Semi-Airborne electromagnetic 2.5D inversion based on a PSO–LCI strategy

2022 ◽  
pp. 104541
Author(s):  
Yiming He ◽  
Weiying Chen ◽  
Kangxin Lei ◽  
Yang Zhao ◽  
Pengfei Lv
Author(s):  
M G Persova ◽  
Y G Soloveichik ◽  
D V Vagin ◽  
D S Kiselev ◽  
O S Trubacheva ◽  
...  

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. F189-F195 ◽  
Author(s):  
Changchun Yin ◽  
Greg Hodges

The traditional algorithms for airborne electromagnetic (EM) inversion, e.g., the Marquardt-Levenberg method, generally run only a downhill search. Consequently, the model solutions are strongly dependent on the starting model and are easily trapped in local minima. Simulated annealing (SA) starts from the Boltzmann distribution and runs both downhill and uphill searches, rendering the searching process to easily jump out of local minima and converge to a global minimum. In the SA process, the calculation of Jacobian derivatives can be avoided because no preferred searching direction is required as in the case of the traditional algorithms. We apply SA technology for airborne EM inversion by comparing the inversion with a thermodynamic process, and we discuss specifically the SA procedure with respect to model configuration, random walk for model updates, objective function, and annealing schedule. We demonstrate the SA flexibility for starting models by allowing the model parameters to vary in a large range (far away from the true model). Further, we choose a temperature-dependent random walk for model updates and an exponential cooling schedule for the SA searching process. The initial temperature for the SA cooling scheme is chosen differently for different model parameters according to their resolvabilities. We examine the effectiveness of the algorithm for airborne EM by inverting both theoretical and survey data and by comparing the results with those from the traditional algorithms.


Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. WA179-WA188 ◽  
Author(s):  
Alan Yusen Ley-Cooper ◽  
James Macnae ◽  
Andrea Viezzoli

Most airborne electromagnetic (AEM) data are processed using successive 1D approximations to produce stitched conductivity-depth sections. Because the current induced in the near surface by an AEM system preferentially circulates at some radial distance from a horizontal loop transmitter (sometimes called the footprint), the section plotted directly below a concentric transmitter-receiver system actually arises from currents induced in the vicinity rather than directly underneath. Detection of paleochannels as conduits for groundwater flow is a common geophysical exploration goal, where locally 2D approximations may be valid for an extinct riverbed or filled valley. Separate from effects of salinity, these paleochannels may be conductive if clay filled or resistive if sand filled and incised into a clay host. Because of the wide system footprint, using stitched 1D approximations or inversions may lead to misleading conductivity-depth images or sections. Near abrupt edges of an extensive conductive layer, the lateral falloff in AEM amplitudes tends to produce a drooping tail in a conductivity section, sometimes coupled with alocal peak where the AEM system is maximally coupled to currents constrained to flow near the conductor edge. Once the width of a conductive ribbon model is less than the system footprint, small amplitudes result, and the source is imaged too deeply in the stitched 1D section. On the other hand, a narrow resistive gap in a conductive layer is incorrectly imaged as a drooping region within the layered conductor; below, the image falsely contains a blocklike poor conductor extending to depth. Additionally, edge-effect responses often are imaged as deep conductors with an inverted horseshoe shape. Incorporating lateral constraints in 1D AEM inversion (LCI) software, designed to improve resolution of continuous layers, more accurately recovers the depth to extensive conductors. The LCI, however, as with any AEM modeling methodology based on 1D forward responses, has limitations in detecting and imaging in the presence of strong 3D lateral discontinuities of dimensions smaller than the annulus of resolution. The isotropic, horizontally slowly varying layered-earth assumption devalues and limits AEM’s 3D detection capabilities. The need for smart, fast algorithms that account for 3D varying electrical properties remains.


Sign in / Sign up

Export Citation Format

Share Document