Reconstructing subsurface sandbody connectivity from temporal evolution of surface networks
Bibliographic record
Abstract
Abstract Characterization of groundwater aquifers and hydrocarbon reservoirs requires an understanding of the distribution and connectivity of subsurface sandbodies. In deltaic environments, distributary channel networks serve as the primary conduits for water and sediment. Once these networks are buried and translated into the subsurface, the coarse‐grained channel fills serve as primary conduits for subsurface fluids such as water, oil or gas. The temporal evolution of channels on the surface therefore plays a first‐order role in the 3D permeability and connectivity of subsurface networks. Land surface imagery is more broadly available than topographic or subsurface data, and time‐series imagery of river networks can hold useful information for constraining the shallow subsurface. However, these reconstructions require an understanding of the degree to which channel bathymetry and river kinematics affect connectivity of subsurface sandbodies. Here, we present a novel method for building synthetic cross sections using overhead images of an experimental delta. We use principal components analysis to extract river networks from surface imagery, then couple this with an inverse‐CDF method to estimate channel bathymetry, to generate a time‐series of synthetic delta topography. This synthetic topography is then transformed, accounting for deposition and subsidence, to produce synthetic stratigraphy that differentiates coarse‐grained channel fill from overbank and offshore deposition. We find that large‐scale subsurface architecture is relatively insensitive to details of channel bathymetry, but instead is primarily controlled by channel location and kinematics. We analyse the connectivity of sand bodies and the geometries of barriers to flow and find that periods of rapid sea‐level rise have more variability in sand body connectivity. We also find that barrier width decreases downstream during all sea‐level phases. Our method generates synthetic stratigraphy that closely resembles the large‐scale architecture and 2‐dimensional connectivity of the real stratigraphy built during the experiment it was based on. We anticipate that it will be broadly applicable to other experimental and field‐scale scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".