Detection of subsurface Qanats by Artificial Neural Network via Microgravity data

Hajian, A. R.; E. Ardestani, V.; Lucas, C.; Saghaiannejad, S. M.

doi:10.22059/jesphys.2009.79977

Detection of subsurface Qanats by Artificial Neural Network via Microgravity data

Authors

A. R. Hajian ¹

V. E. Ardestani ²

C. Lucas ³

S. M. Saghaiannejad ⁴

¹ Instructor, Physics Department, Islamic Azad University, Najaf Abad Branch, Isfahan, Iran

² Associate Professor, Earth Physics Department, Institute of Geophysics, University of Tehran and Center of Excellence in Survey Engineering and Disaster Management, Tehran, Iran

³ Professor of control, Electrical Engineering Department, University of Tehran, Iran

⁴ Associate Professor, Electrical Engineering, Electrical Engineering Department, Isfahan Technical University, Isfahan, Iran

10.22059/jesphys.2009.79977

Abstract

A full automatic algorithm is designed to detect subsurface Qanats (sub terrains) via Artificial Neural Networks .We first gained the residual gravity anomaly from microgravity data and then applied it to a Multi Layer Perceptron (MLP) which was trained for the models of sphere and cylinder.
As a field example, the depth of a subsurface Qanat buried under the north entrance of the Geophysics Institute is determined through MLP (trained with noisy data).

Keywords

Artificial Neural Network

Microgravity

Qanat