2D inverse modeling of residual gravity anomalies from Simple geometric shapes using Modular Feed-forward Neural Network

Annals Of Geophysics

Alireza Hajian¹  Ata Eshaghzadeh³ 

[1] tel: [phone] . Fax: [phone];Department of Physics, Najafabad Branch, Islamic Azad University, Najafabad, Iran (hajian@iaun.a;Graduate student of geophysics, Institute of Geophysics, University of Tehran, Iran ([e-mail]) &

关键词: Modular Feed-forward Neural Network (MFNN);   

DOI  :  10.4401/ag-7540

学科分类：地球化学与岩石

来源: Istituto Nazionale Di Geofisica E Vulcanologia

【 摘 要 】

In this paper, we introduce a new method called Modular Feed-forward Neural Network (MFNN) to find the shape factor, depth and amplitude coefficient parameters related to simple geometric-shaped models such as sphere, horizontal cylinder, and vertical cyl

【 授权许可】

CC BY