Nugraha, Diptya Mas (2017) Aplikasi Transformasi Curvelet Pada Pengolahan Data Seismik 2D. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Noise acak pada data seismik masih menjadi permasalahan utama. Filter tradisional yang digunakan pada pengolahan data seismik masih memiliki kelemahan pada atenuasi noise acak karena masih menggunakan skala tunggal. Perkembangan filter telah mencapai multi skala. Salah satu transformasi tersebut adalah transformasi curvelet. Transformasi curvelet merupakan perkembangan dari transformasi wavelet yang mampu memisahkan noise dari sinyal dalam dimensi frekuensi, dip, azimuth dan lokasi. Pada penelitian ini, diterapkan transformasi curvelet pada data seismik 2D darat PSTM (Post Stack Time Migration) dan dua data seismik sintetik darat dengan bentuk model Antiklin dan geologi komples (model Marmousi). Langkah awal yang dilakukan adalah dengan membuat matrix pada tras data seismik, kemudian melakukan Fast Fourier Trasnfrom (FFT) 2D pada data seismik stack. Selanjutnya dilakukan windowing dalam domain frekuensi sehingga didapatkan koefisien curvelet. Setelah itu reduksi dengan bantuan threshold. Langkah terakhir dilakukan inverse FFT sehingga didapatkan kembali data yang telah di reduksi noise-nya. Berdasarkan hasil uji coba, didapatkan nilai parameter yang baik yaitu dengan nilai finest =1(curvelet), nbscales =3, Angles coarse=8, sigma fine = 10, shift= 3, weighting=8 dan tuning neighbor =10 . Hasil pada data sintetik menunjukkan nilai signal to ratio yang tinggi yaitu pada model sintetik “Antiklin” berkisar antara 27-28 dB dengan nilai SNR pada data noisy yaitu antara 19-25 dB sedangkan pada data seismik sintetik “Marmousi” memiliki nilai antara 15-16 dB pada data noisy dan nilai antara 17-21 dB pada data curvelet. Pada data seismik 2D PSTM menunjukkan spektrum amplitudo dan frekuensi dengan rentang nilai 5-60 HZ dengan nilai dominan yaitu 12-27 Hz, data sebelum transformasi memiliki nilai rentang 10-105 Hz dengan nilai dominan adalah 10-27 Hz. Transformasi Curvelet juga dapat menjadi filter terakhir pada data final stack dan mampu menjadi filter opsional dalam pengolahan data seismik darat 2D.
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Noise on seismic data is a major challenge for geophysicst on seismic data processing. Noise is
divided into two types: random noise and coherent noise.
Several methods have been able to eliminate coherent noise such as F
-
K Filters,
Median Filtering and F
-
X Deconvolution. However, for random noise cases
remains a challenge. Traditional filters used i
n
seismic
data processing still have
weaknesses in random noise attenuation because they still use a single scale. The
development of filters has reached multi
-
scale. One such transformation is the
curvelet transf
orm. Curvelet transform
is the development
of wavelet transforms
capable of separating noise from signals in the dimensions of frequency, dip,
azimuth
and location. In this research use
PSTM 2D (Post Stack Time Migration)
seismic
data and two synthetic ground
seismic
data with Anticline model and
c
omposite geology (Marmousi model). The first step is to create a matrix on the
seismi
c
trace data
, then do the Fast Fourier Trasnfrom (FFT) 2D on the
seismic
data stack. Next is done windowing in the frequency domain so that the curvelet
coefficient is obt
ained. After that reduction with the help of threshold. The last
step is inverse FFT so that the data is recovered in the noise reduction. Based on
the result of the experiment, we got good parameter value with finest value = 1
(curvelet), nbscales = 3, An
gles coarse = 8,
sigma coarse=3,
sigma fine = 10,
shift = 3, weighting = 8 and tuning neighbor = 10. The results of the synthetic
data showed a high signal to ratio value that is in the synthetic model "Antiklin"
ranging from 27
-
28 dB with SNR value in dat
a noisy that is between 19
-
25 dB
whereas in
seismic
synthetic data "Marmousi" has value between 15
-
16 DB on
noisy data and values
between 17
-
21 dB in
curvelet
data
In 2D PSTM seismic
data shows the amplitude and frequency spectrum with the range are 5
-
60
HZ
and the dominant value are 12
-
27 Hz, then the data before the transformation
has a value of 10
-
105 Hz range with the dominant value is 10
-
27 Hz. Curvelet transform can also be the last filter on the final data stack and capable of being an optional fil
ter in the 2D seismic land data processing.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSGf 551.220 287 Nug a |
| Uncontrolled Keywords: | Amplitude and Frequency Spectrum, Curvelet Parameter, Curvelet Transform, Seismic Reflector, Signal to Noise Ratio (SNR) |
| Subjects: | Q Science > QE Geology > QE601 Geology, Structural |
| Divisions: | Faculty of Civil Engineering and Planning > Geophysics Engineering > 33201-(S1) Undergraduate Thesis |
| Depositing User: | Diptya Mas Nugraha |
| Date Deposited: | 17 Nov 2017 02:35 |
| Last Modified: | 06 Mar 2019 03:16 |
| URI: | http://repository.its.ac.id/id/eprint/43842 |
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