June  2021, 15(3): 387-413. doi: 10.3934/ipi.2020073

Simultaneously recovering both domain and varying density in inverse gravimetry by efficient level-set methods

1. 

School of Science, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China

2. 

Departments of Mathematics and CMSE, Michigan State University, East Lansing, MI 48824, USA

* Corresponding author: Jianliang Qian

Received  April 2020 Revised  October 2020 Published  November 2020

We develop new efficient algorithms for a class of inverse problems of gravimetry to recover an anomalous volume mass distribution (measure) in the sense that we design fast local level-set methods to simultaneously reconstruct both unknown domain and varying density of the anomalous measure from modulus of gravity force rather than from gravity force itself. The equivalent-source principle of gravitational potential forces us to consider only measures of the form $ \mu = f\,\chi_{D} $, where $ f $ is a density function and $ D $ is a domain inside a closed set in $ \bf{R}^n $. Accordingly, various constraints are imposed upon both the density function and the domain so that well-posedness theories can be developed for the corresponding inverse problems, such as the domain inverse problem, the density inverse problem, and the domain-density inverse problem. Starting from uniqueness theorems for the domain-density inverse problem, we derive a new gradient from the misfit functional to enforce the directional-independence constraint of the density function and we further introduce a new labeling function into the level-set method to enforce the geometrical constraint of the corresponding domain; consequently, we are able to recover simultaneously both unknown domain and varying density from given modulus of gravity force. Our fast level-set method is built upon localizing the level-set evolution around a narrow band near the zero level-set and upon accelerating numerical modeling by novel low-rank matrix multiplication. Numerical results demonstrate that uniqueness theorems are crucial for solving the inverse problem of gravimetry and will be impactful on gravity prospecting. To the best of our knowledge, our inversion algorithm is the first of such for the domain-density inverse problem since it is based upon the conditional well-posedness theory of the inverse problem.

Citation: Wenbin Li, Jianliang Qian. Simultaneously recovering both domain and varying density in inverse gravimetry by efficient level-set methods. Inverse Problems & Imaging, 2021, 15 (3) : 387-413. doi: 10.3934/ipi.2020073
References:
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W. Li, W. Lu, J. Qian and Y. Li, A multiple level set method for three-dimensional inversion of magnetic data, Geophysics, 82 (2017), J61-J81. doi: 10.1190/segam2017-17729331.1.  Google Scholar

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Y. Li and D. Oldenburg, 3D inversion of gravity data, Geophysics, 63 (1998), 109-119.   Google Scholar

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A. LitmanD. Lesselier and F. Santosa, Reconstruction of a 2-D binary obstacle by controlled evolution of a level-set, Inverse Problems, 14 (1998), 685-706.  doi: 10.1088/0266-5611/14/3/018.  Google Scholar

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W. LuS. Leung and J. Qian, An improved fast local level set method for three-dimensional inverse gravimetry, Inverse Problems and Imaging, 9 (2015), 479-509.  doi: 10.3934/ipi.2015.9.479.  Google Scholar

[22]

W. Lu and J. Qian, A local level set method for three-dimensional inversion of gravity gradiometry data, Geophysics, 80 (2015), G35-G51. Google Scholar

[23]

D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., 42 (1989), 577-685.  doi: 10.1002/cpa.3160420503.  Google Scholar

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S. J. Osher and J. A. Sethian, Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 79 (1988), 12-49.  doi: 10.1016/0021-9991(88)90002-2.  Google Scholar

[25]

D. PengB. MerrimanS. OsherH. K. Zhao and M. Kang, A pde-based fast local level set method, J. Comput. Phys., 155 (1999), 410-438.  doi: 10.1006/jcph.1999.6345.  Google Scholar

[26]

F. Santosa, A level-set approach for inverse problems involving obstacles, Control, Optimizat. Calculus Variat., 1 (1996), 17-33.  doi: 10.1051/cocv:1996101.  Google Scholar

[27]

M. SussmanP. Smereka and S. J. Osher, A level set approach for computing solutions to incompressible two-phase flows, J. Comput. Phys., 114 (1994), 146-159.   Google Scholar

[28]

K. van den DoelU. Ascher and A. Leitao, Multiple level sets for piecewise constant surface reconstruction in highly ill-posed problems, J. Sci. Comput., 43 (2010), 44-66.  doi: 10.1007/s10915-009-9341-x.  Google Scholar

[29]

H.-K. ZhaoT. ChanB. Merriman and S. J. Osher, A variational level set approach for multiphase motion, J. Comput. Phys., 127 (1996), 179-195.  doi: 10.1006/jcph.1996.0167.  Google Scholar

show all references

References:
[1]

J. E. Bain, T. R. Horscroft, J. Weyand, A. H. Saad and D. N. Bulling, Complex salt features resolved by integrating seismic, gravity and magnetics, EAEG/EAPG Annual Meeting, Expanded Abstracts. Google Scholar

[2]

M. K. Ben Hadj Miled and E. L. Miller, A projection-based level-set approach to enhance conductivity anomaly reconstruction in electrical resistance tomography, Inverse Problem, 23 (2007), 2375-2400. doi: 10.1088/0266-5611/23/6/007.  Google Scholar

[3]

M. Burger and S. Osher, A survey on level set methods for inverse problems and optimal design, European J. Appl. Math., 16 (2005), 263-301.  doi: 10.1017/S0956792505006182.  Google Scholar

[4]

O. Dorn and D. Lesselier, Level set methods for inverse scattering, Inverse Problems, 22 (2006), R67-R131. doi: 10.1088/0266-5611/22/4/R01.  Google Scholar

[5]

W. Freeden and M. Zuhair Nashed, Handbook of Mathematical Geodesy: Functional Analytic and Potential Theoretic Methods, Springer Nature, 2018. doi: 10.1007/978-3-319-57181-2.  Google Scholar

[6]

S. HouK. Solna and H.-K. Zhao, Imaging of location and geometry for extended targets using the response matrix, J. Comput. Phys., 199 (2004), 317-338.  doi: 10.1016/j.jcp.2004.02.010.  Google Scholar

[7]

V. Isakov, Inverse Source Problems, American Mathematical Society, Providence, Rhode Island, 1990. doi: 10.1090/surv/034.  Google Scholar

[8]

V. Isakov, S. Leung and J. Qian, A fast local level set method for inverse gravimetry,, Comm. in Computational Physics, 10 (2011), 1044-1070. doi: 10.4208/cicp.100710.021210a.  Google Scholar

[9]

V. IsakovS. Leung and J. Qian, A three-dimensional inverse gravimetry problem for ice with snow caps, Inverse Problems and Imaging, 7 (2013), 523-544.  doi: 10.3934/ipi.2013.7.523.  Google Scholar

[10]

G. J. Jorgensen and J. L. Kisabeth, Joint 3-D inversion of gravity magnetic and tensor gravity fields for imaging salt formations in the deepwater gulf of mexico, in Expanded Abstracts, Soc. Expl. Geophys., Tulsa, OK, 2000, 424-426. doi: 10.1190/1.1816085.  Google Scholar

[11]

B. KirkendallY. Li and D. Oldenburg, Imaging cargo containers using gravity gradiometry, IEEE Transactions on Geoscience and Remote Sensing, 45 (2007), 1786-1797.  doi: 10.1117/12.660979.  Google Scholar

[12]

R. A. Krahenbuhl and Y. Li, Inversion of gravity data using a binary formulation, Geophysical Journal International, 167 (2006), 543-556.  doi: 10.1111/j.1365-246X.2006.03179.x.  Google Scholar

[13]

K. Kunisch and X. Pan, Estimation of interfaces from boundary measurements, SIAM J. Control Optm., 32 (1994), 1643-1674.  doi: 10.1137/S0363012992226338.  Google Scholar

[14]

S. Leung and J. Qian, Transmission traveltime tomography based on paraxial Liouville equations and level set formulations, Inverse Problems, 23 (2007), 799-821.  doi: 10.1088/0266-5611/23/2/019.  Google Scholar

[15]

J. P. Leveille, I. F. Jones, Z. Z. Zhou, B. Wang and F. Liu, Subsalt imaging for exploration, production, and development: A review, Geophysics, 76 (2011), WB3-WB20. doi: 10.1190/geo2011-0156.1.  Google Scholar

[16]

W. Li, W. Lu and J. Qian, A level set method for imaging salt structures using gravity data, Geophysics, 81 (2016), G35-G51. doi: 10.1190/geo2015-0295.1.  Google Scholar

[17]

W. Li, W. Lu, J. Qian and Y. Li, A multiple level set method for three-dimensional inversion of magnetic data, Geophysics, 82 (2017), J61-J81. doi: 10.1190/segam2017-17729331.1.  Google Scholar

[18]

W. Li, J. Qian and Y. Li, Joint inversion of surface and borehole magnetic data: A level-set approach, Geophysics, 85 (2020), J15-J32. doi: 10.1190/segam2018-2996911.1.  Google Scholar

[19]

Y. Li and D. Oldenburg, 3D inversion of gravity data, Geophysics, 63 (1998), 109-119.   Google Scholar

[20]

A. LitmanD. Lesselier and F. Santosa, Reconstruction of a 2-D binary obstacle by controlled evolution of a level-set, Inverse Problems, 14 (1998), 685-706.  doi: 10.1088/0266-5611/14/3/018.  Google Scholar

[21]

W. LuS. Leung and J. Qian, An improved fast local level set method for three-dimensional inverse gravimetry, Inverse Problems and Imaging, 9 (2015), 479-509.  doi: 10.3934/ipi.2015.9.479.  Google Scholar

[22]

W. Lu and J. Qian, A local level set method for three-dimensional inversion of gravity gradiometry data, Geophysics, 80 (2015), G35-G51. Google Scholar

[23]

D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., 42 (1989), 577-685.  doi: 10.1002/cpa.3160420503.  Google Scholar

[24]

S. J. Osher and J. A. Sethian, Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys., 79 (1988), 12-49.  doi: 10.1016/0021-9991(88)90002-2.  Google Scholar

[25]

D. PengB. MerrimanS. OsherH. K. Zhao and M. Kang, A pde-based fast local level set method, J. Comput. Phys., 155 (1999), 410-438.  doi: 10.1006/jcph.1999.6345.  Google Scholar

[26]

F. Santosa, A level-set approach for inverse problems involving obstacles, Control, Optimizat. Calculus Variat., 1 (1996), 17-33.  doi: 10.1051/cocv:1996101.  Google Scholar

[27]

M. SussmanP. Smereka and S. J. Osher, A level set approach for computing solutions to incompressible two-phase flows, J. Comput. Phys., 114 (1994), 146-159.   Google Scholar

[28]

K. van den DoelU. Ascher and A. Leitao, Multiple level sets for piecewise constant surface reconstruction in highly ill-posed problems, J. Sci. Comput., 43 (2010), 44-66.  doi: 10.1007/s10915-009-9341-x.  Google Scholar

[29]

H.-K. ZhaoT. ChanB. Merriman and S. J. Osher, A variational level set approach for multiphase motion, J. Comput. Phys., 127 (1996), 179-195.  doi: 10.1006/jcph.1996.0167.  Google Scholar

Figure 1.  Regularizations by solving equation (37) with $ \lambda_f = 0.01 $. (a) Using Dirichlet boundary condition (38); (b) using Neumann boundary condition (41)
Figure 2.  Example 1. (a) True anomalous mass distribution; (b) initial guess; (c) clean data; (d) data contaminated with $ 5\% $ Gaussian noise
Figure 3.  Example 1. Inversion results. (a) Using $ (g_1,g_2) $ with clean measurements; (b) using $ (g_1,g_2) $ contaminated with $ 5\% $ Gaussian noise; (c) using $ d $ with clean measurement; (d) using $ d $ contaminated with $ 5\% $ Gaussian noise
Figure 4.  Example 2. (a) True anomalous mass distribution; (b) gravity data contaminated with $ 5\% $ Gaussian noise; (c) recovered solution using $ (g_1,g_2) $; (d) recovered solution using $ d $
Figure 5.  Example 3. (a) True mass distribution 1; (b) true mass distribution 2; (c) error of the recovered solution $ (\rho-\rho_{exact}) $ for distribution 1; (d) error $ (\rho-\rho_{exact}) $ for distribution 2
Figure 6.  Example 4: first case. (a) Anomalous mass distribution with $ f = 2-x_2 $; (b) recovered solution; (c) cross section at $ x_1 = 0.5 $
Figure 7.  Example 4: second case. (a) Anomalous mass distribution with $ f = 1+2\sin(2\pi x_2) $; (b) recovered solution; (c) cross section at $ x_1 = 0.5 $
Figure 8.  Example 5. (a) Anomalous mass distribution with $ f = 2-2\,x_2^2 $; (b) modulus data $ d = |\nabla U| $ with $ 5\% $ Gaussian noises
Figure 9.  Example 5. (a) Initial guess of $ \phi $; (b) initial structure of the anomalous mass distribution; (c) labeling function $ F(x) $; (d) recovered solution; (e) error $ \rho-\rho_{exact} $; (f) cross section of $ \rho $ at $ x_1 = 0.25 $
Figure 10.  Example 6. (a) Anomalous mass distribution with $ f = 1+\sin x_2 $; (b) modulus data $ d = |\nabla U| $ with $ 5\% $ Gaussian noise
Figure 11.  Example 6. (a) Initial guess of $ \phi $; (b) initial structure of the anomalous mass distribution; (c) labeling function $ F(x) $; (d) recovered solution; (e) error $ \rho-\rho_{exact} $; (f) cross section of $ \rho $ at $ x_1 = 0.25 $
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