Complexity in the earthquake mechanism is manifested in different forms such as fractal distribution, clustering of seismicity, etc., and characterized as critical phenomenon. Occurrences of earthquakes generally represent the state of metastable equilibrium. The Andaman–Sumatra subduction zone is one of the most seismically active corridors (possibly in metastable state) in the world. Recently, the region faced three major earthquakes of magnitude more than 8.5 (M ~ 9.1 on December 26, 2004; M ~ 8.6 on March 28, 2005; M ~ 8.6 on April 11, 2012). Researchers have suggested multiple causes of earthquake generation in this region including the one with possible correlation of tidal stresses with earthquake occurrences. The latter issue, however, has been hotly debated in view of the fact that a small stress generated due to tidal forcing cannot cause such a bigger magnitude earthquake. We study here the impact of tidal forcing on critically generated earthquake phenomena. We examined the statistical behavior of recurrence time interval of earthquakes using the available data for period of about 40 years from 1973 to 2013. We constrain the simple empirical toy model using the concept of catastrophe theory to evaluate the impact of small tidal forcing on the critical state of earthquakes occurrences. In addition to the major role of Helmholtz free energy during the plate motion, our analysis suggests that the stability and critical behavior of the earthquake in Sumatra region could be associated with tidal forcing, however, only for triggering of some of the “Catastrophic–Chaotic” earthquake phenomenon.
The rock mass rating (RMR) and slope mass rating (SMR) has been carried out to classify the slope in terms of slope instability. To understand the RMR and SMR various geostructural, geomorphologic and hydrological parameters of the slopes were measured and analyzed. 32 rock slopes/rock cum debris slopes were identified in the study area. The present RMR and SMR study is an outcome of extensive field study along a stretch of about 10 km on road leading from Srinagar to Pauriarea along Alaknanda valley. The technique followed incorporates the relation between discontinuities and slope along with rock mass rating (RMR) and slope mass rating (SMR). The analysis of the 32 studied slopes shows that in the Gangadarshan area out of six rock slope facets, two falls in class II (stable) and four in class IV (unstable). It is significant to note that the slope facets coming under class IV are comprised of active landslide portions. While the slopes under class II show minor failure or old landslide debris. 相似文献
In this study, multivariate statistical approaches, namely hierarchical cluster analysis (CA) and principal component analysis (PCA), were employed to understand the impact of copper mining on surface waters located in Central-East India. The data set generated consisted of nine parameters, namely pH, dissolved oxygen (DO), alkalinity, total dissolved solids, copper, iron, manganese, zinc and fluoride, collected in forty sampling points covering all seasons. As delineated by CA, the entire data set for both the surface waters was bifurcated into groups, namely Banjar River inclusion of seepage points (BRISP) and Banjar River exclusion of seepage points (BRESP), Son River inclusion of seepage points (SRISP) and Son River exclusion of seepage points (SRESP). Four latent factors were identified, namely copper, iron, fluoride and manganese, explaining 84.7 % of variance for BRISP, 71.9 % of variance for BRESP, 66.7 % of variance for SRISP and 68 % of variance for SRESP. The extensive application of PCA on BRISP, BRESP, SRISP and SRESP reveals that the main stream of both the rivers remains unaffected by mining operations when seepage points were excluded. Additionally, iron content is considerably significant throughout the stream due to the geogenic sources and it is considered as a major factor for the depletion of DO level in the streams. This study reveals the level of contamination in the studied surface waters and the effectiveness of multivariate statistical techniques for evaluation and interpretation of complex data matrix in understanding the spatial variations and identification of pollution sources. 相似文献
