2d fft
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2021 ◽  
Vol 1 (1) ◽  
pp. 204-210
Author(s):  
Wiji Raharjo ◽  
Indiati Retno Palupi ◽  
Oktavia Dewi Alfiani

Separation between Regional and Residual anomaly in Gravity and Magnetic data processing is very important to get the best result in geological interpretation. Several method were used to solve this problem like upward continuation and polynomial fitting. With the same principle, 2D FFT is applied by make an interactive tools based on Matlab Language Programming, named “Oasis Ala-Ala”. It adopt the algorithm from software Oasis. It started with make visualization map or the original data, then the map divide into some grids. Each of grid contain gravity or magnetic data. Then it transformed from special to wavenumber domain. After that, it convolve with our own filter matrix. And the last step is inverse it to get the regional and residual anomaly map. However, Matlab is powerful in facilitate this process in the GUI Toolbox. One important thing is the size of gravity and magnetic data. It will improve to Filter matrix size before do inverse process.


2021 ◽  
Vol 873 (1) ◽  
pp. 012017
Author(s):  
I R Palupi ◽  
W Raharjo ◽  
S Kiswanti

Abstract Regional and residual Separation anomaly is one thing that must do in gravity processing data. It is important before calculating the depth of anomaly by power spectrum. There are several ways to do this, one of them is using 2D Fast Fourier Transform (FFT). 2D FFT will calculate the two-dimensional power of the gravity map (Bouger anomaly) to change the spatial domain into the wavenumber domain. 2D FFT result has no unit because it works in the wavenumber domain. Power spectrum do in wavenumber domain map. Besides that, to make the wavenumber map in the frequency domain, it should be convolved with some filter (high–pass filter) and then inverse to separate the regional and the residual map. The design of the filter matrix depends on the number of the data and the location of anomalies will be enhanced. It will influence the separation result. The best result gets from the trial and error process. 2D FFT is act like Upward Continuation or Polynomial Fitting in the gravity method with the simple process. In this paper, the process fully done in Python. Python is an effective and simple language programming because it has many modules to support the processing and covering the big data. It also gives the flexibility to the researcher to determine the specific location that will be enhanced


Author(s):  
Emre Timur ◽  
Coşkun Sarı

AbstractThe Dikili geothermal area, in the northern part of Izmir province, is one of the best known geothermal areas in Western Anatolia. This study attempts to analyze and interpret Bouguer gravity data to determine average structural depth values and assess geothermal resources using the Radial Amplitude Spectrum Method (RASM), based on 2D Fast Fourier Transform (FFT) analysis. We selected four different areas to apply the method. The greatest advantage of this method over the conventional power spectrum method is that it can determine the mean depth from 2D FFT spectra, not using a single cross-section taken in one direction. Thus, the user can select an area rather than a direction and average depth can be determined more accurately. The results show that average depth values of the top of the reservoir vary between 314 and 640 m in the region.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lin Cai ◽  
Xiaoyun Wan ◽  
Houtse Hsu ◽  
Jiangjun Ran ◽  
Xiangchao Meng ◽  
...  

AbstractDue to the independence of the gradiometer instrument’s orientation in space, the second invariant $$I_2$$ I 2 of gravity gradients in combination with individual gravity gradients are demonstrated to be valid for gravity field determination. In this contribution, we develop a novel gravity field model named I3GG, which is built mainly based on three novel elements: (1) proposing to utilize the third invariant $$I_3$$ I 3 of the gravity field and steady-state ocean circulation explorer (GOCE) gravity gradient tensor, instead of using the $$I_2$$ I 2 , similar to the previous studies; (2) applying an alternative two-dimensional fast fourier transform (2D FFT) method; (3) showing the advantages of $$I_3$$ I 3 over $$I_2$$ I 2 in the effect of measurement noise from the theoretical and practical computations. For the purpose of implementing the linearization of the third invariant, this study employs the theory of boundary value problems with sphere approximation at an accuracy level of $$O(J_2^2\cdot T_{ij})$$ O ( J 2 2 · T ij ) . In order to efficiently solve the boundary value problems, we proposed an alternative method of 2D FFT, which uses the coherent sampling theory to obtain the relationship between the 2D FFT and the third invariant measurements and uses the pseudo-inverse via QR factorization to transform the 2D Fourier coefficients to spherical harmonic ones. Based on the GOCE gravity gradient data of the nominal mission phase, a novel global gravity field model (I3GG) is derived up to maximum degree/order 240, corresponding to a spatial resolution of 83 km at the equator. Moreover, in order to investigate the differences of gravity field determination between $$I_3$$ I 3 with $$I_2$$ I 2 , we applied the same processing strategy on the second invariant measurements of the GOCE mission and we obtained another gravity field model (I2GG) with a maximum degree of 220, which is 20 degrees lower than that of I3GG. The root-mean-square (RMS) values of geoid differences indicates that the effects of measurement noise of I3GG is about 20% lower than that on I2GG when compared to the gravity field model EGM2008 (Earth Gravitational Model 2008) or EIGEN-5C (EIGEN: European Improved Gravity model of the Earth by New techniques). Then the accuracy of I3GG is evaluated independently by comparison the RMS differences between Global Navigation Satellite System (GNSS)/leveling data and the model-derived geoid heights. Meanwhile, the re-calibrated GOCE data released in 2018 is also dealt with and the corresponding result also shows the similar characteristics.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Homin Kang ◽  
Jaehong Lee ◽  
Duksu Kim
Keyword(s):  

2020 ◽  
pp. invited1-1-invited1-13
Author(s):  
Mattias Mende ◽  
Thomas Wiener

This article describes, how color textures can be reliably detected and classified in the production process independent of external parameters such as brightness, object positions (translation), angulars (rotation), object distances (scaling) or curved surfaces (rotation + scaling). The methods described here are also suitable for reliably classifying at least 18 color textures even if they differ only slightly from each other optically. The online classification of color textures is a classic task in the wood, furniture and textile industry. For example, un- wanted defects or partial soiling on moving webs can be reliably detected regard- less of fluctuations in brightness and/or shadows during process operation. Algo- rithms has been developed for teach-in with RGB-HSI-transform, set fewer seg- ments on the color textures of each class with e.g. 24x24 Pixel, use suitable transformations {HSI}, e.g. 2D-FFT for formation characteristic 2D spectral mountains in these segments, extraction of statistical features and setting up the individual classifiers. Algorithms has been developed for identification & classification in process op- eration with extraction of statistical characteristics and methods of robust classi- fication. The implementation of the methods, the triggering of the color cameras, the processing of the color information including the output of the results to the process control is done with the data analysis program Xeidana®.


Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 164 ◽  
Author(s):  
Wojciech Rafajłowicz

We consider a rather general problem of nonparametric estimation of an uncountable set of probability density functions (p.d.f.’s) of the form: f ( x ; r ) , where r is a non-random real variable and ranges from R 1 to R 2 . We put emphasis on the algorithmic aspects of this problem, since they are crucial for exploratory analysis of big data that are needed for the estimation. A specialized learning algorithm, based on the 2D FFT, is proposed and tested on observations that allow for estimate p.d.f.’s of a jet engine temperatures as a function of its rotation speed. We also derive theoretical results concerning the convergence of the estimation procedure that contains hints on selecting parameters of the estimation algorithm.


The Handwritten Character Recognition has been a challenging task for the past many decades. This is an old application related to the area of pattern recognition. Handwritten character recognition (HCR) can be classified into two types namely, Online and Offline. As per the literature survey, there are no standard databases for HCR [1] [2] [3] since there are very less number of speakers for any Indian language compared to English. Hence, the database of Indian scripts both for testing and training are to be developed in the laboratory environment. The recognition accuracy for printed / typed characters is more than 99 percent, whereas for the HCR it is around 60 percent. Hence the area of HCR is an open area of research. HCR for Indian languages is at nascent stage compared to English since they contain alphabets and also matra’s / sandhi are complex which make the recognition tougher. The freedom of the scriber in writing the script is also another challenge for achieving the better recognition accuracy. This work describes the handwritten character recognition of both Hindi and English scripts by extracting features using 2D FFT and using the Nearest Neighborhood Classifier. The best recognition accuracy for handwritten character recognition of English and Hindi languages obtained is 70%.


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