scholarly journals Separation of diffracted waves via SVD filter

2020 ◽  
Vol 17 (5) ◽  
pp. 1259-1271
Author(s):  
Hong-Yan Shen ◽  
Qin Li ◽  
Yue-Ying Yan ◽  
Xin-Xin Li ◽  
Jing Zhao
Keyword(s):  
Seismic Waves ◽  
Original Data ◽  
Identification Accuracy ◽  
Reflected Waves ◽  
Wave Separation ◽  
Imaging Results ◽  
Diffracted Waves ◽  
Fast Processing ◽  
The Difference ◽  
Value Decomposition

Abstract Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones. However, the energy of diffracted waves is weaker than that of reflections. Therefore, the extraction of diffracted waves is the basis for the effective utilization of diffracted waves. Based on the difference in travel times between diffracted and reflected waves, we developed a method for separating the diffracted waves via singular value decomposition filters and presented an effective processing flowchart for diffracted wave separation and imaging. The research results show that the horizontally coherent difference between the reflected and diffracted waves can be further improved using normal move-out (NMO) correction. Then, a band-rank or high-rank approximation is used to suppress the reflected waves with better transverse coherence. Following, separation of reflected and diffracted waves is achieved after the filtered data are transformed into the original data domain by inverse NMO. Synthetic and field examples show that our proposed method has the advantages of fewer constraints, fast processing speed and complete extraction of diffracted waves. And the diffracted wave imaging results can effectively improve the identification accuracy of geological heterogeneous bodies or zones.

Ground-motion amplitude across ridges

1994 ◽  
Vol 84 (6) ◽  
pp. 1786-1800
Author(s):  
H. Pedersen ◽  
B. Le Brun ◽  
D. Hatzfeld ◽  
M. Campillo ◽  
P.-Y. Bard
Keyword(s):  
Numerical Simulations ◽  
Vertical Component ◽  
Field Work ◽  
Spectral Ratios ◽  
Spectral Matrix ◽  
Wave Separation ◽  
Time Domains ◽  
Diffracted Waves ◽  
Seismic Records ◽  
Value Decomposition

Abstract Local amplification and wave diffraction on an elongated ridge near Sourpi in central Greece were studied by the analysis of seismic records of local and regional earthquakes. Data were obtained during field work especially designed for this purpose. These data were analyzed in the frequency and time domains. In the frequency domain, spectral ratios show amplifications of 1.5 to 3 at the ridge top relative to the base of the ridge. The horizontal components of motion are more amplified than the vertical component and the observed spectral ratios seem stable for different earthquake locations. Theoretical spectral ratios, calculated by the indirect boundary element method, are dependent on earthquake location but are in general agreement with the observed spectral ratios. Another dataset, from Mont St. Eynard in the French Alps, showed similar characteristics with spectral amplitudes on the top of the ridge up to four times those on the flank. These relative amplifications are within the range predicted by numerical simulations. The numerical simulations also show that the topographic effect involves the emission of diffracted waves propagating from the top toward the base of the ridge. The use of a seven-station array on the ridge at Sourpi made it possible to identify such waves. The analysis was performed with wave separation methods using singular value decomposition and spectral matrix filtering. Our results show agreement between experimental data and theoretical results supporting the use of numerical simulations for estimation of purely topography-induced amplification on ridge tops. Our results also show that such amplification is moderate for the ridges under study.

scholarly journals Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1166
Author(s):  
Wei Zhang ◽  
Liang Gong ◽  
Suyue Chen ◽  
Wenjie Wang ◽  
Zhonghua Miao ◽  
...  
Keyword(s):  
Field Experiments ◽  
Absolute Error ◽  
Identification Accuracy ◽  
Depth Image ◽  
Dynamic Identification ◽  
Random Sample Consensus ◽  
Harvesting Efficiency ◽  
Value Decomposition ◽  
Forage Harvester ◽  
Ransac Algorithm

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

Periodically poled KTA crystal investigated using coherent X-ray beams

2000 ◽  
Vol 33 (4) ◽  
pp. 1149-1153 ◽  
Author(s):  
P. Pernot-Rejmánková ◽  
P. A. Thomas ◽  
P. Cloetens ◽  
F. Lorut ◽  
J. Baruchel ◽  
...  
Keyword(s):  
Phase Difference ◽  
Domain Walls ◽  
Structural Information ◽  
Periodically Poled ◽  
Synchrotron Source ◽  
X Ray ◽  
Diffracted Waves ◽  
Coherent Beams ◽  
Diffraction Imaging ◽  
The Difference

The distribution of inverted ferroelectric domains on the surface and within the bulk of a periodically poled KTA (KTiOAsO4) single crystal has been observed using a simple X-ray diffraction imaging setup which takes advantage of the highly coherent beams available at a third-generation synchrotron source, such as the ESRF. This technique allows one to reveal the phase difference between the waves that are Bragg diffracted from adjacent domainsviafree-space propagation (Fresnel diffraction). The phase difference of the diffracted waves is mainly produced by the difference in phases of the structure factors involved, and contains precise structural information about the nature of the domain walls.

scholarly journals The effect of a secondary task on identification accuracy of oxygen saturation ranges using an enhanced pulse oximetry sonification

2016 ◽  
Vol 60 (1) ◽  
pp. 628-632
Author(s):  
Estrella Paterson ◽  
Penelope Sanderson ◽  
Neil Paterson ◽  
David Liu ◽  
Robert Loeb
Keyword(s):  
Oxygen Saturation ◽  
Secondary Task ◽  
Ambient Noise ◽  
Visual Display ◽  
Identification Accuracy ◽  
Future Research ◽  
Identification Performance ◽  
Arithmetic Task ◽  
Disruption Effect ◽  
The Difference

In the operating theatre, anesthesiologists monitor an anesthetized patient’s oxygen saturation (SpO2) with a visual display but also with an auditory tone, or sonification. However, if the anesthesiologist must divide their attention across tasks, they may be less effective at recognising their patient’s SpO2 level. Previous research indicates that a sonification enhanced with additional sound dimensions of tremolo and brightness more effectively supports participants’ identification of SpO2 ranges than a conventional sonification does. This laboratory study explored the effect of a secondary task on participants’ ability to identify SpO2 range when using a conventional sonification (LogLinear sonification) versus an enhanced sonification (Stepped Effects sonification). Nineteen non-clinician participants who used the Stepped Effects sonification were significantly more effective at identifying SpO2 range ( Md = 100%) than were 18 participants using the LogLinear sonification ( Md = 80%). Range identification performance of participants using the Stepped Effects sonification tended to be less disrupted by a concurrent arithmetic task (drop from Md = 100% to 95%) than it was for participants using the LogLinear sonification (drop from Md = 80% to 73%). However, the disruption effect in each case was small, and the difference in disruption across sonifications was not statistically significant. Future research will test the sonifications under more intense cognitive load and in the presence of ambient noise.

scholarly journals Linear traveltime perturbation interpolation: a novel method to compute 3-D traveltimes

2015 ◽  
Vol 203 (1) ◽  
pp. 548-552 ◽  
Author(s):  
Jianzhong Zhang ◽  
Junjie Shi ◽  
Lin-Ping Song ◽  
Hua-wei Zhou
Keyword(s):  
Seismic Waves ◽  
Homogeneous Medium ◽  
Cell Boundary ◽  
Routine Method ◽  
Bilinear Interpolation ◽  
Linear Distribution ◽  
Heterogeneous Model ◽  
Computational Errors ◽  
Novel Method ◽  
The Difference

Abstract The linear traveltime interpolation has been a routine method to compute first arrivals of seismic waves and trace rays in complex media. The method assumes that traveltimes follow a linear distribution on each boundary of cells. The linearity assumption of traveltimes facilitates the numerical implementation but its violation may result in large computational errors. In this paper, we propose a new way to mitigate the potential shortcoming hidden in the linear traveltime interpolation. We use the vertex traveltimes in a calculated cell to introduce an equivalent homogeneous medium that is specific to the cell boundary from a source. Therefore, we can decompose the traveltime at a point on the cell boundary into two parts: (1) a reference traveltime propagating in the equivalent homogeneous medium and (2) a perturbation traveltime that is defined as the difference between the original and reference traveltimes. We now treat that the traveltime perturbation is linear along each boundary of cells instead of the traveltime. With the new assumption, we carry out the bilinear interpolation over traveltime perturbation to complete traveltime computation in a 3-D heterogeneous model. The numerical experiments show that the new method, the linear traveltime perturbation interpolation, is able to achieve much higher accuracy than that based on the linear traveltime interpolation.

A dictionary learning method with atom splitting for seismic footprint suppression

Geophysics ◽  
2021 ◽  
pp. 1-97
Author(s):  
Dawei Liu ◽  
Lei Gao ◽  
Xiaokai Wang ◽  
wenchao Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Original Data ◽  
Morphological Features ◽  
Time Slice ◽  
Learning Method ◽  
Seismic Attribute ◽  
Morphological Component Analysis ◽  
Value Decomposition

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises two components, i.e., useful signals and acquisition footprint. Useful signals describe the spatial distributions of geological structures with local piecewise smooth morphological features. However, acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in the marine seismic acquisition appear as stripes. As useful signals and acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two sub-dictionaries to reconstruct these two components. We propose an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D post-stack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D post-stack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that the proposed method can effectively suppress the acquisition footprint with fidelity to the original data.

Matrix form of interval multivariable gray model and its application

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandang Guo ◽  
Yaqian Jing ◽  
Bingjun Li
Keyword(s):  
Three Dimensional ◽  
Original Data ◽  
Upper Bounds ◽  
Matrix Form ◽  
Gray Model ◽  
Interval Numbers ◽  
Content Type ◽  
The Matrix ◽  
Number Sequences ◽  
The Difference

PurposeThe purpose of this paper is to make multivariable gray model to be available for the application on interval gray number sequences directly, the matrix form of interval multivariable gray model (IMGM(1,m,k) model) is constructed to simulate and forecast original interval gray number sequences in this paper.Design/methodology/approachFirstly, the interval gray number is regarded as a three-dimensional column vector, and the parameters of multivariable gray model are expressed in matrix form. Based on the dynamic gray action and optimized background value, the interval multivariable gray model is constructed. Finally, two examples and comparisons are carried out to verify the effectiveness of IMGM(1,m,k) model.FindingsThe model is applied to simulate and predict expert value, foreign direct investment, automobile sales and steel output, respectively. The results show that the proposed model has better simulation and prediction performance than another two models.Practical implicationsDue to the uncertainty information and continuous changing of reality, the interval gray numbers are used to characterize full information of original data. And the IMGM(1,m,k) model not only considers the characteristics of parameters changing with time but also takes into account information on lower, middle and upper bounds of interval gray numbers simultaneously to make better suitable for practical application.Originality/valueThe main contribution of this paper is to propose a new interval multivariable gray model, which considers the interaction between the lower, middle and upper bounds of interval numbers and need not to transform interval gray number sequences into real sequences. According to combining different characteristics of each bound of interval gray numbers, the matrix form of interval multivariable gray model is established to simulate and forecast interval gray numbers. In addition, the model introduces dynamic gray action to reflect the changes of parameters over time. Instead of white equation of classic MGM(1,m), the difference equation is directly used to solve the simulated and predicted values.

scholarly journals Toward efficacy of piecewise polynomial truncated singular value decomposition algorithm in moving force identification

2019 ◽  
Vol 22 (12) ◽  
pp. 2687-2698 ◽  
Author(s):  
Zhen Chen ◽  
Lifeng Qin ◽  
Shunbo Zhao ◽  
Tommy HT Chan ◽  
Andy Nguyen
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Singular Value ◽  
Decomposition Algorithm ◽  
Identification Accuracy ◽  
Piecewise Polynomial ◽  
Truncated Singular Value Decomposition ◽  
Force Identification ◽  
Moving Force ◽  
Value Decomposition

This article introduces and evaluates the piecewise polynomial truncated singular value decomposition algorithm toward an effective use for moving force identification. Suffering from numerical non-uniqueness and noise disturbance, the moving force identification is known to be associated with ill-posedness. An important method for solving this problem is the truncated singular value decomposition algorithm, but the truncated small singular values removed by truncated singular value decomposition may contain some useful information. The piecewise polynomial truncated singular value decomposition algorithm extracts the useful responses from truncated small singular values and superposes it into the solution of truncated singular value decomposition, which can be useful in moving force identification. In this article, a comprehensive numerical simulation is set up to evaluate piecewise polynomial truncated singular value decomposition, and compare this technique against truncated singular value decomposition and singular value decomposition. Numerically simulated data are processed to validate the novel method, which show that regularization matrix [Formula: see text] and truncating point [Formula: see text] are the two most important governing factors affecting identification accuracy and ill-posedness immunity of piecewise polynomial truncated singular value decomposition.

Vector-acoustic full-waveform inversion: Taking advantage of wavefield separation and dealiasing

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. R409-R423
Author(s):  
Polina Zheglova ◽  
Alison Malcolm
Keyword(s):  
Seismic Waves ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Horizontal Displacement ◽  
Displacement Component ◽  
Full Waveform Inversion ◽  
Directional Information ◽  
Wave Separation ◽  
Full Waveform ◽  
Natural Separation

Vector-acoustic full-waveform inversion (VAFWI) directly inverts vector-acoustic (VA) data, which consist of pressure and particle displacement components, at the cost of conventional acoustic full-waveform inversion (FWI). VA data contain information about the direction of arrival of the recorded seismic waves. In VAFWI, this directional information is taken into account by introducing an appropriate data weighting. With this weighting, in the geometry of a marine seismic experiment, the VAFWI adjoint calculation approximates inverse wavefield extrapolation, resulting in the natural separation of up- and downgoing recorded waves. If the free-surface effects are modeled during the inversion, the wave separation leads to (1) suppression of surface-related artifacts, (2) constructive interference of receiver ghosts with their primaries leading to preservation of the low-frequency content in the adjoint fields, and (3) compensation for insufficient spatial wavefield sampling on the receiver side. The horizontal displacement component helps interpolate the missing data. Synthetic examples demonstrate that for undersampled data, VAFWI consistently recovers the subsurface properties with higher resolution and fewer artifacts than conventional FWI.

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