Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application

We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterization of the cover-basement interface. It consists of a cooperative inversion workflow relying on standalone inversion codes. Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining such structural constraints with petrophysical prior information. First, we perform the 1D probabilistic inversion of MT data for all sites and recover the respective probabilities of observing the cover-basement interface, which we interpolate to the rest of the study area. We then calculate the probabilities of observing the different rock units and partition the model into domains defined by combinations of rock units with non-zero probabilities. Third, we combine such domains with petrophysical information to apply spatially-varying, disjoint interval bound constraints to least-squares magnetic data inversion. We demonstrate the proof-of-concept using a realistic synthetic model reproducing features from the Mansfield area (Victoria, Australia) using a series of uncertainty indicators. We then apply the workflow to field data from the prospective mining region of Cloncurry (Queensland, Australia). Results indicate that our integration methodology efficiently leverages the complementarity between separate MT and magnetic data modelling approaches and can improve our capability to image the cover-basement interface. In the field application case, our findings also suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement.

Magnetic inversion to recover the subsurface block structures based on L1 norm and total variation regularization

Summary This paper presents a new sparse inversion method based on L1 norm regularization for 3D magnetic data. In isolation, L1 norm regularization yields model elements which are unconstrained by the input data to be exactly zero, leading to a sparse model with compact and focused structure. Here, we complement the L1 norm with a penalty minimizing total variation, the L1 norm of the model gradients; it is expected that the sharp boundaries of the subsurface structure are not compromised by incorporating this penalty. Although this penalty is widely used in the geophysical inversion studies, it is often replaced by an alternative quadratic penalty to ease solution of the penalized inversion problem; in this study, the original definition of the total variation, i.e., form of the L1 norm of the model gradients, is used. To solve the problem with this combined penalty of L1 norm and total variation, this study introduces alternative direction method of multipliers (ADMM), which is a primal-dual optimization algorithm that solves convex penalized problems based on the optimization of an augmented Lagrange function. To improve the computational efficiency of the algorithm to make this method applicable to large-scale magnetic inverse problems, this study applies matrix compression using the wavelet transform and the preconditioned conjugate gradient method. The inversion method is applied to both synthetic tests and real data, the synthetic tests demonstrate that, when subsurface structure is blocky, it can be reproduced almost perfectly.

2-D Inversion of magnetic data and gravity modelling of the transition zone between the Pernambuco-Alagoas domain and the northern portion of the Sergipano Belt (NE, Brazil)

Two-dimensional magnetic inversions were performed with the purpose to obtain information about the magnetic susceptibility, depth and volume of true magnetic sources located in the basement adjacent to the Sergipe-Alagoas basin, in the transition zone between the Pernambuco-Alagoas Domain and the northern portion of the Sergipano Belt (Borborema Province, NE Brazil) and to delineate its geometries. The magnetic data processing was performed to interpret geological contacts, source’s edge boundaries and mapping of folds and shear zones. In the results of the magnetic inversion, a source was found in the Rio Coruripe domain between 9 and 21 km in depth with a magnetic susceptibility of 0.0079 (SI) and another in the Pernambuco-Alagoas domain between 7.5 and 9 km with a magnetic susceptibility of 0.0077 (SI). The basement’s depth was obtained through spectral analysis, whose base was found at 37.7 km. In addition, gravity models were created with the purpose to verify the information obtained by the magnetic inversion and spectral analysis, which was useful to understand how these sources interfere in the structure of the basement and how it affected its tectonic model.

Recovering Magnetization of Rock Formations by Jointly Inverting Airborne Gravity Gradiometry and Total Magnetic Intensity Data

Conventional 3D magnetic inversion methods are based on the assumption that there is no remanent magnetization, and the inversion is run for magnetic susceptibility only. This approach is well-suited to targeting mineralization; however, it ignores the situation where the direction of magnetization of the rock formations is different from the direction of the induced magnetic field. We present a novel method of recovering a spatial distribution of magnetization vector within the rock formation based on joint inversion of airborne gravity gradiometry (AGG) and total magnetic intensity (TMI) data for a shared earth model. Increasing the number of inversion parameters (the scalar components of magnetization vector) results in a higher degree of non-uniqueness of the inverse problem. This increase of non-uniqueness rate can be remedied by joint inversion based on (1) Gramian constraints or (2) joint focusing stabilizers. The Gramian constraints enforce shared earth structure through a correlation of the model gradients. The joint focusing stabilizers also enforce the structural similarity and are implemented using minimum support or minimum gradient support approaches. Both novel approaches are applied to the interpretation of the airborne data collected over the Thunderbird V-Ti-Fe deposit in Ontario, Canada. By combining the complementary AGG and TMI data, we generate jointly inverted shared earth models that provide a congruent image of the rock formations hosting the mineral deposit.

A Bayesian framework for simultaneous determination of susceptibility and magnetic thickness from magnetic data

<p>The thickness of the magnetized layer in the crust (or lithosphere) holds valuable information about the thermal state and composition of the lithosphere. Commonly, maps of magnetic thickness are estimated by spectral methods that are applied to individual data windows of the measured magnetic field strength. In each window, the measured power spectrum is fit by a theoretical function which depends on the average magnetic thickness in the window and a ‘fractal’ parameter describing the spatial roughness of the magnetic sources. The limitations of the spectral approach have long been recognized and magnetic thickness inversions are routinely calibrated using heat flow measurements, based on the assumption that magnetic thickness corresponds to Curie depth. However, magnetic spectral thickness determinations remain highly uncertain, underestimate uncertainties, do not properly integrate heat flow measurements into the inversion and fail to address the inherent trade-off between lateral thickness and susceptibility variations.</p><p>We present a linearized Bayesian inversion that works in space domain and addresses many issues of previous depth determination approaches. The ‘fractal’ description used in the spectral approaches translates into a Matérn covariance function in space domain. We use a Matérn covariance function to describe both the spatial behaviour of susceptibility and magnetic thickness. In a first step, the parameters governing the spatial behaviour are estimated from magnetic data and heat flow data using a Bayesian formulation and the Monte-Carlo-Markov-Chain (MCMC) technique. The second step uses the ensemble of parameter solution from MCMC to generate an ensemble of susceptibility and thickness distributions, which are the main output of our approach.</p><p>The newly developed framework is applied to synthetic data at satellite height (300 km) covering an area of 6000 x 6000 km. These tests provide insight into the sensitivity of satellite magnetic data to susceptibility and thickness. Furthermore, they highlight that magnetic inversion benefits greatly from a tight integration of heat flow measurements into the inversion process.</p>

A global environmental crisis 42,000 years ago

Geological archives record multiple reversals of Earth’s magnetic poles, but the global impacts of these events, if any, remain unclear. Uncertain radiocarbon calibration has limited investigation of the potential effects of the last major magnetic inversion, known as the Laschamps Excursion [41 to 42 thousand years ago (ka)]. We use ancient New Zealand kauri trees (Agathis australis) to develop a detailed record of atmospheric radiocarbon levels across the Laschamps Excursion. We precisely characterize the geomagnetic reversal and perform global chemistry-climate modeling and detailed radiocarbon dating of paleoenvironmental records to investigate impacts. We find that geomagnetic field minima ~42 ka, in combination with Grand Solar Minima, caused substantial changes in atmospheric ozone concentration and circulation, driving synchronous global climate shifts that caused major environmental changes, extinction events, and transformations in the archaeological record.

lp Norm Smooth Inversion of Magnetic Anomaly Based on Improved Adaptive Differential Evolution

Due to the approved applicability of differential evolution (DE) in geophysical problems, the algorithm has been widely concerned. The DE algorithms are mostly applied to solve the geophysical parametric estimation based on specific models, but they are rarely used in solving the physical property inverse problem of geophysical data. In this paper, an improved adaptive differential evolution is proposed to solve the lp norm magnetic inversion of 2D data, in which the perturbation direction in the mutation strategy is smoothed by using the moving average technique. Besides, a new way of updating the regularization coefficient is introduced to balance the effect of the model constraint adaptively. The inversion results of synthetic models demonstrate that the presented method can obtain a smoother solution and delineate the distributions of abnormal bodies better. In the field example of Zaohuoxi iron ore deposits in China, the reconstructed magnetic source distribution is in good agreement with the one inferred from drilling information. The result shows that the proposed method offers a valuable tool for magnetic anomaly inversion.