@unpublished {zeng2024IMAGEefw,
	title = {Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation},
	year = {2024},
	month = {3},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slimgroup.github.io/IMAGE2024/Yunlin_Zeng2024SEG/paper.html},
	author = {Yunlin Zeng and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {zhang2023IMAGEssd,
	title = {3D seismic survey design by maximizing the spectral gap},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {The massive cost of 3D acquisition calls for methods to reduce the number of receivers by designing optimal receiver sampling masks. Recent studies on 2D seismic showed that maximizing the spectral gap of the subsampling mask leads to better wavefield reconstruction results. We enrich the current study by proposing a simulation-free method to generate optimal 3D acquisition by maximizing the spectral gap of the subsampling mask via a simulated annealing algorithm. Numerical experiments confirm improvement of the proposed method over receiver sampling locations obtained by jittered sampling.},
	keywords = {Acquisition, matrix factorization, SEG, spectral gap, survey design, wavefield reconstruction},
	doi = {10.1190/image2023-3895546.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/zhang2023IMAGEssd/zhang2023IMAGEssd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/zhang2023IMAGEssd/zhang2023IMAGEssd_pres.pdf},
	author = {Yijun Zhang and Ziyi Yin and Oscar Lopez and Ali Siahkoohi and Mathias Louboutin and 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 {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 {louboutin2022RHPCafa,
	title = {Abstractions for at-scale seismic inversion},
	booktitle = {Rice Oil and Gas High Performance Computing Conference 2022},
	year = {2022},
	note = {Rice Oil and Gas High Performance Computing Conference 2022},
	month = {03},
	pages = {Thursday Workshop: Devito Training and Hackathon},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, RHPC, software, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2022/louboutin2022RHPCafa/RiceHPC22.pdf},
	url2 = {https://youtu.be/scRTbP8w6Wk?t=4542},
	author = {Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Yijun Zhang and Philipp A. Witte and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {zhang2022SEGass,
	title = {A simulation-free seismic survey design by maximizing the spectral gap},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorithms. These technologies are designed to record as little data as possible in the field, while providing accurate wavefield reconstruction in the areas of the survey that are not recorded. This is achieved by designing randomized subsampling masks that allow for accurate wavefield reconstruction via matrix completion methods. Motivated by these recent results, we propose a simulation-free seismic survey design that aims at improving the quality of a given randomized subsampling using a simulated annealing algorithm that iteratively increases the spectral gap of the subsampling mask, a property recently linked to the quality of the reconstruction. We demonstrate that our proposed method improves the data reconstruction quality for a fixed subsampling rate on a realistic synthetic dataset.},
	keywords = {Acquisition, matrix factorization, SEG, spectral gap, survey design, wavefield reconstruction},
	doi = {10.1190/image2022-3751690.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/Yijun2022SEGass.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/index.html},
	software = {https://github.com/slimgroup/opt_spectral_gap},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar 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 {zhang2021ML4SEISMICiss,
	title = {Improved seismic survey design by maximizing the spectral gap with global optimization},
	year = {2021},
	month = {11},
	abstract = {Random subsampling is increasingly being used in the acquisition of seismic data to shorten the acquisition time and to reduce costs. However, the design of optimal acquisition geometries is still an ongoing area of research. Matrix completion (MC) is a computationally efficient method to reconstruct fully sampled wavefields from sparsely sampled seismic data. In MC theory, the spectral gap (SG), which is a measure of the connectedness of the graph in expander graph theory, has been used to predict, and to some degree quantify, the quality of wavefield reconstruction, given a specific subsampling scheme (acquisition mask). Building on these insights, we propose an optimization scheme, based on simulated annealing, which finds subsampling masks with large SGs that improve the quality of wavefield reconstruction with MC. The experimental results show that the proposed method successfully increases the SG of the subsampling mask starting from randomly initialized masks. Increasing the SG leads to improved connectivity between the sources and receivers and therefore of the wavefield reconstruction. Numerical experiments confirm a direct relationship between increased SG and improved reconstruction quality. This confirms the value SG analysis brings to the design of seismic surveys without the need to carry out expensive wavefield reconstructions to optimize the acquisition design.},
	keywords = {Acquisition, Compressive Sensing, ML4SEISMIC, SLIM, survey design, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/zhang2021ML4SEISMICiss/Tue-10-30-Zhang.pdf},
	author = {Yijun Zhang and Felix J. Herrmann}
}
@conference {zhang2021SEGapw,
	title = {A practical workflow for land seismic wavefield recovery with weighted matrix factorization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {145-149},
	abstract = {While wavefield reconstruction through weighted low-rank matrix factorizations has been shown to perform well on marine data, out-of-the-box application of this technology to land data is hampered by ground roll. The presence of these strong surface waves tends to dominate the reconstruction at the expense of the weaker body waves. Because ground roll is slow, it also suffers more from aliasing. To overcome these challenges, we introduce a practical workflow where the ground roll and body wave components are recovered separately and combined. We test the proposed approach blindly on a subset of the 3D SEAM Barrett dataset. With our technique, we recover densely sampled data from 25 percent randomly subsampled receivers. Independent comparisons on a single shot demonstrate significant improvements achievable with the presented workflow.},
	keywords = {3D SEAM Barrett dataset, ground roll, SEG, wavefield reconstruction, weighted matrix factorization},
	doi = {10.1190/segam2021-3594419.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/zhang2021SEGapw/Yijun2021.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/zhang2021SEGapw/zhang2021SEGapw_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/zhang2021SEGapw/zhang2021SEGapw.mp4},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Yijun Zhang and Felix J. Herrmann}
}
@techreport {sharan2020lsh,
	title = {Large scale high-frequency wavefield reconstruction with recursively weighted matrix factorizations},
	number = {TR-CSE-2020-4},
	year = {2020},
	month = {10},
	abstract = {Acquiring seismic data on a regular periodic fine grid is challenging. By exploiting the low-rank approximation property of fully sampled seismic data in some transform domain, low-rank matrix completion offers a scalable way to reconstruct seismic data on a regular periodic fine grid from coarsely randomly sampled data acquired in the field. While wavefield reconstruction have been applied successfully at the lower end of the spectrum, its performance deteriorates at the higher frequencies where the low-rank assumption no longer holds rendering this type of wavefield reconstruction ineffective in situations where high resolution images are desired. We overcome this shortcoming by exploiting similarities between adjacent frequency slices explicitly. During low-rank matrix factorization, these similarities translate to alignment of subspaces of the factors, a notion we propose to employ as we reconstruct monochromatic frequency slices recursively starting at the low frequencies. While this idea is relatively simple in its core, to turn this recent insight into a successful scalable wavefield reconstruction scheme for 3D seismic requires a number of important steps. First, we need to move the weighting matrices, which encapsulate the prior information from adjacent frequency slices, from the objective to the data misfit constraint. This move considerably improves the performance of the weighted low-rank matrix factorization on which our wavefield reconstructions is based. Secondly, we introduce approximations that allow us to decouple computations on a row-by-row and column-by-column basis, which in turn allow to parallelize the alternating optimization on which our low-rank factorization relies. The combination of weighting and decoupling leads to a computationally feasible full-azimuth wavefield reconstruction scheme that scales to industry-scale problem sizes. We demonstrate the performance of the proposed parallel algorithm on a 2D field data and on a 3D synthetic dataset. In both cases our approach produces high-fidelity broadband wavefield reconstructions from severely subsampled data.},
	keywords = {5D reconstruction, compressed sensing, frequency-domain, parallel, signal processing},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2020/sharan2020lsh/sharan2020lsh.html},
	author = {Shashin Sharan and Yijun Zhang and Oscar Lopez and Felix J. Herrmann}
}
@conference {louboutin2020SEGWSspl,
	title = {Sparsity promoting least-squares migration for long offset sparse OBN},
	booktitle = {SEG Workshop on Promises and Challenges with Sparse Node Ultra-long Offset OBN Acquisition in Imaging and Earth Model Building; virtual},
	year = {2020},
	note = {(SEG Workshop, virtual)},
	month = {10},
	keywords = {born, deblending, multiple, obn, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/louboutin2020SEGWSspl/louboutin2020SEGWSspl_pres.pdf},
	author = {Mathias Louboutin and Ziyi Yin and Yijun Zhang and Felix J. Herrmann}
}
@conference {zhang2020SEGtli,
	title = {Transfer learning in large-scale ocean bottom seismic wavefield reconstruction},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {1666-1670},
	abstract = {Achieving desirable receiver sampling in ocean bottom acquisition is often not possible because of cost considerations. Assuming adequate source sampling is available, which is achievable by virtue of reciprocity and the use of modern randomized (simultaneous-source) marine acquisition technology, we are in a position to train convolutional neural networks (CNNs) to bring the receiver sampling to the same spatial grid as the dense source sampling. To accomplish this task, we form training pairs consisting of densely sampled data and artificially subsampled data using a reciprocity argument and the assumption that the source-site sampling is dense. While this approach has successfully been used on the recovery monochromatic frequency slices, its application in practice calls for wavefield reconstruction of time-domain data. Despite having the option to parallelize, the overall costs of this approach can become prohibitive if we decide to carry out the training and recovery independently for each frequency. Because different frequency slices share information, we propose the use the method of transfer training to make our approach computationally more efficient by warm starting the training with CNN weights obtained from a neighboring frequency slices. If the two neighboring frequency slices share information, we would expect the training to improve and converge faster. Our aim is to prove this principle by carrying a series of carefully selected experiments on a relatively large-scale five-dimensional data synthetic data volume associated with wide-azimuth 3D ocean bottom node acquisition. From these experiments, we observe that by transfer training we are able t significantly speedup in the training, specially at relatively higher frequencies where consecutive frequency slices are more correlated.},
	keywords = {SEG, transfer learning, wavefield reconstruction},
	doi = {10.1190/segam2020-3427882.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGtli/zhang2020SEGtli.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGtli/zhang2020SEGtli_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGtli/zhang2020SEGtli_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Mi Zhang and Ali Siahkoohi and Felix J. Herrmann}
}
@conference {zhang2020SEGwrw,
	title = {Wavefield recovery with limited-subspace weighted matrix factorizations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {2858-2862},
	abstract = {Modern-day seismic imaging and monitoring technology increasingly rely on dense full-azimuth sampling. Unfortunately, the costs of acquiring densely sampled data rapidly become prohibitive and we need to look for ways to sparsely collect data, e.g. from sparsely distributed ocean bottom nodes, from which we then derive densely sampled surveys through the method of wavefield reconstruction. Because of their relatively cheap and simple calculations, wavefield reconstruction via matrix factorizations has proven to be a viable and scalable alternative to the more generally used transform-based methods. While this method is capable of processing all full azimuth data frequency by frequency slice, its performance degrades at higher frequencies because monochromatic data at these frequencies is not as well approximated by low-rank factorizations. We address this problem by proposing a recursive recovery technique, which involves weighted matrix factorizations where recovered wavefields at the lower frequencies serve as prior information for the recovery of the higher frequencies. To limit the adverse effects of potential overfitting, we propose a limited-subspace recursively weighted matrix factorization approach where the size of the row and column subspaces to construct the weight matrices is constrained. We apply our method to data collected from the Gulf of Suez, and our results show that our limited-subspace weighted recovery method significantly improves the recovery quality.},
	keywords = {algorithm, data reconstruction, frequency-domain, interpolation, Processing, SEG},
	doi = {10.1190/segam2020-3428365.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Yijun Zhang and Shashin Sharan and Oscar Lopez and Felix J. Herrmann}
}
@article {silva2018alr,
	title = {Applications of low-rank compressed seismic data to full waveform inversion and extended image volumes},
	journal = {Geophysics},
	year = {2019},
	note = {Accepted on January 22, 2019.},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and image, resulting in the call for more complex wave-equation based inversion algorithms that require dense long-offset samplings. Consequently, there is an exponential growth in the size of data volumes and prohibitive demands on computational resources. In this work, we propose a method to compress and process seismic data directly in a low-rank tensor format, which drastically reduces the amount of storage required to represent the data. We first outline how seismic data exhibits low-rank structure in a particular transform-domain, which can be exploited to compress the dense data in one extremely storage-efficient tensor format when the data is fully sampled. In the more realistic case of missing data, we can use efficient interpolation techniques to approximate the fully sampled volume in compressed form. In either case, once we have our data represented in its compressed tensor form, we design an algorithm to extract source or receiver gathers directly from the compressed parameters. This extraction process can be done on-the-fly directly on the compressed data, in a full waveform inversion context, and does not require scanning through the entire dataset in order to form shot gathers. To the best of our knowledge, this work is one of the first major contributions to working with seismic data applications directly in the compressed domain without reconstructing the entire data volume. We use a stochastic inversion approach, which works with small subsets of source experiments at each iteration, further reducing the computational and memory costs of full waveform inversion. We also demonstrate how this data compression and extraction technique can be applied to forming full subsurface image gathers through probing techniques.},
	keywords = {common shot/receiver gather, extended image volume, full waveform inversion, low rank, tensor},
	doi = {10.1190/geo2018-0116.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2019/silva2018alr/silva2018alr.pdf},
	author = {Curt Da Silva and Yiming Zhang and Rajiv Kumar and Felix J. Herrmann}
}
@conference {zhang2019SEGhfw,
	title = {High-frequency wavefield recovery with weighted matrix factorizations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {3959-3963},
	abstract = {Acquired seismic data is normally not the fully sampled data we would like to work with since traces are missing due to physical constraints or budget limitations. Rank minimization is an effective way to recovering the missing trace data. Unfortunately, this technique{\textquoteright}s performance may deteriorate at higher frequency because high-frequency data can not necessarily be captured accurately by low-rank matrix factorizations albeit remedies exist such as hierarchical semi-separable matrices. As a result, recovered data often suffers from low signal to noise ratio S/Rs at the higher frequencies. To deal with this situation, we propose a weighted recovery method that improves the performance at the high frequencies by recursively using information from matrix factorizations at neighboring lower frequencies. Essentially, we include prior information from previously reconstructed frequency slices during the wavefield reconstruction. We apply our method to data collected from the Gulf of Suez, which shows that our method performs well compared to the traditional method without weighting.},
	keywords = {algorithm, data reconstruction, frequency-domain, interpolation, Processing, SEG},
	doi = {10.1190/segam2019-3215103.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/zhang2019SEGhfw/zhang2019SEGhfw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/zhang2019SEGhfw/zhang2019SEGhfw_pres.pdf},
	author = {Yijun Zhang and Shashin Sharan and Felix J. Herrmann}
}
@mastersthesis {zhang2018THlss,
	title = {Large-scale seismic data compression: application to full waveform inversion and extended image volume},
	year = {2018},
	note = {(MSc)},
	month = {04},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and im- age, resulting in a call for more complex wave-equation based inversion al- gorithms that require dense long-o↵set samplings. Consequently, there is an exponential growth in the size of data volumes and prohibitive demands on computational resources. In this work, we propose a method to com- press and process seismic data directly in a low-rank tensor format, which drastically reduces the amount of storage required to represent the data. We first outline how seismic data exhibits low-rank structure in a particular transform-domain, which can be exploited to compress the dense data in one extremely storage-efficient tensor format when the data is fully sampled. In the more realistic case of missing data, we can use interpolation techniques based on the same tensor format to recover fully sampled data volume in compressed form. In either case, once we have our data represented in its compressed tensor form, we design an algorithm to extract source or receiver gathers directly from the compressed parameters. This extraction process can be done on-the-fly directly on the compressed data, in the full wave- form inversion context, and does not require scanning through the entire dataset in order to form shot gathers. To the best of our knowledge, this work is one of the first major contributions to working with seismic data applications directly in the compressed domain without reconstructing the entire data volume. We use a stochastic inversion approach, which works with small subsets of source experiments at each iteration, further to reduce the computational and memory costs of full waveform inversion. We also demonstrate how this data compression and extraction technique can be ap- plied to forming full subsurface image gathers through probing techniques.},
	keywords = {compression, extended image volume, FWI, hierarchical tucker, MSc, multilinear algebra, on-the-fly, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2018/zhang2018THlss/zhang2018THlss.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2018/zhang2018THlss/zhang2018THlss_pres.pdf},
	author = {Yiming Zhang}
}
@conference {zhang2017SEGmsd,
	title = {Massive 3D seismic data compression and inversion with hierarchical Tucker},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {1347-1352},
	abstract = {Modern-day oil and gas exploration, especially in areas of complex geology such as fault belts and sub-salt areas, is an increasingly expensive and risky endeavour. Typically long-offset and dense sampling seismic data are required for subsequent shot based processing procedures, e.g. wave-equation based inversion (WEI) and surface-related multiple elimination (SRME). However, these strict requirements result in an exponential growth in data volume size and prohibitive demands on computational resources, given the multidimensional nature of the data volumes. Moreover the physical constraints and cost limitations impose restrictions on acquiring fully sampled data. In this work, we propose to invert our large-scale data from a set of subsampled measurements, resulting in an estimate of the true volume in a compressed low-rank tensor format. Rather than expanding the data to its fully-sampled form for later downstream processes, we demonstrate how to use this compressed data directly via on-the-fly common shot or receiver gathers extraction. The combination of massive compression and fast on demand data reconstruction of 3D shot or receiver gathers leads to a substantial reduction in memory costs but with minimal effects on results in the subsequent processing procedures. We demonstrate the effective implementation of our proposed framework on full-waveform inversion on a 3D seismic synthetic data set generated from a Overthrust model.},
	keywords = {3D, FWI, SEG, tensor algebra},
	doi = {10.1190/segam2017-17742951.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/zhang2017SEGmsd/zhang2017SEGmsd.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/zhang2017SEGmsd/zhang2017SEGmsd_pres.pdf},
	author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {zhang2017SINBADFmsd,
	title = {Massive seismic data compression \& recovery w/ on-the-fly data extraction},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Industrial seismic exploration has moved towards complex geological areas, which requires typically long-offset and dense sampling data in order to avoid aliasing and inaccuracy in wave-equation based inversion algorithms. These strict requirements lead to massive data volume size and prohibitive demands on computational resources. In this work, we propose to compress our dense data in hierarchical Tucker tensor format by exploiting the low-rank structure of the data in a transformed domain. Then, we devise on-the-fly common shot or receiver gather extraction directly via the highly compressed factors. In subsampling scenarios, by interpolating this novel tensor format, we can also reconstruct the shot or receiver gather on a per-query basis rather than expanding the data to its fully-sampled form. We demonstrate the effective performance of our proposed technique on 3D stochastic full-waveform inversion, which allows the stochastic algorithm to extract shot gathers as it requires them throughout the inversion process. Moreover, we finally show how to computational effectively generate the CIGs from this compressed low-rank tensor representation of the data with the help of fast simultaneous shot or receiver gather generation.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.mov},
	author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@conference {kukreja2016WOLFHPCdaf,
	title = {Devito: automated fast finite difference computation},
	booktitle = {WOLFHPC 2016 Workshop (Super Computing)},
	year = {2016},
	note = {(WOLFHPC Workshop (SC16), Utah)},
	month = {11},
	abstract = {Domain specific languages have successfully been used in a variety of fields to cleanly express scientific problems as well as to simplify implementation and performance optimization on different computer architectures. Although a large number of stencil languages are available, finite difference domain specific languages have proved challenging to design because most practical use cases require additional features that fall outside the finite difference abstraction. Inspired by the complexity of real-world seismic imaging problems, we introduce Devito, a domain specific language in which high level equations are expressed using symbolic expressions from the SymPy package. Complex equations are automatically manipulated, optimized, and translated into highly optimized C code that aims to perform comparably or better than hand-tuned code. All this is transparent to users, who only see concise symbolic mathematical expressions.},
	keywords = {acoustic, compiler, finite differences, high performance computing, Modelling, stencil},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SC/2016/WOLFHPC/kukreja2016WOLFHPCdaf/kukreja2016WOLFHPCdaf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SC/2016/WOLFHPC/kukreja2016WOLFHPCdaf/kukreja2016WOLFHPCdaf_pres.pdf},
	author = {Navjot Kukreja and Mathias Louboutin and Felippe Vieira Zacarias and Fabio Luporini and Michael Lange and Gerard Gorman}
}
@conference {deaguiar2016SCdff,
	title = {DeVito: fast finite difference computation},
	booktitle = {Super Computing (SC16)},
	year = {2016},
	note = {(Super Computing, Utah)},
	month = {11},
	abstract = {Seismic imaging, used in energy exploration, is arguably the most compute and data intensive application in the private sector. The commonly used methods involve solving the wave equations numerically using finite difference formulations. Writing optimized code for these applications involves multiple man-years of effort that need to be repeated every time a new development needs to be factored in {\textendash} for every target platform. DeVito is a new tool for performing optimized Finite Difference (FD) computation from high-level symbolic problem definitions. The application developer needs to provide a differential equation in symbolic form. DeVito performs automated code generation and Just-In-Time (JIT) compilation based on this symbolic equation to create and execute highly optimized Finite Difference kernels on multiple computer platforms. DeVito has been designed to be used as part of complex workflows involving data flows across multiple applications over different nodes of a cluster.},
	keywords = {finite differences, Full-waveform inversion, high performance computing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SC/2016/deaguiar2016SCdff/deaguiar2016SCdff_poster.pdf},
	url2 = {http://sc16.supercomputing.org/sc-archive/tech_poster/tech_poster_pages/post155.html},
	author = {Marcos de Aguiar and Gerard Gorman and Felix J. Herrmann and Navjot Kukreja and Michael Lange and Mathias Louboutin and Felippe Vieira Zacarias}
}
@conference {lange2016dtg,
	title = {Devito: Towards a generic finite difference DSL using symbolic python},
	booktitle = {6th Workshop on Python for High-Performance and Scientific Computing},
	year = {2016},
	note = {(PyHPC, Utah)},
	month = {11},
	pages = {67-75},
	abstract = {Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific Python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the Python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic Python expressions. We demonstrate Devito{\textquoteright}s symbolic API and performance advantages over traditional Python acceleration methods before highlighting its use in the scientific context of seismic inversion problems.},
	keywords = {acoustic, finite differences, HPC, inversion, Modelling, python, software optimization},
	doi = {10.1109/PyHPC.2016.9},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg_pres.pdf},
	author = {Michael Lange and Navjot Kukreja and Mathias Louboutin and Fabio Luporini and Felippe Vieira Zacarias and Vincenzo Pandolfo and Paulius Velesko and Paulius Kazakas and Gerard Gorman}
}
@presentation {kumar2016SINBADFlrm,
	title = {Low-rank methods for on-the-fly slicing \& dicing of seismic data \& image volumes},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and produce, which calls for more complex wave-equation based inversion (WEI) algorithms that require dense long-offset samplings. These requirements result in an exponential growth in data volumes and prohibitive demands on computational resources. In this work, we propose a fast, resilient, and scalable wave-equation based inversion methodology for both the data and image-domain, which can handle complicated wave physics. First we show that both the data and image domains exhibit low-rank structure in a transform-domain, which can be exploited to compress the dense data or image volumes. Then, by accessing information from the compressed volumes on-the fly, we devise a scalable computational inversion framework driven by gradient calculations, which works with small subsets of source experiments. In the full-waveform inversion context, we demonstrate the efficacy of low-rank interpolation to improve downstream inversion results compared to merely inverting the velocity model using the subsampled data volume directly. We demonstrate the effectiveness of the proposed framework on two different waveform-inversion formulations, specifically performing full-waveform inversion on 5D data set generated using the overthrust model, and wave-equation based migration velocity analysis on the Marmousi model. This is joint work with Curt Da Silva and Yiming Zhang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.mov},
	author = {Rajiv Kumar and Curt Da Silva and Yiming Zhang and Felix J. Herrmann}
}
@presentation {gorman2016SINBADFopp,
	title = {Open Performance portablE SeismiC Imaging{\textendash}-OPESCI},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this project, we introduce OPESCI-FD, a Python package built on symbolic mathematics to automatically generate Finite Difference models from a high-level description of the model equations. We investigate applying this framework to generate the propagator program used in seismic imaging. We implement the 3D acoustic and anisotropic FD scheme as an example and demonstrate the advantages of usability, flexibility and accuracy of the framework. The design of OPESCI-FD aims to allow rapid development, analysis and optimisation of Finite Difference programs. OPESCI-FD is the foundation for continuing development by the OPESCI project team, building on the research presented in this report. This talk concludes by reviewing the further developments that are already under way, as well as the scope for extension to cater for other equations and numerical schemes. This joint work with SINBAD and SENAI CIMATEC and has received additional funding from Intel.},
	keywords = {Presentation, private, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.mov},
	author = {Gerard Gorman and Marcos de Aguiar and David Ham and Felix J. Herrmann and Paul H. J. Kelly and Navjot Kukreja and Michael Lange and Mathias Louboutin and Fabio Luporini and Paulius Velesko and Vincenzo Pandolfo and Felippe Vieira Zacarias}
}
@conference {zheglova2014EAGEams,
	title = {Application of matrix square root and its inverse to downward wavefield extrapolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In this paper we propose a method for computation of the square root of the Helmholtz operator and its inverse that arise in downward extrapolation methods based on one-way wave equation. Our approach involves factorization of the discretized Helmholtz operator at each depth by extracting the matrix square root after performing the spectral projector in order to eliminate the evanescent modes. The computation of the square root of the discrete Helmholtz operator and its inverse is done using polynomial recursions and can be combined with low rank matrix approximations to reduce the computational cost for large problems. The resulting square root operator is able to model the propagating modes kinematically correctly at the angles of up to 90 degree. Preliminary results on convergence of iterations are presented in this abstract. Potential applications include seismic modeling, imaging and inversion.},
	keywords = {EAGE, extrapolation, Modelling, one-way wave equation, square root},
	doi = {10.3997/2214-4609.20141184},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/zheglova2014EAGEams/zheglova2014EAGEams.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/zheglova2014EAGEams/zheglova2014EAGEams_pres.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
@presentation {zheglova2014SINBADead,
	title = {Exploring applications of depth stepping in seismic inverse problems},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We are exploring applications of stable depth extrapolation with the full wave equation to imaging and inversion. Depth stepping with full wave equation can be advantageous to the time and frequency domain modelling if special care is taken to stabilize the depth exptrapolator efficiently, since it reduces the higher dimensional modelling problem to a number of lower dimensional subproblems. We are interested in exploring applications in inversion, modelling and imaging. For example, just as the reverse time migration can be shown to be the gradient of the reduced formulation of the full waveform inversion problem, it is interesting to explore whether a formulation of the inversion problem can be achieved whose gradient can be computed using depth stepping techniques. We are also interested in such applications as preconditioning of iterative methods for Helmholtz equation and imaging.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/zheglova2014SINBADead/zheglova2014SINBADead.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
@conference {miao2014SEGrhss,
	title = {Randomized HSS acceleration for full-wave-equation depth stepping migration},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {3752-3756},
	abstract = {In this work we propose to use the spectral projector (Kenney and Laub, 1995) and randomized HSS technique (Chandrasekaran et al., 2006) to achieve a stable and affordable two-way wave equation depth stepping migration algorithm.},
	keywords = {acoustic, depth extrapolation, full wave equation migration, randomized SVD, SEG, spectral projector},
	doi = {http://dx.doi.org/10.1190/segam2014-1417.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/miao2014SEGrhss/miao2014SEGrhss.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/miao2014SEGrhss/miao2014SEGrhss_pres.pdf},
	author = {Lina Miao and Polina Zheglova and Felix J. Herrmann}
}
@presentation {zheglova2013SINBADihss,
	title = {Imaging with hierarchical semi separable matrices},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Hierarchically Semi Separable (HSS) matrices are (in general) dense matrices that have low rank off-diagonal blocks that can be represented economically. Exploiting a specific structure of HSS representation, fast algorithms have been devised for matrix matrix multiplication, addition and computing a matrix inverse. We are interested in developing fast algorithms for seismic imaging using ideas from this approach. An overview of HSS representation and some methods using HSS representations of operators will be shown in this talk.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/zheglova2013SINBADihss/zheglova2013SINBADihss.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
