On the geologic structure of the Explora Escarpment (Weddell Sea,Antarctica) revealed by satellite and Shipboard data evaluation

Bathymetric, gravity, and magnetic data from Antarctic expeditions with RV POLARSTERN and satellite altimeter data from the Geosat Geodetic Mission are analysed using methods from geostatistics and geophysical inverse theory.The Explora Escarpment represents the edge between the Antarctic Continental Shelf and the Weddell Abyssal Plain. It is an important link in the reconstruction of Gondwana breakup, but a feature as large as the 2000 m deep Wegener Canyon was only discovered in 1984, when extensive bathymetric, gravimetric, and magnetic surveys with RV POLARSTERN began.Geostatistics, the theory of regionalized variables, is applied to integrate dense surveys of Wegener Canyon and sparse observations in adjacent areas into maps with full coverage of the 230 km by 330 km area at 10°–20° W/70°–72° S. The resultant highresolution bathymetric and gravity maps reveal detailed structures of the Explora Escarpment. Using geophysical inversion, the gravity terrain effect is calculated. Satellite data are used for their better coverage, but have much lower resolution. Nevertheless, the structures of Wegener Canyon and other more prominent features appear with surprisingly good correlation also in the Geosat altimeter data. While it was initially supposed that Wegener Canyon is purely an erosional structure, the magnetic map now provides evidence of the canyon's tectonic origin.     

Is the ocean floor a fractal?

The topographic structure of the ocean bottom is investigated at different scales of resolution to answer the question: Can the seafloor be described as a fractal process? Methods from geostatistics, the theory of regionalized variables, are used to analyze the spatial structure of the ocean floor at different scales of resolution. The key to the analysis is the variogram criterion: Self-similarity of a stochastic process implies self-similarity of its variogram. The criterion is derived and proved here: it also is valid for special cases of self-affinity (in a sense adequate for topography). It has been proposed that seafloor topography can be simulated as a fractal (an object of Hausdorff dimension strictly larger than its topological dimension), having scaling properties (self-similarity or self-affinity). The objective of this study is to compare the implications of these concepts with observations of the seafloor. The analyses are based on SEABEAM bathymetric data from the East Pacific Rise at 13°N/104°W and at 9°N/104°W and use tracks that run both across the ridge crest and along the ridge flank. In the geostatistical evaluation, the data are considered as a stochastic process. The spatial continuity of this process is described by variograms that are calculated for different scales and directions. Applications of the variogram criterion to scale-dependent variogram models yields the following results: Although the seafloor may be a fractal in the sense of the definition involving the Hausdorff dimension, it is not self-similar, nor self-affine (in the given sense). Mathematical models of scale-dependent spatial structures are presented, and their relationship to geologic processes such as ridge evolution, crust formation, and sedimentation is discussed.     

Optimization techniques for integrating spatial data

Two optimization techniques ta predict a spatial variable from any number of related spatial variables are presented. The applicability of the two different methods for petroleum-resource assessment is tested in a mature oil province of the Midcontinent (USA). The information on petroleum productivity, usually not directly accessible, is related indirectly to geological, geophysical, petrographical, and other observable data. This paper presents two approaches based on construction of a multivariate spatial model from the available data to determine a relationship for prediction. In the first approach, the variables are combined into a spatial model by an algebraic map-comparison/integration technique. Optimal weights for the map comparison function are determined by the Nelder-Mead downhill simplex algorithm in multidimensions. Geologic knowledge is necessary to provide a first guess of weights to start the automatization, because the solution is not unique. In the second approach, active set optimization for linear prediction of the target under positivity constraints is applied. Here, the procedure seems to select one variable from each data type (structure, isopachous, and petrophysical) eliminating data redundancy. Automating the determination of optimum combinations of different variables by applying optimization techniques is a valuable extension of the algebraic map-comparison/integration approach to analyzing spatial data. Because of the capability of handling multivariate data sets and partial retention of geographical information, the approaches can be useful in mineral-resource exploration.     

An Integrated Mapping Approach to Petroleum Exploration on the Pratt Anticline,Pratt County,Kansas

A quantitative map comparison/integration technique to aid in petroleum exploration was applied to an area in south-central Kansas. The visual comparison and integration of maps has become increasingly difficult with the large number and different types of maps necessary to interpret the geology and assess the petroleum potential of an area; therefore, it is desirable to quantify these relationships. The algebraic algorithm used in this application is based on a point-by-point comparison of any number and type of spatial data represented in map form. Ten geological and geophysical maps were compared and integrated, utilizing data from 900 wells located in a nine-township area on the Pratt Anticline in Pratt County, Kansas. Five structure maps, including top of the Lansing Group (Pennsylvanian), Mississippian chert, Mississippian limestone, Viola Limestone (Ordovician), and Arbuckle Group (Cambro-Ordovician), two isopachous maps from top of Mississippian chert to Viola and Lansing to Arbuckle, a Mississippian chert porosity map, Bouguer gravity map, and an aeromagnetic map were processed and interpreted. Before processing, each map was standardized and assigned a relative degree of importance, depending on knowledge of the geology of the area. Once a combination of weights was obtained that most closely resembled the pattern of proved oil fields (target map), a favorability map was constructed based on a coincidence of similarity values and of geological properties of petroleum reservoirs. The resulting favorability maps for the study area indicate location of likely Mississippian chert and lower Paleozoic production.     

Master of the Obscure—Automated Geostatistical Classification in Presence of Complex Geophysical Processes

The topic of this paper is the retrieval of hidden or secondary information on complex spatial variables from geophysical data. Typical situations of obscured geological or geophysical information are the following: (1) Noise may disturb the signal for a variable for which measurements have been collected. (2) The variable of interest may be obscured by other geophysical processes. (3) The information of interest may formally be captured in a secondary variable, whereas data may have been collected for a primary variable only, that is related to the geophysical process of interest. Examples discussed here include mapping of marine-geologic provinces from bathymetric data, identification of sea-ice properties from snow-depth data, analysis of snow surface data in an Alpine environment and association of deformation types in fast-moving glaciers from airborne video material or satellite imagery. Data types include geophysical profile or trackline data, image data, grid or matrix-type data, and more generally, any two-dimensional or three-dimensional discrete or discretizable data sets. The framework for a solution is geostatistical characterization and classification, which typically involves the following steps: (1) calculation of vario functions (which may be of higher order or residual type, or combinations of both), (2) derivation of classification parameters from vario functions, and (3) characterization, classification or segmentation, depending on the applied problem. In some situations, spatial surface roughness is utilized as an auxiliary variable, for instance, roughness of the seafloor may be derived from bathymetric data and be indicative of geological provinces. The objective of this paper is to present components of the geostatistical classification method in a summarizing and synoptical manner, motivated by applied examples and integrating principal and generalized concepts, such as hyperparameters and parameters that relate to the same physical processes and work for data in oversampled and undersampled situations, parameters that facilitate comparison among different data types, data sets and across scales, variograms and vario functions of higher order, and deterministic and connectionist classification algorithms.     

Monitoring changes of ice streams using time series of satellite-altimetry-based digital terrain models

The Antarctic Ice Sheet plays a major role in the global system, and the large ice streams discharging into the circumpolar sea represent its gateways to the world’s oceans. Satellite radar altimeter data provide an opportunity for mapping surface elevation at kilometerresolution with meteraccuracy. Geostaristical methods have been developed for the analysis of these data. Applications to Seasat data and data from the Geosat Exact Repeat Mission indicate that the grounding line of Lambert Glacier/Amery Ice Shelf, the largest ice stream in East Antarctica, has advanced 10–12 km between 1978 and 1987–89. The objectives of this paper are to explore possibilities and limitations of satellite-altimetry-based mapping to capture changes for shorter time windows and for smaller areas, and to investigate some methodological aspects of the data analysis. We establish that one season of radar altimeter data is sufficient for constructing a map. This allows study of interannual variation and is the key for a limeseries analysis approach to study changes in ice streams. Maps of the lower Lambert Glacier and the entire Amery Ice Shelf are presented for austral winters 1978, 1987, 1988, and 1989. As a first step toward understanding the dynamics of the ice-stream/iceshelf system, elevation changes are calculated for grounded ice, the grounding zone, and floating ice. In the absence of (sufficient) surface gravity control for the Lambert Glacier/Amery Ice Shelf area, altimetry-based maps may facilitate improvement of geoid models as they provide constraints on the terrain correction in the inverse gravimetric problem.     

Inverse theory in the earth sciences—an introductory overview with emphasis on Gandin's method of optimum interpolation

In 1963, Gandin published a monograph on “optimum interpolation for the objective analysis of meteorological fields, ? a method that is similar mathematically to geodetical least-squares prediction and collocation, simple kriging, and spectral interpolation. The common problem is the interpolation or extrapolation or estimation of a continuous spatial property from finitely many observations. Gandin 's method is presented in an inverse-theoretical context with focus on a methodological comparison with related methods. Underlying mathematical assumptions as well as geological implications are discussed. An introductory overview of inverse methods in the earth sciences is given, with emphasis on methods with a structure analysis step.     

Is the ocean floor a fractal?

The topographic structure of the ocean bottom is investigated at different scales of resolution to answer the question: Can the seafloor be described as a fractal process? Methods from geostatistics, the theory of regionalized variables, are used to analyze the spatial structure of the ocean floor at different scales of resolution. The key to the analysis is the variogram criterion: Self-similarity of a stochastic process implies self-similarity of its variogram. The criterion is derived and proved here: it also is valid for special cases of self-affinity (in a sense adequate for topography). It has been proposed that seafloor topography can be simulated as a fractal (an object of Hausdorff dimension strictly larger than its topological dimension), having scaling properties (self-similarity or self-affinity). The objective of this study is to compare the implications of these concepts with observations of the seafloor. The analyses are based on SEABEAM bathymetric data from the East Pacific Rise at 13°N/104°W and at 9°N/104°W and use tracks that run both across the ridge crest and along the ridge flank. In the geostatistical evaluation, the data are considered as a stochastic process. The spatial continuity of this process is described by variograms that are calculated for different scales and directions. Applications of the variogram criterion to scale-dependent variogram models yields the following results: Although the seafloor may be a fractal in the sense of the definition involving the Hausdorff dimension, it is not self-similar, nor self-affine (in the given sense). Mathematical models of scale-dependent spatial structures are presented, and their relationship to geologic processes such as ridge evolution, crust formation, and sedimentation is discussed.