Inferring the most probable maps of underground utilities using Bayesian mapping model

Journal of Applied Geophysics

Volumn 150, Issue , 2018, Pages 52-66

  Bilal, Muhammad ^a   Khan, Wasiq ^f   Muggleton, Jennifer ^c   Rustighi, Emiliano ^c   Jenks, Hugo ^d   Pennock, Steve R ^d   Atkins, Phil R ^e   Cohn, Anthony ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF LEEDS   (United Kingdom)

^c UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^d UNIVERSITY OF BATH   (United Kingdom)

^e UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^f MANCHESTER METROPOLITAN UNIVERSITY   (United Kingdom)

Author keywords

Bayesian data fusion; Bayesian regression; Image processing; Most probable maps; MTU sensors

Indexed keywords

AUTOMATION; BINARY ALLOYS; DATA FUSION; DATA INTEGRATION; DATA VISUALIZATION; IMAGE SEGMENTATION; INVERSE PROBLEMS; MAXIMUM PRINCIPLE;

BAYESIAN CLASSIFICATION; BAYESIAN DATA FUSIONS; BAYESIAN MAPPING; BAYESIAN REGRESSION; CLASSIFICATION TECHNIQUE; IMAGES PROCESSING; MAPPING MODELING; MAPPING THE UNDERWORLD SENSOR; MOST PROBABLE MAP; UNDERGROUND UTILITIES;

MAPPING;

ALGORITHM; BAYESIAN ANALYSIS; BURIED STRUCTURE; IMAGE PROCESSING; MACHINE LEARNING; PERFORMANCE ASSESSMENT; SEGMENTATION; THREE-DIMENSIONAL MODELING; TWO-DIMENSIONAL MODELING; UNDERGROUND CONSTRUCTION; VISUALIZATION;

EID: 85044639536     PISSN: 09269851     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jappgeo.2018.01.006     Document Type: Article

References (47)

1. Aleardi, M., Ciabarri, F., Mazzotti, A., Probabilistic estimation of reservoir properties by means of wide-angle AVA inversion and a petrophysical reformulation of the Zoeppritz equations. J. Appl. Geophys. 147:Supplement C (2017), 28–41 Available at: http://www.sciencedirect.com/science/article/pii/S0926985117301192.
2. Ali Khan, M., Sarfraz, M., Motion tweening for skeletal animation by cardinal spline. Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E., (eds.) Informatics Engineering and Information Science, 2011, Springer, Berlin Heidelberg.
3. Al-Nuaimy, W., et al. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J. Appl. Geophys. 43:2 (2000), 157–165 Available at http://www.sciencedirect.com/science/article/pii/S0926985199000555.
4. Ashdown 2000. Mains Location Equipment – A State of the Art Review and Future Research Needs. In: UKWIR (Ed.). (ISBN: 1 84057 233 7).
5. Bailey, T., Durrant-Whyte, H., Simultaneous localization and mapping (SLAM): part II. IEEE Robotics & Automation Magazine 13:3 (2006), 108–117.
6. Bailey, T.L., Elkan, C., Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proceedings International Conference on Intelligent Systems for Molecular Biology, 2, 1994, 28–36.
7. Bar-Shalom, Y., Tracking and Data Association. 1987, Academic Press Professional, Inc., San Diego, CA, USA.
8. Bhalerao, Abhir, Wilson, R., A Fourier Approach to 3D Local Feature Estimation from Volume Data (2001). British Machine Vision Conference. 2001, 461–470.
9. Buntine, W., Variational Extensions to EM and Multinomial PCA. Elomaa, T., Mannila, H., Toivonen, H., (eds.) Machine Learning: ECML 2002, 2002, Springer Berlin Heidelberg.
10. Burtwell, M., E. A., Locating underground plant and equipment - proposals for a research programme. UKWIR, 2004 (ISBN: 1-84057).
11. Chen, Huanhuan, A. G. C, Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data. The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, IEEE, Barcelona.
12. Chen, H., Cohn, A.G., Buried utility pipeline mapping based on multiple spatial data sources: A Bayesian data fusion approach. IJCAI, 2011 Retrieved from https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-402.
13. Chen, T.W., Wang, Q., 3D line segment detection algorithm for large-scale scenes. 2010 International Conference on Audio, Language and Image Processing, 2010, 1056–1061, 10.1109/ICALIP.2010.5685113.
14. Christensen, C.W., et al. Combining airborne electromagnetic and geotechnical data for automated depth to bedrock tracking. J. Appl. Geophys. 119:Supplement C (2015), 178–191 Available at: http://www.sciencedirect.com/science/article/pii/S0926985115001585.
15. Delicado, P., Smrekar, M., Mixture of nonlinear models: a Bayesian fit for principal curves. 2007 International Joint Conference on Neural Networks, 2007, 195–200, 10.1109/IJCNN.2007.4370954.
16. Dettmer, J., et al. Trans-dimensional finite-fault inversion. Geophys. J. Int. 199:2 (2014), 735–751 Available at https://doi.org/10.1093/gji/ggu280.
17. Do, C.B., Batzoglou, S., What is the expectation maximization algorithm?. Nat. Biotechnol., 26, 2008, 897, 10.1038/nbt1406.
18. Dong-Min, W., Dong-Chul, P., 3D line segment detection based on disparity data of area-based stereo. Intelligent systems, 2009. GCIS ‘09. WRI Global Congress on, 19–21 May 2009, 2009, 219–223.
19. Dutta, R., Cohn, A.G., Muggleton, J.M., 3D mapping of buried underworld infrastructure using dynamic Bayesian network based multi-sensory image data fusion. J. Appl. Geophys. 92 (2013), 8–19 Available at: http://www.sciencedirect.com/science/article/pii/S0926985113000311.
20. Esri, Put Your Maps to Work with ArcGIS, the Mapping Platform for Your Organization [Online]. 2015, Esri Available: https://www.arcgis.com.
21. Ester, M., Kriegel, H.-P., Sander, J., Xu, X., A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996, AAAI Press, 226–231.
22. Fernández-Martínez, J.L., et al. From Bayes to Tarantola: new insights to understand uncertainty in inverse problems. J. Appl. Geophys. 98:Supplement C (2013), 62–72.
23. Fiannacca, P., et al. IG-Mapper: A new ArcGIS® toolbox for the geostatistics-based automated geochemical mapping of igneous rocks. Chem. Geol. 470:Supplement C (2017), 75–92.
24. Friedman, J., Popescu, B., Gradient Directed Regularization for Linear Regression and Classification. 2004 (Retrieved from citeulike-article-id:3142778).
25. Gonzalez, R.C., Digital Image Processing. Third Ed, 2008.
26. Guo, R., et al. Non-linearity in Bayesian 1-D magnetotelluric inversion. Geophys. J. Int. 185:2 (2011), 663–675.
27. Ji, Y., et al. Frequency-domain sparse Bayesian learning inversion of AVA data for elastic parameters reflectivities. J. Appl. Geophys. 133:Supplement C (2016), 1–8.
28. Klautau, Aldebaro, N. J. A. A. O, Discriminative Gaussian mixture models: a comparison with Kernel classifiers. Mishra, T.F.A.N., (eds.) Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003.
29. Krishna, A.S., Rao, G.S., Contrast enhancement techniques using histogram equalization methods on color images with poor lightning. Int. J. Comput. Sci. Eng. Appl., 2013, 15–24.
30. Mcmahon, W., Burtwell, M.H., Evans, M., Minimising street works disruption: the real costs of street works to the utility industry and society. UKWIR, 2005.
31. Muggleton, J.M., Brennan, M.J., Gao, Y., Determining the location of buried plastic water pipes from measurements of ground surface vibration. J. Appl. Geophys. 75:1 (2011), 54–61.
32. Neira, J., Tardos, J.D., Data association in stochastic mapping using the joint compatibility test. IEEE Trans. Robot. Autom., 2001, 890–897.
33. Oldenborger, G.A., et al. Bedrock mapping of buried valley networks using seismic reflection and airborne electromagnetic data. Journal of Applied Geophysics 128:Supplement C (2016), 191–201.
34. Otsu, Nobuyuki, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern, SMC-9, 1979, 62–66.
35. Ren, H., et al. Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling. J. Appl. Geophys. 147:Supplement C (2017), 68–80.
36. Ristić A., et al. Point coordinates extraction from localized hyperbolic reflections in GPR data. J. Appl. Geophys. 144:Supplement C (2017), 1–17.
37. Royal Acd, R. C., Atkins Pr, Brennan Mj, Chapman Dn, Chen H, Cohn Ag, Curioni G, Foo Ky, Goddard K, Hao T, Lewin Pl, Metje N, Muggleton Jm, Naji A, Pennock Sr, Redfern Ma, Saul Aj, Swingler Sg and Wang P 2010. Mapping the Underworld: Location, Mapping and Positioning Without Excavation. In: Control, C. (Ed.). (Middle East, Singapore).
38. Royal, A.C.D., Atkins, P.R., Brennan, M.J., Chapman, D.N., Chen, H., Cohn, A.G., Foo, K.Y., Goddard, K.F., Hayes, R., Hao, T., Lewin, P.L., Metje, N., Muggleton, J.M., Naji, A., Orlando, G., Pennock, S.R., Redfern, M.A., Saul, A.J., Swingler, S.G., Wang, P., Rogers, C.D.F., Site assessment of multiple-sensor approaches for buried utility detection. Int. J. Geophys., 2011, 2011, 19.
39. Salojarvi, J., Puolamaki, K., Kaski, S., Expectation maximization algorithms for conditional likelihoods. Proceedings of the 22nd International Conference on Machine Learning, 2005, ACM, Bonn, Germany.
40. Sander, J., Ester, M., Kriegel, H.-P., Xu, X., Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Discov. 2 (1998), 169–194.
41. Sanquer, M., Chatelain, F., El-Guedri, M., Martin, N., A reversible jump MCMC algorithm for Bayesian curve fitting by using smooth transition regression models. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, 22–27 May 2011, 2011, 3960–3963.
42. Thomas, A.M., et al. Stakeholder needs for ground penetrating radar utility location. J. Appl. Geophys. 67:4 (2009), 345–351.
43. Underworld, M.T., Mapping the Underworld [Online]. Available: http://www.mappingtheunderworld.ac.uk, 2011. (Accessed 13 September 2015)
44. Wang, Y., Lu, W., Discontinuity enhancement based on time-variant seismic image deblurring. J. Appl. Geophys. 135:Supplement C (2016), 155–162.
45. Ward, G., Hastie, T., Barry, S., Elith, J., Leathwick, J.R., Presence-only data and the em algorithm. Biometrics 65:2 (2009), 554–563, 10.1111/j.1541-0420.2008.01116.x.
46. Werman, M., Keren, D., A novel Bayesian method for fitting parametric and non-parametric models to noisy data. Computer Vision and Pattern Recognition, 1999, IEEE Computer Society Conference on 1999, Vol. 2, 1999, 1–558.
47. Yangming Li, S.L., Song, Quanjun, Liu, Hai, Meng, M.Q.-H., Fast and robust data association using posterior based approximate joint compatibility test. IEEE Transactions on Industrial Informatics, 2013, 331–339.