Implementasi Wavelet pada Data Seismik Gunungapi

Dairoh Dairoh; Dega Surono Wibowo

doi:10.20527/flux.v17i2.6945

Implementasi Wavelet pada Data Seismik Gunungapi

Dairoh Dairoh, Dega Surono Wibowo

Abstract

Noise decomposition on seismic volcanic data with wavelet using Daubechies wavelet decomposition on X and Y direction record data. Seismic volcanic data is in the form of broadband data, which broadband data has a wide frequency range so that it has a high enough noise level. So that when the noise decomposition process is carried out in selecting the decomposition of the noise it must be appropriate so that the information contained in it does not decompose, this is because the volcanic broadband seismic record data contains volcanic earthquake event information that has an amplitude at the same value as the amplitude of the noise. Therefore noise decomposition is done by implementing wavelet, that is by using Daubechies wavelet decomposition. The results show that Daubechies wavelet decomposition can break down noise properly without losing the information contained in the signal. The decomposition of noise uses Daubechies db 5 level 5 wavelet decomposition in detail. The results of the wavelet using wavelet decomposition obtained vulcanic earthquake events whose signal results showed conformity with volcanic earthquakes with T. Minakmi and wasserman's forms.

Keywords

broadband, wavelet, Daubechies, volcano, Wasserman

Full Text:

PDF (Bahasa Indonesia)

References

Bormann, P. (2009). New Manual of IASPEI Seismological Observatory Practice. New Manual of IASPEI Seismological Observatory Practice (NMSOPM), 1(2002), 1162. https://doi.org/10.2312/GFZ.NMSOP

Chui, C. K. (1992). An Introducrion to Wavelets (volume 1). Texas A&M University, College Station, Texas:Academic Press.

Daubechies, I. (1992). Ten Lectures of Wavelets. Springer-Verlag, 357. http://books.google.nl/books?id=Nxnh48rS9jQC&printsec=frontcover&dq=intitle:Ten+Lectures+of+Wavelets&hl=&cd=1&source=gbs_api%5Cnpapers3://publication/uuid/533E5F2A-F9F5-4D8E-BFF2-A87A8228FE0B

Graps, A. (1995). An Introduction to Wavelets. IEEE Computational Science and Engineering, 2(2), 50–61.

Handoko, W., T, Ardhianto, Eka, & Safriliyant, E. (2011). Analisis Dan Implementasi Image Denoising dengan Metode Normal Shrink sebagai Wavelet Thresholding Analysis. Jurnal Teknologi Informasi DINAMIK, 16(1), 56–63.

Hatmojo, Y. I. (2014). Implementasi wavelet haar dan jaringan tiruan pada pengolahan selaput pelangi mata. Jurnal Jnteti, 3(1), 58–62.

Kawakatsu, H., & Yamamoto, M. (2015). Volcano Seismology. In Treatise on Geophysics: Second Edition (Vol. 4). Elsevier B.V. https://doi.org/10.1016/B978-0-444-53802-4.00081-6

Ratna, D., & Khukmiati, H. (2004). Penerapan Transformasi Wavelet Diskrit Untuk Reduksi Noise Pada Citra Digital. Limits: Journal of Mathematics and Its Applications, 1(1), 47–57. https://doi.org/10.12962/j1829605x.v1i1.1350

Robi’in, B. (2017). Analisis Dekomposisi Wavelet Pada Pengenalan Pola Lurik Dengan Metode Learning Vector Quantization. ILKOM Jurnal Ilmiah, 9(2), 153–160. https://doi.org/10.33096/ilkom.v9i2.133.153-160

Solikhin, A., Gunawan, H., Surono, S., & Jousset, P. (2011). Volcanic tremor analysis during Merapi 2010 explosion sequences. EGU General Assembly, 13, 13937.

Sriani, Ikhwan, A., & Yusnidah. (2017). Aplikasi Metode Transformasi Wavelet Diskrit. Jurnal Ilmiah Saintikom, 16(1), 31–40.

Suma’inna, S., & Gumilar, G. (2013). Implementasi Transformasi Wavelet Daubechies pada Kompresi Citra Digital. Jurnal Cauchy, 2(4), 211–215. https://doi.org/10.18860/ca.v2i4.3117

Sunjoyo, Andriani, L., Santosa, Gunawan, R., & Nugraha, K. A. (2016). Implementasi Transformasi Haar Wavelet untuk Deteksi Citra Jeruk Nipis yang Busuk. Jurnal Informatika, 12(2), 165–173. https://doi.org/10.21460/inf.2016.122.498

Wasserman, J. (2002). Volcano Seismology. Treatise on Geophysics, 4, 389–420. https://doi.org/10.1016/B978-044452748-6.00073-0

DOI: http://dx.doi.org/10.20527/flux.v17i2.6945