Energy Exploitation And Environmental Impact In Nigeria: The Way Forward.

Energy is the basis of industrial civilisation; without energy, modern life would be difficult to live. However, the exploitation and utilisation of energy in this case non-renewable energy (oil, gas and coal) comes with effects that impact negatively on the environment such as environmental pollution which affects weather conditions, soil fertility, aquatic habitats and wildlife animals. This paper, after highlighting the various environmental impact of energy exploitation, emphasizes on the need for the stakeholders, in this context, government, engineers, scientist, investors and community leaders to rise up to the challenges and ensure that the impacts of energy to the environment are addressed. It advocates for interdisciplinary approach and strict adherence to professional ethics by engineers and scientists to address the problems while the appropriate government agencies should enforce the various environmental laws. And judging by the threat the non-renewable energy exploitation poses to humans and the environment, the paper suggests a gradual shift to clean energy technology for sustainable development.Key words: Energy Exploitation, Environment Pollution, Modern Technologies, Sustainable Development

Prediction of Large Events on a Dynamical Model of a Fault

We present results for long term and intermediate term prediction algorithms applied to a simple mechanical model of a fault. We use long term prediction methods based, for example, on the distribution of repeat times between large events to establish a benchmark for predictability in the model. In comparison, intermediate term prediction techniques, analogous to the pattern recognition algorithms CN and M8 introduced and studied by Keilis-Borok et al., are more effective at predicting coming large events. We consider the implications of several different quality functions Q which can be used to optimize the algorithms with respect to features such as space, time, and magnitude windows, and find that our results are not overly sensitive to variations in these algorithm parameters. We also study the intrinsic uncertainties associated with seismicity catalogs of restricted lengths.Comment: 33 pages, plain.tex with special macros include

Universality of Cluster Dynamics

We have studied the kinetics of cluster formation for dynamical systems of dimensions up to $n=8$ interacting through elastic collisions or coalescence. These systems could serve as possible models for gas kinetics, polymerization and self-assembly. In the case of elastic collisions, we found that the cluster size probability distribution undergoes a phase transition at a critical time which can be predicted from the average time between collisions. This enables forecasting of rare events based on limited statistical sampling of the collision dynamics over short time windows. The analysis was extended to L$^p$-normed spaces ($p=1,...,\infty$) to allow for some amount of interpenetration or volume exclusion. The results for the elastic collisions are consistent with previously published low-dimensional results in that a power law is observed for the empirical cluster size distribution at the critical time. We found that the same power law also exists for all dimensions $n=2,...,8$, 2D L$^p$ norms, and even for coalescing collisions in 2D. This broad universality in behavior may be indicative of a more fundamental process governing the growth of clusters

Using synchronization to improve earthquake forecasting in a cellular automaton model

A new forecasting strategy for stochastic systems is introduced. It is inspired by the concept of anticipated synchronization between pairs of chaotic oscillators, recently developed in the area of Dynamical Systems, and by the earthquake forecasting algorithms in which different pattern recognition functions are used for identifying seismic premonitory phenomena. In the new strategy, copies (clones) of the original system (the master) are defined, and they are driven using rules that tend to synchronize them with the master dynamics. The observation of definite patterns in the state of the clones is the signal for connecting an alarm in the original system that efficiently marks the impending occurrence of a catastrophic event. The power of this method is quantitatively illustrated by forecasting the occurrence of characteristic earthquakes in the so-called Minimalist Model.Comment: 4 pages, 3 figure

Scale free networks of earthquakes and aftershocks

We propose a new metric to quantify the correlation between any two earthquakes. The metric consists of a product involving the time interval and spatial distance between two events, as well as the magnitude of the first one. According to this metric, events typically are strongly correlated to only one or a few preceding ones. Thus a classification of events as foreshocks, main shocks or aftershocks emerges automatically without imposing predefined space-time windows. To construct a network, each earthquake receives an incoming link from its most correlated predecessor. The number of aftershocks for any event, identified by its outgoing links, is found to be scale free with exponent $\gamma = 2.0(1)$. The original Omori law with $p=1$ emerges as a robust feature of seismicity, holding up to years even for aftershock sequences initiated by intermediate magnitude events. The measured fat-tailed distribution of distances between earthquakes and their aftershocks suggests that aftershock collection with fixed space windows is not appropriate.Comment: 7 pages and 7 figures. Submitte

Predictability of Self-Organizing Systems

We study the predictability of large events in self-organizing systems. We focus on a set of models which have been studied as analogs of earthquake faults and fault systems, and apply methods based on techniques which are of current interest in seismology. In all cases we find detectable correlations between precursory smaller events and the large events we aim to forecast. We compare predictions based on different patterns of precursory events and find that for all of the models a new precursor based on the spatial distribution of activity outperforms more traditional measures based on temporal variations in the local activity.Comment: 15 pages, plain.tex with special macros included, 4 figure

Application of pattern recognition algorithm in the seismic belts of Indian convergent plate margin - CN algorithm

The earthquake catalogue from 1964 to August 1991 is used to identify the times of increased probabilities (TIPs) of the earthquake mainshocks of magnitudes greater than or equal to 6.4 and are associated with the Indian convergent plate margins, in retrospect. In Pakistan and Indo-Burma regions, the analysis was repeated for magnitude threshold 6.2 and 7.0 respectively. All the earthquakes (except one in the Hindukush region and one in Indo-Burmese region) in Pakistan, Hindukush-Pamir, Himalaya and Indo-Burmese regions were preceded by the special activation and hence were predicted. Approximately 23 ± 10% of the total time (1970 to August 1991) is occupied by the TIPs in all the regions. The reasons for failure to predict the two earthquakes in these regions are discussed. Our analysis gives a better picture of the regionalization and the size of the space-time volume for the preparation of an earthquake. The high success ratio of the algorithm proves that it can be applied in this territory for further prediction in the real time, without any significant changes in its parameters

Aperiodicity in one-way Markov cycles and repeat times of large earthquakes in faults

A common use of Markov Chains is the simulation of the seismic cycle in a fault, i.e. as a renewal model for the repetition of its characteristic earthquakes. This representation is consistent with Reid's elastic rebound theory. Here it is proved that in {\it any} one-way Markov cycle, the aperiodicity of the corresponding distribution of cycle lengths is always lower than one. This fact concurs with observations of large earthquakes in faults all over the world