Remote remanence estimation (RRE)

Pratt, David A.; McKenzie, K. Blair; White, Tony S.

doi:10.1071/eg14031

Cited by 12 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional means of magnetic data processing and modeling assumes that the induced magnetization is the sole factor that contributes to the magnetic field. However, as argued by Li (2017), the occurrence of remanent magnetization is almost ubiquitous; therefore, ignoring remanence could lead to false interpretations in many cases, as evidenced by previous works (e.g., Lelièvre & Oldenburg, 2009;Pratt et al, 2014). from −180°to 180°with an eastward deviation of the magnetic field from the geographic north being positive.…”

Section: Magnetization Directionmentioning

confidence: 98%

Predicting Magnetization Directions Using Convolutional Neural Networks

Nurindrawati

Sun

2020

JGR Solid Earth

View full text Add to dashboard Cite

Magnetic data have been widely used for understanding basin structures, mineral deposit systems, the formation history of various geological systems, and many others. Proper interpretation of magnetic data requires accurate knowledge of total magnetization directions of the source bodies in an area of study. Existing approaches for estimating magnetization directions involve either unstable data processing steps or computationally intensive processes such as 3-D inversions. In this study, we developed a new method of automatically predicting the magnetization direction of a magnetic source body using convolutional neural networks (CNNs). CNNs have achieved great success in many other applications such as computer vision and seismic image interpretation but have not been used to extract parameters from magnetic data. We simulated many magnetic data maps with different magnetization directions from a synthetic source body, all subject to the same background field. Two CNNs were trained separately, one for predicting the inclination and the other for predicting declination. We determined the optimal CNN architectures for predicting inclinations and declinations by systematically comparing 13 different CNN architectures. In addition, we investigated the effect of having different parameters such as magnetization magnitude, source body shape, location, and depth on the performance of our predictive models. We also tested the method using field data from Black Hill Norite, Australia, and Yeshan region, China, for which prior research results are available for comparison. Our study shows that machine learning provides an effective means of automatically predicting magnetization directions based on magnetic data maps.

show abstract

Section: Magnetization Directionmentioning

confidence: 98%

Predicting Magnetization Directions Using Convolutional Neural Networks

Nurindrawati

Sun

2020

JGR Solid Earth

View full text Add to dashboard Cite

show abstract

“…7 (a) the TMI southeast satellite anomaly at Coompana, South Australia, (b) TMI forward computed from the inversion model with outline of the model bodies, (c) NSS anomaly distribution of magnetization depends on the precision with which magnetization directions can be recovered (in favorable circumstances to within 10 or possibly even 5 ) and will be significantly enhanced if high precision can be achieved by an automated search algorithm to enable processing of large data sets. Possible remanent magnetization directions known from a defined apparent polar wander (APW) curve (see ▶ "Paleomagnetism, Polar Wander) for an area can be used to guide inversion of magnetization direction (Cordani and Shukowsky 2009;Pratt et al 2014). As a resource for both subsurface mineral exploration and palaeomagnetic studies, the Australian Remanent Anomalies Database (http://bit.ly/2HvhZdP) (see ▶ "Magnetic, Global Anomaly Map) has been established to compile and distribute magnetic field magnetization results, and this national coverage can be extended using global scale datasets such as EMAG2 (doi: https://doi.org/10.7289/V5H70CVX).…”

Section: Magnetic Field Studies and Paleomagnetismmentioning

confidence: 99%

Recovery of Source Magnetization Direction from Magnetic Field Data

Foss

2020

Encyclopedia of Solid Earth Geophysics

View full text Add to dashboard Cite

show abstract

“…The magnetisation direction can be computed for compact sources such as intrusive pipes and ellipsoid style targets but, further investigation is required to test the credibility of the results over large areas. This is a high priority for further research because it has the potential to expose a range of target styles that are often lost in conventional magnetic data analysis (Pratt, 2013;Pratt et al 2014).…”

Section: Limitationsmentioning

confidence: 99%