“FX-FK filter application to remove or isolate Linear Noise and 3D Linear Noise “;
FX-FK filter application to remove or isolate Linear Noise and 3D Linear Noise
Abstract
In this paper we will apply (FK- FX) filter on 2d and 3d land data. The Filter is applied to remove or isolate linear noise. Close to pie-shape operator is designed in FK domain within defined the apparent velocity range, as well as the noise temporal and spatial frequency ranges. Operator is converted in FX domain and applied using the exact shot/receiver coordinates. The level of noise removal is controlled by the length of the applied operator.3D case: operator is applied on shot gathers in moving azimuthal sectors. As a next step noise is obtained by subtracting FX-FK filtered data from the initial data. Adaptive subtraction is used to accurately remove noise without damaging the signal.
Key words: FX-FK; Linear Noise; 3D Linear Noise; land data
1. INTRODUCTION:
The F-X prediction filter was first introduced by Canales (1984), and formulated to the F-XY filter later by Chase (1992). It is widely used as a tool of random noise attenuation. It appeals to seismic data processing mainly due to its general signal model assumption, i.e., signal is predictable by convolution filters. This is a better signal model than that of other noise attenuation algorithms. For example, the K-L transform assumes that signal is horizontally aligned events with only amplitude variations. The Radon transform is more flexible than the
K-L in allowing signal to follow varied trajectories than simply being flat in time, but a few fixed types of trajectories (hyperbolic, parabolic and straight lines to name some) could not fully cover all signal behavior.
2. Theory, work method and results
2.1 Theory
FK FILTERING is transforming the data to the FK domain, raising the amplitude spectrum to an exponential power and performing the inverse transform is a method sometimes used to reduce the levels of random noise.
FX DECONVOLUTION, This is the commonest modern technique for attenuating random noise since it has few artifacts and can be run in 2D or 3D modes. An important feature is the addback of original signal which can be tailored by the processor to produce a section with a pleasing appearance. Small temporal (e.g. 10 traces) and spatial (e.g. 20ms) windows of input data are Fourier transformed to the FX domain. Deconvolution operators are designed in the lateral (X) dimension to predict the coherent parts of the signal. Subtracting the coherent parts will leave the incoherent parts i.e. random noise which can then be inverse transformed and subtracted from the signal. The next window would then be selected, ensuring some overlap with the previous window.
2.2 work method and results
The process done by four steps, first applying linear noise removal using FK-FX filter on our data, the input is 2d land data for linear noise and 3d land data for 3d Linear Noise Removal. For the Trace Distance Noise Filter 10 m the average step for offsets, Length of the filter in FX domain 2000 m, Apparent minimum slowness of noise 8 ms/trace ,Apparent maximum slowness of noise 9000 ms/trace ,Maximum frequency for band-pass 100 HZ ,Max Frequency (Linear Noise) Maximum frequency of ground roll for operator 50HZ.,Muting (% of Nyquist) Percentage of Nyquist spatial frequency for wave-number muting 60%.For 3D Linear Noise Removal, 3D Azimuth Slice Size 45 degree the size of azimuthal sector. Minimum Offset (0 m), Distance from the shot for borrowing traces to compensate for lack of near offsets in the sector.Second step, is linear noise extraction, subtract filtered data from the input data, the input is the input and output of first, and output is extracted noise. Third step, applying a band pass filter on the extracted linear noise, the input is extracted noise (output second step), the output is filtered noise. The ormsby band pass filter parameters ,low truncation 10HZ ,low cut frequency 15 HZ,high cut frequency 55 HZ,high truncation 60HZ ,filter in frequency domain ,percent zero padding for FFT 10.Step four, Adaptive subtraction input data (first step input) minus filtered noise (third step output ), the input for this step have two inputs, one is the input of first step and the second is filtered noise (output third step).the operator lag for time domain Adaptive subtraction 10 ms ,moving window shift 50%.
The four steps done can be illustrated in fig (1).
Fig (1) shows the flow used for this work
In fig (2) shows the raw data and fig (5) 3d raw data shot point sort, 192 traces, used for flow in fig (1). The first output which is output of first step can be represent in fig (3) for 2d land data, and fig (6) for 3d land data. After applying the flow in fig (1) the output of step four (Adaptive subtraction) is removal noise, which can be illustrated in fig (4) for linear noise removal (2d land data), and 3d linear noise removal for 3d land data.
Fig (2) 2d land data raw data shot point sort, 120 traces
Fig (3) Fk-fx data, 2d land data after applied fk-fx filter, shot point sort, 120 traces
Fig(4)After applied flow fig(1), Removal noise
Fig (5) 3d raw data shot point sort, 192 traces
Fig (6) Fk-fx data, 3d land data after applied fk-fx filter
Fig (7) 3d land data after applied flow in fig(1), 3D Linear Noise removal.
3. CONCOLUTION
Minimum apparent slowness must be smaller than real minimum slowness of linear noise because we limit operator length when we apply.Max slowness can be just huge number, if we want to eliminate all low velocity linear noise. The shorter is operator length, the milder application of operator will be. To eliminate all linear noise this procedure can be applied in iterative manner. The fk-fx linear noise removal was reviewed and tested on 2d land and 3d land seismic data .It was found that On noisy synthetic images, numerical measurements indicate the fk-fx filter performs better at attenuation random noise than the f-k filter only . The residual noise after f-x filtering still appears fairly random, and the filter does not give rise to the same type of coherent In addition, the f-x filter is able to extract the signal without any guidance from the user, whereas an f-k dip reject filter must.
References
1. Canales, L.L. 1984, Random Noise Reduction, 54th Annual SEG meeting, Atlanta
2. Chase, M.K., Random noise reduction by 3-D spatial prediction filtering, 62nd Annual SEG Meeting, New Orleans, USA. 1992.
3. Jones, I.F., and Levy, S., 1987, Signal-to-noise ratio enhancement in multi-channel seismic data via the Karhunen-Loeve transform: Geophysical Prospecting, v. 35, 12-32.Holden-Day.
4. Treitel, S., 1974, The complex wiener f'flter: Geophysics, v. 39, 169-173.
5. Lawton, D., and Harrison, M., 1990, A two-component reflection seismic survey, Springbank, Alberta: in this volume.
6. Robinson, E.A., 1967, Multichannel time series analysis with digital computer programs: San Francisco,
7. Wang, Xi-shuo, Random noise attenuation of pre-stack seismic data by surface consistent prediction in frequency domain, CSEG National Convention, Calgary, Canada, 1996.
8. Wang, Xi-shuo, Surface consistent noise attenuation of seismic data in frequency domain with adaptive pre-whitening, 67th Annual SEG Meeting, Denver, USA. 1997.
MOHAMED MHMOD, PHD, Jilin university Geoexploration science and technology ,Geophysics.
E-MAIL:baveciwan-23@hotmail.com