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README.md

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+---
+tags:
+- seismic
+- ground-roll
+- denoising
+- seg-c3
+- geophysics
+- synthetic
+task_categories:
+- image-to-image
+- other
+size_categories:
+- 10M-100M
+pretty_name: SEG C3 Ground-Roll Dataset
+---
+
+# SEG C3 Ground-Roll Dataset
+
+Paired noisy-input / noise-label SEG-Y volumes for supervised ground-roll attenuation, derived from the SEG C3 synthetic velocity model.
+
+## Task
+
+**Noise-label regression**: given a noisy pre-stack shot gather, predict the additive ground-roll noise component. The clean signal is recovered as:
+
+```
+denoised = noisy_input - predicted_noise
+```
+
+The noise labels serve as regression targets. Both input and label are 3D SEG-Y volumes with identical geometry.
+
+## Dataset Description
+
+- **Source**: SEG C3 synthetic velocity model ([wiki.seg.org/wiki/C3](https://wiki.seg.org/wiki/C3))
+- **Geometry**: 9 regular shot gathers, 201 traces × 625 time samples per shot, dt = 2 ms
+- **Noise modeling**: Reflection signals modeled with the acoustic wave equation; ground roll modeled with the elastic wave equation to capture its dispersive, low-velocity character
+- **Noise intensity levels**: 1.0, 3.0, 5.0, 7.0, 9.0
+- **Format**: Pre-stack SEG-Y (revision 1), IBM float
+
+## File Structure
+
+Each noise level has a matched pair of SEG-Y files:
+
+| Level | Noisy Input | Noise Label | Size (approx) |
+|-------|------------|-------------|---------------|
+| 1.0 | SEGC3_shots1_9_noisy_1.0.sgy | SEGC3_shots1_9_noise_1.0.sgy | ~951 MB × 2 |
+| 3.0 | SEGC3_shots1_9_noisy_3.0.sgy | SEGC3_shots1_9_noise_3.0.sgy | ~951 MB × 2 |
+| 5.0 | SEGC3_shots1_9_noisy_5.0.sgy | SEGC3_shots1_9_noise_5.0.sgy | ~951 MB × 2 |
+| 7.0 | SEGC3_shots1_9_noisy_7.0.sgy | SEGC3_shots1_9_noise_7.0.sgy | ~951 MB × 2 |
+| 9.0 | SEGC3_shots1_9_noisy_9.0.sgy | SEGC3_shots1_9_noise_9.0.sgy | ~951 MB × 2 |
+
+**Total**: 5 noisy + 5 noise SEG-Y files
+
+## Loading Data
+
+```python
+import segyio
+import numpy as np
+
+def read_shot_gather(path, traces_per_shot=201):
+    '''Read a regular SEG-Y file into (n_shots, n_traces, n_time).'''
+    with segyio.open(path, "r", strict=False) as src:
+        n_traces_total = src.tracecount
+        n_shots = n_traces_total // traces_per_shot
+        n_time = src.samples.size
+        data = np.zeros((n_shots, traces_per_shot, n_time), dtype=np.float32)
+        for i in range(n_shots):
+            for j in range(traces_per_shot):
+                data[i, j, :] = src.trace[i * traces_per_shot + j]
+    return data
+
+# Load a level-3.0 pair
+noisy  = read_shot_gather("noisy/SEGC3_shots1_9_noisy_3.0.sgy")
+noise  = read_shot_gather("noise/SEGC3_shots1_9_noise_3.0.sgy")
+signal = noisy - noise   # clean reference
+```
+
+With `huggingface_hub`:
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="GeoBrain/seg-c3-ground-roll",
+    filename="noisy/SEGC3_shots1_9_noisy_3.0.sgy",
+    repo_type="dataset",
+)
+```
+
+## Train / Val / Test Split
+
+Shot-level sequential split by FFID (field file ID), avoiding trace leakage:
+
+| Split | Shots | Fraction |
+|-------|-------|----------|
+| Train | 7     | 77.8%    |
+| Val   | 1     | 11.1%    |
+| Test  | 1     | 11.1%    |
+
+The split is done at loading time (not pre-saved as separate files) so users can adjust the ratios.
+
+## Preprocessing Recipe
+
+The companion benchmark applies:
+
+1. **Normalization**: `max_abs`, global scope — the entire noisy volume scaled to [-1, 1]; same stats applied to the noise label
+2. **Patching**: Overlapping 2D patches (128 traces × 256 time samples), 50% overlap, yielding (1, H, W) tensors
+
+No spherical-divergence correction is applied (raw amplitudes are used).
+
+## Benchmark Results
+
+See the companion model repository for full benchmark results across UNet, ResUNet, DnCNN, and Attention UNet architectures at each noise level.
+
+## Citation
+
+If you use this dataset, please cite the SEG C3 model and the companion benchmark:
+
+```bibtex
+@misc{seg_c3_ground_roll,
+  title={SEG C3 Ground-Roll Attenuation Benchmark},
+  howpublished={https://huggingface.co/datasets/GeoBrain/seg-c3-ground-roll},
+}
+```
+
+## References
+
+- SEG C3 Velocity Model: https://wiki.seg.org/wiki/C3
+- `segyio` library: https://github.com/equinor/segyio