Abstract:
the himalaya covering 20-38° n latitude and 70-98° e longitude, is one of the most seismo-tectonically active and vulnerable regions of the world. visual inspection of the temporal earthquake frequency pattern of the himalayas indicates the nature of the tectonic activity prevailing in this region. however, the quantification of this dynamical pattern is essential for constraining a model and characterizing the nature of earthquake dynamics in this region. we examine the temporal evolution of seismicity (m > 4) of the central himalaya (ch), western himalaya (wh) and northeast himalaya (neh), for the period of 1960-2003 using artificial neural network (ann) technique. we use a multilayer feedforward artificial neural network (ann) model to simulate monthly resolution earthquake frequency time series for all three regions. the ann is trained using a standard back-propagation algorithm with gradient decent optimization technique and then generalized through cross-validation. the results suggest that earthquake processes in all three regions evolved on a high dimensional chaotic plane akin to "self-organized" dynamical pattern. earthquake processes of neh and wh show a higher predictive correlation coefficient (50-55%) compared to the ch (30%), implying that the earthquake dynamics in the neh and wh are better "organized" than in the ch region. the available tectonogeological observations support the model predictions.

Abstract:
The Himalaya covering 20-38° N latitude and 70-98° E longitude, is one of the most seismo-tectonically active and vulnerable regions of the world. Visual inspection of the temporal earthquake frequency pattern of the Himalayas indicates the nature of the tectonic activity prevailing in this region. However, the quantification of this dynamical pattern is essential for constraining a model and characterizing the nature of earthquake dynamics in this region. We examine the temporal evolution of seismicity (M ≥ 4) of the Central Himalaya (CH), Western Himalaya (WH) and Northeast Himalaya (NEH), for the period of 1960-2003 using artificial neural network (ANN) technique. We use a multilayer feedforward artificial neural network (ANN) model to simulate monthly resolution earthquake frequency time series for all three regions. The ANN is trained using a standard back-propagation algorithm with gradient decent optimization technique and then generalized through cross-validation. The results suggest that earthquake processes in all three regions evolved on a high dimensional chaotic plane akin to “self-organized” dynamical pattern. Earthquake processes of NEH and WH show a higher predictive correlation coefficient (50-55%) compared to the CH (30%), implying that the earthquake dynamics in the NEH and WH are better “organized” than in the CH region. The available tectono-geological observations support the model predictions.

Abstract:
A crucial point in the debate on feasibility of earthquake prediction is the dependence of an earthquake magnitude from past seismicity. Indeed, whilst clustering in time and space is widely accepted, much more questionable is the existence of magnitude correlations. The standard approach generally assumes that magnitudes are independent and therefore in principle unpredictable. Here we show the existence of clustering in magnitude: earthquakes occur with higher probability close in time, space and magnitude to previous events. More precisely, the next earthquake tends to have a magnitude similar but smaller than the previous one. A dynamical scaling relation between magnitude, time and space distances reproduces the complex pattern of magnitude, spatial and temporal correlations observed in experimental seismic catalogs.

Abstract:
Nowadays, computer scientists have shown the interest in the study of social insect's behaviour in neural networks area for solving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron (MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of the crucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning the complex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP training with the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time series data.

Abstract:
We investigated possible uncertainties and biases of magnitude estimate arising from instrument characteristics, site conditions and routine data processing at a local seismic network running in Southeastern Sicily. Differences in instrument characteristics turned out to be of minor importance for small and moderate earthquakes. Magnitudes routinely calculated with the HYPOELLIPSE program are obtained from the peak ground velocities applying a correction for the dominant period. This procedure yields slightly lower values than the standard procedure, where magnitudes are estimated from peak ground displacement. In order to provide the operators in the data center with a tool for an immediate estimate of earthquake size from drum records we carried out a bivariate regression relating local magnitude (M L ) to the duration of the signal and the travel time difference of P- and S-waves.

Abstract:
Based on the geodynamics, an earthquake does not take place until the momentum-energy excess a faulting threshold value of rock due to the movement of the fluid layer under the rock layer and the transport and accumulation of the momentum. From the nonlinear equations of fluid mechanics, a simplified nonlinear solution of momentum corresponding the accumulation of the energy could be derived. Otherwise, a chaos equation could be obtained, in which chaos corresponds to the earthquake, which shows complexity on seismology, and impossibility of exact prediction of earthquakes. But, combining the Carlson-Langer model and the Gutenberg-Richter relation, the magnitude-period formula of the earthquake may be derived approximately, and some results can be calculated quantitatively. For example, we forecast a series of earthquakes of 2004, 2009 and 2014, especially in 2019 in California. Combining the Lorenz model, we discuss the earthquake migration to and fro. Moreover, many external causes for earthquake are merely the initial conditions of this nonlinear system.

Abstract:
Investigation of the time and magnitude distribution of the fore- and aftershocks of the Cremasta lake earthquake which occurred on February 5, 1966 is made. The deformation characteristics and spatial distribution of these shocks is also studied. Strong evidence is presented that the foreshocks and the main shock have been triggered by the waterloading of the Cremasta artificial lake.

The article describes a project proposed to determine the epicenter of a future
short-focus earthquake tens of hours before and to reduce the magnitude of
an impending catastrophic earthquake. It focuses on developing a physical
model to determine the conditions necessary for the start of an earthquake,
for a method based on the registration of flows of mercury vapor in the gas
rising from the Earth. This model gives an explanation of why an earthquake
precursor appears so early (such a long period of time can range from a few
to hundreds of hours). Normally, the characteristic times of an earthquake
precursor for seismic methods are tens of seconds. The project is based on the
physical and mathematical models of an earthquake. The derived formula for
the time of the precursor of a future earthquake allows us to explain and to
describe the time increase for the precursor, depending on the magnitude of
the earthquake. The method of reducing the magnitude of an impending catastrophic
earthquake is based on the proposed physical model of the onset of
an earthquake and is implemented by the action of a vibration source in the
region of the detected earthquake epicenter. The proposed system should save
citizens, lives from future short-focus earthquakes.

Abstract:
One of the main interests in seismology is the formulation of models able to describe the clustering in time occurrence of earthquakes. Analysis of the Southern California Catalog shows magnitude clustering in correspondence to temporal clustering. Here we propose a dynamical scaling hypothesis in which time is rescaled in terms of magnitude. This hypothesis is introduced in the context of a generalized trigger model and gives account for clustering in time and magnitude for earthquake occurrence. The model is able to generate a synthetic catalog reproducing magnitude and inter-even time distribution of thirty years California seismicity.

Abstract:
The fabric warmth retention test is a complex process that is influenced by various factors, so errors often appear in the test. There lies a function relation between the fabric thickness, gram weight and warmth retention rate, CLO value. Artificial neural network BP algorithm was used to simulate the function mapping relation, and realized the automatic mapping from basic performance to warmth retention performance, and exhibited high mapping precision, it could also be used to amend the numerical value and reduce errors. It is demonstrated that the method is of high efficiency.