3D forward modelling and inversion of inductive source resistivity data

2010 ◽  
Author(s):  
Dikun Yang ◽  
Douglas W. Oldenburg
Keyword(s):  
Forward Modelling ◽  
Resistivity Data ◽  
And Inversion

Effects of induced stress on seismic forward modelling and inversion

2018 ◽  
Vol 213 (2) ◽  
pp. 851-867 ◽  
Author(s):  
Jeroen Tromp ◽  
Jeannot Trampert
Keyword(s):  
Forward Modelling ◽  
Induced Stress ◽  
And Inversion

Forward modelling and inversion of self-potential anomalies caused by 2D inclined sheets

2013 ◽  
Vol 44 (3) ◽  
pp. 176-184 ◽  
Author(s):  
Mohamad Sadegh Roudsari ◽  
Ali Beitollahi
Keyword(s):  
Forward Modelling ◽  
And Inversion ◽  
Self Potential

Forward modelling and inversion of multi‐source TEM data

2008 ◽  
Author(s):  
D. W. Oldenburg ◽  
E. Haber ◽  
R. Shekhtman
Keyword(s):  
Forward Modelling ◽  
And Inversion

Estimating anisotropy parameters and traveltimes in the τ‐p domain

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1076-1086 ◽  
Author(s):  
Mirko van der Baan ◽  
J. Michael Kendall
Keyword(s):  
Anisotropic Media ◽  
Seismic Wave Propagation ◽  
Linear Process ◽  
Fast Method ◽  
Forward Modelling ◽  
Interval Estimates ◽  
Taylor Series Expansions ◽  
Local Interval ◽  
Anisotropy Parameters ◽  
And Inversion

The presence of anisotropy influences many aspects of seismic wave propagation and has therefore implications for conventional processing schemes. To estimate the anisotropy, we need both forward modelling and inversion tools. Exact forward modelling in anisotropic media is generally done by raytracing. However, we present a new and fast method, using the τ‐p transform, to calculate exact P and SV reflection moveout curves in stratified, laterally homogeneous, anisotropic media which requires no ray tracing. Results are exact even if the SV‐waves display cusps. In addition, we show how the same method can be used for parameter estimation. Since inversion for anisotropic parameters is very nonunique, we develop expressions requiring only a reduced number of parameters. Nevertheless, predictions using these expressions are more accurate than Taylor series expansions and are also able to handle cusps in the SV traveltime curves. In addition, layer stripping is a linear process. Therefore, both effective (average) and local (interval) estimates can be obtained.

Forward Modelling and Inversion with 3D Wavelets

2020 ◽  
Author(s):  
P. Mesdag ◽  
C. Tanase ◽  
H. Debeye
Keyword(s):  
Forward Modelling ◽  
And Inversion

2.5D direct-current resistivity forward modelling and inversion by finite-element-infinite-element coupled method

2015 ◽  
Vol 64 (3) ◽  
pp. 767-779 ◽  
Author(s):  
Yuan Yuan ◽  
Jianke Qiang ◽  
Jingtian Tang ◽  
Zhengyong Ren ◽  
Xiao Xiao
Keyword(s):  
Finite Element ◽  
Direct Current ◽  
Forward Modelling ◽  
Infinite Element ◽  
Coupled Method ◽  
And Inversion

scholarly journals Exploration Disaster Source of Mine Water by Electromagnetic Radiation

2020 ◽  
Vol 26 (5) ◽  
pp. 16-21
Author(s):  
Benyu Su ◽  
Zhixiong Li ◽  
Rongyao Li ◽  
Rongfu Rao ◽  
Jingcun Yu
Keyword(s):  
Coal Seam ◽  
Electromagnetic Radiation ◽  
Underground Mining ◽  
Parameter Distribution ◽  
Forward Modelling ◽  
Electric Parameter ◽  
Inversion Theory ◽  
Deep Underground ◽  
And Inversion ◽  
Inversion Modelling

geological hazard in deep underground mining. Before the rock mass explosion, electromagnetic energy will radiate outward during the deformation and rupture of the coal rocks. Hence, it is possible to use the electromagnetic radiation to predict geological disasters in coal mines. A challenging task using the active source electromagnetic survey technique is to detect geological anomalies, such as disaster water sources and geological structures. To this end, this paper proposes a new electromagnetic radiation solution based on the forward and inversion theory to detect geological anomalies in the coal seam. Based on typical coal mine geological models, the forward modelling and inversion modelling have been performed, respectively. The forward modelling explained the geological anomalies inside the coal seam, which were very sensitive to the response of the radiated electromagnetic field; especially, for the water-bearing geological anomalies. The inversion modelling discovered that the inversion geo-electric parameter distribution agreed well with the actual model. As a result, the proposed method is feasible for geological anomalies detection.

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