A magnetotelluric investigation under the Williston Basin of southeastern Saskatchewan

1988 ◽  
Vol 25 (1) ◽  
pp. 60-67 ◽  
Author(s):  
J. M. Maidens ◽  
K. V. Paulson
Keyword(s):  
Low Frequency ◽  
Singular Value ◽  
Williston Basin ◽  
Frequency Range ◽  
Conductivity Anomaly ◽  
Central Plains ◽  
The North ◽  
Naturally Occurring ◽  
Value Decomposition ◽  
Resistivity Log

A magnetotelluric (MT) survey using naturally occurring ultra low frequency (ULF) and extremely low frequency (ELF) sources was conducted in the frequency range of 0.5 mHz to 20 Hz in order to locate the western edge and the depth of the North American Central Plains (NACP) conductivity anomaly in the Bengough area of southeastern Saskatchewan. The data base was also used to evaluate the complex singular-value decomposition (CSVD) method of MT processing and to corroborate certain geologic interpretations in this part of the Williston Basin.Modelling of the resulting impedance tensors revealed a deep (10–15 km) zone with resistivity (35–85 Ω∙m) significantly lower than typical values (1000–1500 Ω∙m) obtained from a borehole resistivity log of the top 30 m of the Precambrian at a depth of 2.3 km. An increase in depth (to 20 km) and resistivity (150–275 Ω∙m) of this deep zone measured at the survey's west end was interpreted as indicating the anomaly's western edge. The CSVD processing of the data did not show any particular advantages over the conventional cross-spectral method.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

scholarly journals Broadband Adaptive RCS Computation through Characteristic Basis Function Method

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Guohua Wang ◽  
Yufa Sun
Keyword(s):  
Cross Section ◽  
Basis Function ◽  
Function Method ◽  
High Accuracy ◽  
Wide Frequency Range ◽  
Singular Value ◽  
Basis Functions ◽  
Frequency Range ◽  
Arbitrary Frequency ◽  
Value Decomposition

A broadband radar cross section (RCS) calculation approach is proposed based on the characteristic basis function method (CBFM). In the proposed approach, the desired arbitrary frequency band is adaptively divided into multiple subband in consideration of the characteristic basis functions (CBFs) number, which can reduce the universal characteristic basis functions (UCBFs) numbers after singular value decomposition (SVD) procedure at lower subfrequency band. Then, the desired RCS data can be obtained by splicing the RCS data in each subfrequency band. Numerical results demonstrate that the proposed method achieve a high accuracy and efficiency over a wide frequency range.

Electromagnetic images of the Trans-Hudson orogen: the North American Central Plains anomaly revealed

2005 ◽  
Vol 42 (4) ◽  
pp. 457-478 ◽  
Author(s):  
Alan G Jones ◽  
Juanjo Ledo ◽  
Ian J Ferguson
Keyword(s):  
Electrical Properties ◽  
North American ◽  
Upper Mantle ◽  
Lithospheric Mantle ◽  
Three Dimensional ◽  
Conductivity Anomaly ◽  
Central Plains ◽  
Slave Craton ◽  
The North ◽  
Kimberlite Field

Magnetotelluric studies of the Trans-Hudson orogen over the last two decades, prompted by the discovery of a significant conductivity anomaly beneath the North American Central Plains (NACP), from over 300 sites yield an extensive database for interrogation and enable three-dimensional information to be obtained about the geometry of the orogen from southern North Dakota to northern Saskatchewan. The NACP anomaly is remarkable in its continuity along strike, testimony to along-strike similarity of orogenic processes. Where bedrock is exposed, the anomaly can be associated with sulphides that were metamorphosed during subduction and compression and penetratively emplaced deep within the crust of the internides of the orogen to the boundary of the Hearne margin. A new result from this compilation is the discovery of an anomaly within the upper mantle beginning at depths of ~80–100 km. This lithospheric mantle conductor has electrical properties similar to those for the central Slave craton mantle conductor, which lies directly beneath the major diamond-producing Lac de Gras kimberlite field. While the Saskatchewan mantle conductor does not directly underlie the Fort à la Corne kimberlite, which is associated with the Sask craton, the spatial correspondence is close.

scholarly journals The North American Central Plains Conductivity Anomaly

1975 ◽  
Vol 43 (3) ◽  
pp. 815-833 ◽  
Author(s):  
A. O. Alabi ◽  
P. A. Camfield ◽  
D. I. Gough
Keyword(s):  
North American ◽  
Conductivity Anomaly ◽  
Central Plains ◽  
The North

Brain Image Enhancement Approach Based on Singular Value Decomposition in Nonsubsampled Shearlet Transform Domain

2020 ◽  
Vol 10 (8) ◽  
pp. 1785-1794 ◽  
Author(s):  
Liangliang Li ◽  
Yujuan Si ◽  
Linli Wang ◽  
Zhenhong Jia ◽  
Hongbing Ma
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Frequency Component ◽  
Singular Value ◽  
Transform Domain ◽  
Brain Images ◽  
Brain Image ◽  
Frequency Components ◽  
Definition Of ◽  
Value Decomposition

In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency component is estimated. The final enhancement image is obtained by inverse NSST. Results of this experiment demonstrate that the proposed technique has good performance in terms of brain image enhancement when compared to other methods.

A Framework for Image Encryption on Frequency Domain

2019 ◽  
pp. 247-259
Author(s):  
Zhe Liu ◽  
Mee Loong Yang ◽  
Wei Qi Yan
Keyword(s):  
Image Encryption ◽  
Digital Watermarking ◽  
Logistic Map ◽  
Low Frequency ◽  
Singular Value ◽  
Encryption Algorithm ◽  
Discrete Wavelet ◽  
Secret Key ◽  
Value Decomposition ◽  
Image Encryption Algorithm

In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.

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