3D modeling inversion calculation of magnetic data using iterative reweighted least squares at the Lau basin, Southwest Pacific

Abstract:

We performed the marine magnetic and the bathymetry survey in the Lau basin for finding the submarine hydrothermal deposits in October 2009. We acquired magnetic and bathymetry datasets by using Overhouser Proton Magnetometer SeaSPY(Marine Magnetics Co.) and Multi-Beam Echo Sounder EM120(Kongsberg Co.). We conducted the data processing to obtain detailed seabed topography, magnetic anomaly and reduction to the pole(RTP). The Lau basin is one of the youngest back–arc basins in the Southwest Pacific. This region was a lot of hydrothermal activities and hydrothermal deposits. In particular, Tofua Arc(TA) in the Lau basin consists of various and complex stratovolcanos(from Massoth et al., 2007).), We calculated the magnetic susceptibility distribution of the TA19-1 seamount(longitude:176°23.5’W, latitude: 22°42.5’W)area using the RTP data by 3-D magnetic inversion from Jung’s previous study(2013). Based on 2D 'compact gravity inversion' by Last & Kubik(1983), we expend it to the 3D algorithm using iterative reweighted least squares method with some weight matrices. The used weight matrices are two types: 1) the minimum gradient support(MGS) that controls the spatial distribution of the solution from Porniaguine and Zhdanov(1999); 2) the depth weight that are used according to the shape of subsurface structures. From the modeling, we derived the appropriate scale factor for the use of depth weight and setting magnetic susceptibility. Furthermore, we have to enter a very small error value to control the computation of the singular point of the inversion model that was able to be easily calculated for modeling. In addition, we applied separately weighted value for the correct shape and depth of the magnetic source. We selected the best results model by change to converge of RMS. Compared between the final modeled result and RTP values in this study, they are generally similar to the each other. But the input values and the modeled values have slightly little difference. This difference is expected to have been caused by various and complex stratovolcanos, misunderstanding of regional geology distribution, modeling design, limited vertical resolution from non-uniqueness in potential field and etc. We can expect to have the better results of advanced modeling design with more geological survey data.