Interaction between two adjacent grounded sources in frequency domain semi-airborne electromagnetic survey

2016 ◽  
Vol 87 (3) ◽  
pp. 034503 ◽  
Author(s):  
Haigen Zhou ◽  
Jun Lin ◽  
Changsheng Liu ◽  
Lili Kang ◽  
Gang Li ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey

scholarly journals Parallel solution of 3D forward and inverse problems of airborne electromagnetic survey

2021 ◽  
Vol 1019 ◽  
pp. 012020
Author(s):  
M G Persova ◽  
Y G Soloveichik ◽  
D V Vagin ◽  
D S Kiselev ◽  
O S Trubacheva ◽  
...  
Keyword(s):  
Inverse Problems ◽  
Parallel Solution ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey

A HYDROSTRATIGRAPHIC UNDERSTANDING OF BURIED BEDROCK VALLEYS: COMPARING AN AIRBORNE ELECTROMAGNETIC SURVEY WITH MULTIPLE SURFACE GEOPHSICAL TECHNIQUES

2019 ◽  
Author(s):  
Oliver Conway-White ◽  
Colby M. Steelman ◽  
Hernan Ugalde ◽  
Adam Smiarowski ◽  
Emmanuelle Arnaud ◽  
...  
Keyword(s):  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey

scholarly journals Quantifying model structural uncertainty using airborne electromagnetic data

2020 ◽  
Vol 224 (1) ◽  
pp. 590-607
Author(s):  
Burke J Minsley ◽  
Nathan Leon Foks ◽  
Paul A Bedrosian
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Parameter Uncertainty ◽  
Probability Distributions ◽  
Joint Probability ◽  
Model Complexity ◽  
Structural Uncertainty ◽  
Mcmc Algorithm ◽  
Geophysical Parameter ◽  
Airborne Electromagnetic

SUMMARY The ability to quantify structural uncertainty in geological models that incorporate geophysical data is affected by two primary sources of uncertainty: geophysical parameter uncertainty and uncertainty in the relationship between geophysical parameters and geological properties of interest. Here, we introduce an open-source, trans-dimensional Bayesian Markov chain Monte Carlo (McMC) algorithm GeoBIPy—Geophysical Bayesian Inference in Python—for robust uncertainty analysis of time-domain or frequency-domain airborne electromagnetic (AEM) data. The McMC algorithm provides a robust assessment of geophysical parameter uncertainty using a trans-dimensional approach that lets the AEM data inform the level of model complexity necessary by allowing the number of model layers itself to be an unknown parameter. Additional components of the Bayesian algorithm allow the user to solve for parameters such as data errors or corrections to the measured instrument height above ground. Probability distributions for a user-specified number of lithologic classes are developed through posterior clustering of McMC-derived resistivity models. Estimates of geological model structural uncertainty are thus obtained through the joint probability of geophysical parameter uncertainty and the uncertainty in the definition of each class. Examples of the implementation of this algorithm are presented for both time-domain and frequency-domain AEM data acquired in Nebraska, USA.

scholarly journals The Frome Airborne Electromagnetic Survey: Uncovering 10% of South Australia

Preview ◽  
2012 ◽  
Vol 2012 (158) ◽  
pp. 39-42 ◽  
Author(s):  
Marina T. Costelloe ◽  
Ian C. Roach
Keyword(s):  
South Australia ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey

Interpretation of the AusAEM1: insights from the world’s largest airborne electromagnetic survey

2020 ◽  
Author(s):  
S.C.T. Wong ◽  
I.C. Roach ◽  
M.G. Nicoll ◽  
P.M. English ◽  
M.-A. Bonnardot ◽  
...  
Keyword(s):  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey

Saltwater intrusion in Caofeidian, Bohai Bay identified by an airborne electromagnetic survey

2019 ◽  
Author(s):  
Yiyuan He* ◽  
Xuefeng Cao ◽  
Zhanhui Li ◽  
Ziqiang Zhu ◽  
Shengjun Liang ◽  
...  
Keyword(s):  
Saltwater Intrusion ◽  
Bohai Bay ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey
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