New paradigms in seismic data preconditioning and imaging via Deep Neural Networks

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The main goal of geophysical methods is to estimate images of the interior of the earth. These images are central information for exploring resources and understanding processes that occur in the subsurface. For instance, in applied seismology, an experiment is carried out where seismic waves are injected into the subsurface. Geological interfaces reflect seismic waves and propagate them back to the surface. Sensors on the earth's surface record these reflected waves. The data gathered by sensors are processed via computational methods that turn recorded signals into images of the earth's interior. Computational approaches to map seismic data into subsurface images are categorized into two main groups: preconditioning and inversion methods. Preconditioning and inversion require solving numerical problems with additional relevant prior information. This prior information is traditionally provided via constraints that ensure solutions with a given character (e.g. smoothness). In recent years, the field of machine learning has provided technologies that permit learning the aforementioned prior information and, by this means, finding efficient and accurate answers to many numerical problems that involve processing and inversion of data. Of particular relevance to the proposed plan are Deep Neural Networks. These are robust machine learning tools with incredible performance in image classification and segmentation tasks. Deep Neural Networks allow performing complex tasks such as those required for face recognition, autonomous driving, and diagnostic medical imaging. The proposed research plan will investigate different means to incorporate Deep Neural Networks into the solution of data preconditioning and inverse problems that arise in applied seismology. We will explore regularization methods based on Deep Neural Networks to find fast and stable solutions to seismic inverse problems. Similarly, we also plan to investigate signal processing methods based on Deep Neural Networks and use these methods for preconditioning tasks such as seismic signal enhancement and seismic data reconstruction. I will be training 3 Ph.D. students, 3 MSc students, 3 PDFs, and 5 undergraduate research assistants in the foundations of seismic data processing, seismic imaging, and machine learning. This reseach program will increase scientific knowledge on the application of machine learning in geosciences, particularly in the development of seismic data preconditioning and imaging methods. I expect students trained via the proposed plan to pursue careers in either academia or industry and to gain sufficiently flexible training to apply their skills to any industry requiring tools to acquire, enhance and extract valuable information from large datasets.