Massively parallel 3D inversion of gravity and gravity gradiometry data

Reliance on desktop computers limits the scale of 3D inversion of gravity and gravity gradiometry surveys, making it impractical to achieve an appropriate level of resolution and detail for geological interpretation. To begin with, airborne surveys are characterised by very large data volumes. They typically contain hundreds to thousands of line kilometres of data with measurement locations every few metres. Often, surveys cover thousands of square kilometres in area with tens of thousands of line kilometres of data. Regional surveys may be even larger and denser as the result of merging multiple and/or historic surveys. Secondly, 3D modelling of large-scale surveys exceeds the capacity of desktop computing resources. And finally, gravity data are finite and noisy, and their inversion is ill posed. Regularisation must be introduced in order to recover the most geologically plausible solutions from the infinite number of mathematically equivalent solutions. Various strategies for 3D inversion have been previously proposed but few lend themselves to truly large-scale 3D inversion. In this paper, we describe how gravity and gravity gradiometry surveys can be inverted to 3D earth models of unprecedented scale (i.e., hundreds of millions of cells) within hours using cluster computers.

[1]  Feng Liu,et al.  Nanomechanical architecture of semiconductor nanomembranes. , 2011, Nanoscale.

[2]  Xiong Li,et al.  Three-Dimensional Gravity Modeling In All Space , 1998 .

[3]  M. Zhdanov,et al.  3‐D magnetic inversion with data compression and image focusing , 2002 .

[4]  E. Crosman.  Idealized large-Eddy simulation sensitivity studies of sea and lake breezes , 2011 .

[5]  D. Oldenburg,et al.  Fast inversion of large-scale magnetic data using wavelet transforms and a logarithmic barrier method , 2003 .

[6]  Michael S. Zhdanov,et al.  New advances in regularized inversion of gravity and electromagnetic data , 2009 .

[7]  J. Facelli.  Chemical shift tensors: theory and application to molecular structural problems. , 2011, Progress in nuclear magnetic resonance spectroscopy.

[8]  Michael S. Zhdanov,et al.  3D inversion of airborne electromagnetic data using a moving footprint , 2010 .

[9]  M. Dransfield.  Conforming Falcon ‡ gravity and the global gravity anomaly , 2010 .

[10]  L. Pedersen,et al.  Eigenvector analysis of gravity gradient tensor to locate geologic bodies , 2010 .

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

[12]  W. J. Steenburgh,et al.  Snow-to-Liquid Ratio Variability and Prediction at a High-Elevation Site in Utah's Wasatch Mountains , 2009 .

[13]  Michael S. Zhdanov,et al.  Focusing geophysical inversion images , 1999 .

[14]  Formation of hydrogenated graphene nanoripples by strain engineering and directed surface self-assembly , 2010, 1009.5416.

[15]  H. Henkel,et al.  Integrated geophysical modelling of a giant, complex impact structure: anatomy of the Vredefort Structure, South Africa , 1998 .

[16]  Xiong Li,et al.  Three‐dimensional gravity modeling in all space , 1997 .

[17]  T. Cheatham,et al.  A coarse-grained model of DNA with explicit solvation by water and ions. , 2011, The journal of physical chemistry. B.

[18]  Mark Pilkington.  3-D Magnetic Imaging Using Conjugate Gradients , 1996 .

[19]  M. Okabe.  Analytical expressions for gravity anomalies due to homogeneous polyhedral bodies and translations into magnetic anomalies , 1979 .

[20]  R. Carlson,et al.  Ultramafic rocks at the center of the Vredefort structure: Further evidence for the crust on edge model , 1999 .

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

[22]  Mark Pilkington,et al.  3-D magnetic imaging using conjugate gradients , 1997 .