Three-dimensional gravity inversion using simulated annealing: Constraintson the diapiric roots of allochthonous salt structures

Citation
S. Nagihara et Sa. Hall, Three-dimensional gravity inversion using simulated annealing: Constraintson the diapiric roots of allochthonous salt structures, GEOPHYSICS, 66(5), 2001, pp. 1438-1449
Citations number
30
Categorie Soggetti
Earth Sciences
Journal title
GEOPHYSICS
ISSN journal
00168033 → ACNP
Volume
66
Issue
5
Year of publication
2001
Pages
1438 - 1449
Database
ISI
SICI code
0016-8033(200109/10)66:5<1438:TGIUSA>2.0.ZU;2-1
Abstract
In the northern continental slope of the Gulf of Mexico, large oil and gas reservoirs are often found beneath sheetlike, allochthonous salt structures that are laterally extensive. Some of these salt structures retain their d iapiric feeders or roots beneath them. These hidden roots are difficult to image seismically. In this study, we develop a method to locate and constra in the geometry of such roots through 3-D inverse modeling of the gravity a nomalies observed over the salt structures. This inversion method utilizes a priori information such as the upper surface topography of the salt, whic h can be delineated by a limited coverage of 2-D seismic data; the sediment compaction curve in the region; and the continuity of the salt body. The i nversion computation is based on the simulated annealing (SA) global optimi zation algorithm. The SA-based gravity inversion has some advantages over t he approach based on damped least-squares inversion. It is computationally efficient, can solve underdetermined inverse problems, can more easily impl ement complex a priori information, and does not introduce smoothing effect s in the final density structure model. We test this inversion method using synthetic gravity data for a type of salt geometry that is common among th e allochthonous salt structures in the Gulf of Mexico and show that it is h ighly effective in constraining the diapiric root. We also show that carryi ng out multiple inversion runs helps reduce the uncertainty in the final de nsity model.