scholarly journals Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code

2021 ◽  
Author(s):  
Jeremie Giraud ◽  
Vitaliy Ogarko ◽  
Roland Martin ◽  
Mark Jessell ◽  
Mark Lindsay

Abstract. The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review of the different techniques for the utilisation of structural, petrophysical and geological information in single physics and joint inversion as implemented in the Tomofast-x open-source inversion platform. We detail the range of constraints that can be applied to the inversion of potential field data. The inversion examples we show illustrate a selection of scenarios using a realistic synthetic dataset inspired by real-world geological measurements and petrophysical data from the Hamersley region (Western Australia). Using Tomofast-x’s flexibility, we investigate inversions combining the utilisation of petrophysical, structural and/or geological constraints while illustrating the utilisation of the L-curve principle to determine regularisation weights. Our results suggest that the utilisation of geological information to derive disjoint interval bound constraints is the most effective method to recover the true model. It is followed by model smoothness and smallness conditioned by geological uncertainty, and cross-gradient minimisation.

2021 ◽  
Vol 14 (11) ◽  
pp. 6681-6709
Author(s):  
Jérémie Giraud ◽  
Vitaliy Ogarko ◽  
Roland Martin ◽  
Mark Jessell ◽  
Mark Lindsay

Abstract. The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review of the different techniques for the utilisation of structural, petrophysical, and geological information in single physics and joint inversion as implemented in the Tomofast-x open-source inversion platform. We detail the range of constraints that can be applied to the inversion of potential field data. The inversion examples we show illustrate a selection of scenarios using a realistic synthetic data set inspired by real-world geological measurements and petrophysical data from the Hamersley region (Western Australia). Using Tomofast-x's flexibility, we investigate inversions combining the utilisation of petrophysical, structural, and/or geological constraints while illustrating the utilisation of the L-curve principle to determine regularisation weights. Our results suggest that the utilisation of geological information to derive disjoint interval bound constraints is the most effective method to recover the true model. It is followed by model smoothness and smallness conditioned by geological uncertainty and cross-gradient minimisation.


2018 ◽  
Author(s):  
F.R. Castro ◽  
S.P. Oliveira ◽  
J. de Souza ◽  
F.J.F. Ferreira

2014 ◽  
Vol 33 (4) ◽  
pp. 448-450 ◽  
Author(s):  
Leonardo Uieda ◽  
Vanderlei C. Oliveira ◽  
Valéria C. F. Barbosa

In this tutorial, we will talk about a widely used method of interpretation for potential-field data called Euler de-convolution. Our goal is to demonstrate its usefulness and, most important, to call attention to some pitfalls encountered in interpretation of the results. The code and synthetic data required to reproduce our results and figures can be found in the accompanying IPython notebooks ( ipython.org/notebook ) at dx.doi.org/10.6084/m9.figshare.923450 or github.com/pinga-lab/paper-tle-euler-tutorial . The note-books also expand the analysis presented here. We encourage you to download the data and try them on your software of choice. For this tutorial, we will use the implementation in the open-source Python package Fatiando a Terra ( fatiando.org ).


2011 ◽  
Author(s):  
Luis A. Gallardo ◽  
Sergio L. Fontes ◽  
Vinicius R. Pinto ◽  
Max Meju ◽  
Patricia de Lugao

2011 ◽  
Vol 51 (1) ◽  
pp. 295 ◽  
Author(s):  
Russell Korsch ◽  
Heike Struckmeyer ◽  
Alison Kirkby ◽  
Laurie Hutton ◽  
Lidena Carr ◽  
...  

Deep seismic reflection surveys in north Queensland that were collected in 2006 and 2007 discovered a previously unknown sedimentary basin, now named the Millungera Basin, which is completely covered by a thin succession of sediments of the Jurassic–Cretaceous, Eromanga-Carpentaria Basin. Interpretation of regional aeromagnetic data suggests that the basin could have areal dimensions of up to 280 km by 95 km. Apart from regional geophysical data, virtually no confirmed geological information exists on the basin. To complement the seismic data, new magnetotelluric data have been acquired on several lines across the basin. An angular unconformity between the Eromanga and Millungera basins indicates that the upper part of the Millungera Basin was eroded prior to deposition of the Eromanga-Carpentaria Basin. Both the western and eastern margins of the Millungera Basin are truncated by thrust faults, with well-developed hangingwall anticlines occurring above the thrusts at the eastern margin. The basin thickens slightly to the east, to a maximum preserved subsurface depth of ˜3,370 m. Using sequence stratigraphic principles, three discrete sequences have been mapped. The geometry of the stratigraphic sequences, the post-depositional thrust margins, and the erosional unconformity at the top of the succession all indicate that the original succession across much of the basin was thicker–by up to at least 1,500 m–than preserved today. The age of the Millungera Basin is unknown, but petroleum systems modelling has been carried out using two scenarios, that is, that the sediment fill is equivalent in age to (1) the Neoproterozoic-Devonian Georgina Basin, or (2) the Permian–Triassic Lovelle Depression of the Galilee Basin. Using the Georgina Basin analogue, potential Cambrian source rocks are likely to be mature over most of the Millungera Basin, with significant generation and expulsion of hydrocarbons occurring in two phases, in response to Ordovician and Cretaceous sediment loading. For the Galilee Basin analogue, potential Permian source rocks are likely to be oil mature in the central Millungera Basin, but immature on the basin margins. Significant oil generation and expulsion probably occurred during the Triassic, in response to late Permian to Early Triassic sediment loading. Based on the seismic and potential field data, several granites are interpreted to occur immediately below the Millungera Basin, raising the possibility of hot rock geothermal plays. Depending on its composition, the Millungera Basin could provide a thermal blanket to trap any heat which is generated. 3D inversion of potential field data suggests that the inferred granites range from being magnetic to nonmagnetic, and felsic (less dense) to more mafic. They may be part of the Williams Supersuite, which is enriched in uranium, thorium and potassium, and exposed just to the west, in the Mount Isa Province. 3D gravity modelling suggests that the inferred granites have a possible maximum thickness of up to 5.5 km. Therefore, if granites with the composition of the Williams Supersuite occur beneath the Millungera Basin, in the volumes indicated by gravity inversions, then, based on the forward temperature modelling, there is a good probability that the basin is prospective for geothermal energy.


2020 ◽  
Vol 8 (4) ◽  
pp. SS97-SS111
Author(s):  
Arvin Boutik Karpiah ◽  
Maxwell Azuka Meju ◽  
Roger Vernon Miller ◽  
Xavier Legrand ◽  
Prabal Shankar Das ◽  
...  

Accurate mapping of crustal thickness variations and the boundary relationships between sedimentary cover rocks and the crystalline basement is very important for heat-flow prediction and petroleum system modeling of a basin. Using legacy industry 3D data sets, we investigated the potential of 3D joint inversion of marine controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data incorporating resistivity anisotropy to map these parameters across subbasins in the Dangerous Grounds in the southwestern rifted margin of the South China Sea, where limited previous seismic and potential field basement interpretations are available for comparison. We have reconstructed 3D horizontal and vertical resistivity models from the seabed down to [Formula: see text] depth for a [Formula: see text] area. The resistivity-versus-depth profile extracted from our 3D joint inversion models satisfactorily matched the resistivity and lithologic well logs at a wildcat exploration well location chosen for model validation. We found that the maximum resistivity gradients in the computed first derivative of the 3D resistivity volumes predict a depth to basement that matches the acoustic basement. The models predict the presence of 2 to approximately 5 km thick electrically conductive ([Formula: see text]) sedimentary cover atop an electrically resistive ([Formula: see text]) crystalline crust that is underlain by an electrically conductive ([Formula: see text]) upper mantle at depths that vary laterally from approximately 25 to 30 km below sea level in our study area. Our resistivity variation with depth is found to be remarkably consistent with the density distribution at Moho depth from recent independent 3D gravity/gradiometry inversion studies in this region. We suggest that 3D joint inversion of CSEM-MT, seismic, and potential field data is the way forward for understanding the deep structure of such rifted margins.


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