Rift structure, evolution, and earthquakes in the Gulf of Corinth, from reflection seismic images

Continental extension is forming the Gulf of Corinth across the strike of earlier Alpine evolution. Here, we present the first deep reflection sections with pre-stack depth-migration processing across the deep basin of the Corinth active rift, which image structures unpredicted by current models. Resolving the infill as a pile of layers that are broken by faults allows one to follow their subsidence and deformation history. Variation among the profiles suggests that southern normal faults control the rift in a time progression from the east towards its western tip. On the central, Derveni-Itea transect, a 3-km widening of the basin accrued since the initiation of this control that is marked by an unconformity between the two main sedimentary units. This is estimated to have occurred 0.5-0.6 Myr ago, assuming the glacio-eustatic sea-level changes have controlled the stratigraphy of sediments deposited as a succession of layers on the subsiding hangingwall, as they did on the southern footwall in forming the famous flight of marine terraces of Corinth. A roll-over anticline and crestal collapse graben are diagnostic of the control by a normal fault of dip varying with depth. The deeper low-angle part of this bi-planar fault is indeed imaged as a reflector in the basement. The occurrence of the collapse with a breakaway at the steep southern basin-bounding fault of the hangingwall slab can be estimated 0.12-0.2 Myr ago, with a marked increase in extension rate that brought it to its present fastest value over 10 mm/yr. The low-angle part of the active fault might also have controlled earlier evolution upslope and in the basin. When compared with inferences from earthquake studies, this low-angle active fault may not appear to be seismogenic but may participate to the seismic cycle, possibly in a conditionally stable regime. Active faults seen as sea-bottom scarps merely accommodate deformation of its subsiding hangingwall. The footwall of the low-angle faults, which current seismicity shows to be in extension, appears then as being pulled out from beneath the rift, in a motion towards the rolling-back slab that causes the Hellenic subduction retreat.     

High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space

Mathematical Geosciences - The present work proposes a new high-order simulation framework based on statistical learning. The training data consist of the sample data together with a training...     

Integrated Interpolation Methods for Geophysical Data: Applications to Mineral Exploration

Any mine planning requires careful prediction of both the head grade andtonnage ofmineralization. There are various methods of interpolation that attempt to provide reasonable estimatesat unsampled locations. All of these give realizations that are unduly smooth and extremevalues that occur in reality are not reflected in these estimates. Such methods, therefore,provide limited scope for accurate risk assessment. An alternative approach that is rapidlygaining popularity is the method of conditional simulation. This approach attempts to reproduceboth the grade distributions of the sample data as well as its spatial variability. In this paper,a case study is presented on a platinum mineralization to demonstrate and compare sequentialGaussian and sequential conditional simulation techniques and to quantify and discuss therelevant sensitivities.     

High-Order Spatial Simulation Using Legendre-Like Orthogonal Splines

High-order sequential simulation techniques for complex non-Gaussian spatially distributed variables have been developed over the last few years. The high-order simulation approach does not require any transformation of initial data and makes no assumptions about any probability distribution function, while it introduces complex spatial relations to the simulated realizations via high-order spatial statistics. This paper presents a new extension where a conditional probability density function (cpdf) is approximated using Legendre-like orthogonal splines. The coefficients of spline approximation are estimated using high-order spatial statistics inferred from the available sample data, additionally complemented by a training image. The advantages of using orthogonal splines with respect to the previously used Legendre polynomials include their ability to better approximate a multidimensional probability density function, reproduce the high-order spatial statistics, and provide a generalization of high-order simulations using Legendre polynomials. The performance of the new method is first tested with a completely known image and compared to both the high-order simulation approach using Legendre polynomials and the conventional sequential Gaussian simulation method. Then, an application in a gold deposit demonstrates the advantages of the proposed method in terms of the reproduction of histograms, variograms, and high-order spatial statistics, including connectivity measures. The C++ course code of the high-order simulation implementation presented herein, along with an example demonstrating its utilization, are provided online as supplementary material.     

CDFSIM: Efficient Stochastic Simulation Through Decomposition of Cumulative Distribution Functions of Transformed Spatial Patterns

Simulation of categorical and continuous variables is performed using a new pattern-based simulation method founded upon coding spatial patterns in one dimension. The method consists of, first, using a spatial template to extract information in the form of patterns from a training image. Patterns are grouped into a pattern database and, then, mapped to one dimension. Cumulative distribution functions of the one-dimensional patterns are built. Patterns are then classified by decomposing the cumulative distribution functions, and calculating class or cluster prototypes. During the simulation process, a conditioning data event is compared to the class prototype, and a pattern is randomly drawn from the best matched class. Several examples are presented so as to assess the performance of the proposed method, including conditional and unconditional simulations of categorical and continuous data sets. Results show that the proposed method is efficient and very well performing in both two and three dimensions. Comparison of the proposed method to the filtersim algorithm suggests that it is better at reproducing the multi-point configurations and main characteristics of the reference images, while less sensitive to the number of classes and spatial templates used in the simulations.     

The Cassini Enceladus encounters 2005–2010 in the view of energetic electron measurements

The moon Enceladus, embedded in Saturn’s radiation belts, is the main internal source of neutral and charged particles in the Kronian magnetosphere. A plume of water ice molecules and dust released through geysers on the south polar region provides enough material to feed the E-ring and also the neutral torus of Saturn and the entire magnetosphere. In the time period 2005–2010 the Cassini spacecraft flew close by the moon 14 times, sometimes as low as 25 km above the surface and directly through the plume. For the very first time measurements of plasma and energetic particles inside the plume and its immediate vicinity could be obtained. In this work we summarize the results of energetic electron measurements in the energy range 27 keV to 21 MeV taken by the Low Energy Magnetospheric Measurement System (LEMMS), part of the Magnetospheric Imaging Instrument (MIMI) onboard Cassini in the vicinity of the moon in combination with measurements of the magnetometer instrument MAG and the Electron Spectrometer ELS of the plasma instrument CAPS onboard the spacecraft. Features in the data can be interpreted as that the spacecraft was connected to the plume material along field lines well before entering the high density region of the plume. Sharp absorption signatures as the result of losses of energetic electrons bouncing along those field lines, through the emitted gas and dust clouds, clearly depend on flyby geometry as well as on measured pitch angle/look direction of the instrument. We found that the depletion signatures during some of the flybys show “ramp-like” features where only a partial depletion has been observed further away from the moon followed by nearly full absorption of electrons closer in. We interpret this as partially/fully connected to the flux tube connecting the moon with Cassini. During at least two of the flybys (with some evidence of one additional encounter) MIMI/LEMMS data are consistent with the presence of dust in energetic electron data when Cassini flew directly through the south polar plume. In addition we found gradients in the magnetic field components which are frequently found to be associated with changes in the MIMI/LEMMS particles intensities. This indicates that complex electron drifts in the vicinity of Enceladus could form forbidden regions for electrons which may appear as intensity drop-outs.     

Joint High-Order Simulation of Spatially Correlated Variables Using High-Order Spatial Statistics

Joint geostatistical simulation techniques are used to quantify uncertainty for spatially correlated attributes, including mineral deposits, petroleum reservoirs, hydrogeological horizons, environmental contaminants. Existing joint simulation methods consider only second-order spatial statistics and Gaussian processes. Motivated by the presence of relatively large datasets for multiple correlated variables that typically are available from mineral deposits and the effects of complex spatial connectivity between grades on the subsequent use of simulated realizations, this paper presents a new approach for the joint high-order simulation of spatially correlated random fields. First, a vector random function is orthogonalized with a new decorrelation algorithm into independent factors using the so-termed diagonal domination condition of high-order cumulants. Each of the factors is then simulated independently using a high-order univariate simulation method on the basis of high-order spatial cumulants and Legendre polynomials. Finally, attributes of interest are reconstructed through the back-transformation of the simulated factors. In contrast to state-of-the-art methods, the decorrelation step of the proposed approach not only considers the covariance matrix, but also high-order statistics to obtain independent non-Gaussian factors. The intricacies of the application of the proposed method are shown with a dataset from a multi-element iron ore deposit. The application shows the reproduction of high-order spatial statistics of available data by the jointly simulated attributes.