Mauricio Araya-Polo is a Senior R&D Manager working at Total E\&P RT USA. He is also an Adjunct Professor at the Computational and Applied Mathematics Department, Rice University. He is currently leading efforts on diverse areas such as: Seismic Imaging/Modeling, Machine Learning and High-Performance Computing (HPC). Previously, he led Geophysics and Machine Learning efforts for Shell International E\&P Inc. Before that, he worked at Repsol USA researching on near real-time visualization/computing and HPC for geophysical algorithms. Before that position, he was senior computer scientist at the Barcelona Supercomputing Center. He holds a PhD and MSc from INRIA-UNSA in France and Computer Engineering degree from the University of Chile.
We propose a new metric that can address some of the shortcomings of widely use metrics that were originally designed for traditional ML tasks not related to our field. The motivation is automatic quality improvement measurement, which is not quite well addressed by our vibrant community. Also, we will cover a use case for the proposed metric that is related to transfer learning in the context of seismic inversion.