Title :
Position Detection of Adjacent Buried Objects from Their Self-Potential Anomalies Using ICA and LVQ Techniques
Author_Institution :
Dept. of Comput. & Control Eng., Tanta Univ.
Abstract :
The self-potential anomalies produced by simple polarized geologic structures are used in the position detection of buried objects such as rocks or minerals. If these objects are adjacent, a mixed self-potential anomaly data will be measured. However, the detection of the objects position from this mixed self-potential anomaly data is usually not possible. In this paper, the mixed self-potential anomaly data is first separated by a blind signal separation technique called the independent component analysis (ICA), then the learning vector quantization (LVQ) neural network is used in the position detection of the separated self-potential anomalies. The proposed system achieves very high accuracy
Keywords :
blind source separation; buried object detection; geophysical signal processing; independent component analysis; neural nets; vector quantisation; ICA; LVQ neural network; adjacent buried object position detection; blind signal separation; independent component analysis; learning vector quantization; polarized geologic structure; self-potential anomaly; Blind source separation; Buried object detection; Geologic measurements; Geology; Independent component analysis; Minerals; Neural networks; Object detection; Polarization; Vector quantization;
Conference_Titel :
Computer Engineering and Systems, The 2006 International Conference on
Conference_Location :
Cairo
Print_ISBN :
1-4244-0271-9
Electronic_ISBN :
1-4244-0272-7
DOI :
10.1109/ICCES.2006.320485
