Spectral Asteroid Classification Using Wavelet Transform and Auxillary Data to Identify Optimal Mining Targets with Convolutional Neural Networks

The mining of Earth’s finite metals has devastating environmental and humanitarian impact. Asteroid mining provides an elegant alternative to the limited resources on Earth, as well as playing a critical role in the quest for interplanetary travel. Thus, a system to reliably determine the composition of asteroids is imperative to seek out the correct targets. Currently, asteroid spectral graphs are matched using mathematical error methods with known spectra of elements to determine their composition. Unfortunately, these approaches are unidimensional and blind to other factors that influence asteroid composition, breaking down when faced with more unique asteroid spectra. In this paper, a novel approach to asteroid spectral analysis is taken by using a convolutional neural network, CNN, engineered to analyze patterns within asteroid spectral graphs supplemented by auxiliary asteroid data, diameter, absolute magnitude, and albedo, to provide insight not found in current asteroid classification methods. Data was sourced from MIT’s Small Main Belt Asteroid Spectroscopic Survey, Phase II, SMASSII, and the space rocks database. A program was created to automatically aggregate data from the two sources, filling empty values with simulated data. Data augmentation algorithms were created and employed to balance the dataset, ensuring sufficient unique data was present in each spectral class for the CNN to learn from. Reclassifying images of spectral graphs into 18 common spectral classes from the Bus DeMeo taxonomic system, the CNN predicted the correct asteroid class 98.8% of the time, over a 13% improvement from a model solely using spectral graphs. Thus, this study not only showcases the accuracy of CNNs in analyzing asteroid spectral graphs, but also underscores how auxiliary data enhances asteroid composition prediction.