scholarly journals Interpretation for magnetic data at low latitude areas using continuous wavelet transform and marquardt algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. first
Author(s):  
Tin Quoc Chanh Duong ◽  
Đẩu Hiếu Dương ◽  
Ngân Ngọc Phạm ◽  
Hải Thanh Nguyễn ◽  
An Danh
Keyword(s):  
Wavelet Transform ◽  
Magnetic Anomaly ◽  
Continuous Wavelet Transform ◽  
Mekong Delta ◽  
Magnetic Data ◽  
Continuous Wavelet ◽  
Low Latitude ◽  
Wavelet Transform Modulus Maxima ◽  
Marquardt Algorithm ◽  
Geomagnetic Data

As analyzing geomagnetic data at low latitude areas for instance the Mekong Delta (latitudes 11,07o), significant problem is that both of the magnetization and ambient field are not vertical totally, making magnetic anomalies antisymmetrical and often skewed to the location of the sources. In this paper, two-dimensional continuous wavelet transform (2-D CWT), using Farshad-Sailhac complex wavelet function is studied and applied for reducing the magnetic anomaly to a symmetrical one - this located on the source of the anomaly, and then determining the position of the center of the object causing anomalies by wavelet transform modulus maxima (WTMM) method. Next, magnetic data is extracted in two perpendicular directions passing through the center of the source to perform one-dimensional continuous wavelet transform (1-D CWT) to estimate the shape, depth and size of the source. Then, using the Marquardt algorithm to solve the inverse problem by least-squares method to further identify other characteristic parameters of the source such as: vertical size, remanent magnetization vector. The reliability of the proposed method is verified through theoretical models before application for analyzing the geomagnetic data in the Mekong Delta. The results are consistency with deep hole data, having small root mean square error, contribute to a better interpretation of the geological nature of the magnetic anomaly sources in the study area.

Estimating depth and model type using the continuous wavelet transform of magnetic data

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 191-199 ◽  
Author(s):  
Marc A. Vallée ◽  
Pierre Keating ◽  
Richard S. Smith ◽  
Camille St‐Hilaire
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Finite Size ◽  
Magnetic Data ◽  
Homogeneous Field ◽  
Continuous Wavelet ◽  
Upward Continuation ◽  
Extended Sources ◽  
Poisson Wavelets ◽  
Depth Of Burial

The continuous wavelet transform has been proposed recently for the interpretation of potential field anomalies. Using Poisson wavelets, which are equivalent to an upward continuation of the analytic signal, this technique allows one to estimate the depth of burial of homogeneous field sources and to determine the nature of the source in the form of a structural index. Moreau et al. (1999) accomplish this by successively testing the least‐squares misfit on a log–log plot of the wavelet transform amplitude versus the sum of the depth and the dilation (upward continuation height). We extend this methodology by analyzing the ratio of the Poisson wavelet coefficients of the first and second orders. For simple pole sources, this ratio at one dilation is enough to estimate the depth and index uniquely; but for extended sources of finite size, we must analyze the variation of the estimates with dilations. The technique gives good results on synthetic and field examples.

CONTINUOUS WAVELET TRANSFORM MODULUS MAXIMA ANALYSIS OF THE ELECTROCARDIOGRAM: BEAT CHARACTERISATION AND BEAT-TO-BEAT MEASUREMENT

2005 ◽  
Vol 03 (01) ◽  
pp. 19-42 ◽  
Author(s):  
I. ROMERO LEGARRETA ◽  
P. S. ADDISON ◽  
M. J. REED ◽  
N. GRUBB ◽  
G. R. CLEGG ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Positive Predictive Value ◽  
Continuous Wavelet Transform ◽  
Predictive Value ◽  
Royal Infirmary ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

The problem of automatic beat recognition in the ECG is tackled using continuous wavelet transform modulus maxima (CWTMM). Features within a variety of ECG signals can be shown to correspond to various morphologies in the CWTMM domain. This domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of this enhanced time-frequency analysis technique for ECG signals, an R-wave detector is developed and tested using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.70% and a positive predictive value of 99.68%).

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