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GeoBrain/
ground-roll-attenuation

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Ground-Roll Attenuation Benchmark

Deep-learning-based ground-roll suppression on pre-stack seismic shot gathers, using the SEG C3 synthetic dataset.

Task

Given a noisy shot gather contaminated by dispersive ground-roll noise, the model predicts the additive noise component. The denoised signal is obtained by:

denoised = noisy_input - predicted_noise

This is a paired regression task trained with a noise-label objective (the ground-truth noise component). The supervision target is the residual between the noisy input and the clean reference.

Dataset

Source: SEG C3 pre-stack synthetic data, 9 regular shot gathers
Geometry: 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
Split: Shot-level (FFID) sequential 7:1:1 — 7 training shots, 1 validation, 1 held-out test

Noise Intensity Levels

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

Metrics computed on the test set (2D flattened shot gathers) in the normalized domain before denoising.

Model Architectures

DFB-CNN (dfb_cnn) — Dual-Filter-Bank CNN with two DnCNN-style subnetworks (5×5 kernel for low-freq, 3×3 for high-freq) operating in the radial-trace (RT) domain. Low-freq CNN: 9 layers, 100 feat; High-freq CNN: 5 layers, 64 feat.

Preprocessing

Normalization: max_abs, global scope — the entire dataset scaled to [-1, 1]
Patching: Overlapping 2D patches (128 × 256) with 50% overlap, channel-last format (1, H, W)

Repository Structure

models/
├── unet/
│   ├── level1.0_seed42/
│   │   ├── best.pt          # Best checkpoint (minimum validation loss)
│   │   └── config.yaml      # Full training configuration
│   ├── level1.0_seed43/
│   ├── level1.0_seed44/
│   ├── level3.0_seed42/
│   └── ...
└── res_unet/
    └── ...

Each subdirectory corresponds to one experiment: a model architecture trained at a specific noise level with a specific random seed.

Training Details

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)
Scheduler	Cosine annealing (min_lr=1e-6)
Epochs	100–200 (varies per model)
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/coherent-noise-attenuation"
model_key = "res_unet"
level = "3.0"
seed = "42"

ckpt_path = hf_hub_download(
    repo_id=repo,
    filename=f"models/{model_key}/level{level}_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
SEG C3 Velocity Model: https://wiki.seg.org/wiki/C3

GeoBrain/ground-roll-attenuation