The transient electromagnetic inversion of detection signals mainly depends on fast inversion in the half-space state. However, the interpretation results have a certain degree of uncertainty and blindness, so the accuracy and applicability of the three-dimensional full-space inversion need to be investigated. Two different three-dimensional full-space inversions were carried out. First, the numerical characteristic parameters of the response signals were extracted. Then, the correlations between the numerical characteristic parameters and physical parameters of the water-bearing abnormal bodies were judged, and the judgment criterion of the iterative direction was proposed. Finally, the inversion methods of the iterative algorithm and the BP neural network were utilized based on the virtual example samples. The results illustrate that the proposed numerical characteristic parameters can accurately reflect the response curve of the full-space surrounding rock. The difference in the numerical characteristic parameters was used to determine the update direction and correction value. Both inversion methods have their advantages and disadvantages. A single inversion method cannot realize the three-dimensional inversion of the physical parameters of water-bearing abnormal bodies quickly, effectively and intelligently. Therefore, the benefits of different inversion methods need to be considered to comprehensively select a reasonable inversion method. The results can provide essential ideas for the subsequent interpretation of the three-dimensional spatial response signals of water-bearing abnormal bodies.
Citation: Yikang Xu, Zhaohua Sun, Wei Gu, Wangping Qian, Qiangru Shen, Jian Gong. Three-dimensional inversion analysis of transient electromagnetic response signals of water-bearing abnormal bodies in tunnels based on numerical characteristic parameters[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 1106-1121. doi: 10.3934/mbe.2023051
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The transient electromagnetic inversion of detection signals mainly depends on fast inversion in the half-space state. However, the interpretation results have a certain degree of uncertainty and blindness, so the accuracy and applicability of the three-dimensional full-space inversion need to be investigated. Two different three-dimensional full-space inversions were carried out. First, the numerical characteristic parameters of the response signals were extracted. Then, the correlations between the numerical characteristic parameters and physical parameters of the water-bearing abnormal bodies were judged, and the judgment criterion of the iterative direction was proposed. Finally, the inversion methods of the iterative algorithm and the BP neural network were utilized based on the virtual example samples. The results illustrate that the proposed numerical characteristic parameters can accurately reflect the response curve of the full-space surrounding rock. The difference in the numerical characteristic parameters was used to determine the update direction and correction value. Both inversion methods have their advantages and disadvantages. A single inversion method cannot realize the three-dimensional inversion of the physical parameters of water-bearing abnormal bodies quickly, effectively and intelligently. Therefore, the benefits of different inversion methods need to be considered to comprehensively select a reasonable inversion method. The results can provide essential ideas for the subsequent interpretation of the three-dimensional spatial response signals of water-bearing abnormal bodies.
Throughout the paper, we work over an algebraically closed field
Σk=Σk(C,L)⊆Pr |
of
Assume that
σk+1:Ck×C⟶Ck+1 |
be the morphism sending
Ek+1,L:=σk+1,∗p∗L, |
which is a locally free sheaf of rank
Bk(L):=P(Ek+1,L) |
equipped with the natural projection
H0(Bk(L),OBk(L)(1))=H0(Ck+1,Ek+1,)=H0(C,L), |
and therefore, the complete linear system
βk:Bk(L)⟶Pr=P(H0(C,L)). |
The
It is clear that there are natural inclusions
C=Σ0⊆Σ1⊆⋯⊆Σk−1⊆Σk⊆Pr. |
The preimage of
Theorem 1.1. Let
To prove the theorem, we utilize several line bundles defined on symmetric products of the curve. Let us recall the definitions here and refer the reader to [2] for further details. Let
Ck+1=C×⋯×C⏟k+1times |
be the
Ak+1,L:=Tk+1(L)(−2δk+1) |
be a line bundle on
The main ingredient in the proof of Theorem 1.1 is to study the positivity of the line bundle
Proposition 1.2. Let
In particular, if
In this section, we prove Theorem 1.1. We begin with showing Proposition 1.2.
Proof of Proposition 1.2. We proceed by induction on
Assume that
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
is surjective. We can choose a point
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
where all rows and columns are short exact sequences. By tensoring with
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
in which we use the fact that
Since
Lemma 2.1. Let
Proof. Note that
B′/A′⊗A′A′/m′q=B′/(m′qB′+A′)=B′/(m′p+A′)=0. |
By Nakayama lemma, we obtain
We keep using the notations used in the introduction. Recall that
αk,1:Bk−1(L)×C⟶Bk(L). |
To see it in details, we refer to [1,p.432,line –5]. We define the relative secant variety
Proposition 2.2. ([2,Proposition 3.15,Theorem 5.2,and Proposition 5.13]) Recall the situation described in the diagram
αk,1:Bk−1(L)×C⟶Bk(L). |
Let
1.
2.
3.
As a direct consequence of the above proposition, we have an identification
H0(Ck+1,Ak+1,L)=H0(Σk,IΣk−1|Σk(k+1)). |
We are now ready to give the proof of Theorem 1.1.
Proof of Theorem 1.1. Let
b:˜Σk:=BlΣk−1Σk⟶Σk |
be the blowup of
b:˜Σk:=BlΣk−1Σk⟶Σk |
We shall show that
Write
γ:˜Σk⟶P(V). |
On the other hand, one has an identification
ψ:Ck+1⟶P(V). |
Also note that
ψ:Ck+1⟶P(V). |
Take an arbitrary closed point
α−1(x)⊆π−1k(x″)∩β−1k(x′). |
However, the restriction of the morphism
[1] |
M. S. Zhdanov, Electromagnetic geophysics: Notes from the past and the road ahead, Geophys., 75 (2010), 557–560. https://doi.org/10.1190/1.3483901 doi: 10.1190/1.3483901
![]() |
[2] |
E. Auken, A. V. Christiansen, C. Kirkegaard, G. Fiandaca, C. Schamper, A. A. Behroozmand, et al., An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data, Explor. Geophys., 46 (2015), 223–235. https://doi.org/10.1071/EG13097 doi: 10.1071/EG13097
![]() |
[3] |
W. Qian, H. Li, J. Yu, Z. Gu, Theoretical and experimental investigation of vehicle-mounted transient electromagnetic method detection for internal defects of operational tunnels, Appl. Sci., 11 (2021), 6909. https://doi.org/10.3390/app11156906 doi: 10.3390/app11156906
![]() |
[4] |
Z. Li, T. Qi, S. Qin, W. Qian, The analysis of the early electromagnetic response of the receiving coil and its application at close-range TEM detection, J. Appl. Geophys., 193 (2021), 104409. https://doi.org/10.1016/j.jappgeo.2021.104409 doi: 10.1016/j.jappgeo.2021.104409
![]() |
[5] |
S. C. Constable, R. L. Parker, C. G. Constable, Occam's inversion: A practical algorithm for generating smooth models from electromagnetic sounding data, Geophysics, 52 (1987), 289–300. https://doi.org/10.1190/1.1442303 doi: 10.1190/1.1442303
![]() |
[6] |
A. V. Christiansen, N. B Christensen, A quantitative appraisal of airborne and ground-based transient electromagnetic (TEM) measurements in Denmark, Geophysics, 68 (2003), 523–534. https://doi.org/10.1190/1.1567220 doi: 10.1190/1.1567220
![]() |
[7] |
Y. Chang, M. Xiao, Y. Wu, Studies on initial parameter selection of one-dimensional inversion for transient electromagnetic Data, Geophys. Prospect. Petrol., 45 (2010), 295–298. https://doi.org/10.13810/j.cnki.issn.1000-7210.2010.02.016 doi: 10.13810/j.cnki.issn.1000-7210.2010.02.016
![]() |
[8] |
P. Yogeshwar, B. Tezkan, Analyzing two-dimensional effects in central loop transient electromagnetic sounding data using a semi-synthetic tipper approach, Geophys. Prospect., 66 (2018), 444–456. https://doi.org/10.1111/1365-2478.12520 doi: 10.1111/1365-2478.12520
![]() |
[9] |
E. Haber, D. W. Oldenburg, R. Shekhtman, Inversion of time domain three-dimensional electromagnetic data, Geophys. J. Int., 171 (2010), 550–564. https://doi.org/10.1111/j.1365-246X.2007.03365.x doi: 10.1111/j.1365-246X.2007.03365.x
![]() |
[10] |
W. Zhao, T. Yan, Z. Gao, Magnetotelluric nonlinear conjugate gradient inversion experiments: an example from data acquired in the Jarud Basin, Inner Mongolia, China, Prog. Geophys., 29 (2014), 2128–2135. https://doi.org/10.6038/pg20140520 doi: 10.6038/pg20140520
![]() |
[11] | Z. Y. Zhou, The 3-D numerical implementation of the whole tunnel space with TEM, Southwest Jiaotong University, (2014), 89–112. |
[12] |
Y. Li, T. Qi, B. Lei, Z. Li, W. Qian, An iterative inversion method using transient electromagnetic data to predict water-filled caves during the excavation of a tunnel, Geophys., 84 (2019), E89–E103. https://doi.org/10.1190/geo2018-0253.1 doi: 10.1190/geo2018-0253.1
![]() |
[13] |
X. Sun, Y. Wang, X. Yang, Y. Wang, Three-dimensional transient electromagnetic inversion with optimal transport, J. Inverse Ill-posed Probl., 30 (2021), 549–565. https://doi.org/10.1515/jiip-2020-0159 doi: 10.1515/jiip-2020-0159
![]() |
[14] |
X. Wang, N. You, Q. Di, J. Deng, Y. Chang, 3-D parallel inversion of multichannel transient electromagnetic data using a moving footprint, Geophys. J. Int., 226 (2021), 1783–1799. https://doi.org/10.1093/gji/ggab187 doi: 10.1093/gji/ggab187
![]() |
[15] |
M. Liu, H. Cai, H. Yang, Y. Xiong, X. Hu, Three dimensional joint inversion of ground and semi-airborne transient electromagnetic method, Chin. J. Geophys., 65 (2022), 3997–4011. https://doi.org/10.6038/cjg2022P0897 doi: 10.6038/cjg2022P0897
![]() |
[16] | D. Tan, T. Qi, Application of expert system in transient electromagnetic inversion, Technol. Highway Transp., 11 (2009), 124–127. |
[17] |
P. Ertan, Y. Türker, K. Yekta, Ö. Coşkun, Application of particle swarm optimization on self-potential data, J. Appl. Geophys., 75 (2011), 305–318. https://doi.org/10.1016/j.jappgeo.2011.07.013 doi: 10.1016/j.jappgeo.2011.07.013
![]() |
[18] |
M. Wang, G. Liu, D. Wang Y. Liu, Application of artificial bee colony algorithm in the version of transient electromagnetic sounding files, Prog. Geophys., 30 (2015), 133–139. https://doi.org/10.6038/pg20150120 doi: 10.6038/pg20150120
![]() |
[19] |
X. Liang, T. Qi, Z. Jin, W. Qian, Hybrid support vector machine optimization model for inversion of tunnel transient electromagnetic method, Math. Biosci. Eng., 17 (2020), 3998–4017. https://doi.org/10.3934/mbe.2020221 doi: 10.3934/mbe.2020221
![]() |
[20] |
H. Sun, Q. Wu, R. Chen, H. Li, Q. Fan, G. Liu, et al., Experimental study on transient electromagnetic responses to shallow karst, Chin. J. Rock Mech. Eng., 37 (2018), 652–661. https://doi.org/10.13722/j.cnki.jrme.2017.0038 doi: 10.13722/j.cnki.jrme.2017.0038
![]() |
[21] |
W. Qian, T. Qi, X. Liang, S. Qin, Z. Li, Y. Li, Vehicle-borne transient electromagnetic numerical characteristic parameter of water-bearing body behind tunnel linings, Math. Probl. Eng., 19 (2020), 8514913. https://doi.org/10.1155/2020/8514913 doi: 10.1155/2020/8514913
![]() |
[22] |
S. L. Butler, Z. Zhang, Forward modeling of geophysical electromagnetic methods using Comsol, Comput. Geosci., 87 (2016), 1–10. https://doi.org/10.1016/j.cageo.2015.11.004 doi: 10.1016/j.cageo.2015.11.004
![]() |
[23] |
Y. Qi, H. El-Kaliouby, A. Revil, A. Ahmed, A. Ghorbani, J. Li, Three-dimensional modeling of frequency-domain and time-domain electromagnetic methods with induced polarization effects, Comput. Geosci., 124 (2019), 85–92. https://doi.org/10.1016/j.cageo.2018.12.011 doi: 10.1016/j.cageo.2018.12.011
![]() |
[24] | S. Li, Y. Liu, W. Sun, Intelligent Calculation and Parameter Inversion, Science Press, (2008), 29–38. |
[25] | B. Liu, H. Guo, MATLAB Neural Network Super-Learning Manual, China Communications Press, (2014), 143–158. |