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(MarineMagnetics 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).We calculated the magnetic susceptibility distribution of the TA19-1 seamount 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.From the modeling, we derived the appropriate scale factor for the use of depth weight and setting magnetic susceptibility.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 many reasons. We can expect to have the better results of advanced modeling design with more geological survey data.agnetics 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).We calculated the magnetic susceptibility distribution of the TA19-1 seamount 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.From the modeling, we derived the appropriate scale factor for the use of depth weight and setting magnetic susceptibility.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 many reasons. We can expect to have the better results of advanced modeling design with more geological survey data.