We propose in this work a novel active learning methodology based on learning reconstruction manifolds with Deep Autoencoders for seismic interpretation. Autoencoders refer to a family of learning models that are trained to reconstruct their inputs. They are designed so that they are only able to reconstruct data sampled from the training distribution, preventing them from regressing to a simple identity mapping. As a useful by-product, they are able to learn the manifold structure of high dimensional data. We utilize such a learned manifold for the task of anomaly detection on image datasets by thresholding the distribution of reconstruction error-based scores on input training examples.