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A joint inversion algorithm to process geoelectric and sutface wave seismic data. Part I: basic ideas 1
Author(s) -
Hering A.,
Misiek R.,
Gyulai A.,
Ormos T.,
Dobroka M.,
Dresen L.
Publication year - 1995
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/j.1365-2478.1995.tb00128.x
Subject(s) - geology , inversion (geology) , depth sounding , surface wave , rayleigh wave , seismology , geophysics , synthetic data , regional geology , joint (building) , geodesy , algorithm , optics , computer science , architectural engineering , oceanography , physics , engineering , metamorphic petrology , tectonics
For the exploration of near‐surface structures, seismic and geoelectric methods are often applied. Usually, these two types of method give, independently of each other, a sufficiently exact model of the geological structure. However, sometimes the inversion of the seismic or geoelectric data fails. These failures can be avoided by combining various methods in one joint inversion which feads to much better parameter estimations of the model than the independent inversions. A suitable seismic method for exploring near‐surface structures is the use of dispersive surface waves: the dispersive characteristics of Rayleigh and Love surface waves depend strongly on the structural and petrophysical (seismic velocities) features of the near‐surface Underground. Geoelectric exploration of the structure Underground may be carried out with the well‐known methods of DC resistivity sounding, such as the Schlumberger, the radial‐dipole and the two‐electrode arrays. The joint inversion algorithm is tested by means of synthetic data. It is demonstrated that the geoelectric joint inversion of Schlumberger, radial‐dipole and two‐electrode sounding data yields more reliable results than the single inversion of a single set of these data. The same holds for the seismic joint inversion of Love and Rayleigh group slowness data. The best inversion result is achieved by performing a joint inversion of both geoelectric and surface‐wave data. The effect of noise on the accuracy of the solution for both Gaussian and non‐Gaussian (sparsely distributed large) errors is analysed. After a comparison between least‐square (LSQ) and least absolute deviation (LAD) inversion results, the LAD joint inversion is found to be an accurate and robust method.