TY - GEN
T1 - Probabilistic inversion of magnetic UXO data
T2 - 26th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience 2020
AU - Wigh, M. D.
AU - Døssing, A.
AU - Hansen, T. M.
N1 - Publisher Copyright:
© 2019 EAGE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Characterization and discrimination of UXO in magnetic offshore seabed surveys usually have limited success. We investigate how to utilize available prior knowledge on UXO type and quantity in a probabilistic framework, in order to improve on discrimination capabilities. It has previously been demonstrated how Bayesian inference can be utilized in a Markov chain Monte Carlo (MCMC) framework, where the solution can be sampled in a stochastic process. In this project, we extend the previous work containing independent 1-D prior distributions to a more complex case, introducing real quantitative data of actual UXO findings in the North Sea. Here, we develop the methodology to take into account knowledge about known size and shape of different UXO as well as the expected quantities of each type. This enables us to not only sample the posterior distribution of the model parameters, but also assign a probability of each UXO type with respect to the data at hand.
AB - Characterization and discrimination of UXO in magnetic offshore seabed surveys usually have limited success. We investigate how to utilize available prior knowledge on UXO type and quantity in a probabilistic framework, in order to improve on discrimination capabilities. It has previously been demonstrated how Bayesian inference can be utilized in a Markov chain Monte Carlo (MCMC) framework, where the solution can be sampled in a stochastic process. In this project, we extend the previous work containing independent 1-D prior distributions to a more complex case, introducing real quantitative data of actual UXO findings in the North Sea. Here, we develop the methodology to take into account knowledge about known size and shape of different UXO as well as the expected quantities of each type. This enables us to not only sample the posterior distribution of the model parameters, but also assign a probability of each UXO type with respect to the data at hand.
UR - https://www.scopus.com/pages/publications/85102007478
U2 - 10.3997/2214-4609.202020080
DO - 10.3997/2214-4609.202020080
M3 - Article in proceedings
AN - SCOPUS:85102007478
T3 - EAGE Conference Proceedings
BT - 26th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience 2020
PB - European Association of Geoscientists and Engineers
Y2 - 7 December 2020 through 8 December 2020
ER -