Geophysics

Seismic data interpretation problems are t y p ically solved using computationally intensive local search methods which often result in inferior solutions. Here, a traditional hybrid genetic algorithm is compared with different staged hybrid genetic algorithms on the geophysical imaging static corrections problem. The traditional hybrid genetic algorithm used here applied local search to every offspring produced by genetic search. The staged hybrid genetic algorithms were designed to temporally sep arate the local and genetic search components into distinct phases so as to minimize interference between the two search methods. The results show that some staged hybrid genetic algorithms produce higher quality solutions while using significantly less computational time for this problem.

Genetic algorithms have particular potential as a tool for optimization when the evaluation function is noisy. Several types of genetic algorithm are compared against a mutation driven stochastic hill-climbing algorithm on a standard set of benchmark functions which have had Gaussian noise added to them. Di erent criteria for judging the e ectiveness of the search are also considered. The genetic algorithms used in these comparisons include an elitist simple genetic algorithm, the CHC adaptive search algorithm and the delta coding genetic algorithm. Finally several hybrid genetic algorithms are described and compared on a very large and noisy seismic data imaging problem.

Many seismic acquisition surveys today have some form of customization and specialization to reduce cost, address operational issues, or efficiently resolve a difficult geologic objective. This trend has been clearly aided in the recent past by the following: 1) Acquisition systems are more flexible and provide capability to specialize, 2) Geologic and reservoir objectives are getting more demanding and diverse, and 3) Geophysicists have more experience and expertise with acquisition opportunities. This trend of specialized acquisition will probably accelerate in the future because of: 4) Better processing tools that allow for very irregular and non-uniform acquisition, & 5) New processing tools that benefit from specialized acquisition, and 6) Better risk management tools by companies to accept unconventional acquisition. The recent discussions on "compressive sensing" shares strong parallels with specialized acquisition. Both perspectives try to find the best acquisition compromise between cost, geologic resolution, noise, and operational restrictions. Both perspectives rely on using advanced processing for successful imaging with unconventional acquisition. Modifying seismic acquisition based on the performance of new processing tools adds risk and should be done cautiously for this expensive process. But, there is a lot to be gained from finding the best acquisition compromise. Not only can costs be significantly lower, but key geologic & reservoir objectives can be resolved. The new processing methods significantly alter the considerations for the best compromise. Better risk management consideration by companies and some new aggressive risk analysis tools will aid the adoption of these specialized, unconventional acquisition & processing approaches.

We present a new algorithm for modeling a self-consistent set of global plate polygons. Each plate polygon is composed of a finite list of plate margins, all with different Euler poles. We introduce a "continuously closed plate" (CCP), such that, as each margin moves independently, the plate polygon remains closed geometrically as a function of time. This method solves emerging needs in computational geodynamics to combine kinematic with dynamic models. Because they have polygons that are too widely spaced in time and have inconsistent motions between margins and plates, traditional global plate tectonic reconstructions have become inadequate for geodynamics. The CCP algorithm has been incorporated into the GPlates open-source paleogeographic system. The algorithm is a set of procedures and data structures that operate on collections of reconstructed geometric data to form closed plate polygons; the main data structures used for each plate polygon are based on a nested hierarchy of topological elements. Reconstructions with CCPs can be created, edited, visualized, and exported with GPlates. The native storage of the dynamic reconstructions is the GPlates Markup Language, GPML, which uses an XML-based file format called GML. We demonstrate the utility of the CCP method by creating a global reconstruction with continuously closing plates from 140 Ma to the present using data from existing, traditional reconstructions.

The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA) and its embedded Atmospheric Dispersion Model is a new atmospheric simulation system for real-time hazard prediction, conceived out of a need to advance the state of the art in numerical weather prediction in order to improve the capability to predict the transport and diffusion of hazardous releases. OMEGA is based upon an unstructured grid that makes possible a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from a few tens of meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning because its unstructured grid permits the addition of grid elements at any point in space and time. In particular, unstructured grid cells in the horizontal dimension can increase local resolution to better capture topography or the important physical features of the atmospheric circulation and cloud dynamics. This means that OMEGA can readily adapt its grid to stationary surface or terrain features, or to dynamic features in the evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first model to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and hence real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with data.

The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction. In the fall of 1999, OMEGA was used for the first time to examine the structure and evolution of a hurricane (Floyd, 1999). The first simulation of Floyd was conducted in an operational forecast mode; additional simulations exploiting both the static as well as the dynamic grid adaptation options in OMEGA were performed later as part of a sensitivity-capability study. While a horizontal grid resolution ranging from about 120 km down to about 40 km was employed in the operational run, resolutions down to about 15 km were used in the sensitivity study to explicitly model the structure of the inner core. All the simulations produced very similar storm tracks and reproduced the salient features of the observed storm such as the recurvature off the Florida coast with an average 48-h position error of 65 km. In addition, OMEGA predicted the landfall near Cape Fear, North Carolina, with an accuracy of less than 100 km up to 96 h in advance. It was found that a higher resolution in the eyewall region of the hurricane, provided by dynamic adaptation, was capable of generating better-organized cloud and flow fields and a well-defined eye with a central pressure lower than the environment by roughly 50 mb. Since that time, forecasts were performed for a number of other storms including Georges (1998) and six 2000 storms (Tropical Storms Beryl and Chris, Hurricanes Debby and Florence, Tropical Storm Helene, and Typhoon Xangsane). The OMEGA mean track error for all of these forecasts of 101, 140, and 298 km at 24, 48, and 72 h, respectively, represents a significant improvement over the National Hurricane Center (NHC) 1998 average of 156, 268, and 374 km, respectively. In a direct comparison with the GFDL model, OMEGA started with a considerably larger position error yet came within 5% of the GFDL 72-h track error. This paper details the simulations produced and documents the results, including a comparison of the OMEGA forecasts against satellite data, observed tracks, reported pressure lows and maximum wind speed, and the rainfall distribution over land.

This paper describes an adaptive, unstructured mesh generator, which can be used for discretizing regional and global domains for geophysical flow simulations in general, and atmospheric flow simulations in particular. The global mesh generator is a part of the Operational Multiscale Environmental model with Grid Adaptivity (OMEGA) System. For a physically complex and computationally intensive atmospheric flow simulation, such as real-time hurricane prediction, the optimization afforded by unstructured adaptive grids is demonstrated.

Global plate motion models provide a spatial and temporal framework for geological data and have been effective tools for exploring processes occurring at the earth's surface. However, published models either have insufficient temporal coverage or fail to treat tectonic plates in a self-consistent manner. They usually consider the motions of selected features attached to tectonic plates, such as continents, but generally do not explicitly account for the continuous evolution of plate boundaries through time. In order to explore the coupling between the surface and mantle, plate models are required that extend over at least a few hundred million years and treat plates as dynamic features with dynamically evolving plate boundaries. We have constructed a new type of global plate motion model consisting of a set of continuously-closing topological plate polygons with associated plate boundaries and plate velocities since the break-up of the supercontinent Pangea. Our model is underpinned by plate motions derived from reconstructing the seafloor-spreading history of the ocean basins and motions of the continents and utilizes a hybrid absolute reference frame, based on a moving hotspot model for the last 100 Ma, and a true-polar wander corrected paleomagnetic model for 200 to 100 Ma. Detailed regional geological and geophysical observations constrain plate boundary inception or cessation, and time-dependent geometry. Although our plate model is primarily designed as a reference model for a new generation of geodynamic studies by providing the surface boundary conditions for the deep earth, it is also useful for studies in disparate fields when a framework is needed for analyzing and interpreting spatio-temporal data.

Understanding tectonic and geodynamic processes leading to the present-day configuration of the Earth involves studying data and models across a variety of disciplines, from geochemistry, geochronology and geophysics, to plate kinematics and mantle dynamics. All these data represent a 3-dimensional spatial and 1-dimensional temporal framework, a formalism which is not exploited by traditional spatial analysis tools. This is arguably a fundamental limit in both the rigour and sophistication in which datasets can be combined for geological "deep time" analysis, and often confines the extent of data analyses to the present-day configurations of geological objects. The GPlates Geological Information Model (GPGIM) represents a formal specification of geological and geophysical data in a time-varying plate tectonics context, used by the GPlates virtual-globe software. It provides a framework in which relevant types of geological data are attached to a common plate tectonic reference frame, allowing the data to be reconstructed in a time-dependent spatio-temporal plate reference frame. The GPlates Markup Language (GPML), being an extension of the open standard Geography Markup Language (GML), is both the modelling language for the GPGIM and an XML-based data format for the interoperable storage and exchange of data modelled by it. The GPlates software implements the GPGIM allowing researchers to query, visualise, reconstruct and analyse a rich set of geological data including numerical raster data. The GPGIM has recently been extended to support time-dependent georeferenced numerical raster data by wrapping GML primitives into the time-dependent framework of the GPGIM. Coupled with GPlates' ability to reconstruct numerical raster data and import/export from/to a variety of raster file formats, as well as its handling of time-dependent plate boundary topologies, interoperability with geodynamic softwares is established, leading to a new generation of deep-time spatio-temporal data analysis and modelling, including a variety of new functionalities such as 4-D data-mining.

Traditional plate reconstruction methodologies do not allow for plate deformation to be considered. Here we present software to construct and visualize global tectonic reconstructions with deforming plates within the context of rigid plates. Both deforming and rigid plates are defined by continuously evolving polygons. The deforming regions are tessellated with triangular meshes such that either strain rate or cumulative strain can be followed. The finite strain history, crustal thickness and stretching factor of points within the deformation zones are tracked as Lagrangian points. Integrating these tools within the interactive platform GPlates enables specialized users to build and refine deforming plate models and integrate them with other models in time and space. We demonstrate the integrated platform with regional reconstructions of Cenozoic western North America, the Mesozoic South American Atlantic margin, and Cenozoic southeast Asia, embedded within global reconstructions, using different data and reconstruction strategies.