14 citations · 22 across the 12 of their papers we have counts for
12 papers
Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers
Yunlin Zeng, Huseyin Tuna Erdinc, Felix J. Herrmann
We present a multi-parameter simulation-based inference framework for joint Bayesian recovery of subsurface velocity and acoustic impedance from seismic data. A score-based diffusi…
OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI
Ipsita Bhar, Huseyin Tuna Erdinc, Thales Souza +2
The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of traditionally expensive iter…
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco +2
Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Wav…
A reduced-order derivative-informed neural operator for subsurface fluid-flow
Jeongjin Park, Grant Bruer, Huseyin Tuna Erdinc +2
Neural operators have emerged as cost-effective surrogates for expensive fluid-flow simulators, particularly in computationally intensive tasks such as permeability inversion from…
Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring
Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Felix J. Herrmann
Underground energy storage, which includes storage of hydrogen, compressed air, and CO2, requires careful monitoring to track potential leakage pathways, a situation where time-lap…
Power-scaled Bayesian Inference with Score-based Generative Models
Huseyin Tuna Erdinc, Yunlin Zeng, Abhinav Prakash Gahlot +1
We propose a score-based generative algorithm for sampling from power-scaled priors and likelihoods within the Bayesian inference framework. Our algorithm enables flexible control…
Full-waveform variational inference with full common-image gathers and diffusion network
Yunlin Zeng, Huseyin Tuna Erdinc, Rafael Orozco +1
Accurate seismic imaging and velocity estimation are essential for subsurface characterization. Conventional inversion techniques, such as full-waveform inversion, remain computati…
Machine learning-enabled velocity model building with uncertainty quantification
Rafael Orozco, Huseyin Tuna Erdinc, Yunlin Zeng +2
Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration project…
Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models
Huseyin Tuna Erdinc, Rafael Orozco, Felix J. Herrmann
In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality data…
Inference of CO2 flow patterns -- a feasibility study
Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Rafael Orozco +2
As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robus…
De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin +2
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or st…
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot +2
Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential…