Using Linear and Nonlinear Inversion Algorithm Combined with Simple Dislocation Model Inversion of Coal Mine Subsidence Mechanism

2013 ◽  
Vol 5 (1) ◽  
pp. 379-387
Author(s):  
YuFeng ZHUQinWei WUTieDing Lu ◽  
Yan Luo
Keyword(s):  
Coal Mine ◽  
Dislocation Model ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Model Inversion ◽  
Mine Subsidence ◽  
Linear And Nonlinear

scholarly journals Spatial distribution and governance of coal-mine subsidence in China

2019 ◽  
Vol 34 (4) ◽  
pp. 867
Author(s):  
Jia-ming LI ◽  
Jian-hui YU ◽  
Wen-zhong ZHANG
Keyword(s):  
Spatial Distribution ◽  
Coal Mine ◽  
Mine Subsidence

High Resolution Seismic Reflection For Characterizing Longwall Coal Mine Subsidence

1993 ◽  
Author(s):  
James A. Jessop ◽  
Calvin L. Cumerlato ◽  
Kevin M. O‘Connor ◽  
John A. Siekmeier
Keyword(s):  
High Resolution ◽  
Coal Mine ◽  
Seismic Reflection ◽  
Mine Subsidence

An ecological scenario prediction model for newly created wetlands caused by coal mine subsidence in the Yanzhou, China

2019 ◽  
Vol 42 (7) ◽  
pp. 1991-2005
Author(s):  
Mengjie Zhang ◽  
Xingzhong Yuan ◽  
Dongjie Guan ◽  
Hong Liu ◽  
Kuo Sun ◽  
...  
Keyword(s):  
Prediction Model ◽  
Coal Mine ◽  
Created Wetlands ◽  
Mine Subsidence ◽  
Scenario Prediction

Agricultural Impacts of Coal Mine Subsidence: Effects on Corn Yields

1989 ◽  
Vol 18 (3) ◽  
pp. 265-267 ◽  
Author(s):  
R. G. Darmody ◽  
I. J. Jansen ◽  
S.G. Carmer ◽  
J.S. Steiner
Keyword(s):  
Coal Mine ◽  
Agricultural Impacts ◽  
Mine Subsidence ◽  
Subsidence Effects

A generic 1-D imaging method for transient electromagnetic data

Geophysics ◽  
2002 ◽  
Vol 67 (2) ◽  
pp. 438-447 ◽  
Author(s):  
Niels Bøie Christensen
Keyword(s):  
Time Derivative ◽  
Step Response ◽  
Imaging Method ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Electromagnetic Data ◽  
Transient Electromagnetic ◽  
Dependent Part ◽  
Space Step ◽  
Forward Mapping

This paper presents a fast approximate 1-D inversion algorithm for transient electromagnetic (EM) data that can be applied for all measuring configurationsand transmitter waveforms and for all field components. The inversion is based on an approximate forward mapping in the adaptive Born approximation. The generality is obtained through a separation of the forward problem into a configuration-independent part, mapping layer conductivities into apparent conductivity, and a configuration-dependent part, the half-space step response. The EM response from any waveform can then be found by a convolution with the time derivative of the waveform. The approach does not involve inherently unstable deconvolution computations or nonunique transformations, and it is about 100 times faster than ordinary nonlinear inversion. Nonlinear model responses of the models obtained through the approximate inversion fit the data typically within 5%.

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