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, 11800 USM, Pulau Pinang, Malaysia.

*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.

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

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

The South-western part of the Nigerian schist belts Introduction Longitudes 4ᵒ 59’ 47.26''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

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

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.

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.

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

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.

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.

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.

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.

Methodology Figure 5: Pattern Recognition Technique showing SI

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

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

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 50–551.205nT, that have power and influence over the north-east, north-west and south-western areas. Figure 6: Magnetic Intensity map

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

Figure 7: Analytical Signal map. Results Analytical Signal map generated Similarly, low magnetic intensity values that ranged between about 0 to -643.498nT, 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.

Results The minimum total magnetic intensity value of -643.498nT was recorded mostly along the Central flank and some pocket places in the north-western and south-eastern flanks. The highest value of 551.205nT recorded mainly in the northern and southern flanks of the study area could be ascribed to the basement complex granitic intrusions.

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

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

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.

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.

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

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.

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

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

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

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

THE END FOR YOUR KIND ATTENTION.

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

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

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

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

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

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

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

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

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

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