1887
Volume 32, Issue 3-4
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

High-resolution aeromagnetic surveys were acquired for the Albuquerque basin in the central Rio Grande rift, a basin filled with poorly consolidated sediments. The surveys proved successful in efficiently and economically mapping previously unknown hydrogeologic features of the shallow subsurface. This success suggests that aeromagnetic methods may be useful in hydrogeologic studies of other sediment-filled basins.

The aeromagnetic surveys were used primarily to delineate buried igneous rocks and to locate faults within the basin fill, both important for understanding the subsurface hydrogeology. Buried igneous rocks were recognized from their high-frequency, high-amplitude magnetic responses and characteristic map patterns. The horizontal-gradient and local wavenumber methods were used to obtain estimates of their source depths.

The aeromagnetic surveys were also successfully used to locate faults within the basin fill. Magnetic signatures associated with faults are produced by the juxtaposition of sediments having differing magnetic properties rather than the products of secondary processes. Expression of faults is abundant throughout the basin, revealing patterns that cannot be mapped at the surface due to widespread cover.

A fault signature recognized in the high-resolution data that has multiple inflection points is best explained by a fault with a thin magnetic layer on the upthrown block and thick magnetic layer on the downthrown block, called the thin-thick layers model. Geologically, this signature indicates erosion of the upthrown block or a growth-faulting scenario: fault-controlled sedimentation for faults that offset sediments, and successive accumulation of basalt on the downthrown block for faults that offset volcanic rocks.

Loading

Article metrics loading...

/content/journals/10.1071/EG01204
2001-09-01
2026-01-16
Loading full text...

Full text loading...

References

  1. Abdelrahman, E. M., 1990, Discussion on ”A least-squares approach to depth determination from gravity data” by O.P. Gupta: Geophysics, 55, 376-378.
  2. Arzi, A.A. 1975, Microgravity for engineering applications: Geophs. Prosp., 23 408425.
  3. Boulanger, O. and Chouteau, M., 2001, Constraints in 3D gravity inversion, Geophysical Prospecting, 49, 265-280.
  4. Brown, M. P. and Poulton, M. M., 1996, Locating buried objects for environmental site investigations using Neural Networks: JEEG, 1, 179-188.
  5. Butler, D.K., Ed. 1977, Proc. of the symposium on detection of subsurface cavities: U.S Army Engineer Waterways Experiment Station, CE, Vicksburg, MS.
  6. Butler, D.K., 1983, Cavity detection research, Report 1, Microgravimetric and magnetic surveys, Medford Cave Site, Florida: Tech. Rep.GL-83-1, U.S Army Engineer Waterways Experiment Station, CE, Vicksburg, MS.
  7. Butler, D.K., 1984, Microgravimetric and gravity gradient techniques for detection of subsurface cavities: Geophysics, 49, 1084-1096.
  8. Charniak E. and McDermott D., 1985, Introduction to Artificial Intelligence, Addison-Wesley.
  9. Fajklewicz, Z., 1986., Origin of the anomalies of gravity and its vertical gradient over cavities in brittle rock: Gophys. Prosp., 4(8), 1233-1254.
  10. Gupta, O. P., 1983, A least-squares approach to depth determination from gravity data: Geophysics, 48, 537-360.
  11. Li, X., and Chouteau, M., 1998, Three-dimensional gravity modelling in all space, Surveys in Geophysics, 19, 339-368.
  12. Li, Y., and Oldenburg, D.W., 1996, 3-D inversion of magnetic data: Geophysics, 61, 394-408.
  13. Li, Y., and Oldenburg, D.W., 1998, 3-D inversion of gravity data: Geophysics, 63, 109-119.
  14. Nettleton, L. L., 1942, Gravity and magnetic calculation: Geophysics, 7, 293-310.
  15. Owen, T. E., 1983, Detection and mapping of tunnels and caves: Developments, in Geophysical Exploration Methods, A. A. Fitch (ed.), Vol. 5, 161-258 Applied Science Publishers Ltd.
  16. Poulton, M.M., Sternberg, B.K., and Glass, C.E., 1992, Location of subsurface targets in geophysical data using neural networks, Geophysics, 57, 1534-1544.
  17. Rosenblatt. F.,1958, The perceptron: A probabilistic model for information storage and organization in the brain: Psychological Review, 65, 386-408.
  18. Rumelhart, D.E., Hinton, G.E., and Williams, R.J., 1986: Learning internal representations by error propagation, in Rumelhart, D. E., and McClelland, J. L., Eds., Parallel distributed processing: Explorations in the Microstructure of Cognition, v.1, p.318-362, Cambridge, MA: The MIT Press.
  19. Salem, A., Ravat, D., and Ushijima, K.,2001, Subsurface imaging of buried steel drums from magnetic data using Hopfield neural network, Proc. of the 5th SEGJ International Symposium, Tokyo, 369-375.
  20. Spichake, V., and Popova, I., 2000, Artificial neural network in inversion of magnetotelluric data in terms of three-dimensional earth macroparameters: Geophys. J. Intl., 142, 15-26.
  21. Telford, W. M., Geldart, L. P., Sheriff, R. E., and Key, D. A., 1976, Applied Geophysics: London, Cambridge Univ. Press.
  22. Van der Baan, M., and Jutten, C., 2000, Neural networks in geophysics: Geophysics, 65, 1032-1047.
  23. Werbos, P.J., 1994, The roots of back propagation, NY: John Wiley & Sons.
/content/journals/10.1071/EG01204
Loading
  • Article Type: Research Article
Keyword(s): aeromagnetic survey; faults; hydrogeology; igneous rocks; sedimentary basin

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error