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 D C 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 twoelectrode 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 nonGaussian (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.

Until the present time the ‘ rock‐coal‐rock’ layer sequence and offsets in coal‐seams in underground coal mines have been detected with the aid of seismic waves and geoelectric measurements. In order to determine the geometrical and petrophysical parameters of the coal‐seam situation, the data recorded using seismic and geoelectric methods have been inverted independently. In consequence, the inversion of partially inaccurate data resulted in a certain degree of ambiguity. This paper presents the first results of a joint inversion scheme to process underground vertical seismic profiling data, geolectric resistivity and resistance data. The joint inversion algorithm makes use of the damped least‐squares method and its weighted version to solve the linearized set of equations for the seismic and geolectric unknowns. In order to estimate the accuracy and reliability of the derived geometrical and petrophysical layer parameters, both a model covariance matrix and a correlation matrix are calculated. The weighted least‐squares algorithm is based on the method of most frequent values (MFV). The weight factors depend on the difference between measured data and those calculated by an iteration process. The joint inversion algorithm is tested by means of synthetic data. Compared to the damped least‐squares algorithm, the MFV inversion leads to smaller estimation errors as well as lower sensitivities due to the choice of the initial model. It is shown that, compared to an independent inversion, the correlation between the model parameters is definitely reduced, while the accuracy of the parameter estimation is appreciably increased by the joint inversion process. Thus the ambiguity is significantly reduced. Finally, the joint inversion algorithm using the MFV method is applied to underground field data. The model parameters can be derived with a sufficient degree of accuracy, even in the case of noisy data.

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Seismic and geoelectric methods are often used in the exploration of near‐surface structures. Generally, these two methods give, independently of one 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 leads to much better parameter estimations of the near‐surface underground than the independent inversions.  In the companion paper (Part I: basic ideas), it was demonstrated theoretically that a joint inversion, using dispersive Rayleigh and Love waves in combination with the well‐known methods of DC resistivity sounding, such as Schlumberger, radial dipole‐dipole and pole‐pole arrays, provides a better parameter estimation.  Two applications are shown: a five layer structure in Borsod County, Hungary, and a three‐layer structure in Thüringen, Germany. Layer thicknesses, wave velocities and resistivities are determined. Of course, the field data sets obtained from the ‘real world’ are not as complete and as good as the synthetic data sets in the theoretical Part I.  In both applications, relative model distances, in percentages, serve as quality control factors for the different inversions; the lower the relative distance, the better the inversion result.  In the Borsod field case, Love wave group slowness data and Schlumberger, radial dipole‐dipole and pole‐pole (i.e two‐electrode) data sets are processed. The independent inversion performed using the Love wave data leads to a relative model distance of 155%. An independent Schlumberger inversion results in 41%, a joint geoelectric inversion of all data sets in 15%, a joint inversion of Love wave data and all geoelectric data sets in 15% and the robust joint inversion of Love wave data and the three geoelectric data sets in 10%.  In the Thüringen field case, only Rayleigh wave group slowness data and Schlumberger data were available. The independent inversion using Rayleigh wave data results in a relative model distance of 19%. The independent inversion performed using Schlumberger data leads to 34%, the joint and robust joint inversion of Rayleigh wave and Schlumberger data gave results of 18% and 20%, respectively.

BREITZKE, M., DRESEN, L., CSOKAS, J., GYULAI, A . and ORMOS, T. 1987, Parameter Estimation and Fault Detection by Three-Component Seismic and Geoelectrical Surveys in a Coal Mine, Geophysical Prospecting 35,832-863.Three-component seismic and geoelectrical in-mine surveys were carried out in Lyukobanya colliery near Miskolc, Hungary to determine the in situ petrophysical parameter distributions and to detect inhomogeneities in the coal seam. The seismic measurements comprise an underground vertical seismic profile, using body waves, and an in-seam seismic amplitude-depth distribution and transmission survey, using channel waves. The geoelectrical measurements are based on the drift-and seam-sounding method.Interval traveltime-, amplitude-, multiple-filter-and polarization analysis methods are applied to the seismic data. They lead to a five-layer model for the strata including the coal seam. The coal seam and two underlying beds act as a seismic waveguide. The layer sequence supports the propagation of both normal and leaky mode channel waves of the Love-and Rayleigh type. A calculation of the total reflected energy for each interface using Knott's energy coefficients shows that the velocity ranges of high reflection energy and of normal and leaky mode wavegroups coincide. The excitation of wavegroups strongly depends on the seismic source. A simultaneous inversion of a geoelectrical drift-and seam-sounding survey prevents misinterpretations of the seismic data by clearly identifying the low-velocity coal seam as a high-resistivity bed. Calculations of dispersion and sounding curves improve the resolution of the slowness and resistivity in each layer.Both diminished amplitudes and distortions in the polarization of transmission seismograms and decreasing resistivities in a geoelectrical pseudosection of the coal seam are related to an inhomogeneity. 8323-COMPONENT SEISMIC AND GEOELECTRICAL SURVEYS 833 A calculation of synthetic seismograms for Love and Rayleigh channel waves with the finite-difference and the Alekseev-Mikhailenko method agrees well with the field data for the main features, i.e., particular arrivals in the wave train, wavegroups, velocities and symmetries or asymmetries.This in-mine experiment demonstrates that the simultaneous acquisition, processing and interpretation of seismic and geoelectrical data improve the lithological interpretation of petrophysical parameter distributions. Coal seam inhomogeneities can also be detected more reliably by the two independent surveys than by one alone.