GNGTS 2017 - 36° Convegno Nazionale

708 GNGTS 2017 S essione 3.3 from the initial value I = 0°, D = 0°. M-IDI and M-IDC return the similar results. Combining with the magnetization intensity distributions of Fig. 3a and the magnetization direction of Fig. 3b, comprehensively, we can obtain the magnetization vector distributions. Conclusions. The tests and comparisons for synthetic and real data reveal that MMM shows stable iteration convergence and low dependence on initial model. Given that without any constraints, the magnetization inclination and declination are varied in a large range and magnetization vectors appear a divergent trend. Geology and physical property information incorporated as constraints can improve the results effectively. For MID method of spherical framework, the magnetization intensity and direction have different units and sensitivities to magnetic anomaly. It is vital of importance to set appropriate weighting coefficients and initial values. M-ID methods solve the magnetization intensity and direction sequentially. M-IDCG obtains the distributions of magnetization inclinations and declination, while M-IDC and M-IDI achieve an optimal magnetization direction, which are more suitable for the isolated anomalies. For multiple and complicated shaped anomalies, a window is usually used to investigate separately. M-IDC is an exhaustion approach that the total field anomalies are calculated many times, which would increase the computational time specially for 3D big observations and discretization. M-IDI improves the inversion efficiency and returns the same results as M- IDC. Magnetization vectors indicate the comprehensive responses of induced magnetization, remanent magnetization and self-demagnetization. Magnetization vector inversion provides an effective approach to evaluate and investigate the remanence and self-demagnetization. Acknowledgements This study was financially supported by the National Natural Sciences Foundation of China (41604087). References Gerovska, D., Araúzo-Bravo, M.J., 2006. Calculation of magnitude magnetic transforms with high centricity and low dependence on the magnetization vector direction, Geophysics, 71, I21-I30. Gerovska, D., Araúzo-Bravo, M.J., Stavrev, P., 2009. Estimating the magnetization direction of sources from southeast Bulgaria through correlation between reduced-to-the-pole and total magnitude anomalies, Geophysical Prospecting, 57, 491-505. Guo, W., Dentith, M.C., Bird, R.T., Clark, D.A., 2001. Systematic error analysis of demagnetization and implications for magnetic interpretation, Geophysics, 66, 562-570. Lelièvre, P.G., Oldenburg, D.W., 2006. Magnetic forward modelling and inversion for high susceptibility, Geophysical Journal International, 166, 76-90. Lelièvre, P.G., Oldenburg, D.W., 2009. A 3D total magnetization inversion applicable when significant, complicated remanence is present, Geophysics, 74, L21-L30. Li, Y., Shearer, S.E., Haney, M.M., Dannemiller, N., 2010. Comprehensive approaches to 3D inversion of magnetic data affected by remanent magnetization, Geophysics, 75, L1-L11. Liu, S., Hu, X., Xi, Y., Liu, T., Xu, S., 2015. 2D sequential inversion of total magnitude and total magnetic anomaly data affected by remanent magnetization, Geophysics, 80, K1-K12. Liu, S., Hu, X., Zhang, H., Geng, M., Zuo, B., 2017. 3D magnetization vector inversion of magnetic data: improving and comparing methods, Pure and Applied Geophysics. Stavrev, P., Gerovska, D., 2000. Magnetic field transforms with low sensitivity to the direction of source magnetization and high centricity, Geophysical Prospecting, 48, 317-340. Wang, M., Di, Q., Xu, K., Wang, R., 2004. Magnetization vector inversion equations and 2D forward and inversed model study, Chinese Journal of Geophysics, 47, 528-534 (in Chinese with English abstract).