وارون‏ سازی داده های مغناطیسی برای بازسازی کامل بردارتوزیع مغناطیس شوندگی
عنوان دوره: هجدهمین کنفرانس ژئوفیزیک ایران
نویسندگان
1پژوهشگر مهمان دانشگاه ناپلز فدریکو 2- گروه علوم زمین
2دانشگاه علوم زمین ووهانگ چین
چکیده
We develop a fast sequential inversion method (M-IDI) for 3D inversion of distribution of total magnetization vector to estimate both direction and intensity of magnetization. First, the magnetization intensity is retrieved by inversion of the magnitude magnetic anomaly using preconditioned conjugate gradient method. Then, the inclination and declination of magnetization are recovered either using the conjugate gradient algorithm like that is done to recovery of magnetization intensity by inverting the total field data (M-IDCG) or computing the correlation coefficients between the observed and predicted total field anomalies where the most correlated anomaly corresponds to the optimal magnetization direction (M-IDC). The new algorithm is evaluated by applying to synthetic and real data examples, and the results are compared with a previous important method i.e., the magnetization vector inversion in Cartesian framework (MMM) and spherical framework (MID).
کلیدواژه ها
 
Title
Inversion of magnetic data to fully reconstruct the Magnetization vector and its application to mineral exploration
Authors
Jamaledin Baniamerian, Shuang Liu
Abstract
We develop a fast sequential inversion method (M-IDI) for 3D inversion of distribution of total magnetization vector to estimate both direction and intensity of magnetization. First, the magnetization intensity is retrieved by inversion of the magnitude magnetic anomaly using preconditioned conjugate gradient method. Then, the inclination and declination of magnetization are recovered either using the conjugate gradient algorithm like that is done to recovery of magnetization intensity by inverting the total field data (M-IDCG) or computing the correlation coefficients between the observed and predicted total field anomalies where the most correlated anomaly corresponds to the optimal magnetization direction (M-IDC). The new algorithm is evaluated by applying to synthetic and real data examples, and the results are compared with a previous important method i.e., the magnetization vector inversion in Cartesian framework (MMM) and spherical framework (MID).
Keywords
magnetic data, magnetization vector, remanent magnetization, remanence, Inversion, Galinge