Synthesizing Raman Spectra of Minerals with Physically-Informed Generative Adversarial Networks: A Case Study on Albite

The scarcity of high-quality data poses a significant challenge for machine learning applications in geosciences. This work presents a novel and reproducible method for generating realistic Raman spectra of minerals to augment limited datasets. We utilize a Generative Adversarial Network (GAN), specifically a Wasserstein GAN with a Gradient Penalty (WGAN-GP), to overcome common training instabilities like mode collapse. The core innovation of our approach lies in a physically-informed generator architecture that creates spectra as a linear combination of q-Gaussian functions, whose parameters are learned by the model. This method, applied to a small dataset of Albite spectra from the RRUFF database, successfully learned the fundamental "grammar" of the mineral's spectrum, producing diverse and physically plausible outputs. We also demonstrate that a key to successful GAN training is finding a stable adversarial equilibrium, highlighting that more training is not always better. This work not only provides a powerful tool for data augmentation but also illustrates how a collaboration between scientific domain knowledge and advanced AI models can create credible and useful data for computational analysis.