S. T. G. Raghu Kanth

Strong motion compatible source geometry

Strong motion array records are analyzed in this paper to identify and map the source zone of four past earthquakes. The source is represented as a sequence of double couples evolving as ramp functions, triggering at different instants, distributed in a region yet to be mapped. The known surface level ground motion time histories are treated as responses to the unknown double couples on the fault surface. The location, orientation, magnitude, and risetime of the double couples are found by minimizing the mean square error between analytical solution and instrumental data. Numerical results are presented for Chi-Chi, Imperial Valley, San Fernando, and Uttarakashi earthquakes. Results obtained are in good agreement with field investigations and those obtained from conventional finite fault source inversions.

INTRINSIC MODE FUNCTIONS OF EARTHQUAKE SLIP DISTRIBUTION

In this paper, empirical mode decomposition technique is used to analyze the spatial slip distribution of five past earthquakes. It is shown that the finite fault slip models exhibit five empirical modes of oscillation. The last intrinsic mode is positive and characterizes the non-stationary mean of the slip distribution. This helps in splitting the spatial variability of slip into trend and the remaining modes sum as the fluctuation in the data. The fluctuation component indicates that it can be modeled as an anisotropic random field. Important parameters of this random field have been estimated. The effect of these modes on ground motion is presented by simulating both acceleration and displacement time histories.

FORECASTING THE AIR TRAFFIC FOR NORTH-EAST INDIAN CITIES

In this article, a new strategy for modeling and forecasting the air traffic data series is presented. The empirical mode decomposition technique is used to decompose the monthly air traffic time series into finite number of intrinsic modes. This helps in identifying the last empirical mode as a trend and the summation of remaining modes as the fluctuation in the data. The fluctuation part is handled by artificial neural network (ANN) techniques, whereas the trend is amenable for modeling through simple regression concepts. It is found that the proposed model explains 46–89% of the variability of five air traffic time series considered here. The model is efficient in statistical forecasting of air traffic as verified on an independent subset of the data series.

STOCHASTIC MODELING OF SPT N-VALUE AND EVALUATION OF PROBABILITY OF LIQUEFACTION AT GUWAHATI CITY

Standard penetration test (SPT) reveals the spatial complexity of standard penetration resistance (N-value) with depth. In this paper, a 1D stochastic characterization of spatial complexity of N-values is developed by considering data obtained from sixty two boreholes in Guwahati City. The N-value profile is modeled as the sum of deterministic part and a stochastic component. The deterministic part which characterizes the non-stationary mean of the data is determined by linear regression analysis. The remaining error is modeled as a spatial random field. The characterization of error heterogeneity as a homogeneous Gaussian random field successfully captures the observed auto-correlation function. The proposed stochastic model is used to compute the probability of factor of safety against liquefaction by Monte-Carlo simulation. The results obtained are presented in form of fragility surfaces, expressing the probability of liquefaction as a function of magnitude of the earthquake and epicentral distance. It is observed that the probability of liquefaction at Guwahati city due to strong earthquakes occurring even at large distances is very high.

Modeling and synthesis of strong ground motion

Success of earthquake resistant design practices critically depends on how accurately the future ground motion can be determined at a desired site. But very limited recorded data are available about ground motion in India for engineers to rely upon. To identify the needs of engineers, under such circumstances, in estimating ground motion time histories, this article presents a detailed review of literature on modeling and synthesis of strong ground motion data. In particular, modeling of seismic sources and earth medium, analytical and empirical Green’s functions approaches for ground motion simulation, stochastic models for strong motion and ground motion relations are covered. These models can be used to generate realistic near-field and far-field ground motion in regions lacking strong motion data. Numerical examples are shown for illustration by taking Kutch earthquake-2001 as a case study.