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Densifying Sparse Ocean Depth Observations

DepthDif explores conditional diffusion for reconstructing dense subsurface ocean temperature fields from sparse, masked observations.

The repository currently supports:
- EO-conditioned multi-band reconstruction (surface condition + deeper target bands)
- cross-source conditioning where EO surface SST can come from OSTIA while deeper targets remain Copernicus reanalysis
- latent diffusion workflow with autoencoder-based depth compression (see Autoencoder)

Model Description

depthdif_schema

DepthDif is a conditional diffusion model: it reconstructs dense depth fields from corrupted submarine observations, conditioned on EO (surface) data plus sparse corrupted subsurface input. Synthetic sparse inputs are generated with continuous curved trajectory masks to mimic submarine movement; in the current dataset version, each track keeps one measurement every few pixels (random 2-8 pixel stride) until the configured corruption percentage is reached. It can inject coordinate/date context via FiLM conditioning and reconstruct the full target image. See the full model details in Model.

In the OSTIA setup, EO surface conditioning comes from mid-month OSTIA SST snapshots (15th, 12:00 UTC), while subsurface targets remain monthly Copernicus reanalysis.

Ambient diffusion (short): at step t, x_t = sqrt(alpha_bar_t) * x_0 + sqrt(1 - alpha_bar_t) * epsilon, epsilon ~ N(0, I).
For ambient-occlusion training with observed mask m and further-corrupted mask m' <= m, optimize
L on the original x support intersected with valid target support (x_valid_mask ∩ y_valid_mask) while conditioning on the stronger corruption m'.

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