Magnetic Field Imaging of Salt Structures at Nordkapp Basin, Barents Sea

Paoletti, Valeria; Milano, Maurizio; Baniamerian, Jamaledin; Fedi, Maurizio

doi:10.1029/2020gl089026

Cited by 21 publications

(10 citation statements)

References 54 publications

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“…To overcome this difficulty, a multiphysics approach is required to properly illuminate the complete geologic picture. Gravity gradiometry, TMI, and EM data have been acquired and inverted to fill in the gaps left by the incomplete seismic picture (Gernigon et al, 2011;Stadtler et al, 2014;Paoletti et al, 2020;Tao et al, 2021;Zhdanov, 2021, 2022). We examine the Uranus diapir in this example, which ultimately failed to present the HC deposit after drilliing.…”

Section: Geologic Setting Of the Nordkapp Basinmentioning

confidence: 99%

Probabilistic approach to Gramian inversion of multiphysics data

Zhdanov

Jorgensen²,

Tao³

2023

Front. Earth Sci.

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We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various components of the geological system. By joint inversion of the multiphysics data, one can produce enhanced subsurface images of the physical properties distribution, which improves our ability to explore natural resources. One powerful method of joint inversion is based on Gramian constraints. This technique enforces the relationships between different model parameters during the inversion process. We demonstrate that the Gramian can be interpreted as a determinant of the covariance matrix between different physical models representing the subsurface geology in the framework of the probabilistic approach to inversion theory. This interpretation opens the way to use all the power of the modern probability theory and statistics in developing novel methods for joint inversion of the multiphysics data. We apply the developed joint inversion methodology to inversion of gravity gradiometry and magnetic data in the Nordkapp Basin, Barents Sea to image salt diapirs.

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Section: Geologic Setting Of the Nordkapp Basinmentioning

confidence: 99%

Probabilistic approach to Gramian inversion of multiphysics data

Zhdanov

Jorgensen²,

Tao³

2023

Front. Earth Sci.

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“…On the other hand, because of the diamagnetic effect of salt deposits, magnetic studies can be integrated with the gravity method as a complementary method (e.g. Barbosa and Silva, 2011; Abedi, 2018; Paoletti et al ., 2020).…”

Section: Field Datamentioning

confidence: 99%

Sequential joint inversion of gravity and magnetic data via the cross‐gradient constraint

Tavakoli

Kalateh

Rezaie

et al. 2021

Geophysical Prospecting

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Different geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross‐gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross‐gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two‐dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross‐gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross‐gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north‐western Iran. In this application case, the lower density of salt minerals such as potash, compared to its surrounding sedimentary sequences, motivates a gravity study. Furthermore, the relative lower susceptibility of these salt minerals, alongside their diamagnetic effect, makes them a suitable target for magnetic surveys. Separate gravity and magnetic studies had been performed over the deposit; however, a constitutive relationship between density and magnetization within the area of interest supporting a joint inversion had not been established. In this paper, we apply the sequential cross‐gradient approach to perform the first full joint inversion for the AjiChai salt deposit. The magnetic inversion here is performed to recover the magnetization amplitudes rather than the magnetization vector. In fact, we assume there is no remanent magnetization and, therefore, that the magnetization vector is constant and parallel to the geomagnetic field direction. The constructed density and magnetization models are of high concordance with available geological information and previous studies including drilling results. In addition, unlike previous separate inversion models, the models are structurally and geometrically similar.

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“…The magnetic fields measured in field surveys are generated by the integrated effects of all magnetic rocks, which contain various magnetizations, magnetization directions, and distributions (Fedi & Quarta, 1998). The magnetic information of target sources recovered from the observed magnetic anomaly field is often used in geological research and mineral exploration (Li et al., 2021; Paoletti et al., 2016, 2020; Wang et al., 2020). For example, igneous rocks usually contain a range of different lithologies and distributions in horizontal and vertical directions (C. Zhang et al., 2020; Feng & Zheng, 2021; H. Zhang et al., 2020; M. Zhang et al., 2020), and large‐scale magma chambers generally form at great depth, whereas smaller‐scale stocks and dikes typically occur at shallow depth.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Helbig's method, cross‐correlation methods, and deep learning methods have all been developed to evaluate the magnetization direction of a single and isolated geological body (Gerovska et al., 2009; Liu et al., 2020; Nurindrawati & Sun, 2020), and magnetization inversion methods have been developed to study magnetic geological bodies of horizontal discontinuities (e.g., igneous rocks, ores, and salt structures). However, measured magnetic anomalies are often superimposed by magnetic layers (e.g., a metamorphic basement), which can introduce errors into the magnetization inversion (Hightower et al., 2020; Paoletti et al., 2020; Prutkin et al., 2011). Therefore, appropriate inversion methods are required to study magnetic anomalies caused by rocks with different distribution patterns.…”

Section: Introductionmentioning

confidence: 99%

Can Targeted Source Information Be Extracted From Superimposed Magnetic Anomalies?

Zhu

2022

JGR Solid Earth

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Magnetic anomalies commonly contain anomalies generated by crustal rocks that have variable mineral compositions and natural remanent magnetizations. Recovery of magnetic parameters from superimposed magnetic anomaly sources is therefore important for mineral exploration and geological studies. Superposition of magnetic anomalies leads to nonnegligible errors of inversion and interpretation. Therefore, extraction of magnetic anomalies is essential for magnetic data processing; however, existing methods cannot extract magnetic anomalies caused by sources without substantial depth differences. A novel low‐rank method is proposed for extracting a target magnetic anomaly from a superimposed anomaly. The low‐rank feature of the magnetic anomaly is used to obtain the target anomaly by reducing the Schatten‐p norm of the superimposed anomaly according to the possible target source distribution. The target magnetic anomaly can then be extracted from the data generated by the source interference at the same or different depths. The accuracy of the proposed method was verified using synthetic modeling. Magnetization vector information for individual igneous rocks was recovered from extracted magnetic anomalies via 3‐D inversion of field data from Yeshan (eastern China) that contain complex magnetic anomaly superpositions. The distributions and lithologies of five buried igneous rocks, including basalt, diabase, and granodiorite, were then identified. The proposed method expands the problem of separating magnetic information from rocks in different layers with large depth differences to rocks within the same or other layers and recovers geometric and physical information for each targeted magnetic source from the superimposed anomaly.

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Magnetic Field Imaging of Salt Structures at Nordkapp Basin, Barents Sea

Cited by 21 publications

References 54 publications

Probabilistic approach to Gramian inversion of multiphysics data

Probabilistic approach to Gramian inversion of multiphysics data

Sequential joint inversion of gravity and magnetic data via the cross‐gradient constraint

Can Targeted Source Information Be Extracted From Superimposed Magnetic Anomalies?

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