scholarly journals Seismicity Pattern Changes Prior to the 2008 Ms7.3 Yutian Earthquake

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 118
Author(s):  
Qinghua Huang
Keyword(s):  
Background Level ◽  
Temporal Variations ◽  
Seismic Quiescence ◽  
Normal Faulting ◽  
Retrospective Case ◽  
Strong Earthquakes ◽  
Seismicity Pattern ◽  
Anomalous Zone ◽  
Potential Applications ◽  
Spatio Temporal

Seismicity pattern changes that are associated with strong earthquakes are an interesting topic with potential applications for natural hazard mitigation. As a retrospective case study of the Ms7.3 Yutian earthquake, which was an inland normal faulting event that occurred on 21 March 2008, the Region-Time-Length (RTL) method is applied to the seismological data of the China Earthquake Administration (CEA) to analyze the features of the seismicity pattern changes before the Yutian earthquake. The temporal variations of the RTL parameters of the earthquake epicenter showed that a quiescence anomaly of seismicity appeared in 2005. The Yutian main shock did not occur immediately after the local seismicity recovered to the background level, but with a time delay of about two years. The spatial variations of seismic quiescence indicated that an anomalous zone of seismic quiescence appeared near the Yutian epicentral region in 2005. This result is consistent with that obtained from the temporal changes of seismicity. The above spatio-temporal seismicity changes prior to the inland normal faulting Yutian earthquake showed similar features to those reported for some past strong earthquakes with inland strike faulting or thrust faulting. This study may provide useful information for understanding the seismogenic evolution of strong earthquakes.

scholarly journals Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

2020 ◽  
Vol 12 (3) ◽  
pp. 576 ◽  
Author(s):  
Zhonghua He ◽  
Liping Lei ◽  
Yuhui Zhang ◽  
Mengya Sheng ◽  
Changjiang Wu ◽  
...  
Keyword(s):  
Temporal Variations ◽  
Atmospheric Co2 ◽  
Total Carbon ◽  
Kriging Method ◽  
Good Reliability ◽  
Potential Applications ◽  
Satellite Sensors ◽  
Spatio Temporal ◽  
Atmospheric Co2 Concentrations

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

Spatio-Temporal Variations in Mangrove Vegetation in conjunction with Related Environmental Factors in Pichavaram (India) 1996-2016

2019 ◽  
Vol 6 (2) ◽  
pp. 15-25
Author(s):  
ripa M K ◽  
Ashwini Mudaliar ◽  
Nikhil Lele ◽  
Archana U Mankad ◽  
T V R Murthy
Keyword(s):  
Environmental Factors ◽  
Temporal Variations ◽  
Mangrove Vegetation ◽  
Spatio Temporal

Spatio-temporal Variations and Typicality in Pansori saseol - Kim So-hee Style Chunhyang-ga -

2019 ◽  
Vol 40 ◽  
pp. 213-241
Author(s):  
Sun-ja Jo
Keyword(s):  
Temporal Variations ◽  
Spatio Temporal

Spatio-temporal variations of winter wheat water requirement and climatic causes in Huang-Huai-Hai Farming Region

2012 ◽  
Vol 20 (3) ◽  
pp. 356-362 ◽  
Author(s):  
Xiao-Lin YANG ◽  
Zhen-Wei SONG ◽  
Hong WANG ◽  
Quan-Hong SHI ◽  
Fu CHEN ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Temporal Variations ◽  
Water Requirement ◽  
Spatio Temporal

ASSESSMENT OF THE SPATIO-TEMPORAL VARIATIONS IN GROUND WATER STORAGE USING DOWN-SCALED GRACE DATA

2018 ◽  
Author(s):  
Hossein Sahour ◽  
◽  
Mohamed Sultan ◽  
Karem Abdelmohsen ◽  
Sita Karki ◽  
...  
Keyword(s):  
Ground Water ◽  
Water Storage ◽  
Temporal Variations ◽  
Grace Data ◽  
Spatio Temporal

scholarly journals A robust machine learning approach to SDG data segmentation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kassim S. Mwitondi ◽  
Isaac Munyakazi ◽  
Barnabas N. Gatsheni
Keyword(s):  
Temporal Variations ◽  
Data Repository ◽  
The Sustainable Development ◽  
Complex Interactions ◽  
Data Segmentation ◽  
Technological Advances ◽  
Machine Learning Approach ◽  
Development Goals ◽  
Data Source ◽  
Spatio Temporal

Abstract In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

Spatio-temporal variations of sea surface halocarbon concentrations and fluxes from southern Yellow Sea

2014 ◽  
Vol 121 (2) ◽  
pp. 369-388 ◽  
Author(s):  
Gui-Peng Yang ◽  
Bin Yang ◽  
Xiao-Lan Lu ◽  
Hai-Bing Ding ◽  
Zhen He
Keyword(s):  
Yellow Sea ◽  
Temporal Variations ◽  
Sea Surface ◽  
Southern Yellow Sea ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document