Project Name :
Buried objects detection and discrimination: combining fast, accurate and physically complete forward and inverse models for UXO identification problems
Donor Organization :
GNSF
Budget:
150 000 GEL
Grant number :
GNSF/ST08/5-450
Research Direction :
9 Earth Sciences and Environment
Sub Direction :
9-210 Pollution and Remediation
Keyword(s) :
UXO, MAS
Description :
The proposed research into Electromagnetic forward and inverse modeling approaches that is based on the Method of auxiliary sources, is aimed at developing efficient methodologies to enhance detection and discrimination of subsurface objects.
In particular, the proposal develops new forward and inverse modeling approaches to address today’s most pressing environmental problem: detection and discrimination a buried subsurface objects, such as landmines, unexploded ordnances (UXO), geophysical deposits, and etc. The landmines and UXOs cleanup problems are not only well-defined issues for Georgia, (Note: in last decade several ethnic conflicts took place in Georgia) these are as one of most urgent problem around the world as well. The high costs that are associated of excavating all geophysical anomalies are well known and are one of the greatest impediments to efficient clean-up of landmines and UXO contaminated lands at former military sites. Recently, low frequency electromagnetic induction sensing has been identified as one of most promising technologies for both detection and discrimination; however, classifying between an object of interests and metallic clutter continues to be a major environmental cleanup problem. For example, field experiences indicate that 75% of the costs to clean up UXO sites are currently spent on excavating targets that pose no threat. Innovative discrimination techniques that can reliably distinguish between object of interests and non-important metallic items are required, particularly in areas where the geological background causes a measurable response in the sensor.
Objectives:
The objective of the proposed work is to understand how an objects’ host medium affects on an object’s EMI responses, and develop robust, practical methodologies based on the MAS for inverting soil’s electromagnetic parameters. There are four specific technical objectives and each will encompass a different phase of the project:
1. Extend the MAS methodology for buried objects to take into account soils EMI responses. This will involve a modification of existing MAS approach for buried objects. Tackling this problem first will allow us to figure out and document what is soils response for different sensors;
2. Formulate and study EMI scattering problem for rough surfaces. Here, the method will extend to model more a realistic soils response for different sensors. Study and document what is soil’s roughness effect and how it affects on the state of the art sensors performance.
3. Develop a new MAS based inversion methodology to invert an object’s and its surrounding medium’s EM parameters simultaneously. Until now the MAS has been used for solving different types forward EM problems. Here, in a first time the MAS technique will apply to geophysical data inversion problems.
4. Implement the MAS inversion methodologies for the state of the art EMI sensors. Write a user friendly, advanced software package for understanding geological soils effect on the state of the art sensor, and for geophysical data inversion to enhance buried objects detection and discrimination.
This research will investigate and document how the geological soil affects on the EM sensors performances, and will provide tools that will improve upon the existing capabilities of reliably detecting and discriminating subsurface objects.
Transition plan for the proposed will be technical publications and presentations. And a final outcome of this proposal will be a program code for advanced inversion and modeling algorithms for EM data that will be useful to the wider EM community and good perspective for commercialization and for adoption as a new technology.
Duration :
22/01/2009 - 29/02/2012
Project Leader :
No leader
Project manager :
Giorgi Ghvedashvili
22/01/2009 - 29/02/2012
Project participant(s) :
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