Hendrik Paasche

Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy c-means cluster analyses

Inversions of an individual geophysical data set can be highly nonunique, and it is generally difficult to determine petrophysical parameters from geophysical data. We show that both issues can be addressed by adopting a statistical multiparameter approach that requires the acquisition, processing, and separate inversion of two or more types of geophysical data. To combine information contained in the physical-property models that result from inverting the individual data sets and to estimate the spatial distribution of petrophysical parameters in regions where they are known at only a few locations, we demonstrate the potential of the fuzzy c -means (FCM) clustering technique. After testing this new approach on synthetic data, we apply it to limited crosshole georadar, crosshole seismic, gamma-log, and slug-test data acquired within a shallow alluvial aquifer. The derived multiparameter model effectively outlines the major sedimentary units observed in numerous boreholes and provides plausible estimates ...

Cooperative inversion of 2D geophysical data sets: A zonal approach based on fuzzy c-means cluster analysis

In many near-surface geophysical applications, it is now common practice to use multiple geophysical methods to explore subsurface structures and parameters. Such multimethod-based exploration strategies can significantly reduce uncertainties and ambiguities in geophysical data analysis and interpretation. We propose a novel 2D approach based on fuzzy c -means cluster analysis for the cooperative inversion of disparate data sets. We show that this approach results in a single zonal model of subsurface structures in which each zone is characterized by a set of different parameters. This finding implies that no further structural interpretation of geophysical parameter fields is needed, which is a major advantage compared with conventional inversions that rely on a single input data set and cooperative inversion approaches.

Rapid integration of large airborne geophysical data suites using a fuzzy partitioning cluster algorithm: a tool for geological mapping and mineral exploration targeting

Unsupervised classification techniques, such as cluster algorithms, are routinely used for structural exploration and integration of multiple frequency bands of remotely sensed spectral datasets. However, up to now, very few attempts have been made towards using unsupervised classification techniques for rapid, automated, and objective information extraction from large airborne geophysical data suites. We employ fuzzy c-means (FCM) cluster analysis for the rapid and largely automated integration of complementary geophysical datasets comprising airborne radiometric and magnetic as well as ground-based gravity data, covering a survey area of approximately 5000 km2 located 100 km east-south-east of Johannesburg, South Africa, along the south-eastern limb of the Bushveld layered mafic intrusion complex. After preparatory data processing and normalisation, the three datasets are subjected to FCM cluster analysis, resulting in the generation of a zoned integrated geophysical map delineating distinct subsurface units based on the information the three input datasets carry. The fuzzy concept of the cluster algorithm employed also provides information about the significance of the identified zonation. According to the nature of the input datasets, the integrated zoned map carries information from near-surface depositions as well as rocks underneath the sediment cover. To establish a sound geological association of these zones we refer the zoned geophysical map to all available geological information, demonstrating that the zoned geophysical map as obtained from FCM cluster analysis outlines geological units that are related to Bushveld-type, other Proterozoic- and Karoo-aged rocks.

Detecting voids in masonry by cooperatively inverting P-wave and georadar traveltimes

Collecting different geophysical data sets at the same object and site offers the opportunity to reduce uncertainties and ambiguities in data analysis and interpretation. To be effective, the different available data sets should be linked during the model-generation process, e.g. by cooperative inversion. In this study, we apply a recently developed zonal cooperative inversion approach based on fuzzy c-means cluster analysis to a non-destructive testing experiment. After briefly reviewing the fundamentals of the inversion strategy, we present a synthetic study investigating the potential of the method to detect air-filled voids in masonry by using ultrasonic and georadar traveltime data. Then, we present and discuss laboratory experiments including the results of cooperatively inverted ultrasonic and georadar traveltimes collected at a masonry test specimen. The geometry of the specimen is known and is thus an ideal test object for a first-time real application of the novel zonal cooperative inversion procedure. Compared to the results of separate inversions of ultrasonic and georadar traveltimes, the zonal cooperative inversion allows for an improved delineation of the size and position of the cavities. The P-wave and georadar velocities determined for the model regions corresponding to the cavities are also improved.

Near-surface seismic traveltime tomography using a direct-push source and surface-planted geophones

Information about seismic velocity distribution in heterogeneous near-surface sedimentary deposits is essential for a variety of environmental and engineering geophysical applications. We have evaluated the suitability of the minimally invasive direct-push technology for near-surface seismic traveltime tomography. Geophones placed at the surface and a seismic source installed temporarily in the subsurface by direct-push technology quickly acquire reversed multioffset vertical seismic profiles (VSPs). The first-arrival traveltimes of these data were used to reconstruct the 2D seismic velocity distribution tomographically. After testing this approach on synthetic data, we applied it to field data collected over alluvial deposits in a former river floodplain. The resulting velocity model contains information about high- and low-velocity anomalies and offers a significantly deeper penetration depth than conventional refraction tomography using surface-planted sources and receivers at the investigated site. A combination of refraction seismic and direct-push data increases resolution capabilities in the unsaturated zone and enables reliable reconstruction of velocity variations in near-surface unconsolidated sediments. The final velocity model structurally matches the results of cone-penetration tests and natural gamma-radiation data acquired along the profile. The suitability of multiple rapidly acquired reverse VSP surveys for 2D tomographic velocity imaging of near-surface unconsolidated sediments was explored.

Automated compilation of pseudo-lithology maps from geophysical data sets: a comparison of Gustafson-Kessel and fuzzy c-means cluster algorithms

Abstract The fuzzy partitioning Gustafson-Kessel cluster algorithm is employed for rapid and objective integration of multi-parameter Earth-science related databases. We begin by evaluating the Gustafson-Kessel algorithm using the example of a synthetic study and compare the results to those obtained from the more widely employed fuzzy c-means algorithm. Since the Gustafson-Kessel algorithm goes beyond the potential of the fuzzy c-means algorithm by adapting the shape of the clusters to be detected and enabling a manual control of the cluster volume, we believe the results obtained from Gustafson-Kessel algorithm to be superior. Accordingly, a field database comprising airborne and ground-based geophysical data sets is analysed, which has previously been classified by means of the fuzzy c-means algorithm. This database is integrated using the Gustafson-Kessel algorithm thus minimising the amount of empirical data processing required before and after fuzzy c-means clustering. The resultant zonal geophysical map is more evenly clustered matching regional geology information available from the survey area. Even additional information about linear structures, e.g. as typically caused by the presence of dolerite dykes or faults, is visible in the zonal map obtained from Gustafson-Kessel cluster analysis. Fuzzy Gustafson–Kessel cluster analysis is employed to integrate suites of disparate data sets. The fuzzy c-means algorithm is used as a reference to discuss the advantages of the Gustafson–Kessel algorithm and revise a database comprising airborne and ground-based geophysical data sets while minimising preparatory data processing required for fuzzy c-means cluster analysis.

Are Earth Sciences lagging behind in data integration methodologies

This article reflects discussions German and South African Earth scientists, statisticians and risk analysts had on occasion of two bilateral workshops on Data Integration Technologies for Earth System Modelling and Resource Management. The workshops were held in October 2012 at Leipzig, Germany, and April 2013 at Pretoria, South Africa, and were attended by about 70 researchers, practitioners and data managers of both countries. Both events were arranged as part of the South African-German Year of Science 2012/2013. The South African National Research Foundation (NRF, UID 81579) has supported the two workshops as part of the South African–German Year of Science activities 2012/2013 established by the German Federal Ministry of Education and Research and the South African Department of Science and Technology.

Join global inversion of GPR and P-wave seismic traveltimes using particle swarm optimization

Particle swarm optimization (PSO) is a relatively new global optimization approach inspired by the social behavior of birds and fishes. Although this approach has proven to provide excellent convergence rates in different optimization problems, it has seldom been used in geophysical inversion. Here, we propose a PSO-based inversion strategy to jointly invert GPR and P-wave seismic traveltimes from co-located crosshole experiments. Using a synthetic data example, we demonstrate the potential of our approach. Comparing our results to the input models as well as to velocity models found by separately inverting the data using a standard linearized inversion approach, illustrates the benefits of using an efficient global optimization approach for such a joint inversion problem. These include a straightforward appraisal of uncertainty, non-uniqueness, and resolution issues as well as the possibility of an improved and more objective interpretation.

Combining cross-hole georadar velocity and attenuation tomography for site characterization: a case study in an unconsolidated aquifer

Information extracted from crosshole georadar data has been used to characterize a gravel- and sand-dominated aquifer. Inversions of direct arrival traveltimes and amplitudes have provided electromagnetic velocity and attenuation tomograms that have allowed critical hydrological structures and parameters to be determined. An integrated interpretation of the velocity and attenuation tomograms was performed via a k-means cluster analysis. As a result of this multivariate statistical analysis, major trends in the relationship between velocity and attenuation were identified, thus enabling us to outline the major hydrostratigraphicu nits ofthe surveyedd eposit.

2D probabilistic prediction of sparsely measured earth properties constrained by geophysical imaging fully accounting for tomographic reconstruction ambiguity

Many hydrological, environmental, or engineering exploration tasks require predicting spatially continuous scenarios of sparsely measured borehole logging data. We present a methodology to probabilistically predict such scenarios constrained by ill-posed geophysical tomography. Our approach allows for transducing tomographic reconstruction ambiguity into the probabilistic prediction of spatially continuous target parameter scenarios. It is even applicable to data sets where petrophysical relations in the survey area are non-unique, i.e., different facies related petrophysical relations may be present. We employ static two-layer artificial neural networks (ANNs) for prediction and additionally evaluate, whether the training performance of the ANNs can be used to rank geophysical tomograms, which are mathematically equal reconstructions of physical parameter distributions in the ground. We illustrate our methodology using a realistic synthetic database for maximal control about the prediction performance and ranking potential of the approach. For doing so, we try to link geophysical radar and seismic tomography as input parameters to porosity of the ground as target parameter of ANN. However, the approach is flexible and can cope with any combination of geophysical tomograms and hydrologic, environmental or engineering target parameters. Ranking of equivalent geophysical tomograms based on additional borehole logging data is found to be generally possible, but risks remain that the ranking based on the ANN training performance does not fully coincide with the closeness of geophysical tomograms to ground truth. Since geophysical field data sets do usually not offer control options similar to those used in our synthetic database, we do not recommend the utilization of recurrent ANNs to learn weights for the individual geophysical tomograms used in the prediction procedure.