scholarly journals 1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

2021 ◽  
Vol 13 (19) ◽  
pp. 3881
Author(s):  
Peng Bai ◽  
Giulio Vignoli ◽  
Thomas Mejer Hansen
Keyword(s):  
Forward Modelling ◽  
Modeling Error ◽  
Simple Modification ◽  
Electromagnetic Data ◽  
Time Domain Electromagnetic ◽  
Airborne Electromagnetic ◽  
Correct Estimation ◽  
High Level ◽  
Modelling Error ◽  
Conductivity Models

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

Induced polarization in airborne EM

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. E317-E327 ◽  
Author(s):  
Terence Kratzer ◽  
James C. Macnae
Keyword(s):  
Induced Polarization ◽  
Basis Functions ◽  
Electromagnetic Data ◽  
Delay Times ◽  
Time Domain Electromagnetic ◽  
Airborne Electromagnetic ◽  
Synthetic Modeling ◽  
2D And 3D ◽  
Airborne Em ◽  
Major Impediment

A major impediment in the path toward airborne induced polarization (IP) is an effective method to quantify data from inductive sources, such as those used in airborne electromagnetic systems. We modeled inductive IP using a combination of Warburg and exponential decay models as a basis for fitting electromagnetic data from ground time-domain electromagnetic (TEM) and airborne versatile TEM (VTEM) surveys. Observed decays were deconvolved into electromagnetic and IP constituents by constrained least-squares fitting of basis functions modified to account for transmitter waveforms. The method was confirmed through synthetic modeling of 2D and 3D structures, and when applied to ground TEM or airborne TEM data, obtained an estimate of apparent chargeability at each station or fiducial. In the case of a VTEM survey in Africa, the apparent chargeabilities mapped graphitic sediments and provided spatially consistent indications of clay concentrations. A limitation on this airborne IP for airborne applications is motion noise, which places a lower limit on usable base frequency and begins to significantly affect the signal at the later delay times, when IP effects are most visible.

3D modeling of buried valley geology using airborne electromagnetic data

2015 ◽  
Vol 3 (4) ◽  
pp. SAC9-SAC22 ◽  
Author(s):  
Vincenzo Sapia ◽  
Greg A. Oldenborger ◽  
Flemming Jørgensen ◽  
André J.-M. Pugin ◽  
Marco Marchetti ◽  
...  
Keyword(s):  
Time Domain ◽  
Electric Resistivity ◽  
Electric Property ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Aquifer System ◽  
Electromagnetic Data ◽  
Time Domain Electromagnetic ◽  
Airborne Electromagnetic ◽  
Geologic Model

Buried valleys are important hydrogeologic features of glaciated terrains. They often contain valuable groundwater resources; however, they can remain undetected by borehole-based hydrogeologic mapping or prospecting campaigns. Airborne electromagnetic (AEM) surveys provide high-density information that can allow detailed features of buried valleys to be efficiently mapped over large geographic areas. Using AEM data for the Spiritwood Valley Aquifer system in Manitoba, Canada, we developed a 3D electric property model and a geologic model of the buried valley network. The 3D models were derived from voxel-based segmentation of electric resistivity obtained via spatially constrained inversion of two separate helicopter time-domain electromagnetic data sets (AeroTEM and versatile time-domain electromagnetic [VTEM]) collected over the survey area. Because the electric resistivity do not provide unequivocal information on subsurface lithology, we have used a cognitive procedure to interpret the electric property models of the aquifer complex, while simultaneously incorporating supporting information for the assignment of lithology in the 3D geologic model. For the Spiritwood model, supporting information included seismic reflection data and borehole records. These data constrained valley geometry and provided lithologic benchmarks at specific borehole sites and along seismic transects. The large-scale AeroTEM survey provided the basis for modeling the regional extent and connectivity of the Spiritwood Valley Aquifer system, whereas the local-scale VTEM survey provided higher near-surface resolution and insight into a detailed shallow architecture of individual buried valleys and their fill.

Machine learning based fast forward modelling of ground-based time-domain electromagnetic data

2021 ◽  
Vol 187 ◽  
pp. 104290
Author(s):  
Thue Sylvester Bording ◽  
Muhammad Rizwan Asif ◽  
Adrian S. Barfod ◽  
Jakob Juul Larsen ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Forward Modelling ◽  
Electromagnetic Data ◽  
Time Domain Electromagnetic

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Sign in / Sign up

Export Citation Format

Share Document