Acta Geodaetica et Geophysica最新文献

2D electrical resistivity tomography (ERT) data inversion and ground penetrating radar (GPR) techniques were used to investigate a cave in New Netim area, southeastern Nigeria. The aim of the research was to apply the non-destructive geophysical techniques in delineating a cave in New Netim. The Wenner electrode configuration was used during the electrical resistivity tomography data acquisition while the common-offset mode was used in the ground penetrating radar scanning. The ERT inversion and GPR results quantitatively show strong contrast in electrical resistivity and dielectric permittivity between the host rock (i.e., limestone) and the void. The differences in the electrical properties and dielectric permittivities of the top loamy soils, weathered limestone and the host rock (i.e., Mfamosing limestone) observed, in contrast to the response from the void were explored. The two combined geophysical techniques with their inversion results gave a reliable near surface geologic model, where both methods show relatively good correlation with regards to delineating the depth to top of the cave (~2.7 m). The joint interpretation of the ERT and GPR was effective in reducing the uncertainty associated with geophysical methods used in locating cavities on a standalone basis. The low cost, fast, environmental friendly and high resolving abilities though at shallow depths of the ERT and GPR techniques have continued to favour their usage in resolving near surface environmental and engineering problems. Generally, caves provide important geotourism attraction sites, hence certain recommendations were put forward that can guide further studies, like the application of Steiner’s geostatistical inversion techniques and other post-exploration development strategies.

During the conventional petrophysical interpretation fixed (predefined) zone parameters are applied for every interpretation zone (depth intervals). Their inclusion in the inversion process requires the extension of a likelihood function for the whole zone. This allows to define the extremum problem for fitting the parameter set to the full interval petrophysical model of the layers crossed by the well, both for the parameters associated with the depth points (e.g. porosity, saturations, rock matrix composition etc.) and for the zone parameters (e.g. formation water resistivity, cementation factor etc.). In this picture the parameters form a complex coupled and correlated system. Even the local parameters associated with different depths are coupled through the zone parameter change. In this paper, the statistical properties of the coupled parameter system are studied which fitted by the Interval Maximum Likelihood (ILM) method. The estimated values of the parameters are coupled through the likelihood function and this determines the correlation between them.

In recent years water-related issues are increasing globally, some researchers even argue that the global hydrological cycle is accelerating, while the number of meteorological extremities is growing. With the help of large number of available measured data, these changes can be examined with advanced mathematical methods. In the outlined research we were able to collect long precipitation datasets from two different climatical regions, one sample area being Ecuador, the other one being Kenya. Using the methodology of spectral analysis based on the discrete Fourier-transformation, several deterministic components were calculated locally in the otherwise stochastic time series, while by the comparison of the results, also with previous calculations from Hungary, several global precipitation cycles were defined in the time interval between 1980 and 2019. The results of these calculations, the described local, regional, and global precipitation cycles can be a helpful tool for groundwater management, as precipitation is the major resource of groundwater recharge, as well as with the help of these deterministic cycles, precipitation forecasts can be delivered for the areas.

QDaedalus is an automated, computer-controlled astro-geodetic measurement system. Astronomical deflections of the vertical measured by the QDaedalus system are significantly influenced by atmospheric refraction. Therefore, the measuring system was further improved by recording the environmental parameters influencing the refraction (air pressure, temperature, humidity) with accurate and high time resolution. In addition to meteorological parameters, refraction also depends on the spectrum of the stars. Both the continuously measured meteorological parameters and the color of the stars were taken into account in the calculation of the refraction. To control the method, we used the deflection of the vertical values of the Pistahegy point in the southern part of Budapest which were determined over 7 years during 260 night measurements. The corrected measurements fit within 0.01" with the average value of previous Pistahegy measurements. The standard deviation of the differences due to the corrections, however, may reach 0.015" for the DOV components.

A new histogram-based robust filter developed for noise reduction of digital elevation model data is presented. When large percentage of data points in data matrices are contaminated with outlier noise, the noise reduction process can give better results than traditional median filtering, if elements with a potentially higher chance of being noise are eliminated by weighting from the input dataset before the median value is calculated. However, on the same matrices, there are likely to be subsets of data where unfiltered input is more reasonable for the calculation. The new method implementing weighting between these two cases is presented below, with its initial tuning and a comparison with both standard median filtering and the Most Frequent Value (MFV) method, as the latter being much more efficient than the usual methods. Following the description of the procedures, their effectiveness is compared for noise reduction in digital elevation model data systems, at various noise levels. The comparison is done mainly by three measures, with most of the focus on the ({L}_{1}) norm data distance results. Finally, a modified version of the method—which includes Steiner’s MFV filter as a core part—is also introduced, with similar examination. The method to be presented has been shown to be superior to conventional median filtering for most noise rates, and in many cases also to Steiner' MFV, for handling non-zero mean noises. The modified version of the method—with the help of Steiner's MFV—has also achieved this in handling zero mean noise, in the field of application described in the paper.

In the tomographic reconstruction of seismic travel time data, care must be taken to keep the propagation of data errors to the model space under control. The non-Gaussian noise distribution—especially the outliers in the data sets- can cause appreciable distortions in the tomographic imaging. To reduce the noise sensitivity well-developed tomography algorithms can be used. On the other hand, the quality of the tomogram can further be improved by using image processing tools. In the paper, a newly developed robust filter is presented, in which the Most Frequent Value (MFV) method developed by Steiner is applied. To analyze the noise reduction capability of the new filter (called Steiner-filter) and to compare it to smoothing filters based on arithmetic- and binomial mean, as well as median, medium-sized tomographic images are used. The MFV-based filter is successfully tested also in edge detection procedures.

The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L₂ norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

A new inversion based Fourier transformation technique named as Legendre-Polynomials Least-Squares Fourier Transformation (L-LSQ-FT) and Legendre-Polynomials Iteratively Reweighted Least-Squares Fourier Transformation (L-IRLS-FT) are presented. The introduced L-LSQ-FT algorithm establishes an overdetermined inverse problem from the Fourier transform. The spectrum was approximated by a series expansion limited to a finite number of terms, and the solution of inverse problem, which gives the values of series expansion coefficients, was obtained by the LSQ method. Practically, results from the least square method are responsive to data outliers, thus scattered large errors and the estimated model values may be far from reality. A definitely better option is attained by introducing Steiner’s Most Frequent Value method. By combining the IRLS algorithm with Cauchy-Steiner weights, the Fourier transformation process was robustified to give the L-IRLS-FT method. In both cases Legendre polynomials were applied as basis functions. Thus the approximation of the continuous Fourier spectra is given by a finite series of Legendre polynomials and their coefficients. The series expansion coefficients were obtained as a solution to an overdetermined non-linear inverse problem. The traditional DFT and the L-IRLS-FT were tested numerically using synthetic datasets as well as field magnetic data. The resulting images clearly show the reduced sensitivity of the newly developed L-IRLS-FT methods to outliers and scattered noise compared to the traditional DFT. Conclusively, the newly developed L-IRLS-FT can be considered to be a better alternative to the traditional DFT.

In this paper, a new inversion method is proposed to process laboratory-measured induced polarization (IP) data. In the new procedure, the concept of the series expansion-based inversion is combined with a more general definition of the objective function. The time constant spectrum of the IP effect is assumed a line spectrum approximated by a series of Dirac’s delta function resulting in a square-integrable forward problem formula. This gives the applicability of the generalized objective function. The expansion coefficients as unknowns represent the model parameters of the inversion procedure. We use the new inversion procedure on an apparent polarizability dataset measured on a rock sample originated from the Recsk ore complex, northeast Hungary. The inversion results was compared to those of three additional laboratory datasets, which were measured on samples rich in ore minerals collected from the same area. The results are compared to those given by the traditional series expansion-based least squares method. It is shown that the newly proposed method gives more accurate and stable parameter estimation.

In this paper, a set of machine learning (ML) tools is applied to estimate the water saturation of shallow unconsolidated sediments at the Bátaapáti site in Hungary. Water saturation is directly calculated from the first factor extracted from a set of direct push logs by factor analysis. The dataset observed by engineering geophysical sounding tools as special variants of direct-push probes contains data from a total of 12 shallow penetration holes. Both one- and two-dimensional applications of the suggested method are presented. To improve the performance of factor analysis, particle swarm optimization (PSO) is applied to give a globally optimized estimate for the factor scores. Furthermore, by a hyperparameter estimation approach, some control parameters of the utilized PSO algorithm are automatically estimated by simulated annealing (SA) to ensure the convergence of the procedure. The result of the suggested ML-based log analysis method is compared and verified by an independent inversion estimate. The study shows that the PSO-based factor analysis aided by hyperparameter estimation provides reliable in situ estimates of water saturation, which may improve the solution of environmental end engineering problems in shallow unconsolidated heterogeneous formations.