# Effective Residual and Regional Gravity Anomaly Separation - Using 1-D & 2-D Stationary Wavelet Transform

### Naeim Mousavi, Vahid E. Ardestani, Hassan Moosavi

#### Abstract

Numerous studies on capabilities of de-noising and separation by wavelet were performed, and their all aims more and less was elimination of possible largest nongeological factors, noise, and to achieve pure regional effects free from residuals. De-noising could be used for removal of non-desired effects like latitude, terrain, tides, drift etc., from our desired portion of data as target. Separations of anomalies that are not of interest conclude shallow structure is suitable to be optimal. Hence detection and removal of ever larger surface anomalies to obtain optimal separation is of interest. At up to now studies, large deviation of primarily original signal has been prevented. In this paper controlling factors which limit the overall deviation of transformed signal from the original one have been replaced with two new parameters that simultaneously cause extracting the maximum surplus signals, residuals, and also preserving the original form ever possible. Results of artificial models along with application of separation to real data indicate the usefulness of discrete stationary wavelet transform in order to optimal separation of anomalies with various wavelengths.

#### References

- Coifman, R. R., and Donoho, D. L., 1995: Translationinvariant denoising, in Antoniadis, A., and Oppenheim, G., Eds., Wavelets in statistics: SpringerVerlag, 125-150.
- Deng, X. Y., Yang, D. H., Peng, J. M., Guan, X., Yang, B. J., 2011: Noise reduction and drift removal using least-squares support vector regression with the implicit bias term, Geophysics, Vol. 75, No. 6, pp. V119-V127
- Donoho, D. L., 1993: Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data, in Daubechies, I., Ed., Different perspectives on wavelets: Proc. Symp. Appl. Math, 47, 173-205.
- Donoho, D. L. Johnstone I. M., 1994: Ideal spatial adaptation by wavelet shrinkage, Biometrika, Vol. 81, pp. 425-455.
- Fedi, M., Lenarduzzi,L., Primiceri, R., Quarta,T., 2000: Localized Denoising Filtering Using the Wavelet Transform Pure appl. geophys. VOL157, pp 1463- 1491.
- Fedi, M., Primiceri, R., Quarta, T., Villani, A. V., 2004: Joint application of continuous and discrete wavelet transform on gravity data to identify shallow and deep sources, Geophys. J. Int. VOL156, 7-21.
- Leblanc, G. E., Morris, W. A., 2001, Denoising of aeromagnetic data via the wavelet transform, GEOPHYSICS, VOL. 66 NO. 6 , Pages 1793-1804.
- Moreau, F., Gibert, D., Holschneider, M., and Saracco, G., 1999: Identification of sources of potential fields with the continuous wavelet transform: basic theory: J. Geophys. Res., 104, B3, 5003-5013.
- Neumann, M. H., and Von Sachs, R., 1995: Wavelet thresholding beyond the Gaussian i.i.d. situation, in Antoniadis, A., and Oppenheim, G., Eds.,Wavelets in Statistics: Springer-Verlag, 301-329.
- Ridsdill-Smith, T. A., and Dentith, M. C., 1999: The wavelet transform in aeromagnetic processing: Geophysics, 64, no. 4, 1003-1013.
- Saito, N., 1994, Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum description length criteria, in Foufoula-Georgiou, E., and Kumar, P., Eds.,Wavelets in geophysics: Academic Press, 299-324.
- Soares, J. C., Tenorio, L., Li, Y. G., 2004: “Efficient automatic denoising of gravity gradiometry data,” Geophysics, Vol. 69, No. 3, pp. 772-782.
- Yan. P., Wu, Y., 2011: Application of wavelet threshold denoising method in gravity data processing, IEEE, International Conference on Multimedia Technology (ICMT),pages 2972-2975.
- Zou, C. C., Yang, X. D., Pan, L. Z., 1999: A new technique for denoising log curve on the basis of wavelet transform, Geophysical and Geochemical Exploration, Vol. 23, No. 6, pp. 462-466.

#### Paper Citation

#### in Harvard Style

Mousavi N., E. Ardestani V. and Moosavi H. (2013). **Effective Residual and Regional Gravity Anomaly Separation - Using 1-D & 2-D Stationary Wavelet Transform** . In *Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: PRG, (ICPRAM 2013)* ISBN 978-989-8565-41-9, pages 659-668. DOI: 10.5220/0004219806590668

#### in Bibtex Style

@conference{prg13,

author={Naeim Mousavi and Vahid E. Ardestani and Hassan Moosavi},

title={Effective Residual and Regional Gravity Anomaly Separation - Using 1-D & 2-D Stationary Wavelet Transform},

booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: PRG, (ICPRAM 2013)},

year={2013},

pages={659-668},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0004219806590668},

isbn={978-989-8565-41-9},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: PRG, (ICPRAM 2013)

TI - Effective Residual and Regional Gravity Anomaly Separation - Using 1-D & 2-D Stationary Wavelet Transform

SN - 978-989-8565-41-9

AU - Mousavi N.

AU - E. Ardestani V.

AU - Moosavi H.

PY - 2013

SP - 659

EP - 668

DO - 10.5220/0004219806590668