Use of Artificial Intelligence towards Automation of Analog Seismogram Digitization

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Seismometers have been recording ground motion since the late 1800s, and about 100 years of the recordings are in analog form (e.g., paper recordings) that cannot be examined using modern techniques. These data contain information about earthquakes, volcano eruptions, subsurface changes, changes in weather pattern, to name the few, and are vital for understanding various phenomena that affect the Earth and how they evolve over time. This project aims to make a significant step forward in converting these analog data into usable digital format by introducing artificial intelligence to the conversion process. Currently, there is a software that takes a record image and generates digital seismograms, but it requires substantial human interaction making this process slow, impractical, or impossible. Successful implementation of artificial intelligence will allow more data to be processed quickly for use by the scientific community, which is a significant broader impact. The project will start by examining the digitized analyses to determine and build the training database to be used for the construction of the neural network. The investigators will also identify steps that will benefit most from implementation of artificial intelligence procedures to decrease human interaction and improve accuracy of the digitization of seismograms. Neural networks for image classification and object identification are now available and will be examined to find the algorithm that is most suitable for the seismogram digitization process. The improved digitization software will be openly available to increase users and provide a robust tool to convert analog seismogram images to research-quality digital seismograms. It will enable the seismological community to retrieve data that for application of modern analyses, and open opportunities for new types of research to be done.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.