Modeling of broadband airborne electromagnetic responses from saline environments

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1624-1632 ◽  
Author(s):  
G. Buselli ◽  
D. R. Williamson
Keyword(s):  
Vertical Direction ◽  
Relative Magnitude ◽  
High Salt ◽  
Salt Accumulation ◽  
Simultaneous Inversion ◽  
Quadrature Component ◽  
Electromagnetic Responses ◽  
Resistivity Model ◽  
Airborne Electromagnetic ◽  
Salt Profile

The removal of vegetation for the development of nonirrigated agriculture and the associated increase in groundwater recharge and discharge has caused significant areas of salinization of surface soil and water resources in Australia. At least three types of salt profiles are known to indicate the relative magnitude of recharge. These profiles may be differentiated by their resistivity structure. Since a broadband airborne electromagnetic (AEM) method offers the possibility of readily obtaining resistivity soundings, modeling was carried out to investigate the ability of a broadband AEM system to distinguish different salt profile types. Salt profile types may be represented by a four‐layer resistivity model. The use of a broadband AEM system to distinguish the relative magnitude of the resistivity of a layer of high salt accumulation and the underlying layer forms the basis for efficiently identifying areas of high or low recharge. Where the resistivity of the underlying layer is greater than that of the salt accumulation, high recharge is indicated, and a lower resistivity of this layer implies low recharge. The response of each of the salt profile models was calculated in the frequency domain and then inverted back to a layered model. With noise added to the calculated responses, the inversion results show that the depth, thickness, and resistivity of a layer of high salt accumulation can be resolved by AEM measurements. Furthermore, the resistivity of this layer can be distinguished from the resistivity of the underlying layer. A high‐recharge profile may therefore be differentiated from a low‐recharge profile with AEM measurements. Since the quadrature component of the AEM response is relatively unaffected by noise caused by the primary field, the effect of using solely the quadrature component of the response was examined briefly as a second part of the AEM modeling investigation. It is found that simultaneous inversion of the quadrature part of the spatial components measured along the line of flight and in a vertical direction gives results similar to those when both the in‐phase and quadrature parts of these components are used in the inversion.

scholarly journals Slopes of an airborne electromagnetic resistivity model interpolated jointly with borehole data for 3D geological modelling

2016 ◽  
Vol 65 (4) ◽  
pp. 1085-1096
Author(s):  
P.-A. Reninger ◽  
G. Martelet ◽  
J. Perrin ◽  
J. Deparis ◽  
Y. Chen
Keyword(s):  
Borehole Data ◽  
Geological Modelling ◽  
Resistivity Model ◽  
Airborne Electromagnetic

Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural network

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Minkyu Bang ◽  
Seokmin Oh ◽  
Kyubo Noh ◽  
Soon Jee Seol ◽  
Joongmoo Byun
Keyword(s):  
Neural Network ◽  
Magnetic Fields ◽  
Recurrent Neural Network ◽  
Field Data ◽  
Vertical Direction ◽  
Prediction Performance ◽  
Electromagnetic Data ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Data ◽  
2D Resistivity

Conventional interpretation of airborne electromagnetic data has been conducted by solving the inverse problem. However, with recent advances in machine learning (ML) techniques, a one-dimensional (1D) deep neural network inversion that predicts a 1D resistivity model using multi-frequency vertical magnetic fields and sensor height information at one location has been applied. Nevertheless, bacause the final interpretation of this 1D approach relies on connecting 1D resistivity models, 1D ML interpretation has low accuracy for the estimation of an isolated anomaly, as in conventional 1D inversion. Thus, we propose a two-dimensional (2D) interpretation technique that can overcome the limitations of 1D interpretation, and consider spatial continuity by using a recurrent neural network (RNN). We generated various 2D resistivity models, calculated the ratio of primary and induced secondary magnetic fields of vertical direction in ppm scale using vertical magnetic dipole source, and then trained the RNN using the resistivity models and the corresponding electromagnetic (EM) responses. To verify the validity of 2D RNN inversion, we applied the trained RNN to synthetic and field data. Through application of the field data, we demonstrated that the design of the training dataset is crucial to improve prediction performance in a 2D RNN inversion. In addition, we investigated changes in the RNN inversion results of field data dependent on the data preprocessing. We demonstrated that using two types of data, logarithmic transformed data and linear scale data, which having different patterns of input information can enhance the prediction performance of the EM inversion results.

Simultaneous inversion of airborne electromagnetic data for resistivity and magnetic permeability

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1556-1564 ◽  
Author(s):  
Les P. Beard ◽  
Jonathan E. Nyquist
Keyword(s):  
Magnetic Permeability ◽  
Phase Response ◽  
Low Frequencies ◽  
Phase Measurements ◽  
Oak Ridge ◽  
Simultaneous Inversion ◽  
Relative Magnetic Permeability ◽  
Frequency Independent ◽  
Airborne Electromagnetic ◽  
Using Data

Where the magnetic permeability of rock or soil exceeds that of free space, the effect on airborne electromagnetic systems is to produce a frequency‐independent shift in the in‐phase response of the system while altering the quadrature response only slightly. The magnitude of the in‐phase shift increases as (1) the relative magnetic permeability is increased, (2) the amount of magnetic material is increased, and (3) the airborne sensor gets nearer the earth’s surface. Over resistive, magnetic ground, the shift may be evinced by negative in‐phase measurements at low frequencies; but over more conductive ground, the same shift may go unnoticed because of the large positive in‐phase response. If the airborne sensor is flown at low levels, the magnitude of the shift may be large enough to affect automatic inversion routines that do not take this shift into account, producing inaccurate estimated resistivities, usually overestimates. However, layered‐earth inversion algorithms that incorporate magnetic permeability as an additional inversion parameter may improve the resistivity estimates. We demonstrate this improvement using data collected over hazardous waste sites near Oak Ridge, Tennessee, USA. Using resistivity inversion without magnetic permeability, the waste sites are almost invisible to the sensors. When magnetic permeability is included as an inversion parameter, the sites are detected, both by improved resistivity estimates and by estimated magnetic permeability.

Three-dimensional frequency-domain modeling of airborne electromagnetic responses

1998 ◽  
Vol 29 (1-2) ◽  
pp. 111-119 ◽  
Author(s):  
Dmitry B. Avdeev ◽  
Alexei V. Kuvshinov ◽  
Oleg V. Pankratov ◽  
Gregory A. Newman
Keyword(s):  
Frequency Domain ◽  
Three Dimensional ◽  
Domain Modeling ◽  
Electromagnetic Responses ◽  
Airborne Electromagnetic

scholarly journals Unexpectedly High Salt Accumulation inside Carbon Nanotubes Soaked in Dilute Salt Solutions

2018 ◽  
Vol 121 (22) ◽  
Author(s):  
Xueliang Wang ◽  
Guosheng Shi ◽  
Shanshan Liang ◽  
Jian Liu ◽  
Deyuan Li ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
High Salt ◽  
Salt Solutions ◽  
Salt Accumulation

Predicting controlled-source electromagnetic responses from seismic velocities

2014 ◽  
Vol 2 (3) ◽  
pp. SH115-SH131 ◽  
Author(s):  
Dieter Werthmüller ◽  
Anton Ziolkowski ◽  
David Wright
Keyword(s):  
Standard Deviation ◽  
Feasibility Studies ◽  
Real Data ◽  
Second Step ◽  
Seismic Velocities ◽  
Well Control ◽  
Controlled Source ◽  
Electromagnetic Responses ◽  
Resistivity Model ◽  
Electric Anisotropy

We created a workflow to predict controlled-source electromagnetic (CSEM) responses from seismic velocities and compared the predicted responses with CSEM data. The first step was to calculate a resistivity model from seismic velocities in a Bayesian framework to account for the uncertainties. The second step was to estimate the electric anisotropy and improve the resistivity model for the depths at which there was no well control. The last step was to use this updated resistivity model to forward-model CSEM responses and compare the result with CSEM data. The comparison with real data revealed that the measured CSEM responses were generally within plus and minus one standard deviation of the predicted responses. This workflow was able to predict CSEM responses, which can prove very useful for feasibility studies before acquisition and interpretation after acquisition of CSEM data.

scholarly journals Imagery of internal structure and destabilization features of active volcano by 3D high resolution airborne electromagnetism

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Marc Dumont ◽  
Aline Peltier ◽  
Else Roblin ◽  
Pierre-Alexandre. Reninger ◽  
Stéphanie Barde-Cabusson ◽  
...  
Keyword(s):  
High Resolution ◽  
Internal Structure ◽  
Ground Surface ◽  
Active Volcano ◽  
Magma Intrusion ◽  
Material Resources ◽  
Resistivity Model ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey ◽  
Inherited Faults

AbstractPresent-day volcano imaging and monitoring relies primarily on ground surface and satellite remote sensing observations. The overall understanding of the volcanic edifice and its dynamics is thus limited by surface investigation, spatial resolution and penetration depth of the ground methods, but also by human and material resources, and harsh environments. Here, we show for the first time that an airborne electromagnetic survey provides a 3D global resistivity model of an active volcano. The high-resolution survey acquired at the Piton de la Fournaise volcano on La Réunion Island, Indian Ocean, shows unprecedented details of the internal structure of the edifice, highlighting the upwelling hydrothermal system below the craters, magma intrusion pathways and inherited faults. Together with surface monitoring, such airborne imagery have a high potential to better characterize volcano internal structure and magmatic processes, and therefore to better anticipate catastrophic events such as phreato-magmatic eruptions or volcano destabilizations.

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