2Dinversion for borehole magnetic data in the presence of significant remanence and demagnetization
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Significant remanence and demagnetization alter the intensity and orientation of total magnetization,which complicates interpretation of magnetic data.To deal with the problem,we propose a method of inverting 2D magnetization vector distributions using the 2D borehole magnetic data.We firstly invert the magnetization intensity distributions based on magnetic anomaly amplitudes of borehole magnetic data. With the known magnetization intensity distributions,subsequently,we invert the magnetization orientation distributions by fitting the magnetic component anomalies.Both magnetization intensity and orientation are solved by conjugate gradients.And a preconditioned matrix is utilized to improve the inverse quality of magnetization intensity. All synthetic examples involving significant remanence and high susceptibility demonstrate that this method is capable of accurately recovering the magnetization vector distributions. The magnetization vector distributions comprehensively include the influences of induced magnetization,remanent magnetization and self-demagnetization.Therefore,it provides an effective approach to study the ore deposits when high susceptibility and significant remanent magnetization are present.Keywords:
Stoner–Wohlfarth model
Rock magnetism
Natural remanent magnetization
Intensity
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We propose a new method for estimating the total magnetization direction of magnetic sources based on the inversion of total-field anomaly via equivalent-layer technique. This technique is commonly used for processing magnetic data by estimating a magnetic-moment distribution over a planar layer of dipoles having an uniform magnetization direction. It can be shown that this distribution is all-positive if the magnetization direction of the dipoles is equal to that of the arbitrary magnetic sources producing the observed data. Based on this theoretical property, we propose an iterative algorithm to estimate the magnetization direction of 3D magnetic sources by imposing a positivity constraint on the magnetic-moment distribution of the dipoles forming the equivalent layer. Our approach imposes neither information on the shape nor on the depth of magnetic sources. Tests with synthetic data and field data, from an alkaline intrusion in southern Brazil, show that our method is able to correctly estimate the magnetization direction of the sources. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Time: 2:15 PM Location: 214D Presentation Type: Oral
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SummaryThe remote determination of magnetic remanence in rocks is a method that has largely been ignored because of the ambiguity associated with the estimation of both the Koenigsberger ratio and remanent magnetization direction. Our research shows that the resultant magnetization direction can be derived directly through inversion of magnetic data for an isolated magnetic anomaly. The resultant magnetization direction is a property of the target magnetic rocks and a robust inversion parameter. The departure angle of the resultant magnetization vector from that of the inducing magnetic field is an important indicator of the existence of remanent magnetization and the inversion process can detect departures that are not easily detected by visual inspection. This departure angle is called the Apparent Resultant Rotation Angle or ARRA.The induced field vector, remanent magnetization vector and resultant magnetization vector lie on a great circle. We find the intersection of the polar wander vector trace with the great circle to obtain one or more possible solutions for the remanent magnetization direction. Geological deduction will normally allow us to reduce the ambiguity for multiple solutions to obtain the most likely remanent magnetization direction. Once the remanent magnetization direction is established, it is then possible to determine the Koenigsberger ratio and magnetic susceptibility for the target.We illustrate the methodology with some synthetic models and targets from Australian magnetic surveys. Magnetic remanence is a physical property of the rock that is distinct from susceptibility and this methodology provides a new tool to help with the categorization and prioritization of exploration targets.Key words:: resultantmagnetizationremanencesusceptibilityinversion.
Prioritization
Rock magnetism
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Inversion of magnetic data is complicated by the presence of remanent magnetization. To deal with this problem, we invert magnetic data for a three-component subsurface magnetization vector, as opposed to magnetic susceptibility (a scalar). The magnetization vector can be cast in a Cartesian or spherical framework. In the Cartesian formulation, the total magnetization is split into one component parallel and two components perpendicular to the earth’s field. In the spherical formulation, we invert for magnetization amplitude and the dip and azimuth of the magnetization direction. Our inversion schemes contain flexibility to obtain different types of magnetization models and allow for inclusion of geologic information regarding remanence. Allowing a vector magnetization increases the nonuniqueness of the magnetic inverse problem greatly, but additional information (e.g., knowledge of physical properties or geology) incorporated as constraints can improve the results dramatically. Commonly available information results in complicated nonlinear constraints in the Cartesian formulation. However, moving to a spherical formulation results in simple bound constraints at the expense of a now nonlinear objective function. We test our methods using synthetic and real data from scenarios involving complicated remanence (i.e., many magnetized bodies with many magnetization directions). All tests provide favorable results and our methods compare well against those of other authors.
Stoner–Wohlfarth model
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The knowledge of the total magnetization direction has always been a drawback in among geological and geophysical exploration when using magnetic data. Remanent magnetization, present in almost all magnetic bodies, can significantly alter this direction. Usually the reduced to pole transform is applied to magnetic data but, without the knowledge of the total magnetization direction the data will not be reduced to pole correctly. Consequently, the interpretation will be erroneous. The need of a more precise and accurate interpretation always leads us to new methods and techniques. In this paper a method to determine the magnetization direction was tested and analyzed. The methodology is based on the cross-correlation of the vertical gradient and the total gradient of the reduced to pole anomaly. The method is only applicable to isolated anomalies and showed consistence and efficiency when applied to synthetic and real data, resulting in a more symmetrical and centered anomaly.
Anomaly (physics)
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Natural remanent magnetization
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The unknown magnetization directions in the presence of remanence have posed great challenges for interpreting magnetic data. Estimating magnetization directions based on magnetic measurements, therefore, has been an active area of research within the applied geophysics community. Despite the availability of several methods for estimating magnetization directions, quantifying the uncertainty of such estimates has remained untackled. We have investigated the use of the magnetization-clustering inversion (MCI) method for the purpose of assessing the uncertainty of the recovered magnetization directions. Specifically, we have leveraged the fact that the number of clusters that one expects to see among the magnetization directions recovered from MCI needs to be supplied by a user. We propose to implement a sequence of MCIs by assuming a series of different cluster numbers, and subsequently, to calculate the standard deviations of the recovered magnetization directions at each location in a model as a practical way of quantifying the uncertainty of the estimated magnetization directions. We have developed two different methods for the calculations of the standard deviations, and have also investigated the maximum number of clusters that one needs to consider to reliably assess the uncertainty. After the proof-of-concept study on a synthetic data set, we applied our methods to a field data set from an iron-oxide-copper-gold deposit exploration in the Carajás Mineral Province, Brazil. The high-confidence zones that correspond to low-uncertainty zones indicate a high spatial correspondence with the mineralization zones inferred from the drillholes and geology.
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The interpretation and characterization of magnetic anomalies is reliant upon the knowledge of the total magnetization direction, usually assumed to be caused solely or primarily by induced magnetization. The presence of strong remanent magnetization can adversely affect the interpretation of the magnetic data and lead to erroneous size or shape. Therefore, it is imperative to know the total magnetization direction. We propose a method that is based upon the optimal correlation between two tools in magnetic data interpretation: the vertical gradient and the total gradient of the RTP field. In this paper we discuss the need for such a method, outline the theory and numerical implementation, and test it on both synthetic and field data sets.
Characterization
Stoner–Wohlfarth model
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Following recent advances in SQUID technology, airborne full tensor magnetic gradiometry (FTMG) is emerging as a practical mineral exploration method that is intended to recover information about remanent magnetization. In this paper, we introduce 3D regularized inversion of FTMG data that recovers the total magnetization vector in each cell of the 3D earth model. If a priori information about the susceptibility or remanent magnetization is available, the 3D inversion can be constrained to recover the remanent magnetization vector. If a priori information is not available, it is possible to recover attributes of remanent magnetization such as the amplitude and angle of the magnetization vector relative to the inducing field. We present a case study for data acquired over a dyke swarm in South Africa that compares our 3D FTMG inversion for magnetization with a 3D total magnetic intensity (TMI) inversion for a positively-constrained susceptibility distribution. Given the significant remanent magnetization present, the 3D FTMG inversion for magnetization recovers results that are most consistent with the known geology.
Natural remanent magnetization
Stoner–Wohlfarth model
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Remanent magnetization and self-demagnetization change the magnitude and direction of the magnetization vector, which complicates the interpretation of magnetic data. To deal with this problem, we evaluated a method for inverting the distributions of 2D magnetization vector or effective susceptibility using 3C borehole magnetic data. The basis for this method is the fact that 2D magnitude magnetic anomalies are not sensitive to the magnetization direction. We calculated magnitude anomalies from the measured borehole magnetic data in a spatial domain. The vector distributions of magnetization were inverted methodically in two steps. The distributions of magnetization magnitude were initially solved based on magnitude magnetic anomalies using the preconditioned conjugate gradient method. The preconditioner determined by the distances between the cells and the borehole observation points greatly improved the quality of the magnetization magnitude imaging. With the calculated magnetization magnitude, the distributions of magnetization direction were computed by fitting the component anomalies secondly using the conjugate gradient method. The two-step approach made full use of the amplitude and phase anomalies of the borehole magnetic data. We studied the influence of remanence and demagnetization based on the recovered magnetization intensity and direction distributions. Finally, we tested our method using synthetic and real data from scenarios that involved high susceptibility and complicated remanence, and all tests returned favorable results.
Rock magnetism
Stoner–Wohlfarth model
Single domain
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