pure and applied geophysics最新文献

A monthly rainfall dataset for India at country, regional and meteorological sub-divisional scales was developed by the Indian Institute of Tropical Meteorology (IITM) based on a fixed network of 306 rain gauges. This dataset has been constructed when long period data was not available at many locations and there was not much computing power available. This data has been used worldwide for rainfall analysis over India. In this study, this rainfall dataset has been compared with a larger network of rain gauges maintained by the India Meteorological Department (IMD) for the southwest monsoon period of 1901–2010 at meteorological sub-divisional scale. Two different rain gauge networks can give rise to divergent estimates of rainfall, in general from differences in network density or location of individual rain gauges in each network, assuming measurement errors have small effect. Although mean monthly and seasonal monsoon rainfall and their interannual variability in both IITM and IMD datasets are similar, IITM dataset shows larger difference from IMD data for several meteorological sub-divisions. The long-term trends and frequency of occurrence of deficient and excess monsoon rainfall also show considerable differences between these two rainfall datasets. Data from a sparse network is not representative at meteorological sub-divisions associated with rather larger spatial variations in the southwest monsoon rainfall. For instance, IITM dataset has 11 rain gauges compared to 147 IMD rain gauges over a meteorological sub-division—South Interior Karnataka, and mean absolute difference in monthly monsoon rainfall estimates becomes about 25% when compared for rather shorter period using station data. It is also demonstrated that inclusion of additional rain gauges substantially improves the quality of IITM monthly rainfall estimates over this specific meteorological sub-division.

Although many earthquake precursors have been proposed to forecast earthquakes, even in this modern era, short-term earthquake forecasting remains challenging due to the heterogeneous nature of the earthquake’s occurrence. This study mainly focused on how the impending earthquake influences pre-earthquake scenarios using minor shocks and further confirmed by atmospheric parameters such as Outgoing Longwave Radiation (OLR). The Himalayan belt is one of the most at-risk areas during a continental-continental collision. The spatiotemporal analysis of the pre-earthquake scenario is carried out to identify the most vulnerable seismic risk zone and to forecast the probable magnitude of the earthquake. From the analysis, it is found that the accumulation of strain energy focussing near the epicenter of the impending earthquake. Furthermore, the study also revealed that abnormal changes in atmospheric parameters observed several days before an earthquake, which could serve as a precursor of seismic activity. On certain days, the anomalous OLR due to the radon gas emanation was observed at the different locations around the epicenter of the impending earthquakes. This phenomenon probably due to the transfer of accumulated strain from one side of the fault to other side of the fault through epicenter of the impending earthquake. This gives vital clue in determining the possible epicenter of the earthquake. The statistical analysis of minor shocks associated with significant earthquakes made it possible to determine the magnitude and depth range of minor shocks that may trigger the nucleation process for major earthquakes. The magnitude and depth ranges of microshocks involved in the nucleation process differed among fault types. This research highlights the importance of monitoring seismic and atmospheric activity to improve earthquake forecasting and preparedness. Hence, it is possible to identify the most vulnerable seismic zone, location of the epicenter and probable magnitude spatio-temporal analysis.

A crisp step function is not an adequate threshold for studies of Markovian occurrence of large earthquakes, because it can lead to missing or pseudo links in an observed sequence that should be a Markov chain. A more realistic threshold is a fuzzy one where there is a transition magnitude band, located between those magnitudes that are too small for the earthquakes to be part of a Markovian process and those who are certainly large enough for the earthquakes to be part of it, where earthquakes may or may not be part of the process. This fuzzy threshold is described by a membership function that gives the probability of an earthquake with a given magnitude belonging to the process. We propose a membership function with probabilities in the transition band proportional to the seismic moment. To estimate empirical transition probabilities when considering a fuzzy magnitude threshold, we propose a counting strategy for the observed transitions and justify it through Monte Carlo simulations. The counting strategy is illustrated by application to the model from a previous seismic study of the Japan area by testing, through Monte Carlo simulations, how well the counting strategy results resemble optimum estimations of the transition probabilities. The simulations are also used to study the behavior of three Markovianity measures, and it is found that the peak values of these measures are not useful in identifying the true transition band, but that this band may be better identified by using the whole set of values taken by each measure for different transition band models. As an illustration, the measures were applied to real data from the previous study, a short set corresponding to a single realization, and found that the behavior of the measures does not agree with those expected from a crisp threshold, but agree, within the limitations of the data, with either a fuzzy threshold going from zero probability for magnitudes (Mle 6.9) to probability one for (Mge 7.2) or from zero probability for magnitudes (Mle 7.0) to probability one for (Mge 7.2).

This study examines trends in minimum and maximum temperatures at various climate stations located in different regions of Morocco for a period of five decades (1970 to 2019). Mann–Kendall, Sen’s estimator, Innovative Trend Analysis (ITA) and Innovative Polygon Trend Analysis (IPTA) were used in the analysis. The results show significant fluctuations, at different time scales, between minimum and maximum temperatures at all stations. In coastal areas, such as Rabat Sale, minimum temperatures fell during January and February while other months saw increases. Average minimum temperatures in Rabat Sale tend to fall by 0.5 °C. On the other hand, maximum temperatures in Rabat Sale rose by 0.2 °C. A decrease of 0.4 °C for T_min and 1.6 °C for T_max were observed in higher continental regions, such as Meknes. Other stations, such as Fez Sais (0.6 °C T_min and 2.6 °C T_max) and Taza (1.1 °C T_min and 2.6 °C T_max) showed an upward trend. Trends also vary, with notable increases in minimum and maximum temperatures, indicating different climatic dynamics according to altitude and locality. In particular, the ITA highlights a significant increase in annual maximum temperatures, with a P-value < 0.05 and trend slopes ranging from 0.0015 °C per year in Rabat Sale to 0.0076 °C per year in Taza. In addition, the IPTA results confirm diversity of upward and downward trends on monthly and seasonal scales, highlighting impact of geographical factors such as proximity to sea, topography, and continentality that contribute to formation of regional microclimates. The results highlight significant impact of climate change in Morocco.

This study explores the feasibility of using fluctuations in the recurrence magnitude dispersion factor (b-value) as a seismic precursor for the Mizoram earthquake that occurred on November 26, 2021, in the Indo-Burma region of northeast India. Employing a comprehensive and homogeneous earthquake catalog spanning from 1900 to 2020, the seismic analysis involved delustering and completeness testing. The research implements a sub-sectional b-value calculation method, dividing the study area into uniformly sized grid cells (2° × 2°) and performing temporal b-value mapping for each grid. The epicenter of the Mizoram earthquake was located within a grid cell characterized by an intermediate b-value. Time-series analysis of the b-value indicated a notable decline preceding the main event, suggesting its potential as a seismic precursor. The study also examines depth-dependent variations in the b-value, revealing an inverse relationship between the b-value and crustal stress. To evaluate the significance of b-value anomalies, the Kolmogorov–Smirnov (K-S) statistic was employed instead of visual inspection. Additionally, the research provides probabilistic estimates of seismic hazard parameters, including the most probable maximum yearly earthquake, mean return period, and probabilities of earthquakes of varying magnitudes. These findings contribute to a deeper understanding of the complex seismotectonic framework and high lithospheric variability in the investigated region.