Bidirectional long short-term memory-based subsurface sound speed profile inversion from satellite data with sparse observations.

Satellite-derived sea surface remote sensing data enable large-scale subsurface sound speed profile (SSP) inversion; however, considerable inversion errors commonly arise due to the lack of in situ constraints. To address this limitation, this Letter proposes a bidirectional long short-term memory-based framework that integrates sparse observations to improve inversion accuracy. Experiments in the Kuroshio Extension and the Philippine Sea demonstrate that sparse observations yield improved inversion accuracy, with remarkable improvement without sea surface data. Depth-layer sensitivity analysis reveals that optimal sparse sampling depth intervals differ under scenarios with and without sea surface data, offering guidance for rational underwater observation deployment.