The singular value decomposition (SVD) enjoys use in genomics, recommender systems, and image processing. We provide a pedagogical overview of the SVD for the reader's convenience. We then reason that the SVD of our data matrix A identifies an approximate lower dimensional subspace in which the 3D porosity images lay and a latent space of the cavities of the porous cages.