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Optimal Interpolation (OI) is a widely used, highly trusted algorithm forinterpolation and reconstruction problems in geosciences. With the influx ofmore satellite missions, we have access to more and more observations and it isbecoming more pertinent to take advantage of these observations in applicationssuch as forecasting and reanalysis. With the increase in the volume ofavailable data, scalability remains an issue for standard OI and it preventsmany practitioners from effectively and efficiently taking advantage of theselarge sums of data to learn the model hyperparameters. In this work, weleverage recent advances in Neural Fields (NerFs) as an alternative to the OIframework where we show how they can be easily applied to standardreconstruction problems in physical oceanography. We illustrate the relevanceof NerFs for gap-filling of sparse measurements of sea surface height (SSH) viasatellite altimetry and demonstrate how NerFs are scalable with comparableresults to the standard OI. We find that NerFs are a practical set of methodsthat can be readily applied to geoscience interpolation problems and weanticipate a wider adoption in the future.

Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables