Dataset and Trained Models for "Depth-progressive 3D seismic velocity model building via 2D generative diffusion models"
Abstract
Overview This repository provides the training dataset, test dataset, and pre-trained model weights for the paper: "Depth-progressive 3D seismic velocity model building via 2D generative diffusion models" by Shijun Cheng, Tariq Alkhalifah, et al. The associated open-source code is available at: https://github.com/DeepWave-KAUST/DiffVMB3D-pub Contents This Zenodo record contains three archive files: File Description train.zip Training dataset (5,500 3D velocity models) test.zip Test dataset (4 benchmark 3D velocity models) trained_model.zip Pre-trained model weights for result reproduction Dataset Generation Source models The training data is built from both 2D and 3D industrial velocity models: 2D sources (5 models, 500 samples each = 2,500 samples): BP1994, BP2004, Hess, Otway, and Sigsbee. Each 2D model is resized to 128 × 128 grid points. 3D sources (3 models, 1,000 samples each = 3,000 samples): 2D slices extracted from the Overthrust, SEAM Arid, and SEG/EAGE 3D industrial models, each resized to 128 × 128. This yields 5,500 base samples before augmentation. 2D-to-3D extension Each 3D velocity model (128 × 128 × 128) is generated from a 2D base model using an undulation-based extension. For each sample, two random displacement curves are independently generated along the y-direction. These curves consist of a B-spline long-wavelength component and a multi-sine short-wavelength component, which control the lateral structural variations in the z- and x-directions,…
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