Improvement of Shallow Seismic Characterization Using the Singular Value Decomposition (SVD) Method in Seismic Data Inversion: A Case Study of a Site in Northeast Mexico

2020 ◽  
Vol 25 (4) ◽  
pp. 447-462
Author(s):  
Victor E. Infante-Pacheco ◽  
Juan C. Montalvo-Arrieta ◽  
Ignacio Navarro de León ◽  
Fernando Velasco-Tapia
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
Seismic Inversion ◽  
Singular Value ◽  
Clastic Rocks ◽  
Data Inversion ◽  
Value Decomposition ◽  
A Site ◽  
Geological Units ◽  
Svd Method

Several approaches can be taken to conduct seismic data inversion. However, usually, these approaches are unable to distinguish vertical and horizontal heterogeneities. Seismic inversion through the singular value decomposition (SVD) method offers an adequate and simple way to improve these traditional inversion models. For this study P and S wave data were acquired at a site located in northeastern Mexico, obtaining their travel times. An inversion algorithm involving the SVD analysis was then developed to establish the seismic velocities of the lithological units. Further, images of compressional and shear-wave velocities ( Vp and Vs, respectively), Vp/ Vs ratio, and elastic moduli (bulk, shear and Young's moduli, Lamé's constant, and Poisson's ratio) were obtained. These were compared with two geotechnical soundings positioned over a geophysical profile line. The geological features of the exposed units were recognized on some trenches. Further, seismic images demonstrated correlations with the thickness and distribution of the geological units. Unconsolidated sediments and fine-grain clastic rocks (in the Méndez formation) were clearly distinguished by the high velocity contrast. SVD seismic inversion has shown the ability to distinguish small physical heterogeneities of shallow geological units. Its application in civil engineering, hydrogeology, and to solve soil pollution problems can be relevant.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

scholarly journals Satellite image classification using proposed singular value decomposition method

2019 ◽  
Vol 13 (28) ◽  
pp. 52-67
Author(s):  
Noor Zubair Kouder
Keyword(s):  
Singular Value Decomposition ◽  
Classification Accuracy ◽  
Satellite Image ◽  
Field Work ◽  
Mean Value ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Erdas Imagine ◽  
Value Decomposition ◽  
Svd Method

In this work, satellite images for Razaza Lake and the surrounding areadistrict in Karbala province are classified for years 1990,1999 and2014 using two software programming (MATLAB 7.12 and ERDASimagine 2014). Proposed unsupervised and supervised method ofclassification using MATLAB software have been used; these aremean value and Singular Value Decomposition respectively. Whileunsupervised (K-Means) and supervised (Maximum likelihoodClassifier) method are utilized using ERDAS imagine, in order to getmost accurate results and then compare these results of each methodand calculate the changes that taken place in years 1999 and 2014;comparing with 1990. The results from classification indicated thatwater and hills are decreased, while vegetation, wet land and barrenland are increased for years 1999 and 2014; comparable with 1990.The classification accuracy was done by number of random pointschosen on the study area in the field work and geographical data thencompared with the classification results, the classification accuracy forthe proposed SVD method are 92.5%, 84.5% and 90% for years1990,1999,2014, respectivety, while the classification accuracies forunsupervised classification method based mean value are 92%, 87%and 91% for years 1990,1999,2014 respectivety.

scholarly journals Non-Negative K-SVD as an element of the forecasting electricity demand system

2019 ◽  
Vol 84 ◽  
pp. 01003
Author(s):  
Marcin Drechny
Keyword(s):  
Singular Value Decomposition ◽  
Sparse Coding ◽  
Demand Forecasting ◽  
Singular Value ◽  
Demand System ◽  
Power Demand ◽  
Power Load ◽  
Fast Power ◽  
Value Decomposition ◽  
Svd Method

The article describes the NN-K-SVD method based on the use of sparse coding and the singular value decomposition to specific values. An example of using the method is the compression of load profiles. The experiment of compression of 125022 power load profiles has been carried out with the use of registered profiles in households and small offices. Two matrices: patterns (atoms) and scaling factors are the result of the discussed algorithm. Features of the created matrices, which can be used in the creation of fast power demand forecasting systems, have been characterized.

Tube Wave Suppression in High Frequency Mine Seismic Data by Singular Value Decomposition

1995 ◽  
Vol 26 (2-3) ◽  
pp. 512-517 ◽  
Author(s):  
Geraldine Teakle ◽  
Shunhua Coa ◽  
Stewart Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
High Frequency ◽  
Singular Value ◽  
Tube Wave ◽  
Value Decomposition

Computing Surface Parameter of Conic Aspheric Mirror with Singular Value Decomposition

2013 ◽  
Vol 631-632 ◽  
pp. 1363-1366
Author(s):  
Yong Luo
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Radius Of Curvature ◽  
Surface Parameter ◽  
Aspheric Mirror ◽  
Value Decomposition ◽  
Svd Method

The result of null testing is usually used as the criterion in the fabricating process of aspheric mirrors. To ensure the accuracy of paraxial radius of curvature and conic constant is important when the null compensator emerges a problem. From the equation of conic aspheric mirror, we derive a set of algorithm from which the paraxial radius of curvature R and conic constant k can be obtained by using Singular Value Decomposition (SVD) method. The simulating result of an aspheric mirror with an aperture of 1229mm is presented and the solving precision reaches △R=0.1% and △k=0.14%. Thus the supplement to null testing of aspheric mirror is achieved effectively.

scholarly journals Receiver phase alignment using fitted SVD derived sensitivities from routine prescans

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256700
Author(s):  
Olivia W. Stanley ◽  
Ravi S. Menon ◽  
L. Martyn Klassen
Keyword(s):  
Singular Value Decomposition ◽  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Singular Value ◽  
7 Tesla ◽  
Signal To Noise ◽  
Phase Signal ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Method

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.

scholarly journals A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3301 ◽  
Author(s):  
Liang Wu ◽  
Qian Xu ◽  
Janne Heikkilä ◽  
Zijun Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Calibration Method ◽  
Angular Distance ◽  
Singular Value ◽  
Optical Parameters ◽  
Star Sensor ◽  
Calibration Methods ◽  
Value Decomposition ◽  
Svd Method

The navigation accuracy of a star sensor depends on the estimation accuracy of its optical parameters, and so, the parameters should be updated in real time to obtain the best performance. Current on-orbit calibration methods for star sensors mainly rely on the angular distance between stars, and few studies have been devoted to seeking new calibration references. In this paper, an on-orbit calibration method using singular values as the calibration reference is introduced and studied. Firstly, the camera model of the star sensor is presented. Then, on the basis of the invariance of the singular values under coordinate transformation, an on-orbit calibration method based on the singular-value decomposition (SVD) method is proposed. By means of observability analysis, an optimal model of the star combinations for calibration is explored. According to the physical interpretation of the singular-value decomposition of the star vector matrix, the singular-value selection for calibration is discussed. Finally, to demonstrate the performance of the SVD method, simulation calibrations are conducted by both the SVD method and the conventional angular distance-based method. The results show that the accuracy and convergence speed of both methods are similar; however, the computational cost of the SVD method is heavily reduced. Furthermore, a field experiment is conducted to verify the feasibility of the SVD method. Therefore, the SVD method performs well in the calibration of star sensors, and in particular, it is suitable for star sensors with limited computing resources.

An Improved Seismic Data Completion Algorithm using Low-Rank Tensor Optimization: Cost Reduction and Optimal Data Orientation

Geophysics ◽  
2021 ◽  
pp. 1-88
Author(s):  
Jonathan Popa ◽  
Susan E. Minkoff ◽  
Yifei Lou
Keyword(s):  
Fourier Transform ◽  
Singular Value Decomposition ◽  
Seismic Data ◽  
Convex Sets ◽  
Singular Spectrum Analysis ◽  
Singular Value ◽  
Low Rank ◽  
Traditional Methods ◽  
Value Decomposition ◽  
Conjugate Symmetry

Seismic data are often incomplete due to equipment malfunction, limited source and receiver placement at near and far offsets, and missing cross-line data. Seismic data contain redundancies as they are repeatedly recorded over the same or adjacent subsurface regions, causing the data to have a low-rank structure. To recover missing data one can organize the data into a multidimensional array or tensor and apply a tensor completion method. We can increase the effectiveness and efficiency of low-rank data reconstruction based on the tensor singular value decomposition (tSVD) by analyzing the effect of tensor orientation and exploiting the conjugate symmetry of the multidimensional Fourier transform. In fact, these results can be generalized to any order tensor. Relating the singular values of the tSVD to those of a matrix leads to a simplified analysis, revealing that the most square orientation gives the best data structure for low-rank reconstruction. After the first step of the tSVD, a multidimensional Fourier transform, frontal slices of the tensor form conjugate pairs. For each pair a singular value decomposition can be replaced with a much cheaper conjugate calculation, allowing for faster computation of the tSVD. Using conjugate symmetry in our improved tSVD algorithm reduces the runtime of the inner loop by 35% to 50%. We consider synthetic and real seismic datasets from the Viking Graben Region and the Northwest Shelf of Australia arranged as high-dimensional tensors. We compare tSVD based reconstruction to traditional methods, projection onto convex sets and multichannel singular spectrum analysis, and see that the tSVD based method gives similar or better accuracy and is more efficient, converging with runtimes that are an order of magnitude faster than the traditional methods. Additionally, we verify the most square orientation improves recovery for these examples by 10-20% compared to the other orientations.

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