The analysis of a specific target domain, or the nature of the data to be analyzed by the model, is critical after the evaluation of available training data. The nature and diversity of data in the source domain should ideally match that of the target domain for models to learn the anticipated data distribution. The effects of data diversity may become especially apparent when using different equipment, environments, and processing conditions which result in deviations of the collected pattern from simulated data. Data pre-processing and augmentation steps, such as those already covered to introduce experimental artifacts, may be used to supplement training data in such cases.