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.

The quality analysis of well-logging inversion results has always been an important part of formation evaluation. The precise calculation of hydrocarbon reserves requires the most accurate possible estimation of porosity, water saturation, and shale and rock-matrix volumes. The local inversion method conventionally used to predict the above model parameters depth by depth represents a marginally overdetermined inverse problem, which is rather sensitive to the uncertainty of observed data and limited in estimation accuracy. To reduce the harmful effect of data noise on the estimated model, we have suggested the interval inversion method, in which an increase of the overdetermination ratio allows a more accurate solution of the well-logging inverse problem. The interval inversion method inverts the data set of a longer depth interval to predict the vertical distributions of petrophysical parameters in a joint inversion procedure. In formulating the forward problem, we have extended the validity of probe response functions to a greater depth interval assuming the petrophysical parameters are depth dependent, and then we expanded the model parameters into a series using the Legendre polynomials as basis functions for modeling inhomogeneous formations. We solved the inverse problem for a much smaller number of expansion coefficients than data to derive the petrophysical parameters in a stable overdetermined inversion procedure. The added advantage of the interval inversion method is that the layer thicknesses and suitably chosen zone parameters can be estimated automatically by the inversion procedure to refine the results of inverse and forward modeling. We have defined depth-dependent model covariance and correlation matrices to compare the quality of the local and interval inversion results. A detailed study using well logs measured from a Hungarian gas-bearing unconsolidated formation revealed that the greatly overdetermined interval inversion procedure can be effectively used in reducing the estimation errors in shaly sand formations, which may refine significantly the results of reserve calculation.

Matra Gravitational and Geophysical Laboratory (MGGL) has been established near Gyöngyösoroszi, Hungary in 2015, in the cavern system of an unused ore mine. The Laboratory is located at 88 m below the surface, with the aim to measure and analyse the advantages of the underground installation of third generation gravitational wave detectors. Specialized instruments have been installed to measure seismic, infrasound, electromagnetic noise, and the variation of the cosmic muon flux. In the preliminary (RUN-0) test period, March-August 2016, data collection has been accomplished. In this paper we describe the research potential of the MGGL, list the installed equipments and summarize the experimental results of RUN-0. Here we report RUN-0 data, that prepares systematic and synchronized data collection of the next run period.

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.