A global geochemical database for environmental and resource management : recommendations for International Geochemical Mapping, final report of IGCP Project 259

Research conducted since 1988 as part of the International Geochemical Mapping (IGM) project has confirmed that the presently available data concerning the geochemical composition of the Earth’s surface are substantially incomplete and internally inconsistent. Many of the older data sets have serious deficiencies and do not meet basic requirements for establishing the range of natural geochemical background values. As a result of natural geological and environmental processes, element abundances in natural materials can vary by several orders of magnitude within short distances. These variations are inadequately documented and their existence is often overlooked in the setting of public policy. A high quality geochemical database is pertinent to a wide range of investigations in the earth and life sciences, and should be considered as an essential component of environmental knowledge. Detailed information about the natural variability of the geochemical background is pertinent to administrative and legal issues as much as to scientific research. Sustainable long-term management of environmental and mineral resources is dependent upon a comprehensive and reliable database. The International Geosphere-Biosphere Program on Global Change requires information on current conditions. Important aspects of change cannot be measured, or their consequences anticipated, unless the present composition of the earth’s surface materials is known. The International Geochemical Mapping project, which was endorsed in 1988 as a contribution to the IGBP (IGBP, 1989), is a multi-stage project established to consider how best to provide quantitative data to portray the geochemical diversity of the earth’s land surface. Participants in IGCP 259 have undertaken a comprehensive review of methods of regional and national geochemical mapping and examined the results obtained. Many problems have been identified and a variety of solutions discussed. Field and laboratory research has been carried out. The resulting recommendations are contained in this report. They are directed towards geochemists and those institutions, which have a mandate for providing an earth science and/or environmental database

Using Random Quasi-Monte-Carlo Within Particle Filters, With Application to Financial Time Series.

This article presents a new particle filter algorithm which uses random quasi-Monte-Carlo to propagate particles. The filter can be used generally, but here it is shown that for one-dimensional state-space models, if the number of particles is N, then the rate of convergence of this algorithm is N−1. This compares favorably with the N−1/2 convergence rate of standard particle filters. The computational complexity of the new filter is quadratic in the number of particles, as opposed to the linear computational complexity of standard methods. I demonstrate the new filter on two important financial time series models, an ARCH model and a stochastic volatility model. Simulation studies show that for fixed CPU time, the new filter can be orders of magnitude more accurate than existing particle filters. The new filter is particularly efficient at estimating smooth functions of the states, where empirical rates of convergence are N−3/2; and for performing smoothing, where both the new and existing filters have the same computational complexity