Assessing data reliability for AI-driven volcanic rock dating: A comparison of electron microprobe and laser ablation mass spectroscopy

This study explores the integrationof artificial intelligence (AI) and modern data analytics for accurately predicting and classifying three distinct periods of volcanic activity. By leveraging previously dated volcanic samples, we assess whether existing age and geochemical data can reliably group and predict volcanic episodes. Our study focuses on the Kula Volcanic Province (Turkey). We compare the effectiveness of two analytical techniques—Electron Microprobe Analysis (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)—in producing high-quality datasets for training deep learning models. While EPMA provides major and minor elemental compositions, LA-ICP-MS offers a broader range of trace elements, which may improve classification accuracy. Two experiments were conducted to evaluate the feasibility of AI-based volcanic rock age estimation. In the first experiment, an autoencoder and unsupervised clustering were applied to reduce dimensionality and group samples based on their elemental composition. The results revealed that EPMA data lacked sufficient detail to form well-defined clusters, whereas LA-ICP-MS data produced clusters that closely aligned with true age classes due to their higher sensitivity to trace elements. In the second experiment, a deep neural network (DNN) was trained to classify rock ages. The LA-ICP-MS-based model achieved a classification accuracy of 95%, significantly outperforming the EPMA-based model (72%). These findings underscore the importance of data quality and analytical technique selection in AI-powered geochronology, demonstrating that high-quality trace element data enhances AI model performance for volcanic rock age estimation. • Determining the age of volcanic rocks helps scientists understand Earth’s geological history and volcanic activity. • The study examines two geochemical analysis techniques: Electron Microprobe Analysis (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). • These methods generate data that can be used to train AI models for estimating rock ages. • Data quality impacts AI model accuracy, as errors in data collection reduce reliability. • The research tested volcanic rock samples using both EPMA and LA-ICP-MS and analyzed results with machine learning. • Findings show that LA-ICP-MS provides more accurate and consistent geochemical data, leading to AI models with 95% accuracy in predicting rock ages. • EPMA data resulted in lower accuracy due to inconsistencies in detecting trace elements. • High-quality data improves AI predictions, enhancing automated geochronology. • More precise rock age estimation helps refine geological timelines, assess volcanic hazards, and improve models of Earth’s evolution. • The study highlights the value of AI and advanced geochemical techniques in improving rock dating methods.