Reply to Comment by M. Narkiewicz on “Depth-to-basement for the East European craton and Teisseyre-Tornquist Zone in Poland based on potential field data, by Mikołajczak et al., International Journal of Earth Sciences (2019) 108:547–567”

2019 ◽  
Vol 108 (5) ◽  
pp. 1767-1771
Author(s):  
Mateusz Mikołajczak ◽  
Stanislaw Mazur ◽  
Łukasz Gągała
Keyword(s):  
Earth Sciences ◽  
Potential Field ◽  
Field Data ◽  
East European Craton ◽  
Potential Field Data ◽  
East European

scholarly journals Depth-to-basement for the East European Craton and Teisseyre-Tornquist Zone in Poland based on potential field data

2018 ◽  
Vol 108 (2) ◽  
pp. 547-567 ◽  
Author(s):  
Mateusz Mikołajczak ◽  
Stanisław Mazur ◽  
Łukasz Gągała
Keyword(s):  
Potential Field ◽  
Field Data ◽  
East European Craton ◽  
Potential Field Data ◽  
East European

The evolution of the southern margin of the East European Craton based on seismic and potential field data

2004 ◽  
Vol 381 (1-4) ◽  
pp. 101-118 ◽  
Author(s):  
S.L. Kostyuchenko ◽  
A.F. Morozov ◽  
R.A. Stephenson ◽  
L.N. Solodilov ◽  
A.G. Vedrentsev ◽  
...  
Keyword(s):  
Potential Field ◽  
Field Data ◽  
East European Craton ◽  
Potential Field Data ◽  
East European ◽  
Southern Margin

An improved approach for detecting the locations of the maxima in interpreting potential field data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Luan Thanh Pham ◽  
Ozkan Kafadar ◽  
Erdinc Oksum ◽  
Ahmed M. Eldosouky
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Potential Field Data

An improved approach for detecting ridge locations to interpret the potential field data for more accurate structural mapping: A case study from Vredefort dome area (South Africa)

2020 ◽  
pp. 104099
Author(s):  
Luan Thanh Pham ◽  
Erdinc Oksum ◽  
Minh Duc Vu ◽  
Quynh Thanh Vo ◽  
Khuong Du Le-Viet ◽  
...  
Keyword(s):  
South Africa ◽  
Potential Field ◽  
Field Data ◽  
Structural Mapping ◽  
Potential Field Data

Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Potential Field ◽  
Adaptive Sampling ◽  
Field Data ◽  
Computational Cost ◽  
Large Field ◽  
Magnetic Data ◽  
Data Sampling ◽  
Data Types ◽  
Data Set ◽  
Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

Interpretation of seismic and potential field data from western New York State and Lake Ontario

2002 ◽  
Vol 353 (1-4) ◽  
pp. 115-149 ◽  
Author(s):  
Khaled Ouassaa ◽  
David A. Forsyth
Keyword(s):  
New York ◽  
Potential Field ◽  
Field Data ◽  
New York State ◽  
Lake Ontario ◽  
Potential Field Data ◽  
York State

Combining Genetic Algorithm and Generalized Least Squares for Geophysical Potential Field Data Optimized Inversion

2010 ◽  
Vol 7 (4) ◽  
pp. 660-664 ◽  
Author(s):  
Ning Qiu ◽  
Qing-Sheng Liu ◽  
Quan-Ye Gao ◽  
Qing-Li Zeng
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Potential Field ◽  
Field Data ◽  
Generalized Least Squares ◽  
Potential Field Data
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