@unpublished {yin2024tfp,
	title = {Time-lapse full-waveform permeability inversion: a feasibility study},
	year = {2024},
	month = {3},
	abstract = {Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study of an end-to-end inversion framework that directly inverts for permeability from prestack time-lapse seismic data. To assess the method{\textquoteright}s robustness, we design experiments focusing on its sensitivity to initial models and potential errors in modeling. Our study leverages the Compass model to simulate CO2 storage in saline aquifers, which is derived from well and seismic data from the North Sea, a candidate site for geological carbon storage.},
	keywords = {4D, CCS, coupled inversion, end-to-end, fluid-flow, GCS, inversion, monitoring, time-lapse},
	doi = {10.48550/arXiv.2403.04083},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2024/yin2024tfp/paper.html},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@article {yin2022TLEdgc,
	title = {Derisking geologic carbon storage from high-resolution time-lapse seismic to explainable leakage detection},
	journal = {The Leading Edge},
	volume = {42},
	number = {1},
	year = {2023},
	note = {(The Leading Edge)},
	month = {01},
	pages = {69-76},
	abstract = {Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO$_2$ concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO$_2$ remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO$_2$ plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO$_2$ leakage automatically on unseen data and with a reasonable accuracy.},
	keywords = {CAM, CCS, classification, explainability, JRM, resolution, seismic imaging, time-lapse},
	doi = {10.1190/tle42010069.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2022/yin2022TLEdgc/paper.html},
	software = {https://github.com/slimgroup/GCS-CAM},
	author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@article {herrmann2023dte,
	title = {Digital twins in the era of generative AI},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge, President{\textquoteright}s Page)},
	month = {11},
	keywords = {CCS, deep learning, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	doi = {10.1190/tle42110730.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/herrmann2023dte/PresidentsPage.pdf},
	author = {Felix J. Herrmann}
}
@mastersthesis {zhang2023THlsw,
	title = {Large scale wavefield reconstruction via weighted matrix factorization and seismic survey design},
	year = {2023},
	note = {(PhD)},
	month = {06},
	school = {Georgia Institute of Technology},
	type = {phd},
	address = {Atlanta},
	abstract = {Seismic data acquisition plays a crucial role in identifying potential oil and gas reservoirs during the early phases of exploration. However, obtaining finely sampled seismic data can be costly and physically impossible. Recent developments in Compressive Sensing have resulted in seismic data being increasingly collected at random along spatial coordinates. Although random sampling improves acquisition efficiency, it shifts the burden from seismic acquisition to data processing. Wavefield recovery is one of the required processes for reconstructing fully seismic data from coarsely subsampled data. Among the various techniques proposed for wavefield reconstruction, matrix completion methods are computationally efficient and straightforward to implement. These methods exploit the low-rank structure of fully seismic data. However, matrix completion performs well at low-to-mid frequencies and degrades at higher frequencies due to the failure of low-rank structure to accurately approximate higher frequencies. To address this issue, this thesis proposed a recursively weighted matrix completion method. Although effective, this method is computationally expensive, and a more efficient method for handling 2D seismic data was also proposed. Compared to 2D seismic data, 3D seismic data can detect reflections outside of the 2D plane but poses a computational challenge due to its large scale. To overcome this challenge, this thesis proposed a parallel weighted reconstruction method to improve the reconstruction of 3D seismic data. Land seismic data presents a greater challenge due to contamination by ground roll, which consists of surface waves with a high spatial frequency content and large amplitude. To address this issue, a practical workflow was proposed in this thesis to improve the recovery of land seismic data. Although matrix completion is an efficient technique for reconstructing fully seismic data, the optimal acquisition design is still being investigated. Recent studies have shown that the spectral gap can be used to predict and characterize the quality of wavefield reconstruction via matrix completion for a given subsampling mask. Based on these findings, a simulation-free seismic survey design for both 2D and 3D seismic data was proposed in this thesis to obtain an improved subsampling survey by minimizing the spectral gap ratio. Furthermore, this concept was extended to the design of a time-lapse seismic survey, which is essential for reservoir management and monitoring geological carbon storage but is difficult and expensive to acquire. To improve the reconstruction of the time-lapse wavefield, a joint recovery model was proposed that leverages the benefits of the non-replicated baseline and monitor subsampled seismic data. A time-lapse seismic survey design that incorporates the joint recovery model with spectral gap was proposed to generate sparse, non-replicated time-lapse acquisition geometries that favor wavefield recovery.},
	keywords = {5D reconstruction, Acquisition, compressed sensing, frequency-domain, JRM, matrix factorization, parallel, PhD, signal processing, spectral gap, survey design, time-lapse, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2023/zhang2023THlsw/zhang2023THlsw.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2023/zhang2023THlsw/zhang2023THlsw_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Thesis/2023/zhang2023THlsw},
	author = {Yijun Zhang}
}
@article {louboutin2023lmi,
	title = {Learned multiphysics inversion with differentiable programming and machine learning},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge)},
	month = {07},
	pages = {452-516},
	abstract = {We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	url = {https://library.seg.org/doi/10.1190/tle42070474.1},
	presentation = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav M{\o}yner and Gerard J. Gorman and Felix J. Herrmann}
}
@article {zhang2023otl,
	title = {Optimized time-lapse acquisition design via spectral gap ratio minimization},
	journal = {Geophysics},
	volume = {88},
	number = {4},
	year = {2023},
	note = {(Geophysics)},
	month = {07},
	pages = {A19-A23},
	abstract = {Modern-day reservoir management and monitoring of geological carbon storage increasingly call for costly time-lapse seismic data collection. In this letter, we show how techniques from graph theory can be used to optimize acquisition geometries for low-cost sparse 4D seismic. Based on midpoint-offset domain connectivity arguments, the proposed algorithm automatically produces sparse non-replicated time-lapse acquisition geometries that favor wavefield recovery.},
	keywords = {Acquisition, JRM, matrix factorization, spectral gap, survey design, time-lapse, wavefield reconstruction},
	doi = {10.1190/geo2023-0024.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2023/zhang2023otl/Spectral_Gap_Paper.html},
	author = {Yijun Zhang and Ziyi Yin and Oscar Lopez and Ali Siahkoohi and Mathias Louboutin and Rajiv Kumar and Felix J. Herrmann}
}
@conference {yin2023EMIutm,
	title = {Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates},
	booktitle = {Engineering Mechanics Institute Conference},
	year = {2023},
	note = {(EMI, Atlanta)},
	month = {06},
	abstract = {Time-lapse seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics and wave physics. It also requires differentiation through the solvers with respect to properties of interest in the subsurface. In this talk, we present a learned end-to-end inversion framework, which uses a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator in order to greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. Through synthetic experiments, we demonstrate the efficacy of this framework on inverting the subsurface permeability of the reservoir and on monitoring CO2 plumes. We further quantify the uncertainty of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can also forecast the growth of CO2 plumes in the future with uncertainty estimation without any acquired seismic data.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, EMI, end-to-end, Fourier neural operators, GCS, inversion, machine learning, Normalizing flows, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/yin2023EMIutm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/index.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Felix J. Herrmann}
}
@conference {yin2022SEGlci,
	title = {Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to effectively invert for the subsurface properties of interest. To drastically reduce the computational cost, we introduce a learned coupled inversion framework based on the wave modeling operator, rock property conversion and a proxy fluid-flow simulator. We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost. We demonstrate the efficacy of our proposed method by means of a synthetic experiment. Finally, our framework is extended to carbon sequestration forecasting, where we effectively use the surrogate Fourier neural operator to forecast the CO$_2$ plume in the future at near-zero additional cost.},
	keywords = {CCS, deep learning, Fourier neural operators, inversion, machine learning, multiphysics, SEG, time-lapse},
	doi = {10.1190/image2022-3722848.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/paper.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/index.html},
	software = {https://github.com/slimgroup/FNO4CO2},
	author = {Ziyi Yin and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yu2022ML4SEISMICmsb,
	title = {Monitoring with sequential Bayesian inference},
	year = {2022},
	month = {11},
	abstract = {For this study, we apply sequential Bayesian inference to monitor the time evolution of subsurface flow of CO2 from indirect acoustic measurements at the surface. Upon receiving new acoustic measurements, we infer the current state of the CO2 by sampling from a learned posterior. Using the incoming data, we then perform online updates of the current posterior. This is accomplished by using the fluid flow model to advance the estimated state variable forward in time in order to update the learned posterior. With a synthetic experiment, we demonstrate this method can track the flow evolution accurately as measured by PSNR metrics. Since the posterior is a learned network, we can compute estimates faster than traditional least squares methods. This method can also quantify the uncertainty due to stochasticity in fluid flow model and the limited-azimuth imaging configuration.},
	keywords = {CCS, conditional normalizing flows, GCS, ML4SEISMIC, monitoring, sequential Bayes, SLIM, time-lapse, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yu2022ML4SEISMICmsb/index.html},
	author = {Ting-ying Yu and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICsfg,
	title = {Simulation-based framework for geological carbon storage monitoring},
	year = {2022},
	month = {11},
	abstract = {While various monitoring modalities exist to track the behavior of CO2 plumes to ensure safe operations and compliance with regulatory requirements, active 3D time-lapse seismic monitoring has proven superior but costly. At SLIM, we aim to reduce the operating costs by optimizing acquisition design, to help drive innovations in seismic monitoring acquisition design and imaging, and to test novel time-lapse acquisition and imaging technologies in silico at scale. In this talk, we will introduce our open-source software platform simulation-based monitoring design framework. We demonstrate how to make use of proxy models for seismic properties derived from real 3D imaged seismic and well data to conduct realistic synthetic geological carbon storage projects. Furthermore, we discuss our proposed sparse non-replicated seismic acquisition and cutting-edge methodology to recover the dense data or to directly image the sparse non-replicated via joint recovery model. This automatic workflow ends with deep neural classifiers to detect potential CO2 leakage over seal through pressure-induced fault openings. We envisage the development of an automatic workflow to handle the large number of continuously monitored CO2 injection sites needed to help combat climate change.},
	keywords = {CAM, CCS, classification, deep learning, explainability, GCS, Imaging, JRM, machine learning, ML4SEISMIC, SLIM, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICsfg/index.html},
	author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {zhang2022ML4SEISMICtss,
	title = {Time-lapse seismic survey design by maximizing the spectral gap},
	year = {2022},
	month = {11},
	abstract = {While time-lapse seismic has been applied successfully to CO2 sequestration monitoring, it remains a challenging problem since replicated dense surveys come at a very high cost in the field. Wavefield reconstruction based on matrix completion (MC) from randomized subsampled data is an efficient way to reduce operational costs. This technique allows for accurate time-lapse reconstruction by employing the joint recovery model (JRM), which capitalizes on the fact that different vintages share a common component. However, combining JRM with optimal time-lapse acquisition survey design remains an unexplored area of research. In expander graph theory, spectral gap (SG) reveals the source-receiver layout connectivity and is related to reconstruction quality during MC. Building on these insights, we proposed a simulation free time-lapse survey design based on JRM that aims to get similar reconstructed quality without insisting on replicate surveys, which significantly reduces the cost in the field. This approach uses the simulated annealing algorithm to find subsampling masks for each vintage. Numerical experiments confirm a direct correlation between increased spectral gap and promising time-lapse reconstruction quality.},
	keywords = {Acquisition, JRM, matrix factorization, ML4SEISMIC, SLIM, spectral gap, survey design, time-lapse, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/zhang2022ML4SEISMICtss/index.html},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Oscar Lopez and Felix J. Herrmann}
}
@presentation {herrmann2022DIRACCudt,
	title = {Uncertainty-aware Digital Twin for Monitoring Geological Carbon Storage},
	year = {2022},
	note = {(Diracc inaugural meeting, Atlanta)},
	month = {11},
	keywords = {CAM, CCS, classification, digital twin, Diracc, explainability, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Diracc/2022/herrmann2022DIRACCudt/index.html},
	author = {Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICutc,
	title = {Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion},
	year = {2022},
	month = {11},
	abstract = {Seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics modeling and wave physics and differentiation through the solvers with respect to the subsurface properties of interest. In this talk, we demonstrate the effectiveness of learned coupled inversion framework using a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator, which greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. We study the effectiveness and correctness of inversion based on Fourier neural operator surrogate and a normalizing flow prior. We also demonstrate the efficacy of this framework on monitoring the growth of CO2 plumes during sequestration, and on uncertainty quantification of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can further forecast the CO2 plume in the future without any acquired seismic data with uncertainty estimation.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, Fourier neural operators, GCS, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICutc/index.html},
	author = {Ziyi Yin and Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yin2021SEGcts,
	title = {Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {3434-3438},
	abstract = {Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO$_2$ plumes is weak in amplitude and therefore difficult to detect seismically. This situation is compounded by the fact that the surveys are often coarsely sampled and not replicated to reduce costs. As a result, images obtained for different vintages (baseline and monitor surveys) often contain artifacts that may be attributed wrongly to time-lapse changes. To address these issues, we propose to invert the baseline and monitor surveys jointly. By using the joint recovery model, we exploit information shared between multiple time-lapse surveys. Contrary to other time-lapse methods, our approach does not rely on replicating the surveys to detect time-lapse changes. To illustrate this advantage, we present a numerical sensitivity study where CO$_2$ is injected in a realistic synthetic model. This model is representative of the geology in the southeast of the North Sea, an area currently considered for carbon sequestration. Our example demonstrates that the joint recovery model improves the quality of time-lapse images allowing us to monitor the CO$_2$ plume seismically.},
	keywords = {CCS, Compressive Sensing, Imaging, JRM, marine, SEG, time-lapse},
	doi = {10.1190/segam2021-3569087.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/yin2021SEGcts.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/Tue-9-28-Yin.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/yin2021SEGcts.mp4},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2021ML4SEISMICism,
	title = {Improved seismic monitoring of CO2 sequestration with the weighted joint recovery model},
	year = {2021},
	month = {11},
	abstract = {Time-lapse seismic monitoring of CO2 sequestration is challenging because the time-lapse signature of CO2 plumes is weak in amplitude and often contaminated by imaging artifacts due to coarsely sampled, noisy, and non-replicated surveys. In this talk, we present a sparsity-promoting least-squares imaging method where the baseline, and the current and past monitor surveys are inverted jointly. We demonstrate that the sensitivity of seismic monitoring can be improved by inverting for the common component{\textemdash}i.e., the component shared by all vintages, and innovations with respect to this common component. Combining this joint approach with weighted l1,2-norm minimization leads to a monitoring scheme capable of detecting irregular CO2-plume growth in a realistic geological setting.},
	keywords = {CCS, Compressive Sensing, Imaging, JRM, ML4SEISMIC, SLIM, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/yin2021ML4SEISMICism/Tue-11-20-Yin.html},
	author = {Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {wason2015EAGEcsm,
	title = {Compressed sensing in 4-D marine{\textendash}-recovery of dense time-lapse data from subsampled data without repetition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We present an extension of our time-jittered marine acquisition for time-lapse surveys by working on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we are interested in recovering the densely sampled baseline and monitor, and then the (complete) 4-D difference from subsampled/incomplete baseline and monitor data.},
	keywords = {EAGE, NFFT, off-the-grid, simultaneous acquisition, time-lapse},
	doi = {10.3997/2214-4609.201413088},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm_poster.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2015CSEGctl,
	title = {Compressive time-lapse seismic data processing using shared information},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2015},
	note = {(CSEG, Calgary)},
	month = {05},
	abstract = {Time-lapse images void of acquisition and processing artifacts can provide more useful information about subsurface changes compared to those with acquisition footprints and other unwanted anomalies. Although, several pre-processing techniques are being developed and used to mitigate these unwanted artifacts, these operations can be very expensive, challenging and data dependent. Migration, as a processing tool, using a sparsity constraint has been shown to reduce artifacts drastically but little is known about the significance for compressed time-lapse seismic data. Leveraging ideas from distributed compressed sensing, and motivated by our earlier work on recovery of densely sampled time-lapse data from compressively sampled measurements, we present a sparsity-constrained migration for time-lapse data that uses a common component shared by the baseline and monitor data. Our algorithm tested on a synthetic example highlights the advantages of exploiting the common information, compared to ad hoc methods that involve parallel processing of the time-lapse data before differencing.},
	keywords = {CSEG, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/oghenekohwo2015CSEGctl/oghenekohwo2015CSEGctl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/oghenekohwo2015CSEGctl/oghenekohwo2015CSEGctl_pres.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2015PIMSntc,
	title = {A new take on compressive time-lapse seismic acquisition, imaging and inversion},
	booktitle = {PIMS Workshop on Advances in Seismic Imaging and Inversion},
	year = {2015},
	note = {(PIMS Workshop on Advances in Seismic Imaging and Inversion, University of Alberta, Edmonton)},
	month = {05},
	abstract = {Compressive sensing (CS), a sampling paradigm that is changing how data is acquired and processed in many fields, has attracted tremendous attention in recent years. In exploration seismology, particularly in 3D seismic technology, there have been published work on the application of CS to seismic data acquisition, processing and inversion. However, very little is known about the implications of CS in time-lapse (4D) seismic. Leveraging ideas from distributed CS where more than one signal is acquired and processed, as in 4D seismic, we present a joint recovery algorithm for time-lapse seismic data acquisition, imaging and inversion. The algorithm exploits the common information in the data sets (baseline and monitor) as part of an inversion procedure. Application of our algorithm to realistic synthetic examples highlight its advantages over other conventional approaches for processing time-lapse seismic data.},
	keywords = {PIMS, time-lapse, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2015/oghenekohwo2015PIMSntc/oghenekohwo2015PIMSntc_pres.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2015EAGEuci,
	title = {Using common information in compressive time-lapse full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {The use of time-lapse seismic data to monitor changes in the subsurface has become standard practice in industry. In addition, full-waveform inversion has also been extended to time-lapse seismic to obtain useful time-lapse information. The computational cost of this method are becoming more pronounced as the volume of data increases. Therefore, it is necessary to develop fast inversion algorithms that can also give improved time-lapse results. Rather than following existing joint inversion algorithms, we are motivated by a joint recovery model which exploits the common information among the baseline and monitor data. We propose a joint inversion framework, leveraging ideas from distributed compressive sensing and the modified Gauss-Newton method for full-waveform inversion, by using the shared information in the time-lapse data. Our results on a realistic synthetic example highlight the benefits of our joint inversion approach over a parallel inversion method that does not exploit the shared information. Preliminary results also indicate that our formulation can address time-lapse data with inconsistent acquisition geometries.},
	keywords = {EAGE, FWI, time-lapse},
	doi = {10.3997/2214-4609.201413086},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci_poster.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {oghenekohwo2014SEGrsw,
	title = {Randomized sampling without repetition in time-lapse surveys},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {4848-4852},
	abstract = {Vouching for higher levels of repeatability in acquisition and processing of time-lapse (4D) seismic data has become the standard with oil and gas contractor companies, with significant investment in the design of acquisition systems and processing algorithms that attempt to address some of the current 4D challenges, in particular, imaging weak 4D signals. Recent developments from the field of compressive sensing have shown the benefits of variants of randomized sampling in marine seismic acquisition and its impact for the future of seismic exploration. Following these developments, we show that the requirement for accurate survey repetition in time-lapse seismic data acquisition can be waived provided we solve a sparsity-promoting convex optimization program that makes use of the shared component between the baseline and monitor data. By setting up a framework for inversion of the stacked sections of a time-lapse data, given the pre-stack data volumes, we are able to extract 4D signals with relatively highfidelity from significant subsamplings. Our formulation is applied to time-lapse data that has been acquired with different source/receiver geometries, paving the way for an efficient approach to dealing with time-lapse data acquired with initially poor repeatability levels, provided the survey geometry details are known afterwards.},
	keywords = {4D, Acquisition, random, repetition, SEG, time-lapse},
	doi = {http://dx.doi.org/10.1190/segam2014-1627.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Felix J. Herrmann}
}
@conference {oghenekohwo2014EAGEtls,
	title = {Time-lapse seismic without repetition: reaping the benefits from randomized sampling and joint recovery},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In the current paradigm of 4-D seismic, guaranteeing repeatability in acquisition and processing of the baseline and monitor surveys ranks highest amongst the technical challenges one faces in detecting time-lapse signals. By using recent insights from the field of compressive sensing, we show that the condition of survey repeatability can be relaxed as long as we carry out a sparsitypromoting program that exploits shared information between the baseline and monitor surveys. By inverting for the baseline and monitor survey as the common "background", we are able to compute high-fidelity 4-D differences from carefully selected synthetic surveys that have different sets of source/receivers missing. This synthetic example is proof of concept of an exciting new approach to randomized 4-D acquisition where time-lapse signal can be computed as long as the survey details, such as source/receiver locations are known afterwards.},
	keywords = {4-D seismic, EAGE, joint recovery, time-lapse},
	doi = {10.3997/2214-4609.20141478},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls_pres.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@conference {oghenekohwo2013CSEGnratld,
	title = {Assessing the need for repeatability in acquisition of time-lapse data},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2013},
	month = {05},
	abstract = {There are several factors that affect the repeatability of 4D(time-lapse) seismic data. One of the most significant factors is the repeatability of the acquisition, particularly the locations of the sources and receivers. It is important to repeat the source-receiver locations, used during the baseline survey, in the monitor or repeat survey. Also, it is essential that the stacked data volumes used for time-lapse analysis are created using the same offset ranges for each survey. This condition is crucial in order to be able to produce an image of the same location over a period of time and enhances proper reservoir characterization. The cost of repeating the seismic acquisition is very expensive, as often times, the receiver array has to be left at the same location over the period for which the data will be acquired. In other words, it is important to repeat the acquisition geometry as much as possible. In this talk, we investigate the results of changing the acquisition geometry, by a random placement of the receivers for both the baseline surveys and newer (monitor) surveys. Results show that we are still able to observe any time-lapse effects from the proposed acquisition geometry. Our experiments have been performed on a synthetic model.},
	keywords = {Acquisition, CSEG, migration, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/oghenekohwo2013CSEGnratld/oghenekohwo2013CSEGnratld.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/oghenekohwo2013CSEGnratld/oghenekohwo2013CSEGnratld_pres.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@techreport {oghenekohwo2013SEGtlswrs,
	title = {Time-lapse seismics with randomized sampling},
	number = {TR-EOAS-2013-3},
	year = {2013},
	institution = {UBC},
	abstract = {In time-lapse or 4D seismics, repeatability of the acquisition is a very crucial step, as we do not want spurious events that are not there. In this paper, we propose an approach which avoids any requirement to repeat the surveys, by using randomized sampling technique which allows us to be more efficient in the acquisition. Our method applies to sampling data using ocean bottom nodes (OBN) as receivers. We test the efficacy of our proposed randomized acquisition geometry for time-lapse survey on two different models. In the first example, model properties does not change with time, while in the second example, model exhibit a time-lapse effect which may be caused by the migration of fluid within the reservoir. We perform two types of randomized sampling - uniform randomized sampling and jittered sampling to visualize the effects of non-repeatability in time-lapse survey. We observe that jittered randomized sampling is a more efficient method compared to randomized sampling, due to it{\textquoteright}s requirement to control the maximum spacing between the receivers. The results are presented, in the image space, as a least-squares migration of the model perturbation and they are shown for a subset of a synthetic model - the Marmousi model},
	keywords = {Acquisition, migration, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/oghenekohwo2013SEGtlswrs/oghenekohwo2013SEGtlswrs.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
