1. APPLICATION OF THE PATTERN RECOGNITION TECHNIQUE TO LOCATE SUBSURFACE MAGNETIC ANOMALY 1Kayode, J.S.*, 1Nawawi, M.N.M., 1Abdullah, K., and 2Yusup, Y. 1School of Physics 2School of Industrial Technology Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia.

2. *Presenter John Stephen Kayode
To begin with, I want to appreciate the 35IGC Committee and the Mineral exploration sub-theme for the acceptance of this paper for oral presentation.

3. Discussion of Results Concluding Remarks Acknowledgement References
35th IGC
2016
Summary of objectives
Introduction
Discussion of Results
Concluding Remarks
Acknowledgement
References

4. Location of the study area
Figure 1: Map of Nigeria showing Aeromagnetic and Gravity survey coverage.

5. The South-western part of the Nigerian schist belts
Introduction
Longitudes 4ᵒ 59’ ''E and 5ᵒ 29' 57.95''E, and Latitudes 7ᵒ 59' 45.6''N and 8ᵒ 30' 22.21''N.
The study area
Bordered by Longitudes, 40E to 80E and Latitudes, 60N to 100N, to which the present study area is sited.
The South-western part of the Nigerian schist belts

6. Introduction
Figure 2: Geological map showing the survey area as adapted from the Nigerian Geological Survey Agency.

7. Data Acquisition
The magnetic data was acquired at right angles to the foremost local geological strike alongside a sequence of NW-SE flight lines with a spacing of 0.5km, and an average flight elevation of about 80m, while the tie lines were recorded at average intervals of about 2km.
Fugro Airborne Survey Services and Patterson Grant and Watson (PGW) in Canada carried out the survey across the country from 2007, and up to the end of 2009.

8. Methodology
The pattern recognition technique is aimed to classify data (patterns) based on either a priori knowledge or on statistical information extracted from these patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space.

9. Processing flow-chart
Methodology
Processing flow-chart
Figure 3: Aeromagnetic data processing flow-chart.

10. Non-linear filtering technique.
Methodology
Low Pass Filter (LPF) to eliminate very low frequency and noisy signals
The Application of various filtering techniques
For the removal of high amplitudes attributes and slows moving wavelength noisy signals from the data.
Non-linear filtering technique.

11. Fig. 4: Map of the study area showing the 12 profiles.
Methodology
RTP and 3-D Euler
Deconvolution
technique was applied
For quantitative
modeling of the
subsurface structures
along a 6 x 6 lines of
Horizontal and vertical
parallel profiles.
Fig. 4: Map of the study area showing the 12 profiles.

12. Methodology It has the ability to Delineate contacts and
sources for subsurface anomalies thereby
making it a powerful technique for estimating
the depth and the geometry of the buried
geologic source rocks. Two main structural
trends was observed in the study area. The
first trend seems to be dominant roughly in
the NNE-SSW directions, whilst the other
trend is nearly NE-SW.

13. Methodology
The major steps adopted for the pattern recognition technique are:
i) Observation of the magnetic anomaly patterns in
the study area.
ii) The interactions among the various subsurface
magnetic anomaly structural patterns were
examined.
Location of the positions of the magnetic anomaly source rocks, i.e., depths determination of the various structures in the area.
Identifications of various structural shapes and ascribing colours to differentiate them.
Prediction of the subsurface structural patterns in the Omu-Aran Schist belt zone SW Nigeria.

14. Methodology
Figure 5: Pattern Recognition Technique showing SI

15. Methodology
Table 1: Structural Indices of magnetic anomalies in Omu-Aran Schist belt area.

16. Methodology Name of the rock type
Table 2: Interpretation of the Pattern Recognition Image Parameters.
Images
Name of the rock type AG
African Granitoids
MGC
Migmatite Gneiss Complex
NPBC
Nigerian Precambrian Basement Complex
SG & HG
Syntectonic Granites and Homogeneous Gneisses
Schist
Undifferentiated Schist

17. Results Magnetic Data Interpretations
The maps of Figures 6 and 7 demonstrate deviations in the magnetic intensity that replicates the magnetic susceptibility of the various subsurface rock units across the diverse terrains present in the Omu-Aran Schist Belt. The Total magnetic intensity map of the study area shows high magnetic values that ranged between about 50– nT, that have power and influence over the north-east, north-west and south-western areas.
Figure 6: Magnetic Intensity map

18. Figure 6: Magnetic Intensity map
Results
Magnetic Data Interpretations
The light to deep red colours as an indications of reasonably high magnetic susceptibility subsurface rocks being present there that is characteristic of crystalline basement rocks (i.e., hornblende granite and migmatite gneisses).
Figure 6: Magnetic Intensity map

19. Figure 7: Analytical Signal map.
Results
Analytical Signal map generated
Similarly, low magnetic intensity values that ranged between about 0 to nT, dominated the west, northwest, and some places in southern and south- eastern parts on the map, with green to deep blue colours as a suggestion of subsurface materials of low magnetic susceptibility (i.e., meta- sediments materials), that overlain the bedrocks.
Figure 7: Analytical Signal map.

20. Results The minimum total magnetic intensity value
of nT was recorded mostly along the
Central flank and some pocket places in the
north-western and south-eastern flanks. The
highest value of nT recorded mainly
in the northern and southern flanks of the
study area could be ascribed to the basement
complex granitic intrusions.

21. Results Interpreted Structural indices along the Horizontal Profile 1
Figure 8: At a depth of 1Km

22. Results Interpreted Structural indices along the Vertical Profile 1
Figure 9: At a depth of 1Km

23. Results
Deductions from the study: The detailed subsurface geologic features i.e., the geometry and configuration of individual basement blocks as shown by the Total Magnetic Intensity and the Analytical Signal maps Figs. 6 & 7 correlates well with the map of Fig. 10.
Fig. 10: Structural
map obtained from
Pattern Recognition Analysis.

24. Results
The magnetic anomalies clearly delineated changes in rock types across the basement block boundaries in the study area
Fig. 10: Structural
map obtained from
Pattern Recognition Analysis.

25. Results
The common interpretation of a magnetic increase or decrease as an indicative of the presence of faults or fractured zones. The TMI values varied from high to very low along the NNE-SSW and NE-SW directions as shown in Fig. 6.
Figure 10

26. Results
These basement structural patterns, in the Omu-Aran Schist belt as presented in Figure 10, controls, to a great extent, the topography and lithology of the area, and also the surface stratigraphy as shown.

27. Results
Figure 2: Geological Map
Figure 10: Structural pattern map

28. Results Fig. 10: Structural pattern map.
Fig. 7: Analytical signal map.

29. Conclusion
Application of the Pattern Recognition Technique to outline subsurface magnetic anomalies Ore rock bodies in the study area helps to
Create a well-built, solid models of the subsurface, magnetic anomalies basement geological structures in Omu-Aran Schist belt zone, SW Nigeria.
Better results were achieved through this techniques compares to the conventional method.
This study clearly delineated the complex basement features in the area as shown in Fig. 10
The varieties of subsurface geological structural models obtained in the Omu-Aran Schist belt using the Pattern Recognition Technique

30. The Authors are grateful to:
ACKNOWLEDGMENTS
The Authors are grateful to:
The Universiti Sains Malaysia For the Postgraduate Fellowship awards
The International Association of Mathematical Geosciences, IAMG, for the travel support
To Attend the 35IGC 2016

31. THE END
FOR YOUR KIND ATTENTION.

32. References
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33. Results Figure 11: At a depth of 1Km
Interpreted Structural indices along the Horizontal Profile 2
Figure 11: At a depth of 1Km

34. Results Figure 12: At a depth of 1Km
Interpreted Structural indices along the Horizontal Profile 3
Figure 12: At a depth of 1Km

35. Results Figure 13: At a depth of 1Km
Interpreted Structural indices along the Horizontal Profile 4
Figure 13: At a depth of 1Km

36. Results Figure 14: At a depth of 1Km
Interpreted Structural indices along the Horizontal Profile 5
Figure 14: At a depth of 1Km

37. Results Figure 15: At a depth of 1Km
Interpreted Structural indices along the Horizontal Profile 6
Figure 15: At a depth of 1Km

38. Results Figure 16: At a depth of 1Km
Interpreted Structural indices along the Vertical Profile 2
Figure 16: At a depth of 1Km

39. Results Figure 17: At a depth of 1Km
Interpreted Structural indices along the Vertical Profile 3
Figure 17: At a depth of 1Km

40. Results Figure 18: At a depth of 1Km
Interpreted Structural indices along the Vertical Profile 4
Figure 18: At a depth of 1Km

41. Results Figure 19: At a depth of 1Km
Interpreted Structural indices along the Vertical Profile 5
Figure 19: At a depth of 1Km

42. Results Figure 20: At a depth of 1Km
Interpreted Structural indices along the Vertical Profile 6
Figure 20: At a depth of 1Km