new pubs

_publications/article/2022-05-01-Lossy-Checkpoint-Compression-in-Full-Waveform-Inversion.md

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+---
+title: "Lossy Checkpoint Compression in Full Waveform Inversion"
+etype: 'article'
+collection: publications
+permalink: /publication/2022-05-01-Lossy-Checkpoint-Compression-in-Full-Waveform-Inversion
+excerpt: '(Geoscientific Model Development)'
+date: 2022-05-01
+venue: 'Lossy Checkpoint Compression in Full Waveform Inversion'
+paperurl: 'https://arxiv.org/pdf/2009.12623.pdf'
+citation: ' Navjot Kukreja,  Jan Hueckelheim,  Mathias Louboutin,  John Washbourne,  Paul Kelly,  Gerard Gorman, "Lossy Checkpoint Compression in Full Waveform Inversion." Lossy Checkpoint Compression in Full Waveform Inversion, 2022.'
+---
+(Geoscientific Model Development)
+
+[Access paper here](https://arxiv.org/pdf/2009.12623.pdf){:target="_blank"}
+
+This paper proposes a new method that combines check-pointing methods with error-controlled lossy compression for large-scale high-performance Full-Waveform Inversion (FWI), an inverse problem commonly used in geophysical exploration. This combination can significantly reduce data movement, allowing a reduction in run time as well as peak memory. In the Exascale computing era, frequent data transfer (e.g., memory bandwidth, PCIe bandwidth for GPUs, or network) is the performance bottleneck rather than the peak FLOPS of the processing unit. Like many other adjoint-based optimization problems, FWI is costly in terms of the number of floating-point operations, large memory footprint during backpropagation, and data transfer overheads. Past work for adjoint methods has developed checkpointing methods that reduce the peak memory requirements during backpropagation at the cost of additional floating-point computations. Combining this traditional checkpointing with error-controlled lossy compression, we explore the three-way tradeoff between memory, precision, and time to solution. We investigate how approximation errors introduced by lossy compression of the forward solution impact the objective function gradient and final inverted solution. Empirical results from these numerical experiments indicate that high lossy-compression rates (compression factors ranging up to 100) have a relatively minor impact on convergence rates and the quality of the final solution.

_publications/article/2022-09-01-De-risking-geological-carbon-storage-from-high-resolution-time-lapse-seismic-to-explainable-leakage-detection.md

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+---
+title: "De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection"
+etype: 'article'
+collection: publications
+permalink: /publication/2022-09-01-De-risking-geological-carbon-storage-from-high-resolution-time-lapse-seismic-to-explainable-leakage-detection
+excerpt: 'Just accepted in the January 2023 special section in seismic resolution'
+date: 2022-09-01
+venue: 'De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection'
+paperurl: 'https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2022/yin2022TLEdgc/paper.html'
+citation: ' Ziyi Yin,  Huseyin Erdinc,  Abhinav Gahlot,  Mathias Louboutin,  Felix Herrmann, "De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection." De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection, 2022.'
+---
+Just accepted in the January 2023 special section in seismic resolution
+
+[Access paper here](https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2022/yin2022TLEdgc/paper.html){:target="_blank"}
+
+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.

_publications/conference/2019-10-01-Accelerating-ideation-and-innovation-cheaply-in-the-Cloud-the-power-of-abstraction-collaboration-and-reproducibility.md

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+---
+title: "Accelerating ideation and innovation cheaply in the Cloud the power of abstraction, collaboration and reproducibility"
+etype: 'conference'
+collection: publications
+permalink: /publication/2019-10-01-Accelerating-ideation-and-innovation-cheaply-in-the-Cloud-the-power-of-abstraction-collaboration-and-reproducibility
+excerpt: '(EAGE HPC Workshop, Dubai)'
+date: 2019-10-01
+venue: 'Accelerating ideation and innovation cheaply in the Cloud the power of abstraction, collaboration and reproducibility'
+citation: ' Felix Herrmann,  Charles Jones,  Gerard Gorman,  Jan H{\"u}ckelheim,  Keegan Lensink,  Paul Kelly,  Navjot Kukreja,  Henryk Modzelewski,  Michael Lange,  Mathias Louboutin,  Fabio Luporini,  James Selvages,  Philipp Witte, "Accelerating ideation and innovation cheaply in the Cloud the power of abstraction, collaboration and reproducibility." Accelerating ideation and innovation cheaply in the Cloud the power of abstraction, collaboration and reproducibility, 2019.'
+---
+(EAGE HPC Workshop, Dubai)
+
+Use [Google Scholar](https://scholar.google.com/scholar?q=Accelerating+ideation+and+innovation+cheaply+in+the+Cloud+the+power+of+abstraction,+collaboration+and+reproducibility){:target="_blank"} for full citation

_publications/conference/2021-06-01-Learned-wave-based-imaging-variational-inference-at-scale.md

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+---
+title: "Learned wave-based imaging - variational inference at scale"
+etype: 'conference'
+collection: publications
+permalink: /publication/2021-06-01-Learned-wave-based-imaging-variational-inference-at-scale
+excerpt: '(Delft, virtual)'
+date: 2021-06-01
+venue: 'Learned wave-based imaging - variational inference at scale'
+paperurl: 'https://slim.gatech.edu/Publications/Public/Conferences/Delft/2021/herrmann2021Delftlwi/herrmann2021Delftlwi.pdf'
+citation: ' Felix Herrmann,  Ali Siahkoohi,  Rafael Orozco,  Gabrio Rizzuti,  Philipp Witte,  Mathias Louboutin, "Learned wave-based imaging - variational inference at scale." Learned wave-based imaging - variational inference at scale, 2021.'
+---
+(Delft, virtual)
+
+[Access paper here](https://slim.gatech.edu/Publications/Public/Conferences/Delft/2021/herrmann2021Delftlwi/herrmann2021Delftlwi.pdf){:target="_blank"}
+
+High dimensionality, complex physics, and lack of access to the ground truth rank medical ultrasound and seismic imaging amongst the most challenging problems in the computational imaging sciences. If these challenges were not bad enough, modern applications of computational imaging increasingly call for the assessment of uncertainty on the image itself and on subsequent tasks. During this talk, I will show how recent developments in Normalizing Flows, a new type of invertible neural networks, can be used to cast wave-based imaging into a scalable Bayesian framework. Contrary to conventional methods, where sample images are drawn from the posterior distribution during inversion, our approach trains Normalizing Flows capable of generating samples from the posterior. Aside from greatly reducing the computational cost, this approach gives us access to the image itself (via Maximum a posteriori or mean estimation) and its multidimensional statistical distribution including its pointwise variance.

_publications/conference/2021-09-01-Compressive-time-lapse-seismic-monitoring-of-carbon-storage-and-sequestration-with-the-joint-recovery-model.md

@@ -13,4 +13,4 @@ citation: ' Ziyi Yin,  Mathias Louboutin,  Felix Herrmann, "Compressive tim
 
 [Access paper here](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/yin2021SEGcts.html){:target="_blank"}
 
-Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO2 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 CO2 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 CO2 plume seismically.
+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.

_publications/conference/2021-10-01-Low-cost-time-lapse-seismic-imaging-of-CCS-with-the-joint-recovery-model.md

@@ -3,11 +3,11 @@ title: "Low-cost time-lapse seismic imaging of CCS with the joint recovery model
 etype: 'conference'
 collection: publications
 permalink: /publication/2021-10-01-Low-cost-time-lapse-seismic-imaging-of-CCS-with-the-joint-recovery-model
-excerpt: '(SEG Workshop, virtual)'
+excerpt: '(IMAGE Workshop, virtual)'
 date: 2021-10-01
 venue: 'Low-cost time-lapse seismic imaging of CCS with the joint recovery model'
 citation: ' Felix Herrmann,  Mathias Louboutin,  Ziyi Yin,  Philipp Witte, "Low-cost time-lapse seismic imaging of CCS with the joint recovery model." Low-cost time-lapse seismic imaging of CCS with the joint recovery model, 2021.'
 ---
-(SEG Workshop, virtual)
+(IMAGE Workshop, virtual)
 
 Use [Google Scholar](https://scholar.google.com/scholar?q=Low+cost+time+lapse+seismic+imaging+of+CCS+with+the+joint+recovery+model){:target="_blank"} for full citation

_publications/conference/2022-03-01-Abstractions-for-at-scale-seismic-inversion.md

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+---
+title: "Abstractions for at-scale seismic inversion"
+etype: 'conference'
+collection: publications
+permalink: /publication/2022-03-01-Abstractions-for-at-scale-seismic-inversion
+excerpt: 'Rice Oil and Gas High Performance Computing Conference 2022'
+date: 2022-03-01
+venue: 'Abstractions for at-scale seismic inversion'
+paperurl: 'https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2022/louboutin2022RHPCafa/RiceHPC22.pdf'
+citation: ' Mathias Louboutin,  Ali Siahkoohi,  Ziyi Yin,  Rafael Orozco,  Thomas II,  Yijun Zhang,  Philipp Witte,  Gabrio Rizzuti,  Felix Herrmann, "Abstractions for at-scale seismic inversion." Abstractions for at-scale seismic inversion, 2022.'
+---
+Rice Oil and Gas High Performance Computing Conference 2022
+
+[Access paper here](https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2022/louboutin2022RHPCafa/RiceHPC22.pdf){:target="_blank"}
+
+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.

_publications/conference/2022-05-01-A-simulation-free-seismic-survey-design-by-maximizing-the-spectral-gap.md

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+---
+title: "A simulation-free seismic survey design by maximizing the spectral gap"
+etype: 'conference'
+collection: publications
+permalink: /publication/2022-05-01-A-simulation-free-seismic-survey-design-by-maximizing-the-spectral-gap
+excerpt: '(IMAGE, Houston)'
+date: 2022-05-01
+venue: 'A simulation-free seismic survey design by maximizing the spectral gap'
+paperurl: 'https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/Yijun2022SEGass.html'
+citation: ' Yijun Zhang,  Mathias Louboutin,  Ali Siahkoohi,  Ziyi Yin,  Rajiv Kumar,  Felix Herrmann, "A simulation-free seismic survey design by maximizing the spectral gap." A simulation-free seismic survey design by maximizing the spectral gap, 2022.'
+---
+(IMAGE, Houston)
+
+[Access paper here](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/Yijun2022SEGass.html){:target="_blank"}
+
+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.

_publications/conference/2022-05-01-Accelerating-innovation-with-software-abstractions-for-scalable-computational-geophysics.md

@@ -0,0 +1,16 @@
+---
+title: "Accelerating innovation with software abstractions for scalable computational geophysics"
+etype: 'conference'
+collection: publications
+permalink: /publication/2022-05-01-Accelerating-innovation-with-software-abstractions-for-scalable-computational-geophysics
+excerpt: '(IMAGE, Houston)'
+date: 2022-05-01
+venue: 'Accelerating innovation with software abstractions for scalable computational geophysics'
+paperurl: 'https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/louboutin_seg22.html'
+citation: ' Mathias Louboutin,  Philipp Witte,  Ali Siahkoohi,  Gabrio Rizzuti,  Ziyi Yin,  Rafael Orozco,  Felix Herrmann, "Accelerating innovation with software abstractions for scalable computational geophysics." Accelerating innovation with software abstractions for scalable computational geophysics, 2022.'
+---
+(IMAGE, Houston)
+
+[Access paper here](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/louboutin_seg22.html){:target="_blank"}
+
+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.

_publications/conference/2022-05-01-Learned-coupled-inversion-for-carbon-sequestration-monitoring-and-forecasting-with-Fourier-neural-operators.md

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+---
+title: "Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators"
+etype: 'conference'
+collection: publications
+permalink: /publication/2022-05-01-Learned-coupled-inversion-for-carbon-sequestration-monitoring-and-forecasting-with-Fourier-neural-operators
+excerpt: '(IMAGE, Houston)'
+date: 2022-05-01
+venue: 'Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators'
+paperurl: 'https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/paper.html'
+citation: ' Ziyi Yin,  Ali Siahkoohi,  Mathias Louboutin,  Felix Herrmann, "Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators." Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators, 2022.'
+---
+(IMAGE, Houston)
+
+[Access paper here](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/paper.html){:target="_blank"}
+
+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.