Accurate measures of lightning activity can be used to predict extreme weather events in advance, saving lives and property. However, the current hand-crafted filtering algorithm for identifying true lightning events from data captured by the GLM onboard NOAA's GOES-R satellites is only 70% accurate, with a 5% false alarm rate. Given the large volume and high temporal resolution, this work applies unsupervised learning techniques in an effort to detect lightning within raw data signals. We present a novel data processing pipeline for the GLM Level 0 products and case study comparison of two approaches to dimensionality reduction and clustering to sort the data by similar patterns. These clusters could then be labeled by a domain expert to accurately distinguish between noise and true lightning events. We demonstrate that autoencoders with graph convolution layers are able to learn a translationally invariant representation of the dataset which allows for k-means clustering to group samples that have similar spatiotemporal patterns together. This work is a first step towards building a machine learning pipeline for improving false event filtering to identify lightning and enhance predictive abilities in the face of increasingly frequent extreme weather events.