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1. A compressed data approach for image-domain least-squares migration

   Ram Tuvi, Zeyu Zhao (September 2021, Geophysics)

   Jeffrey Shragge (Ed.)

   We consider the problem of image-domain least-squares migration (LSM) based on efficiently constructing the Hessian matrix with sparse beam data. Specifically, we use the ultra-wide-band phase space beam summation method, in which beams are used as local basis functions to represent scattered data collected at the surface. The beam domain data are sparse. One can identify seismic events with significant contributions so that only beams with nonnegligible amplitudes need to be used to image the subsurface. In addition, due to the beams’ spectral localization, only beams that pass near an imaging point need to be taken into account. These two properties reduce the computational complexity of computing the Hessian matrix — an essential ingredient for LSM. As a result, we can efficiently construct the Hessian matrix based on analyzing the sparse beam domain data 

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2. A compressed data approach for image-domain least-squares migration

   Ram Tuvi, Zeyu Zhao (September 2022, Geophysics)

   Jeffrey Shragge (Ed.)

   We consider the problem of image-domain least-squares migration (LSM) based on efficiently constructing the Hessian matrix with sparse beam data. Specifically, we use the ultra-wide-band phase space beam summation method, in which beams are used as local basis functions to represent scattered data collected at the surface. The beam domain data are sparse. One can identify seismic events with significant contributions so that only beams with nonnegligible amplitudes need to be used to image the subsurface. In addition, due to the beams’ spectral localization, only beams that pass near an imaging point need to be taken into account. These two properties reduce the computational complexity of computing the Hessian matrix — an essential ingredient for LSM. As a result, we can efficiently construct the Hessian matrix based on analyzing the sparse beam domain data. 

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3. An Efficient and Stable High‐Resolution Seismic Imaging Method: Point‐Spread Function Deconvolution

   https://doi.org/10.1029/2021JB023281  

   Yang, Jidong; Huang, Jianping; Zhu, Hejun; McMechan, George; Li, Zhenchun (July 2022, Journal of Geophysical Research: Solid Earth)

   Abstract By fitting observed data with predicted seismograms, least‐squares migration (LSM) computes a generalized inverse for a subsurface reflectivity model, which can improve image resolution and reduce artifacts caused by incomplete acquisition. However, the large computational cost of LSM required for simulations and migrations limits its wide applications for large‐scale imaging problems. Using point‐spread function (PSF) deconvolution, we present an efficient and stable high‐resolution imaging method. The PSFs are first computed on a coarse grid using local ray‐based Gaussian beam Born modeling and migration. Then, we interpolate the PSFs onto a fine‐image grid and apply a high‐dimensional Gaussian function to attenuate artifacts far away from the PSF centers. With 2D/3D partition of unity, we decompose the traditional adjoint migration results into local images with the same window size as the PSFs. Then, these local images are deconvolved by the PSFs in the wavenumber domain to reduce the effects of the band‐limited source function and compensate for irregular subsurface illumination. The final assembled image is obtained by applying the inverse of the partitions for the deconvolved local images. Numerical examples for both synthetic and field data demonstrate that the proposed PSF deconvolution can significantly improve image resolution and amplitudes for deep structures, while not being sensitive to velocity errors as the data‐domain LSM. 

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4. The LSM RUM-Tree: A Log Structured Merge R-Tree for Update-intensive Spatial Workloads

   https://doi.org/10.1109/ICDE51399.2021.00238  

   Shin, Jaewoo; Wang, Jianguo; Aref, Walid G. (April 2021, 2021 IEEE 37th International Conference on Data Engineering (ICDE))

   null (Ed.)

   Many applications require update-intensive work-loads on spatial objects, e.g., social-network services and shared-riding services that track moving objects (devices). By buffering insert and delete operations in memory, the Log Structured Merge Tree (LSM) has been used widely in various systems because of its ability to handle insert-intensive workloads. While the focus on LSM has been on key-value stores and their optimizations, there is a need to study how to efficiently support LSM-based secondary indexes. We investigate the augmentation of a main-memory-based memo structure into an LSM secondary index structure to handle update-intensive workloads efficiently. We conduct this study in the context of an R-tree-based secondary index. In particular, we introduce the LSM RUM-tree that demonstrates the use of an Update Memo in an LSM-based R-tree to enhance the performance of the R-tree's insert, delete, update, and search operations. The LSM RUM-tree introduces novel strategies to reduce the size of the Update Memo to be a light-weight in-memory structure that is suitable for handling update-intensive workloads without introducing significant over-head. Experimental results using real spatial data demonstrate that the LSM RUM-tree achieves up to 9.6x speedup on update operations and up to 2400x speedup on query processing over the existing LSM R-tree implementations. 

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5. Rayleigh-scattering microscopy for tracking and sizing nanoparticles in focused aerosol beams

   https://doi.org/10.1107/s2052252518010837  

   Hantke, Max F; Bielecki, Johan; Kulyk, Olena; Westphal, Daniel; Larsson, Daniel_S D; Svenda, Martin; Reddy, Hemanth_K N; Kirian, Richard A; Andreasson, Jakob; Hajdu, Janos; et al (November 2018, IUCrJ)

   Ultra-bright femtosecond X-ray pulses generated by X-ray free-electron lasers (XFELs) can be used to image high-resolution structures without the need for crystallization. For this approach, aerosol injection has been a successful method to deliver 70–2000 nm particles into the XFEL beam efficiently and at low noise. Improving the technique of aerosol sample delivery and extending it to single proteins necessitates quantitative aerosol diagnostics. Here a lab-based technique is introduced for Rayleigh-scattering microscopy allowing us to track and size aerosolized particles down to 40 nm in diameter as they exit the injector. This technique was used to characterize the `Uppsala injector', which is a pioneering and frequently used aerosol sample injector for XFEL single-particle imaging. The particle-beam focus, particle velocities, particle density and injection yield were measured at different operating conditions. It is also shown how high particle densities and good injection yields can be reached for large particles (100–500 nm). It is found that with decreasing particle size, particle densities and injection yields deteriorate, indicating the need for different injection strategies to extend XFEL imaging to smaller targets, such as single proteins. This work demonstrates the power of Rayleigh-scattering microscopy for studying focused aerosol beams quantitatively. It lays the foundation for lab-based injector development and online injection diagnostics for XFEL research. In the future, the technique may also find application in other fields that employ focused aerosol beams, such as mass spectrometry, particle deposition, fuel injection and three-dimensional printing techniques. 

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