@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}
}
@conference {herrmann2023CCUSdgs,
	title = {Derisking geological storage with simulation-based seismic monitoring design and machine learning},
	booktitle = {Carbon, Capture, Utilization, and Storage},
	year = {2023},
	note = {(CCUS, Houston)},
	month = {04},
	keywords = {CAM, CCS, CCUS, classification, explainability, JRM, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2023/herrmann2023CCUSdgs/CCUS2023.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2023/herrmann2023CCUSdgs},
	author = {Felix J. Herrmann and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin}
}
@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}
}
@presentation {gahlot2023ML4SEISMICtsm,
	title = {Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model},
	year = {2023},
	month = {11},
	abstract = {During time-lapse seismic monitoring of CO2 plumes, a weak 4D signal below the level of inversion or migration artifacts poses challenges. To address these, low-cost randomized non-replicated acquisitions and a linear joint recovery model (JRM) have been introduced. It takes advantage of the shared information between different vintages in the time-lapse seismic data and subsurface structure undergoing localized changes. Since the relationship between seismic data and subsurface properties is seldom linear, we propose a more versatile nonlinear JRM (nJRM) to invert for the squared slowness of the vintages. The nJRM takes advantage of the full nonlinear relation between these squared slownesses and time-lapse data through the wave equation. Also, careful derivation of the gradients makes the computational cost of nJRM equivalent to the independent recovery. We present a synthetic study for geological carbon storage (GCS) which shows that the non-replication can be beneficial to time-lapse imaging, making seismic monitoring of GCS less costly for the long term sustainability of the technology.},
	keywords = {CCS, JRM, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/gahlot2023ML4SEISMICtsm},
	author = {Abhinav Prakash Gahlot and Mathias Louboutin and Ziyi Yin and Felix J. Herrmann}
}
@conference {erdinc2022AAAIdcc,
	title = {De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images},
	booktitle = {AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges},
	year = {2022},
	note = {(AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, Arlington)},
	month = {11},
	abstract = {With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO$_2$ leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO$_2$ storage has shown promising results in its ability to monitor the growth of the CO$_2$ plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO$_2$ concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO$_2$ plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO$_2$ plumes by leveraging Class Activation Mapping (CAM) methods.},
	keywords = {AAAI, CAM, CCS, classification, explainability, JRM, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/erdinc2022AAAIdcc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {erdinc2022ML4SEISMICdgp,
	title = {De-risking GCS projects with explainable CO$_2$ leakage detection in time-lapse seismic images},
	year = {2022},
	month = {11},
	abstract = {With the global deployment of Carbon, capture and storage (CCS) technology to combat climate change, there is an associated risk of contamination with CO2 leaking back to the atmosphere. Thus, it requires continuous monitoring of CO2 after the injection stops at the storage site. In this work, we generated synthetic CO2 plume development data with both leakage and no leakage scenarios. We trained a convolutional neural network (CNN) discriminative classifier and also a generative classifier and compared their performances in CO2 leakage detection. The accuracy of our discriminative classifier on the test data is 85\% and that of the generative classifier is 90\%. The Class Activation Mapping (CAM) results of the discriminative classifier and the latent space representation of our dataset in the case of generative classifier strengthens our claims about trustworthy leakage classification.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, JRM, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/erdinc2022ML4SEISMICdgp/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin 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}
}
@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 {herrmann2021SEGWSlts,
	title = {Low-cost time-lapse seismic imaging of CCS with the joint recovery model},
	booktitle = {SEG Workshop on Geophysical Challenges in Presalt Carbonates; virtual},
	year = {2021},
	note = {(IMAGE Workshop, virtual)},
	month = {10},
	keywords = {born, CCS, Imaging, JRM, multiple, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/herrmann2021SEGWSlts/herrmann2021SEGWSlts_pres.pdf},
	author = {Felix J. Herrmann and Mathias Louboutin and Ziyi Yin and Philipp A. Witte}
}
