Magnetic amplitude inversion for depth-to-basement and apparent magnetization-intensity estimates

We have developed an inversion method to recover the depth and the total magnetization intensity of the basement under a sedimentary basin using the amplitude of the magnetic anomaly vector (amplitude data). Because the amplitude data are weakly dependent on the magnetization direction, our method is suitable for interpreting areas with remanent magnetization. Our method assumes constant magnetized basement rocks overlain by nonmagnetic sediments. To overcome the inherent ambiguity of potential field data, we assume knowledge of the average depth of the basement and use it as a constraint to regularize the inversion. A sensitivity analysis with synthetic data shows the weak dependency of the magnetic amplitude inversion on the magnetization direction. Different combinations of magnetization directions recover the interface separating sediments from basement rocks. Test on field data over the Foz do Amazonas Basin, Brazil, recovers the shape of the basement relief without any knowledge about the magnetization intensity and direction. The estimated basement relief reveals a smooth basement framework with basement highs in the central part of the area. In a regional-scale perspective, the deeper and constant estimated basement relief at the northernmost limit of the area may suggest changing in crustal domains from a hyperextended continental crust to homogeneous oceanic crust.

Constraining magnetic amplitude inversion with magnetotelluric data to image volcanic units: A case study

We present a case study on imaging volcanic units in gas exploration by constraining magnetic amplitude inversions using magnetotelluric (MT) sounding data at sparse locations. Magnetic data can be effective in mapping volcanic units because they have remanent magnetization and significant susceptibility contrast with surrounding rocks. Although magnetic data can identify the lateral distribution of volcanic units, they often have difficulties in defining the depth extent. For this reason, additional structural constraints from other geophysical methods can often help improve the vertical resolution. Among the independent geophysical methods, MT data can provide the needed structural information at a low cost. We have investigated an approach to combine a set of sparse MT soundings with magnetic amplitude data to image the distribution of volcanics in a basin environment. We first use a blocky 1D MT inversion based on Ekblom norm to obtain the structural constraint, and then we perform a constrained 3D magnetic amplitude inversion to recover the distribution of effective susceptibility by incorporating the structural information from MT soundings. We determine that even a small number of MT stations (e.g., 20) in a [Formula: see text] area is sufficient to drastically improve the magnetic amplitude inversion. Our results indicate that magnetic amplitude inversion with structural constraint from MT soundings form a practical and cost-effective means to map the lateral and vertical distribution of volcanics.

Quantifying the error level in computed magnetic amplitude data for 3D magnetization inversion

Three-dimensional inversion plays an important role in the quantitative interpretation of magnetic data in exploration problems, and magnetic amplitude data can be an effective tool in cases in which remanently magnetized materials are present. Because amplitude data are typically calculated from total-field anomaly data, the error levels must be characterized for inversions. Lack of knowledge of the error in amplitude data hinders the ability to properly estimate the data misfit associated with an inverse model and, therefore, the selection of the appropriate regularization parameter for a final model. To overcome these challenges, we have investigated the propagation of errors from total-field anomaly to amplitude data. Using parametric bootstrapping, we find that the standard deviation of the noise in amplitude data is approximately equal to that of the noise in total-field anomaly data when the amplitude data are derived from the conversion of total-field data to three orthogonal components. We then illustrate how the equivalent source method can be used to estimate the error in total-field anomaly data when needed. The obtained noise estimate can be applied to amplitude inversion to recover an optimal inverse model by applying the discrepancy principle. We test this method on synthetic and field data and determine its effectiveness.