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README.md

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+---
+tags:
+- seismic
+- multiples
+- denoising
+- marine-seismic
+- geophysics
+- synthetic
+task_categories:
+- image-to-image
+- other
+size_categories:
+- 1G-10G
+pretty_name: Marine Multiples Attenuation Dataset
+---
+
+# Marine Multiples Attenuation Dataset
+
+Paired noisy-input / multiples-noise-label SEG-Y volumes for supervised marine multiples attenuation.
+
+## Task
+
+**Noise-label regression**: given a noisy pre-stack shot gather, predict the additive multiples component. The denoised signal is recovered as:
+
+```text
+denoised = noisy_input - predicted_multiples
+```
+
+The uploaded noise label is the supervised target. The clean reference used by the benchmark is computed as:
+
+```text
+clean_reference = noisy_input - multiples_label
+```
+
+## Dataset Description
+
+- **Noisy input**: `noisy/total_nodw.sgy`
+- **Multiples label**: `noise/multiples.sgy`
+- **Geometry used by benchmark configs**: 638 traces per shot, 1,976 time samples per trace
+- **Format**: Pre-stack SEG-Y, paired volumes with matching geometry
+
+
+### Noisy Shot Gather
+
+<div align="center">
+
+  <img src="assets/multiples/noisy1.png" alt="Noisy Shot Gather example 1" width="48%">
+  <img src="assets/multiples/noisy2.png" alt="Noisy Shot Gather example 2" width="48%">
+  <img src="assets/multiples/noisy3.png" alt="Noisy Shot Gather example 3" width="48%">
+
+</div>
+
+<div align="center"><i>Example 1 &nbsp;|&nbsp; Example 2 &nbsp;|&nbsp; Example 3 - noisy shot gather for a representative shot gather.</i></div>
+
+
+### Clean Reference Signal
+
+<div align="center">
+
+  <img src="assets/multiples/clean1.png" alt="Clean Reference Signal example 1" width="48%">
+  <img src="assets/multiples/clean2.png" alt="Clean Reference Signal example 2" width="48%">
+  <img src="assets/multiples/clean3.png" alt="Clean Reference Signal example 3" width="48%">
+
+</div>
+
+<div align="center"><i>Example 1 &nbsp;|&nbsp; Example 2 &nbsp;|&nbsp; Example 3 - clean reference signal for a representative shot gather.</i></div>
+
+
+### Multiples Noise Label
+
+<div align="center">
+
+  <img src="assets/multiples/noise1.png" alt="Multiples Noise Label example 1" width="48%">
+  <img src="assets/multiples/noise2.png" alt="Multiples Noise Label example 2" width="48%">
+  <img src="assets/multiples/noise3.png" alt="Multiples Noise Label example 3" width="48%">
+
+</div>
+
+<div align="center"><i>Example 1 &nbsp;|&nbsp; Example 2 &nbsp;|&nbsp; Example 3 - multiples noise label for a representative shot gather.</i></div>
+
+
+## File Structure
+
+| Kind | Path | Size |
+|------|------|------|
+| noise | `noise/multiples.sgy` | 3161.4 MB |
+| noisy | `noisy/total_nodw.sgy` | 3161.4 MB |
+
+**Total**: 1 noisy + 1 noise SEG-Y files
+
+## Loading Data
+
+```python
+import segyio
+import numpy as np
+
+def read_shot_gather(path, traces_per_shot=638):
+    '''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
+
+noisy = read_shot_gather("noisy/total_nodw.sgy", traces_per_shot=638)
+multiples = read_shot_gather("noise/multiples.sgy", traces_per_shot=638)
+clean_reference = noisy - multiples
+```
+
+With `huggingface_hub`:
+
+```python
+from huggingface_hub import hf_hub_download
+
+noisy_path = hf_hub_download(
+    repo_id="GeoBrain/multiples",
+    filename="noisy/total_nodw.sgy",
+    repo_type="dataset",
+)
+```
+
+## Benchmark Split
+
+The companion benchmark uses shot-level FFID splitting to avoid trace leakage. The current multiples configs use:
+
+| Split | Shots |
+|-------|-------|
+| Train | 510 |
+| Val | 64 |
+| Test | 64 |
+
+The split is done at loading time, so users can adjust it in their own configs.
+
+## Preprocessing Recipe
+
+The companion benchmark applies:
+
+1. **Normalization**: `max_abs`, global scope on the noisy input; the same scale is applied to the multiples label.
+2. **Patching**: overlapping 2D patches on the trace-time plane.
+3. **Metric handling**: SNR can skip near-zero clean-reference patches with `min_signal_energy`.
+
+No spherical-divergence correction is applied in the denoising training script.
+
+## Citation
+
+If you use this dataset, cite the dataset repository:
+
+```bibtex
+@misc{marine_multiples_attenuation,
+  title={Marine Multiples Attenuation Dataset},
+  howpublished={https://huggingface.co/datasets/GeoBrain/multiples},
+}
+```
+
+## References
+
+- `segyio` library: https://github.com/equinor/segyio