Integrating geostatistical methods and deep learning for enhanced ⁸⁷Sr/⁸⁶Sr isoscape Estimation: A case study in South Korea

The ⁸⁷Sr/⁸⁶Sr isotopic ratio has emerged as a valuable geochemical tracer in fields such as environmental forensics, archaeology, and provenance research. However, generating accurate and spatially continuous isoscape maps from sparse isotopic measurements remains a major challenge due to limited data availability and spatial heterogeneity. To address this, we propose a hybrid framework for ⁸⁷Sr/⁸⁶Sr isoscape mapping that integrates a kriging-based data augmentation method with a deep learning (DL) classifier. The kriging component generates synthetic training samples by interpolating sparse isotopic data while preserving underlying spatial correlations and geological anisotropy. These augmented data, along with spatial geological features (e.g., lithology, tectonic settings) and geochemical compositions, are used as input variables for training a feedforward deep neural network. The approach was applied to 409 soil samples collected across South Korea, and its performance was benchmarked against conventional kriging and convolutional neural networks (CNN). The proposed model achieved significantly higher classification accuracy (91.67%) compared to kriging-based and CNN-based models (76.7% and 86.7%, respectively). Furthermore, the isoscape outputs revealed meaningful isotopic patterns linked to geological and geomorphological controls, such as metamorphic rock distributions, fault density, and surface slope. This framework demonstrates the effectiveness of combining geostatistics with DL to improve predictive accuracy and interpretability in isotopic provenance research and environmental monitoring.