| --- |
| tags: |
| - seismic |
| - multiples |
| - denoising |
| - unet |
| - resunet |
| - dncnn |
| - attention-unet |
| - pytorch |
| library_name: pytorch |
| --- |
| |
| # Marine Multiples Attenuation Benchmark |
|
|
| Deep-learning-based free-surface multiple attenuation on marine pre-stack seismic shot gathers. |
|
|
| ## Task |
|
|
| Given a shot gather containing primaries and free-surface multiples, the model predicts the additive multiple component. The attenuated primary estimate is obtained by: |
|
|
| ``` |
| denoised = noisy_input - predicted_noise |
| ``` |
|
|
| This is a **paired regression** task trained with a noise-label objective. The supervision target is the multiple wavefield stored in the paired noise-label volume. |
|
|
| ## Dataset |
|
|
| - **Input volume**: `/data/shared/benchmark/multiples/noisy/total_nodw.sgy` |
| - **Multiple-label volume**: `/data/shared/benchmark/multiples/noise/multiples.sgy` |
| - **Geometry**: regular shot gathers with 638 traces per shot |
| - **Split**: shot-level sequential split from the training configs, typically 510 train shots, 64 validation shots, and 64 held-out test shots |
|
|
| The uploaded checkpoints are trained on the fixed paired marine multiples benchmark used by `scripts/multiples_attenuation`. |
|
|
| Metrics are computed on the held-out test shots in the normalized domain. If a batch-evaluation workbook is supplied, its values are embedded below. |
|
|
| ## Model Architectures |
|
|
| - **DNNDAT** (`dnndat`) — DNNDAT-style convolutional encoder-decoder for marine multiple suppression (Wang et al., 2022). U-Net-like encoder-decoder with 28 convolutional layers and dropout. |
| - **SAGAN** (`sagan`) — Self-attention GAN generator for seismic surface-related multiple suppression (Tao et al., 2022). U-Net generator with a bottleneck self-attention block. |
|
|
| ## Uploaded Checkpoints |
|
|
| - DNNDAT: 4 checkpoints |
| - SAGAN: 3 checkpoints |
|
|
| ### Preprocessing |
|
|
| - **Normalization**: `max_abs`, global scope — the entire dataset scaled to [-1, 1] |
| - **Patching**: overlapping 2D patches, usually 256 traces × 512 time samples with 50% overlap |
| - **Tensor format**: PyTorch NCHW patches `(batch, 1, trace, time)` |
|
|
| ## Repository Structure |
|
|
| ``` |
| models/ |
| ├── unet/ |
| │ ├── seed42/ |
| │ │ ├── best.pt # Best checkpoint (minimum validation loss) |
| │ │ └── config.yaml # Full training configuration |
| │ ├── seed43/ |
| │ ├── seed44/ |
| │ └── ... |
| └── res_unet/ |
| └── ... |
| ``` |
|
|
| Each subdirectory corresponds to one experiment: a model architecture trained with a specific random seed. |
|
|
| ## Training Details |
|
|
| | Hyperparameter | Value | |
| |----------------|-------| |
| | Loss | MSE on the predicted multiple/noise component | |
| | Optimizer | Adam / AdamW / SGD, depending on model config | |
| | Scheduler | Cosine annealing (min_lr=1e-6) | |
| | Epochs | 100-200, depending on model config | |
| | Gradient clipping | 1.0 (max norm) | |
| | Seeds | 42, 43, 44 per experiment | |
| |
| ## Usage |
| |
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
|
|
| # Download a checkpoint |
| repo = "GeoBrain/multiples-attenuation" |
| model_key = "res_unet" |
| seed = "42" |
|
|
| ckpt_path = hf_hub_download( |
| repo_id=repo, |
| filename=f"models/{model_key}/seed{seed}/best.pt", |
| ) |
| |
| # Load state dict |
| state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True) |
|
|
| # For full model loading, instantiate the corresponding architecture |
| # and load the state dict (see config.yaml for exact architecture params). |
| ``` |
| |
| See the companion benchmark documentation for detailed experimental setup and full evaluation results. |
| |
| ## Results |
| |
| *Results pending — run batch_evaluate.py to populate.* |
| |
| |
| ## References |
| |
| - Ronneberger et al., U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI 2015 |
| - He et al., Deep Residual Learning for Image Recognition, CVPR 2016 |
| - Zhang et al., Image Denoising via Deep CNN (DnCNN), IEEE TIP 2017 |
| - Oktay et al., Attention U-Net: Learning Where to Look for the Pancreas, MIDL 2018 |
| - Kiraz et al., Attenuating free-surface multiples and ghost reflection from seismic data using a trace-by-trace convolutional neural network approach, Geophysical Prospecting 2024 |
| - Tao et al., Seismic Surface-Related Multiples Suppression Based on SAGAN, IEEE Geoscience and Remote Sensing Letters 2022 |
| - Wang et al., Seismic multiple suppression based on a deep neural network method for marine data, Geophysics 2022 |
| |
