scholarly journals Analisis Continuous Wavelet Transform (CWT) Anomali Residual Medan Gravitasi Di Situs Geologi Karangsambung Kebumen Jawa Tengah

2019 ◽  
Vol 2 (2) ◽  
pp. 61
Author(s):  
Wahyu Hidayat ◽  
Wrego Seno Giamboro
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Gravity Method ◽  
Rock Density ◽  
Bouguer Anomalies ◽  
Central Java ◽  
Free Air ◽  
Data Survey ◽  
High Degree

Gravity method is a passive geophysical method that provides information on the distribution of rock density below the surface. The gravity method has a weakness at the level of ambiguity in determining the depth of the anomaly. This study aims to determine the depth of the anomaly using Continuous Wavelet Transform (CWT) analysis to overcome the value of ambiguity, so that the results obtained have a high degree of accuracy. The research method is data survey / acquisition and data analysis. This research was conducted in Karangsambung Kebumen, Central Java with the acquisition of gravitational data as many as 56 measurement points. The results of data acquisition then analyzed included reading to mGal, tool height correction, drift, tides, latitude, free air correction, Bouguer correction, and field correction. The results of this correction obtained Complete Bouguer Anomalies (ABL) values which were then reduced to flat fields and regional-residual anomaly filters. The next step is CWT analysis by making incisions on residual anomaly maps. The results showed that the source of the anomaly was between ± 39.2 - 122.9 meters.

Application of global geopotential models to high-precesion levelling processing

2017 ◽  
Vol 926 (8) ◽  
pp. 2-9
Author(s):  
V.V. Popadyev ◽  
D.A. Kuliev
Keyword(s):  
Field Model ◽  
Direct Evaluation ◽  
Gravity Field Model ◽  
Bouguer Anomalies ◽  
Free Air ◽  
Geopotential Models ◽  
Integral Characteristics ◽  
Indirect Evaluation ◽  
Gravity Map ◽  
High Degree

The article studies the properties of the high-degree gravity field model EGM-2008 in the calculation of integral characteristics at large distances several times greater than the spatial resolution of the model. As an example of an indirect evaluation of a high-degree model, a gravimetric correction was computed into the sum of the measured elevations along the line of the high-precision I class leveling of the Krasnovodsk – Chardzhou line located in Turkmenistan. Using the calculator ICGEM, the Bouguer anomalies were calculated at each point of the line, then the attraction of the Bouguer layer (used heights are from catalog) excluded for the transition to free-air anomalies. In parallel, for a direct evaluation of the model, a regular grid of Bouguer anomalies with a step of 2 angular minutes within line area was also obtained, which were then compared with the anomalies from the gravity map J-40 of scale 1

scholarly journals Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1106
Author(s):  
Jagdish N. Pandey
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Continuous Wavelet Transform ◽  
Inversion Formula ◽  
Continuous Wavelet ◽  
The Real ◽  
Schwartz Distributions ◽  
Distributional Sense ◽  
Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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