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An adaptive coupling strategy for joint inversions that use petrophysical information as constraints

Heincke, Björn; Jegen, Marion; Moorkamp, Max; Hobbs, Richard; Chen, Jin

An adaptive coupling strategy for joint inversions that use petrophysical information as constraints Thumbnail


Björn Heincke

Marion Jegen

Max Moorkamp

Richard Hobbs

Jin Chen


Joint inversion strategies for geophysical data have become increasingly popular as they allow for the efficient combination of complementary information from different data sets. The algorithm used for the joint inversion needs to be flexible in its description of the subsurface so as to be able to handle the diverse nature of the data. Hence, joint inversion schemes are needed that 1) adequately balance data from the different methods, 2) have stable convergence behavior, 3) consider the different resolution power of the methods used and 4) link the parameter models in a way that they are suited for a wide range of applications. Here, we combine active source seismic P-wave tomography, gravity and magnetotelluric (MT) data in a petrophysical joint inversion that accounts for these issues. Data from the different methods are inverted separately but are linked through constraints accounting for parameter relationships. An advantage of performing the inversions separately is that no relative weighting between the data sets is required. To avoid perturbing the convergence behavior of the inversions by the coupling, the strengths of the constraints are readjusted at each iteration. The criterion we use to control the adaption of the coupling strengths is based on variations in the objective functions of the individual inversions from one to the next iteration. Adaption of the coupling strengths makes the joint inversion scheme also applicable to subsurface conditions, where assumed relationships are not valid everywhere, because the individual inversions decouple if it is not possible to reach adequately low data misfits for the made assumptions. In addition, the coupling constraints depend on the relative resolutions of the methods, which leads to an improved convergence behavior of the joint inversion. Another benefit of the proposed scheme is that structural information can easily be incorporated in the petrophysical joint inversion (no additional terms are added in the objective functions) by using mutually controlled structural weights for the smoothing constraints. We test our scheme using data generated from a synthetic 2-D sub-basalt model. We observe that the adaption of the coupling strengths makes the convergence of the inversions very robust (data misfits of all methods are close to the target misfits) and that final results are always close to the true models independent of the parameter choices. Finally, the scheme is applied on real data sets from the Faroe-Shetland Basin to image a basaltic sequence and underlying structures. The presence of a borehole and a 3-D reflection seismic survey in this region allows direct comparison and, hence, evaluate the quality of the joint inversion results. The results from joint inversion are more consistent with results from other studies than the ones from the corresponding individual inversions and the shape of the basaltic sequence is better resolved. However, due to the limited resolution of the individual methods used it was not possible to resolve structures underneath the basalt in detail, indicating that additional geophysical information (e.g. CSEM, reflection onsets) needs to be included.


Heincke, B., Jegen, M., Moorkamp, M., Hobbs, R., & Chen, J. (2017). An adaptive coupling strategy for joint inversions that use petrophysical information as constraints. Journal of Applied Geophysics, 136, 279-297.

Journal Article Type Article
Acceptance Date Oct 27, 2016
Online Publication Date Nov 1, 2016
Publication Date Jan 1, 2017
Deposit Date Dec 22, 2016
Publicly Available Date Nov 1, 2017
Journal Journal of Applied Geophysics
Print ISSN 0926-9851
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 136
Pages 279-297


Accepted Journal Article (1 Mb)

Copyright Statement
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license

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