MODEL_CARD.md

metadata

library_name: diffusers
pipeline_tag: image-to-image
tags:
  - seismic-inversion
  - impedance-inversion
  - diffusion
  - ddpm
  - overthrust

Seismic-LDDPM

Seismic-LDDPM is a latent DDPM pipeline for seismic impedance inversion. The pipeline takes a low-frequency impedance image (dipin) and a synthetic seismic record (record) and predicts the impedance image.

This repository includes:

• Diffusers-format model components: vq_model, unet, scheduler, and condition_encoder.
• SeismicImpInvLDDPMPipeline in codes/pipeline.py.
• A complete Overthrust benchmark sample at data/Overthrust_trueimp.mat.
• Root infer.py and supporting inference code under codes/.

Installation

git clone https://huggingface.co/mally-2000/saii-cldm-synthetic
cd saii-cldm-synthetic
pip install -r requirements.txt

Overthrust Evaluation

The Overthrust evaluation script is intentionally fixed to the bundled data/Overthrust_trueimp.mat. It cuts the full model into six 256 x 256 patches, synthesizes the seismic records and low-frequency impedance inputs, runs inference, stitches the six predictions back together, and computes the metrics.

python codes/eval_overthrust.py \
  --model . \
  --output outputs/overthrust \
  --num-inference-steps 1000

Outputs:

• outputs/overthrust/full_target.npy
• outputs/overthrust/full_prediction.npy
• outputs/overthrust/full_reconstruction.npy
• outputs/overthrust/comparison_impedance.png
• outputs/overthrust/metrics_summary.json

Benchmark Result

Evaluated locally on the bundled Overthrust benchmark with 1000 DDPM steps, noise_snr=15, dipin_v=0.012, f0=30, phase=0, seed=1234, and patch indices [0, 1, 2, 3, 4, 5].

Space	PSNR	SSIM	PCC	RRE	NMSE
Normalized	30.7698	0.9339	0.9963	0.0435	0.001894
Impedance	33.4413	0.9554	0.9957	0.0324	0.001050
VQ reconstruction	37.7954	0.9677	0.9983	0.0209	0.000435

Single-Sample Inference

For a single default Overthrust patch:

python infer.py

The script builds one Overthrust test sample internally, synthesizes the low-frequency impedance and seismic record, and saves prediction.npy, target.npy, and comparison.png under outputs/infer_LDDPM.

For SAII-CLDM model-driven sampling:

python infer.py CLDM

Python Usage

import torch
from codes.pipeline import SeismicImpInvLDDPMPipeline

pipe = SeismicImpInvLDDPMPipeline.from_pretrained(
    "mally-2000/saii-cldm-synthetic",
    torch_dtype=torch.float32,
    trust_remote_code=True,
).to("cuda")

result = pipe(
    dipin=dipin,      # torch.Tensor, BCHW
    record=record,    # torch.Tensor, BCHW
    num_inference_steps=1000,
    seed=1234,
)

prediction = result.impedance_samples

Notes

• codes/dataset.py contains a lightweight SeismicBase and OverthrustTrueimpDataset; it does not depend on the original training repository's ldm.data.seisimic.
• Synthetic record generation is seeded through the benchmark configuration so the published Overthrust evaluation is reproducible.
• The bundled Overthrust file is used only as a compact benchmark input for reproducing this model's inference pipeline.