Constraining 3D geometric gravity inversion with 2D reflection seismic profile using a generalized level-set approach: application to Eastern Yilgarn craton

Abstract. One of the main tasks in 3D geological modelling is the boundary parametrization of the subsurface from geological observations and geophysical inversions. Several approaches have been developed for geometric inversion and joint inversion of geophysical datasets. However, the robust, quantitative integration of models and datasets with different spatial coverage, resolution, and levels of sparsity remains challenging. One promising approach for recovering the boundary of the geological units is the utilization of a level-set inversion method with potential field data. We focus on constraining 3D geometric gravity inversion with sparse lower-uncertainty information from a 2D seismic section. We use a level-set approach to recover the geometry of geological bodies using two synthetic examples and data from the geologically complex Yamarna terrane (Yilgarn craton, Western Australia). In this study, a 2D seismic section has been used for constraining the location of rock unit boundaries being solved during the 3D gravity geometric inversion. The proposed work is the first we know of that automates the process of adding spatially distributed constraints to the 3D level-set inversion. In many hard-rock geoscientific investigations, seismic data is sparse and our results indicate that unit boundaries from gravity inversion can be much better constrained with seismic information even though they are sparsely distributed within the model. Thus, we conclude that it has the potential to bring the state of the art a step further towards building a 3D geological model incorporating several sources of information in similar regions of investigation.

[1]  Michael A. Saunders,et al.  LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares , 1982, TOMS.

[2]  L. Vincent Grayscale area openings and closings, their efficient implementation and applications , 1993 .

[3]  D. Oldenburg,et al.  3-D inversion of gravity data , 1998 .

[4]  Henk J. A. M. Heijmans,et al.  Mathematical Morphology: A Modern Approach in Image Processing Based on Algebra and Geometry , 1995, SIAM Rev..

[5]  Fred Aminsadeh,et al.  Chapter 28: 3-D Salt and Overthrust Seismic Models , 1996 .

[6]  D. Calvetti,et al.  Tikhonov regularization and the L-curve for large discrete ill-posed problems , 2000 .

[7]  M. Chouteau,et al.  Constraints in 3D gravity inversion , 2001 .

[8]  Pierre Soille,et al.  Advances in mathematical morphology applied to geoscience and remote sensing , 2002, IEEE Trans. Geosci. Remote. Sens..

[9]  R. Anand,et al.  Regolith geology of the Yilgarn Craton, Western Australia: Implications for exploration , 2002 .

[10]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[11]  T. Chan,et al.  Multiple level set methods with applications for identifying piecewise constant functions , 2004 .

[12]  B. Goleby,et al.  Deep seismic reflection profiling in the Archaean northeastern Yilgarn Craton, Western Australia: implications for crustal architecture and mineral potential , 2004 .

[13]  Ron Kimmel,et al.  Fast Marching Methods , 2004 .

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[15]  Mariusz Jankowski,et al.  Erosion, dilation and related operators , 2006 .

[16]  C. Farquharson,et al.  Geologically constrained gravity inversion for the Voisey's Bay ovoid deposit , 2008 .

[17]  Frank Shih,et al.  Introduction to Mathematical Morphology , 2009 .

[18]  P. Kitanidis,et al.  Bayesian inversion for facies detection: An extensible level set framework , 2009 .

[19]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[20]  C. Farquharson,et al.  Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration , 2010 .

[21]  R. Blewett,et al.  Scale-integrated architecture of a world-class gold mineral system: The Archaean eastern Yilgarn Craton, Western Australia , 2010 .

[22]  M. Jessell,et al.  Towards an integrated inversion of geoscientific data: What price of geology? , 2010 .

[23]  Implicit structural inversion of gravity data using linear programming, a validation study* , 2010 .

[24]  Richard J. Blakely,et al.  Unique geologic insights from "non-unique" gravity and magnetic interpretation , 2011 .

[25]  L. Gallardo,et al.  New insights into Archean granite-greenstone architecture through joint gravity and magnetic inversion , 2012 .

[26]  M. Bernard,et al.  Joint inversion of P-wave velocity and density, application to La Soufrière of Guadeloupe hydrothermal system , 2012 .

[27]  M. Pawley,et al.  Adding pieces to the puzzle: episodic crustal growth and a new terrane in the northeast Yilgarn Craton, Western Australia , 2012 .

[28]  M. Perrin Shared Earth Modeling: Knowledge Driven Solutions for Building and Managing Subsurface 3D Geological Models , 2013 .

[29]  M. Jessell,et al.  Making the link between geological and geophysical uncertainty: geodiversity in the Ashanti Greenstone Belt , 2013 .

[30]  Roland Martin,et al.  Next Generation Three-Dimensional Geologic Modeling and Inversion , 2014 .

[31]  M. Zhdanov,et al.  Application of Cauchy-type integrals in developing effective methods for depth-to-basement inversion of gravity and gravity gradiometry data , 2015 .

[32]  C. Farquharson,et al.  3D Potential Field Inversion for Wireframe Surface Geometry , 2015 .

[33]  D. Sanderson,et al.  The use of topology in fracture network characterization , 2015 .

[34]  Yaoguo Li,et al.  Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering , 2015 .

[35]  Anya M. Reading,et al.  Spatial-Contextual Supervised Classifiers Explored: A Challenging Example of Lithostratigraphy Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  J. Qian,et al.  A level-set method for imaging salt structures using gravity data , 2016 .

[37]  Samuel T. Thiele,et al.  The topology of geology 1: Topological analysis , 2016 .

[38]  Hongzhu Cai,et al.  Joint Inversion of Gravity and Magnetotelluric Data for the Depth-to-Basement Estimation , 2017, IEEE Geoscience and Remote Sensing Letters.

[39]  M. Jessell,et al.  Uncertainty estimation for a geological model of the Sandstone greenstone belt, Western Australia – insights from integrated geological and geophysical inversion in a Bayesian inference framework , 2017, Special Publications.

[40]  J. Qian,et al.  A multiple level-set method for 3D inversion of magnetic data , 2017 .

[41]  Inversion of geophysical potential field data using the finite element method , 2017 .

[42]  Roland Martin,et al.  Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion , 2017 .

[43]  Giulia Boato,et al.  Detecting Morphological Filtering of Binary Images , 2017, IEEE Transactions on Information Forensics and Security.

[44]  Simonetta Boria,et al.  Kriging-guided level set method for crash topology optimization , 2017 .

[45]  Guillaume Caumon,et al.  RINGMesh: A programming library for developing mesh-based geomodeling applications , 2017, Comput. Geosci..

[46]  Mark Lindsay,et al.  Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization , 2018 .

[47]  Roland Martin,et al.  Impact of uncertain geology in constrained geophysical inversion , 2018, ASEG Extended Abstracts.

[48]  M. Jessell,et al.  Drillhole uncertainty propagation for three-dimensional geological modeling using Monte Carlo , 2018, Tectonophysics.

[49]  C. Farquharson,et al.  Multiple level-set joint inversion of traveltime and gravity data with application to ore delineation: A synthetic study , 2018 .

[50]  Roland Martin,et al.  Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization , 2019, Solid Earth.

[51]  Roland Martin,et al.  Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis , 2019, Geophysical Journal International.

[52]  Florian Wellmann,et al.  GemPy 1.0: open-source stochastic geological modeling and inversion , 2019, Geoscientific Model Development.

[53]  M. Dentith,et al.  Petrophysics and mineral exploration: a workflow for data analysis and a new interpretation framework , 2019, Geophysical Prospecting.

[54]  J. Giraud Development and application of multidisciplinary workflows for geologically and/or petrophysically constrained geophysical inversion , 2019 .

[55]  Three-dimensional gravity anomaly data inversion in the Pyrenees using compressional seismic velocity model as structural similarity constraints , 2020 .

[56]  M. Jessell,et al.  Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging , 2020, Solid Earth.

[57]  M. Lindsay,et al.  Constrained 3D geometric gravity inversion , 2021 .

[58]  Roland Martin,et al.  Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code , 2021 .

[59]  Roland Martin,et al.  Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data , 2021 .

[60]  Jérémie Giraud,et al.  Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework , 2021, GEOPHYSICS.