TITLE: A novel fusion Python application of data mining techniques to evaluate airborne magnetic datasets 

ABSTRACT: A novel fusion python application of data mining techniques (DMT) was
designed and implemented to locate, identify, and delineate the subsurface
structural pattern (SSP) of source rocks for the features of interest underlain
the study area. The techniques of machine learning tools (MLT) helped to define
magnetic anomaly source (MAS) rock and the various depths of these subsurface
source rock features. The principal objective is to use straightforward DMT to
locate magnetic anomaly features of interest that host mineralization. The
required geo-referenced radiometric data, which facilitated the delineation of
SSP, were sufficiently covered by combining the application of the Oasis
Montaj\c{opyright} 2014 source parameter imaging functions. Relevance basic
filtering techniques of data reduction were used to improve the signal-to-noise
(S/N) ratio and hence automatically determine depths to the various engrossed
features from gridded geo-referenced airborne magnetic datasets before the DMT
application was performed. Geological source rock models (GSRM) (i.e., rock
contacts, dykes) served as the delineated features based on their structural
index (SI) values. The anomalies were perpendicularly oriented, with few
inconsequential nonvertical features, and all were generally aligned in NNE-SSW
and NE-SW directions. The DMT approach showed that magnetic anomaly patterns
(MAP) control the SSP and the ground surface stratigraphy (GSS) on a geological
time-scale (GTS) by fusing the subsurface gravitational structural features
(SGSF) in the area. The DMT facilitated the determination of depths to these
subsurface geological source rock features with a maximum depth of
approximately 1.277 km using a 3x3 window size to map the concealed features of
interest.
A novel fusion python application of data mining techniques (DMT) was
designed and implemented to locate, identify, and delineate the subsurface
structural pattern (SSP) of source rocks for the features of interest underlain
the study area. The techniques of machine learning tools (MLT) helped to define
magnetic anomaly source (MAS) rock and the various depths of these subsurface
source rock features. The principal objective is to use straightforward DMT to
locate magnetic anomaly features of interest that host mineralization. The
required geo-referenced radiometric data, which facilitated the delineation of
SSP, were sufficiently covered by combining the application of the Oasis
Montaj\c{opyright} 2014 source parameter imaging functions. Relevance basic
filtering techniques of data reduction were used to improve the signal-to-noise
(S/N) ratio and hence automatically determine depths to the various engrossed
features from gridded geo-referenced airborne magnetic datasets before the DMT
application was performed. Geological source rock models (GSRM) (i.e., rock
contacts, dykes) served as the delineated features based on their structural
index (SI) values. The anomalies were perpendicularly oriented, with few
inconsequential nonvertical features, and all were generally aligned in NNE-SSW
and NE-SW directions. The DMT approach showed that magnetic anomaly patterns
(MAP) control the SSP and the ground surface stratigraphy (GSS) on a geological
time-scale (GTS) by fusing the subsurface gravitational structural features
(SGSF) in the area. The DMT facilitated the determination of depths to these
subsurface geological source rock features with a maximum depth of
approximately 1.277 km using a 3x3 window size to map the concealed features of
interest.
A novel fusion Python application of data mining techniques to evaluate airborne magnetic datasets
http://arxiv.org/abs/2006.07236v1