mineralization. This study highlights the potential of coupling between machine learning and SWIR spectral data of alteration minerals to uncover the concealed lode gold mineralization.