Machine Learning-Enhanced Magnetic Calibration for Airborne Magnetic Anomaly Navigation

REPORT OF THE SEG COMMITTEE FOR A NATIONAL MAGNETIC ANOMALY MAP

Geophysics ◽

10.1190/1.1440663 ◽

1976 ◽

Vol 41 (5) ◽

pp. 1055-1055

Keyword(s):

Magnetic Field ◽

Magnetic Anomaly ◽

Developed Countries ◽

Magnetic Survey ◽

Accurate Representation ◽

Anomalous Magnetic Field ◽

Anomaly Map ◽

The U.S ◽

Airborne Magnetic ◽

Airborne Magnetic Survey

Our country’s urgent need to find new sources for minerals and energy and its need to know more about the planet on which we live could be greatly assisted by preparation of a national magnetic anomaly map (NMAM)—a map which will provide an accurate representation of the earth’s anomalous magnetic field. It is startling to note that the U.S. is one of the few developed countries which has not commissioned a detailed airborne magnetic survey of the whole country, followed by production of a national magnetic anomaly map.

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Inversion of Magnetic Anomaly using Machine Learning Regression Techniques along with PSO

10.3997/2214-4609.202112801 ◽

2021 ◽

Author(s):

A. Kumar ◽

S.R. Gangumalla

Keyword(s):

Machine Learning ◽

Magnetic Anomaly ◽

Regression Techniques

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Characteristics Analysis of Magnetic Anomaly Signals in Airborne Magnetic Anomaly Detection

Proceedings of the 2017 2nd International Conference on Communication and Information Systems - ICCIS 2017 ◽

10.1145/3158233.3159380 ◽

2017 ◽

Author(s):

Jiaxin Zhou ◽

Jianyong Chen ◽

Zhichao Shan ◽

Changkang Chen

Keyword(s):

Anomaly Detection ◽

Magnetic Anomaly ◽

Characteristics Analysis ◽

Airborne Magnetic

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Localization Estimation of Magnetic Targets Using Airborne Magnetic Anomaly Detection

2021 IEEE 4th International Conference on Electronics Technology (ICET) ◽

10.1109/icet51757.2021.9451092 ◽

2021 ◽

Author(s):

Dunge Liu ◽

Xin Liu ◽

Yao Zhang ◽

Zhenghua Xia ◽

Zhilong Zhao ◽

...

Keyword(s):

Anomaly Detection ◽

Magnetic Anomaly ◽

Airborne Magnetic

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Position detection of unexploded ordnance from airborne magnetic anomaly data using 3-D self organized feature map

Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005. ◽

10.1109/isspit.2005.1577117 ◽

2006 ◽

Cited By ~ 7

Author(s):

T. El Tobely ◽

A. Salem

Keyword(s):

Magnetic Anomaly ◽

Unexploded Ordnance ◽

Feature Map ◽

Self Organized ◽

Position Detection ◽

Airborne Magnetic

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Airborne Magnetic Anomaly Navigation

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.2017.2649238 ◽

2017 ◽

Vol 53 (1) ◽

pp. 67-80 ◽

Cited By ~ 15

Author(s):

Aaron Canciani ◽

John Raquet

Keyword(s):

Magnetic Anomaly ◽

Airborne Magnetic

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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