subsurfacegen/field-scale-dataset-preview

Field-Scale Dataset — Preview Sample

A curated single-family preview of the subsurfacegen/field-scale-dataset full release. Designed for reviewers to download in under 5 minutes and inspect the dataset's content + structure without pulling the full 12 TB.

📊 Quick visual tour

All five figures from this preview are pre-rendered below (and again, fully interactive, in view_preview.ipynb). The notebook is rendered inline by HuggingFace with embedded plot outputs, so reviewers can see every figure on this page without downloading or running anything.

Above: shot-gather panels. Velocity model on the left shows the three color-coded source positions (Left/Middle/Right = source idx 8/32/56); the three dark panels on the right are the corresponding 8 s gathers recorded by the 1,000-receiver streamer. Generated from the included shot_gathers/8s/3-25Hz/...gom_151_il_0507.h5.

This preview ships exactly the four HDF5 files that produce the manuscript's intro figure, so reviewers can reproduce that figure 1-for-1. Contents:

File	Size	What it is
models/gom_d619/gom_151_sos.h5	2.19 GB	3D SOS-smoothed velocity volume — Gulf of Mexico realization 151. Shape (619, 1000, 1000) on a 10 m grid. HDF5 key velocity.
slices/slice_gom_151_il_0507.h5	1.1 MB	2D inline-507 velocity slice extracted from the volume above. Shape (619, 1000). HDF5 key velocity.
wavefields/5s/3-6Hz/wavefield_gom_151_il_0507_srchorizontal4.958km.h5	508 MB	5 s acoustic wavefield, 3-6 Hz band, single source at horizontal x = 4.958 km. Shape (358, 1000, 619) = (nt, nx, nz). HDF5 key wavefield.
shot_gathers/8s/3-25Hz/shot_gather_cube_gom_151_il_0507.h5	100 MB	8 s shot-gather cube, 3-25 Hz band, 64 sources stacked. Shape (64, 572, 1000) = (n_src, nt, n_rec). HDF5 key shot_gather_cube.
Total	~2.80 GB

Plus a slimmed data/all.parquet (4 rows) covering only these four files and the same 25-column schema as the full repo.

Visual tour (all 5 figures)

The five panels below are PNGs rendered by view_preview.py from the four HDF5 files included here. The same plots appear interactively in view_preview.ipynb.

1. 3D cutaway of the SOS-smoothed velocity volume

Three exposed faces (top, front, left) of the 619 × 1000 × 1000 volume on a 10 m grid, diverging seismic colormap centered at 3100 m/s.

2. Three orthogonal slices through the volume

The 2D slice we ship (slice_gom_151_il_0507.h5) is the inline-507 panel on the left. Red dashed lines mark where each panel's slice was taken in the other two views.

3. 2D velocity slice with acquisition geometry

slice_gom_151_il_0507.h5 overlaid with the streamer (every 25th of 1000 receivers @ 10 m depth, 0.6–9.4 km) and the gold star marking the wavefield source x = 4.958 km.

4. Wavefield time progression

Six time snapshots (0.20 → 5.00 s) from the included 3-6 Hz wavefield. Velocity model in grayscale (alpha 0.4) sits behind the wavefield amplitude (seismic-alpha cmap, transparent near zero).

5. Shot-gather panels (manuscript intro figure)

1×4 layout: velocity model with the three color-coded source positions on the left, then the three corresponding shot-gather panels (Left/Middle/Right = source idx 8/32/56) recorded over the 8 s simulation.

Quick start

# 1. Download the preview repo (replace ./preview_data with any local path)
pip install huggingface_hub h5py hdf5plugin numpy matplotlib
huggingface-cli download subsurfacegen/field-scale-dataset-preview \
    --repo-type=dataset --local-dir=./preview_data
cd ./preview_data

# 2. Render all 5 figures (3D cutaway, orthogonal slices, 2D slice w/ geometry,
#    wavefield time progression, shot-gather panels) to ./figs/
python view_preview.py --plot all --output-dir ./figs

The view_preview.ipynb notebook in this repo also embeds all five figures inline — HuggingFace renders the saved outputs directly on the dataset page, so you can read through the visual narrative without downloading anything.

How this sample was created

We selected the gom_151_il_0507 family because it is the exact sample family shown in the manuscript's intro figure. The recipe is fully reproducible against the full dataset:

import pandas as pd
df = pd.read_parquet("data/all.parquet")  # full dataset's parquet
family = df[df.slice_id == "gom_151_il_0507"]
# Returns 1 slice + 5 wavefields (one per band) + 5 gathers (one per band) = 11 rows.
# We kept the 3-6 Hz wavefield (matches the manuscript figure) and the 3-25 Hz
# shot-gather cube (richest source bandwidth). Total = 4 files.

Properties of this sample family in the full parquet index:

Property	Value
model_id	gom_151
model_type	gom (Gulf of Mexico)
slice_id	gom_151_il_0507
orientation	inline
slice_index	507
slice_location_m	5070 (= 507 × 10 m grid)
split (slice + wavefield + gather)	train
split (3D model row)	null (3D models are not split-assigned by design)
Wavefield source x	4.958031 km (uniformly random along the slice)
Wavefield band	3-6 Hz (1 of 5 bands; lowest-frequency option)
Gather band	3-25 Hz (1 of 5 bands; widest bandwidth)
Gather sources	64 equally-spaced from 0.6 to 9.4 km

Schema (matches the full dataset)

The slimmed data/all.parquet has the same 25 columns as the full repo. The key columns for joining the four files are:

• slice_id — links a slice to its derived wavefields and shot-gathers
• model_id — links a slice to its source 3D volume
• data_type — one of model | slice | wavefield | gather

See the full repo's README for the complete column list and definitions.

Numerical simulation parameters

Parameter	Value
Solver	Devito FDTD (Louboutin et al., 2019)
Grid spacing	10 m × 10 m
Time step (native)	1.0 ms
Wavefield temporal subsample	factor 14 (effective dt = 14 ms)
FD stencil space order	8
Absorbing boundary	60-cell sponge
Top boundary	Free surface
Receivers	1,000 per slice, streamer at 10 m depth, 0.6-9.4 km
Source depth	10 m
Wavefield source wavelet	band-limited Ricker, 3-6 Hz, peak f₀ = 4.5 Hz
Gather source wavelet	band-limited Ricker, 3-25 Hz, peak f₀ = 14.0 Hz
Random seed	42 (reproducible source placement)

Loading the files

import h5py
import hdf5plugin   # registers the compression filter used by wavefields + gathers
import json

# 3D velocity volume — root attr "metadata" is a JSON string
with h5py.File("models/gom_d619/gom_151_sos.h5", "r") as f:
    vol = f["velocity"][:]            # (619, 1000, 1000) float32
    meta = json.loads(f.attrs["metadata"])

# 2D slice — dataset attr "metadata_json"
with h5py.File("slices/slice_gom_151_il_0507.h5", "r") as f:
    sl = f["velocity"][:]             # (619, 1000) float32
    sl_meta = json.loads(f["velocity"].attrs["metadata_json"])

# Wavefield — dataset attr "metadata_json"
with h5py.File("wavefields/5s/3-6Hz/wavefield_gom_151_il_0507_srchorizontal4.958km.h5", "r") as f:
    wf = f["wavefield"][:]            # (358, 1000, 619)  = (nt, nx, nz)
    wf_meta = json.loads(f["wavefield"].attrs["metadata_json"])

# Shot-gather cube — dataset attr "metadata_json"
with h5py.File("shot_gathers/8s/3-25Hz/shot_gather_cube_gom_151_il_0507.h5", "r") as f:
    cube = f["shot_gather_cube"][:]   # (64, 572, 1000) = (n_src, nt, n_rec)
    sg_meta = json.loads(f["shot_gather_cube"].attrs["metadata_json"])

License

This preview is released under CC BY 4.0, matching the full dataset's license.

Citation

@dataset{field_scale_dataset_preview,
  title={Field-Scale Dataset (Preview): single-family sample of SOS-smoothed velocity volumes,
         2D slices, wavefields, and shot-gather cubes},
  author={Anonymous},
  year={2026},
  url={https://huggingface.co/datasets/subsurfacegen/field-scale-dataset-preview},
}

Contact

Removed for anonymous review. Full dataset and contact information will be restored after acceptance.