A Biological-Inspired Deep Learning Framework for Big Data Mining and Automatic Classification in Geosciences

MycelialNet is a novel deep neural network (DNN) architecture inspired by natural mycelial networks. Mycelia, the vegetative part of fungi, form extensive underground networks that, in a very efficient way, connect biological entities, transport nutrients and signals, and dynamically adapt to environmental conditions. Drawing inspiration from these properties, MycelialNet integrates dynamic connectivity, self-optimization, and resilience into its artificial structure. This paper explores how mycelial-inspired neural networks can enhance big data analysis, particularly in mineralogy, petrology, and other Earth disciplines, where exploration and exploitation must be efficiently balanced during the process of data mining. We validate our approach by applying MycelialNet to synthetic data first, and then to a large petrological database of volcanic rock samples, demonstrating its superior feature extraction, clustering, and classification capabilities with respect to other conventional machine learning methods.