scholarly journals Time-Domain Output Data Identification Model for Pipeline Flaw Detection Using Blind Source Separation Technique Complexity Pursuit

Acoustics ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 199-219
Author(s):  
Zia Ullah ◽  
Xinhua Wang ◽  
Yingchun Chen ◽  
Tao Zhang ◽  
Haiyang Ju ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Blind Source Separation ◽  
Field Data ◽  
Structural Parameters ◽  
Source Separation ◽  
Flaw Detection ◽  
Modal Identification ◽  
Complex Signals ◽  
Magnetic Field Data ◽  
Complexity Pursuit

Vital defect information present in the magnetic field data of oil and gas pipelines can be perceived by developing such non-parametric algorithms that can extract modal features and performs structural assessment directly from the recorded signal data. This paper discusses such output-only modal identification method Complexity Pursuit (CP) based on blind signal separation. An application to the pipeline flaw detection is presented and it is shown that the complexity pursuit algorithm blindly estimates the modal parameters from the measured magnetic field signals. Numerical simulations for multi-degree of freedom systems show that the method can precisely identify the structural parameters. Experiments are performed first in a controlled laboratory environment secondly in real world, on pipeline magnetic field data, recorded using high precision magnetic field sensors. The measured structural responses are given as input to the blind source separation model where the complexity pursuit algorithm blindly extracted the least complex signals from the observed mixtures that were guaranteed to be source signals. The output power spectral densities calculated from the estimated modal responses exhibit rich physical interpretation of the pipeline structures.

An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Maysam Abedi
Keyword(s):  
Magnetic Field ◽  
Magnetic Susceptibility ◽  
Least Squares ◽  
Minimization Problem ◽  
Potential Field ◽  
Field Data ◽  
Least Squares Method ◽  
Porphyry Copper ◽  
Magnetic Data ◽  
Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit.  The real case study located in  Semnan province of  Iran  consists  of  an arc-shaped  porphyry  andesite  covered  by  sedimentary  units  which  may  have  potential  of  mineral  occurrences, especially  porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

scholarly journals A reexamination of “interstellar ion waves” previously identified in Pioneer 10 magnetic field data

1998 ◽  
Vol 25 (19) ◽  
pp. 3721-3724 ◽  
Author(s):  
Neil Murphy ◽  
Edward J. Smith ◽  
Joyce Wolf ◽  
Devrie S. Intriligator
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Magnetic Field Data ◽  
Ion Waves ◽  
Pioneer 10

Blind Complex Source Separation Based on Cyclostationary Statistics

2014 ◽  
Vol 519-520 ◽  
pp. 1051-1056
Author(s):  
Jie Guo ◽  
An Quan Wei ◽  
Lei Tang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Performance ◽  
Separation Algorithm ◽  
Complex Signals ◽  
Cyclic Frequency ◽  
Complex Source ◽  
Simulation Results ◽  
Better Than ◽  
Separation Model

This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Indirect Measurement ◽  
Rate Of Change ◽  
Induction Coil ◽  
Voltage Response ◽  
Magnetic Field Data ◽  
Transient Electromagnetic ◽  
Different Characteristics ◽  
The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

scholarly journals The geomagnetic field at 1982 from DE-2 and other magnetic field data.

1988 ◽  
Vol 40 (9) ◽  
pp. 1103-1127 ◽  
Author(s):  
R. A. LANGEL ◽  
J. R. RIDGWAY ◽  
M. SUGIURA ◽  
K. MAEZAWA
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Field Data ◽  
Magnetic Field Data
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