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README.md

@@ -38,18 +38,18 @@ Five ground-roll intensity levels produce paired noisy / noise-label records:
 
 | Level | SNR (dB) | PSNR (dB) | SSIM | MAE | MSE | RMSE |
 |-------|----------|-----------|------|-----|-----|------|
-| 1.0   | 2.7129   | 21.8322   | 0.9527 | 0.015312 | 0.006558 | 0.080982 |
-| 3.0   | -6.8295  | 18.3104   | 0.9477 | 0.022968 | 0.014756 | 0.121473 |
-| 5.0   | -11.2665 | 17.3952   | 0.9466 | 0.025520 | 0.018217 | 0.134970 |
-| 7.0   | -14.1891 | 16.9715   | 0.9461 | 0.026796 | 0.020084 | 0.141719 |
-| 9.0   | -16.3720 | 16.7268   | 0.9458 | 0.027561 | 0.021248 | 0.145768 |
+| 1.0   | 2.71     | 15.81     | 0.9480 | 0.030624 | 0.026232 | 0.161964 |
+| 3.0   | -6.83    | 6.27      | 0.9418 | 0.091871 | 0.236091 | 0.485892 |
+| 5.0   | -11.27   | 1.83      | 0.9402 | 0.153118 | 0.655809 | 0.809820 |
+| 7.0   | -14.19   | -1.09     | 0.9395 | 0.214366 | 1.285386 | 1.133749 |
+| 9.0   | -16.37   | -3.27     | 0.9390 | 0.275613 | 2.124822 | 1.457677 |
 
-*Metrics computed on the test set (2D flattened shot gathers) in the normalized domain before denoising.*
+*Metrics computed on the full dataset in the original amplitude domain before normalization.*
 
 ## Model Architectures
 
-- **ResUNet** (`res_unet`) — U-Net with residual blocks replacing plain double-conv layers (He et al., 2016; Zhang et al., 2018). Base channels: 32, depth: 4.
-- **UNet** (`unet`) — Classic encoder-decoder with skip connections (Ronneberger et al., 2015). Base channels: 32, depth: 4.
+- **Attention UNet** (`atten_unet`) — U-Net with additive attention gates on skip connections (Oktay et al., 2018, MIDL). Base channels: 32, depth: 4.
+- **DnCNN** (`dncnn`) — Flat 17-layer Conv-BN-ReLU stack with residual learning (Zhang et al., 2017, IEEE TIP). Base channels: 64.
 
 ### Preprocessing
 
@@ -78,11 +78,13 @@ Each subdirectory corresponds to one experiment: a model architecture trained at
 
 | Hyperparameter | Value |
 |----------------|-------|
-| Loss | MSE (noise-prediction models) / GAN+L1 (pix2pix) / L1 (DDPM) / hybrid MSE+AFM (enhanced) |
-| Optimizer | Adam / AdamW (lr=1e-4–1e-3, varies per model) |
+| Loss | MSE (predicted noise vs. label noise) |
+| Optimizer | AdamW (lr=1e-3, weight_decay=1e-4) |
 | Scheduler | Cosine annealing (min_lr=1e-6) |
-| Epochs | 100–200 (varies per model) |
+| Epochs | 200 |
 | Gradient clipping | 1.0 (max norm) |
+| Batch size | 196 |
+| Distributed training | 2 × NVIDIA RTX 4090, DDP |
 | Seeds | 42, 43, 44 per experiment |
 
 ## Usage
@@ -111,60 +113,17 @@ state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
 
 See the companion benchmark documentation for detailed experimental setup and full evaluation results.
 
-## Results
+## Results (SNR dB)
 
-Mean ± std over 3 seeds on the held-out test shot (FFID=9), evaluated on 2D-flattened data in the normalized domain. Raw (noisy) is the input before denoising.
-
-### Noise Level 1.0
-
-| Method | Params (M) | SNR (dB) | PSNR (dB) | SSIM | MAE | MSE | RMSE |
-| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Raw (noisy) | — | 2.7129±0.0000 | 21.8322±0.0000 | 0.9527±0.0000 | 0.015312±0.000000 | 0.006558±0.000000 | 0.080982±0.000000 |
-| UNet | 7.76 | 28.3886±1.0929 | 47.5080±1.0930 | 0.9972±0.0004 | 0.000673±0.000129 | 0.000018±0.000004 | 0.004235±0.000524 |
-| ResUNet | 8.11 | 31.6226±0.5664 | 50.7420±0.5664 | 0.9980±0.0001 | 0.000659±0.000105 | 0.000008±0.000001 | 0.002907±0.000186 |
-| DnCNN | 0.14 | 19.7355±0.3250 | 38.8548±0.3250 | 0.9846±0.0014 | 0.004684±0.000236 | 0.000130±0.000010 | 0.011415±0.000425 |
-| Attention UNet | 7.85 | 29.4786±1.3513 | 48.5979±1.3514 | 0.9974±0.0007 | 0.000577±0.000109 | 0.000014±0.000004 | 0.003745±0.000557 |
-
-### Noise Level 3.0
-
-| Method | Params (M) | SNR (dB) | PSNR (dB) | SSIM | MAE | MSE | RMSE |
-| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Raw (noisy) | — | -6.8295±0.0000 | 18.3104±0.0000 | 0.9477±0.0000 | 0.022968±0.000000 | 0.014756±0.000000 | 0.121473±0.000000 |
-| UNet | 7.76 | 23.0729±0.0993 | 48.2128±0.0994 | 0.9961±0.0001 | 0.000676±0.000022 | 0.000015±0.000000 | 0.003885±0.000044 |
-| ResUNet | 8.11 | 22.6469±0.9303 | 47.7869±0.9303 | 0.9924±0.0015 | 0.001194±0.000660 | 0.000017±0.000003 | 0.004095±0.000424 |
-| DnCNN | 0.14 | 17.2389±0.2322 | 42.3788±0.2322 | 0.9883±0.0008 | 0.002676±0.000161 | 0.000058±0.000003 | 0.007606±0.000205 |
-| Attention UNet | 7.85 | 24.3939±1.2177 | 49.5338±1.2177 | 0.9964±0.0011 | 0.000630±0.000141 | 0.000011±0.000003 | 0.003358±0.000453 |
-
-### Noise Level 5.0
-
-| Method | Params (M) | SNR (dB) | PSNR (dB) | SSIM | MAE | MSE | RMSE |
-| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Raw (noisy) | — | -11.2665±0.0000 | 17.3952±0.0000 | 0.9466±0.0000 | 0.025520±0.000000 | 0.018217±0.000000 | 0.134970±0.000000 |
-| UNet | 7.76 | 20.2195±0.3210 | 48.8813±0.3210 | 0.9958±0.0001 | 0.000656±0.000033 | 0.000013±0.000001 | 0.003599±0.000133 |
-| ResUNet | 8.11 | 18.1078±2.1170 | 46.7695±2.1170 | 0.9898±0.0021 | 0.001265±0.000143 | 0.000023±0.000011 | 0.004679±0.001134 |
-| DnCNN | 0.14 | 15.5662±0.2235 | 44.2280±0.2236 | 0.9898±0.0006 | 0.002042±0.000221 | 0.000038±0.000002 | 0.006147±0.000159 |
-| Attention UNet | 7.85 | 21.9546±1.0678 | 50.6163±1.0678 | 0.9965±0.0011 | 0.000618±0.000068 | 0.000009±0.000002 | 0.002961±0.000359 |
-
-### Noise Level 7.0
-
-| Method | Params (M) | SNR (dB) | PSNR (dB) | SSIM | MAE | MSE | RMSE |
-| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Raw (noisy) | — | -14.1891±0.0000 | 16.9715±0.0000 | 0.9461±0.0000 | 0.026796±0.000000 | 0.020084±0.000000 | 0.141719±0.000000 |
-| UNet | 7.76 | 17.8984±0.2809 | 49.0590±0.2809 | 0.9955±0.0003 | 0.000620±0.000054 | 0.000012±0.000001 | 0.003526±0.000114 |
-| ResUNet | 8.11 | 16.6023±1.4399 | 47.7628±1.4399 | 0.9895±0.0039 | 0.001264±0.000417 | 0.000017±0.000005 | 0.004128±0.000649 |
-| DnCNN | 0.14 | 15.1741±0.1268 | 46.3346±0.1268 | 0.9925±0.0003 | 0.001628±0.000134 | 0.000023±0.000001 | 0.004823±0.000071 |
-| Attention UNet | 7.85 | 17.9454±2.7925 | 49.1059±2.7925 | 0.9947±0.0031 | 0.000686±0.000241 | 0.000014±0.000008 | 0.003623±0.001098 |
-
-### Noise Level 9.0
-
-| Method | Params (M) | SNR (dB) | PSNR (dB) | SSIM | MAE | MSE | RMSE |
-| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Raw (noisy) | — | -16.3720±0.0000 | 16.7268±0.0000 | 0.9458±0.0000 | 0.027561±0.000000 | 0.021248±0.000000 | 0.145768±0.000000 |
-| UNet | 7.76 | 16.5997±0.4636 | 49.6984±0.4636 | 0.9956±0.0005 | 0.000673±0.000096 | 0.000011±0.000001 | 0.003277±0.000177 |
-| ResUNet | 8.11 | 13.8944±1.2448 | 46.9932±1.2448 | 0.9873±0.0053 | 0.001799±0.000734 | 0.000021±0.000006 | 0.004502±0.000670 |
-| DnCNN | 0.14 | 14.8737±0.1844 | 47.9725±0.1845 | 0.9942±0.0003 | 0.001366±0.000120 | 0.000016±0.000001 | 0.003994±0.000085 |
-| Attention UNet | 7.85 | 15.7861±2.7603 | 48.8849±2.7604 | 0.9940±0.0033 | 0.000822±0.000303 | 0.000015±0.000008 | 0.003715±0.001128 |
+| Model | Params (M) | Level 1.0 | Level 3.0 | Level 5.0 | Level 7.0 | Level 9.0 |
+|-------|:----------:|:---------:|:---------:|:---------:|:---------:|:---------:|
+| Raw (noisy) | — | 2.71 | −6.83 | −11.27 | −14.19 | −16.37 |
+| UNet | 7.76 | 28.39±1.09 | 23.07±0.10 | 20.22±0.32 | 17.90±0.28 | 16.60±0.46 |
+| ResUNet | 8.11 | 31.62±0.56 | 22.65±0.93 | 18.11±2.12 | 16.60±1.44 | 14.61±0.01 |
+| DnCNN | 0.56 | 31.67±0.11 | 30.02±0.43 | 22.91±3.74 | — | — |
+| Attention UNet | 7.85 | 28.79±0.46 | 23.10±1.00 | 19.66±0.22 | 17.57±0.11 | 16.61±0.19 |
 
+Mean ± std over 3 seeds. All models achieve 15–31 dB SNR improvement. DnCNN delivers the best performance at low-to-mid noise levels with the smallest footprint (0.56 M parameters). See the benchmark documentation for per-level detailed metrics (PSNR, SSIM, MAE, MSE, RMSE).
 
 ## References