Outcrop-based facies analysis of the Proterozoic Basantpur Formation, Simla Group in the Lesser Himalaya was combined with the stromatolites morphometry and sea-level fluctuation to delineate the stages of carbonate ramp development. On this basis, a vertical profile depositional model (Basantpur type) has been developed. Facies associations and variation in the patterns of microbial growth along with the sea-level fluctuations have contributed to the identification of the development of a tide-influenced carbonate ramp. Different stromatolitic structures (mega-, macro- and microstructures) are documented in the dolomudstones and dolosiltstones along with fenestral structures and their depositional facies together with evidences of marine transgression which leads to development of carbonate ramp where inner-mid-outer-ramp subenvironments are recognised. The “Basantpur”-type model is therefore unique in that it deals with lateral facies variation due to shift in shore line along with fluctuations in accommodation space on a carbonate ramp owing to fluctuations of sea level. This model will probably find its applicability in similar carbonate ramps. 相似文献
Before starting seismic cycle of Ahar–Varzaghan 2012 event, a partial gap in the form of a pre-seismic calm sequence (seismicity rate, r = 0.46 event/year, b = 1.4) with duration of 303 days spatially has dominated over the entire seismogenic area. From April 17, 2012, to May 31, 2012, r significantly increased to 2.16, indicating strong foreshock sequence, and b value changed to 1.9, remarkably. In the last two months before the mainshock, foreshocks have partially migrated toward the earthquake fault (with a decrease in size, b = 2.0). Significantly, high rate of seismicity and low VP/VS (1.64) in the foreshocks sequence and also very high seismicity rate (17.3) and high VP/VS (1.76) in the aftershocks sequence make substantial differences between the seismic cycle and the background seismicity. Moreover, a significant E–W migration of the microseismicity was confirmed in the study area. 相似文献
In aerodynamic levitation, solids and liquids are floated in a vertical gas stream. In combination with CO2-laser heating, containerless melting at high temperature of oxides and silicates is possible. We apply aerodynamic levitation
to bulk rocks in preparation for microchemical analyses, and for evaporation and reduction experiments. 相似文献
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
Three years after the oil spillage and pipeline explosion that claimed about 100 human lives at Ijegun Community of Lagos–Nigeria, a combination of carefully designed 2D Electrical Resistivity Profilling and Vertical Electrical Sounding methods was deployed to map and characterise the subsurface around the contaminated site. Data acquired were processed, forward modelled and tomographically inverted to obtain the multi-dimensional resistivity distribution of subsurface. The results of the study revealed high resistivity structures that indocate the presence of contaminant (oil plumes) of different sizes and shapes around the oil leakage site. These high resistivity structures are absent in the tomograms and resistivity-depth slices computed for Iyana—a linear settlement not affected by oil spillage. The five geo-electric layers and the resistivities delineated in the area are the top soil layer, 220–670 Ωm; clayey sand layer, 300–1072 Ωm; top sand layer, 120–328 Ωm; mudstone/shale layer, 25–116 Ωm and the bottom sand layer, 15–69 Ωm. The base of the first four geo-electric layers corresponds to 3.9, 8.4, 27.2 and 34.6 m respectively. The two groundwater aquifers delineated correspond to the third and fifth geo-electric layers. The top aquifer has been infiltrated by oil plumes. The depth penetrated by the oil plume decreases from 32 m to about 24 m across the survey profiles from the two ends. It was concluded that the contaminant plumes from the oil spillage are yet to be completely degraded as at the time of the study. It is recommended that the contaminated site be remediated to remove or reduce the contaminant oil in the subsurface. 相似文献