Multiorder Sequential Joint Inversion of Gravity Data With Inhomogeneous Depth Weighting: From Near Surface to Basin Modeling Applications

Abstract

We have established a workflow for a multi-order sequential joint inversion (MOSJI) of gravity and gravity gradients, that aims at modelling of vertically-stacked sources in various geological scenarios. We consider the joint inversion of the gravity data and one of the h th order derivative of the gravity data. The first step involves separate inversions, which are fundamental to fully exploit the different wavelength-content of the two quantities to invert. The joint inversion is warranted by using the scheme of a sequential joint inversion with a cross-gradient constraint. The algorithm is able to exploit different types of a priori information, such as compactness and inhomogeneous model-weighting function. Firstly, we test this approach on a realistic synthetic model from the SEg Advanced Modelling (SEAM) Phase I model, involving salt and mother salt structures. Then, we consider a synthetic model containing either shallower or deeper karst cavities. These tests produced a better modelling of both shallower and deeper sources, when compared to the separate unconstrained inversions. Thanks to these good results, we apply our method to a real case for cavity detection in Southern Spain. The method shows an accurate modelling of the expected sources. In all the aforementioned tests, we obtain a strong decrease of the cross-gradient values and a meaningful linearization in the scatter plots of physical parameters, both indicating the good performance of the joint inversion.

Magnetic and GPR Data Modelling via Multiscale Methods in San Pietro in Crapolla Abbey, Massa Lubrense (Naples)

“…For the magnetic field T, the DEXP transformation is given by where Ω r, z i is the DEXP-scaled field at each altitude z i ; r is the position vector at points in the harmonic region; T r, z i is the upward continued field at power-law of altitudes; q is the order of differentiation of is the structural index, which is a parameter characterizing the type of source (Bianco et al 2024;Fedi 2016), namely, ranging from 0 to 3, for simple sources such as contact, sheet, line and sphere, respectively. When dealing with real sources, this parameter is not constant versus the altitude and assumes fractional values (Bianco et al 2023) and intermediate versus those of the aforementioned sources (Fedi, Florio, and Paoletti 2012).…”

Extremely compact sources (ECS): a new potential field filtering method

“…We will here use a model-weighting function W z , based on the depth weighting function. Such function, originally proposed by [8][9][10] , has its elements at a given depth z in the 3D model defined as: So, the weighting function (Eq. 4) becomes:…”