Geological Interpretation of Aeromagnetic Data (David J. Isles and Leigh R. Rankin)

2014 ◽  
Vol 109 (5) ◽  
pp. 1495-1496
Author(s):  
N. C. White
Keyword(s):  
Aeromagnetic Data ◽  
Geological Interpretation

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2019 ◽  
Vol 2019 (199) ◽  
pp. 17-17
Author(s):  
Dave Isles ◽  
Leigh Rankin
Keyword(s):  
Aeromagnetic Data ◽  
Geological Interpretation

AEROMAGNETIC AND GEOLOGICAL INTERPRETATION OF A SECTION OF THE APPALACHIAN BELT IN CANADA

1967 ◽  
Vol 4 (6) ◽  
pp. 1015-1037 ◽  
Author(s):  
B. K. Bhattacharyya ◽  
B. Raychaudhuri
Keyword(s):  
Prince Edward Island ◽  
Bouguer Anomaly ◽  
Polarization Vector ◽  
Fault System ◽  
Aeromagnetic Data ◽  
Eastern Canada ◽  
Geological Interpretation ◽  
Vertical Displacements ◽  
Regional Tectonic ◽  
Anomaly Map

Studies were made of total field aeromagnetic data over an area covering a section of the Appalachian belt in eastern Canada. This area is bounded by latitudes 45 °N and 47°40′ N and longitudes 62°30′ W and 67 °W. The residual magnetic values were filtered analytically in order to accentuate the effects of regional tectonic trends in the area. The second vertical derivative values were evaluated for outlining the contacts of magnetized geological formations with a reasonable degree of accuracy. Results of the analysis of the Bouguer anomaly map for the area seemed to correspond well with the tectonic trends indicated by the aeromagnetic data. Sixty-five anomalies were chosen from the residual and filtered maps to determine the following parameters of the causative bodies: (1) intensity of polarization; (2) direction-cosines of the polarization vector; and (3) depths to the top and bottom of the bodies.The picture of the pre-Carboniferous basement, as inferred from aeromagnetic data, is that of a valley and ridge configuration characterized by a series of subparallel, elongated basement blocks with relative vertical displacements. The basement blocks are bounded by major fault systems, known or inferred, mostly of pre-Carboniferous age. These blocks are aligned mostly in the direction of major tectonic trend in the area. The details of subsurface Basement topography are discussed on the basis of the results of interpretation of aeromagnetic data. Most interesting of all is a belt of high magnetic intensity running roughly in a NW–SE direction over the Gulf of St. Lawrence and Prince Edward Island. It has been suggested that this belt is caused by a pre-Taconic topographic high, or alternatively, by a pre-Carboniferous basement high bounded by fault zones subparallel with the fault system under the Cabot Strait.

Optimizing the use of aeromagnetic data for predictive geological interpretation: an example from the Grenville Province, Quebec

2006 ◽  
Vol 32 (5) ◽  
pp. 565-576 ◽  
Author(s):  
Sharon Parsons ◽  
Léopold Nadeau ◽  
Pierre Keating ◽  
Chang-Jo Chung
Keyword(s):  
Aeromagnetic Data ◽  
Grenville Province ◽  
Geological Interpretation

Geological Interpretation of Aeromagnetic Data

2013 ◽  
Author(s):  
David J. Isles ◽  
Leigh R. Rankin
Keyword(s):  
Aeromagnetic Data ◽  
Geological Interpretation

Convolution Neural Networks Applied to the Interpretation of Lineaments in Aeromagnetic Data

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Tomas Naprstek ◽  
Richard Smith
Keyword(s):  
Neural Networks ◽  
Mineral Exploration ◽  
Physical Parameters ◽  
Small Range ◽  
Aeromagnetic Data ◽  
Geological Interpretation ◽  
Convolution Neural Networks ◽  
North Eastern ◽  
Seismic Sections ◽  
Synthetic Datasets

Parameter estimation in aeromagnetics is an important tool for geological interpretation. Due to aeromagnetic data being highly prevalent around the world, it can often be used to assist in understanding the geology of an area as a whole, or for locating potential areas of further investigation for mineral exploration. Methods that automatically provide information such as the location and depth to the source of anomalies are useful to the interpretation, particularly in areas where a large number of anomalies exist. Unfortunately, many of the current methods rely on high-order derivatives, and are therefore susceptible to noise in the data. Convolution neural networks (CNNs) are a subset of machine learning methods that are well-suited to image processing tasks, and have been shown to be effective at interpreting other geophysical data, such as seismic sections. Following several similar successful approaches, we have developed a CNN methodology for estimating the location and depth of lineament-type anomalies in aeromagnetic maps. To train the CNN model, we utilized a synthetic aeromagnetic data modeler to vary relevant physical parameters, and developed a representative dataset of approximately 1.4 million images. These were then used for training classification CNNs, with each class representing a small range of depth values. We first applied the model to a series of difficult synthetic datasets with varying amounts of noise, comparing the results against the tilt-depth method. We then applied the CNN model to a dataset from north-eastern Ontario, Canada, that contained a dyke with known depth, which was correctly estimated. This method is shown to be robust to noise, and can easily be applied to new datasets using the trained model, which has been made publicly available.

ENHANCING LINEAR FEATURES IN AEROMAGNETIC DATA USING DIRECTIONAL HORIZONTAL GRADIENT AT WADI HAIMUR AREA, SOUTH EASTERN DESERT, EGYPT,

2020 ◽  
Vol 15 (2) ◽  
pp. 323-326
Author(s):  
Ahmed Mohammed ELDOSOUKY ◽  
◽  
Sayed Omar ELKHATEEB ◽  
Abeer ALI ◽  
Sherif KHARBISH
Keyword(s):  
Eastern Desert ◽  
Horizontal Gradient ◽  
Aeromagnetic Data ◽  
Linear Features ◽  
South Eastern
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