Discover our reproducible research

These frameworks feature open-source GitHub repositories containing executable examples available as Python scripts or Jupyter notebooks. Click through to explore these tools and seamlessly integrate them into your own work.

Self-supervised surface-related multiple suppression with multidimensional convolution

https://github.com/DeepWave-KAUST/SSL-Multiples-Attenuation-pub

Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near offsets of towed-streamer data

https://github.com/DeepWave-KAUST/SSLDiff-Interpolation-pub

Guided Diffusion PosteriorSampling for Elastic ParameterInversion with Angle-Stack SeismicData

https://github.com/DeepWave-KAUST/dpsAVAinversion-pub

Full Waveform Inversion with Max-Pooling-Based Approximate Envelope

https://github.com/DeepWave-KAUST/Maxpool-patch-fwi-pub

Implicit Full Waveform Inversion Imaging

https://github.com/DeepWave-KAUST/ifwi-pub

Depth-progressive 3D seismic velocity model building via 2D generative diffusion models

https://github.com/DeepWave-KAUST/DiffVMB3D-pub

Generative Wave Propagator

https://github.com/DeepWave-KAUST/GenWP-pub

Full-waveform inversion with integrated seismic data processing via a shared representation network

https://github.com/DeepWave-KAUST/SRNFWI-pub

Plug-and-Play Regularized Multi-Dimensional Deconvolution for Accurate Wavefield Retrieval and Imaging

https://github.com/DeepWave-KAUST/MDD-PnP-pub

Physics-informed conditional diffusion model for generalizable elastic wave-mode separation

https://github.com/DeepWave-KAUST/DiffSeparation-pub

Separation-PINN: Seismic P- and S-waves mode separation with physics informed neural networks

https://github.com/DeepWave-KAUST/Separation-PINN-pub

SiameseFit: Latent Seismic Data Comparison for Enhanced Full Waveform Inversion and Cycle Skipping Mitigation

https://github.com/DeepWave-KAUST/SiameseFit-pub

Seismic wavefield solutions via physics-guided generative neural operator

https://github.com/DeepWave-KAUST/GNO-pub

Shallow-to-deep velocity model building via diffusion models

https://github.com/DeepWave-KAUST/DiffVMB-pub

Efficient Upside-Down Rayleigh-Marchenko Imaging through Machine-Learned Focusing Function Estimation

https://github.com/DeepWave-KAUST/FF_pred_DL_UDRM-pub

Distributed Acoustic Sensing Denoising Using a Self-supervised Conditional Diffusion Model

https://github.com/DeepWave-KAUST/DiffusionDAS_Denoising-pub

A Conditional Diffusion Model for CO₂ Monitoring and Forecasting in Heterogeneous Geological Formations

https://github.com/DeepWave-KAUST/DMFCO2-pub

LAViG-FLOW: Latent Autoregressive Video Generation for Fluid Flow Simulations

https://github.com/DeepWave-KAUST/LAViG-FLOW-pub

Diffusion model-based posterior sampling in full waveform inversion and Accelerating Stein variational gradient descent in full waveform inversion with diffusion models

https://github.com/DeepWave-KAUST/guidedfwi-pub

Post-FWI Injection of Learned Priors Using a Flow Matching Model

https://github.com/DeepWave-KAUST/PostFWI-pub

An effective physics-informed neural operator framework for predicting wavefields

https://github.com/DeepWave-KAUST/PICNO-pub

Conditional Image Prior for Uncertainty Quantification in Full Waveform Inversion

https://github.com/DeepWave-KAUST/FWI_Conditional-CNN_UQ-pub

A Generative Foundation Model for an All-in-One Seismic Processing Framework

https://github.com/DeepWave-KAUST/GSFM_pub

Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network

https://github.com/DeepWave-KAUST/Meta-LRPINN-pub

A Deep Learning-based time shift objective function for Full Waveform Inversion

https://github.com/DeepWave-KAUST/DeepTimeshift-pub

An Optimization-Based Inverse Solver to Accelerate the Marchenko Method

https://github.com/DeepWave-KAUST/Inverse-solver-Marchenko-pub

Learned frequency-domain scattered wavefield solutions using neural operators

https://github.com/DeepWave-KAUST/neuroseismic

Bayesian Seismic Inversion with Implicit Neural Representations

https://github.com/DeepWave-KAUST/B-IntraSeismic-pub

Slope assisted ground roll removal and seismic signal separation with Physics Informed Neural Networks

https://github.com/DeepWave-KAUST/PINNs_signal_separation-pub

Self-supervised multi-stage deep learning network for seismic data denoising

https://github.com/DeepWave-KAUST/MSMHA_Denoising-pub

Joint Microseismic Event Detection and Location with a Detection Transformer

https://github.com/DeepWave-KAUST/Microseismic_with_DETR-pub

Velocity model building with uncertainty quantification using a multi-modal autoregressive generative network

https://github.com/DeepWave-KAUST/VelocityGPT2-pub

SiameseFWI: A Deep Learning Network for Enhanced Full Waveform Inversion

https://github.com/DeepWave-KAUST/Siamese_FWI-pub

Enhancing Multi-parameter Elastic Full Waveform Inversion with a Siamese Network

https://github.com/DeepWave-KAUST/ElasticSiameseFWI-pub

F-SiameseFWI: A Novel Deep Learning Framework for Multi-Source Full Wave Inversion

https://github.com/DeepWave-KAUST/FreqSiameseFWI-pub

Self-supervised Seismic Resolution Enhancement

https://github.com/DeepWave-KAUST/SSL-Resolution-Enhancement

Reproducible material for Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion

https://github.com/DeepWave-KAUST/AnnealedSVGD_FWI

Deep Learning-based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data

https://github.com/DeepWave-KAUST/Microseismic_with_SF_BH

Regularization strategies for Seis2Rock-based petrophysical inversion of pre-stack seismic data

https://github.com/DeepWave-KAUST/Regularized_Seis2Rock

Wavenumber-aware diffusion sampling to regularize multi-parameter elastic full waveform inversion

https://github.com/DeepWave-KAUST/ilvrefwi

Multiple Wavefield Solutions in Physics-Informed Neural Networks using Latent Representation

https://github.com/DeepWave-KAUST/latentpinn

Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network

https://github.com/DeepWave-KAUST/DLDAS_Denoising-pub

A self-supervised learning framework for seismic low-frequency extrapolation

https://github.com/DeepWave-KAUST/SSL-Lowfrequency-extrapolation

Seis2Rock: A Data-Driven Approach to Direct Petrophysical Inversion of Pre-Stack Seismic Data

https://github.com/DeepWave-KAUST/Seis2Rock

Learned regularizations for elastic full waveform inversion using diffusion models

https://github.com/DeepWave-KAUST/diffefwi

Robust Full Waveform Inversion with deep Hessian deblurring

https://github.com/DeepWave-KAUST/DeepFWIHessian

IntraSeismic: Using Coordinate-Based Learning for Seismic Inversion

https://github.com/DeepWave-KAUST/IntraSeismic

Meta-Processing: A robust framework for multi-tasks seismic processing

https://github.com/DeepWave-KAUST/Meta-Processing

PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks

https://github.com/DeepWave-KAUST/PINNslope

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks

https://github.com/DeepWave-KAUST/PINNgabor