Machine learning(ML)efficiently and accurately processes dense seismic array data,improving earthquake catalog creation,which is crucial for understanding earthquake sequences and fault systems;analyzing its reliabili...Machine learning(ML)efficiently and accurately processes dense seismic array data,improving earthquake catalog creation,which is crucial for understanding earthquake sequences and fault systems;analyzing its reliability is also essential.An M5.8 earthquake struck Alxa Left Banner,Inner Mongolia,China on April 15,2015,a region with limited CENC monitoring capabilities,making analysis challenging.However,abundant data from ChinArray provided valuable observations for assessing the event.This study leveraged ChinArray data from the 2015 Alxa Left Banner earthquake sequence,employing machine learning(specifically PhaseNet,a deep learning method,and GaMMA,a Bayesian approach)for automated seismic phase picking,association,and location analysis.Our generated catalog,comprising 10,432 phases from 708 events,is roughly ten times larger than the CENC catalog,encompassing all CENC events with strong consistency.A slight magnitude overestimation is observed only at lower magnitudes.Furthermore,the catalog adheres to the Gutenberg-Richter and Omori laws spatially,temporally,and in magnitude distribution,demonstrating its high reliability.Double-difference tomography refined locations for 366 events,yielding a more compact spatial distribution with horizontal errors within 100m,vertical errors within 300m,and travel-time residuals within 0.05s.Depths predominantly range from 10-30km.Aftershocks align primarily NEE,with the mainshock east of the aftershock zone.The near-vertical main fault plane dips northwestward,exhibiting a Y-shaped branching structure,converging at depth and expanding towards the surface.FOCMEC analysis,using first motion and amplitude ratios,yielded focal mechanism solutions for 10 events,including the mainshock.These solutions consistently indicate a strike-slip mechanism with a minor extensional component.Integrating the earthquake sequence's spatial distribution and focal mechanisms suggests the seismogenic structure is a negative flower structure,consistent with the Dengkou-Benjing fault.Comparing the CENC and ML-generated catalogs using the maximum curvature(MAXC)method reveals a 0.6 decrease in completeness magnitude(M_(C)).However,magnitude-frequency distribution discrepancies above the MAXC-estimated M_(C)suggest MAXC may underestimate both M_(C)and the b-value.This study analyzes the 2015 Alxa Left Banner M5.8 earthquake using a reliable,MLgenerated earthquake catalog,revealing detailed information about the sequence,faulting structure,aftershock distribution,and stress characteristics.展开更多
The evolvement of a vulnerable ecological region is a dynamic process, which is affected by various factors. During the evolvement process, human activities have a decisive effect. The purpose of studying vulnerable e...The evolvement of a vulnerable ecological region is a dynamic process, which is affected by various factors. During the evolvement process, human activities have a decisive effect. The purpose of studying vulnerable ecological region is to control human economic activities and to develop a negative feedback modulation mechanism.This paper established a model of vulnerable ecological region's evolvement by considering four synthetic variables.These synthetic variables are ecological carrying capacity, ecological resilience, economic development intensity, and economic development velocity. Finally, Ongniud Banner and Aohan Banner in North China were taken as study cases to simulate the evolvement processes of vulnerable ecological regions under different conditions of economic development. The results show that human activities have an important influence on the evolvement trend of vulnerable ecological region.展开更多
Radar remote sensing can acquire information of sub-surface covered by sand in arid area, detect surface roughness and vegetation coronet's layer and linear feature such as linear structure and channel sensitively...Radar remote sensing can acquire information of sub-surface covered by sand in arid area, detect surface roughness and vegetation coronet's layer and linear feature such as linear structure and channel sensitively. With sediment facies analysis, this paper studies the features of environmental evolution in mid-late Epipleistocene (60 ka BP-20 ka BP) in northeastern Ejin Banner. The conclusions are listed as follows: (1) The evolution of the three lakes, i.e. Gaxunnur, Sugunur and Tian'e lakes, are dominated by faults and regional climate. (2) By analyzing sedimentary section of old Juyanze Lake, the three lakes used to be a large outflow lake before 50 ka BP in northeastern Ejin Banner, and at 50 ka BP, temperature declined rapidly in northwestern China. The event caused the lake's shrinkage. (3) By fault activity uplift in the northern part of old Juyan Lake and depression in the southern part, the lake's water followed from north to south at around 35 ka BP, old Juyanze fluvial fan was formed. At the same time, Juyan Lake separated from Sugunur Lake and Wentugunr old channel was abandoned. (4) In recent 2000 years, Ruoshui River is a wandering river, sometimes it flows into Juyan Lake and sometimes Sugunur and Gaxunnur lakes. Due to human activities and over exploitation, the oasis ecosystem is rapidly degenerated in 15 years (1986-2000).展开更多
It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast...It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.展开更多
Topography-induced potential vorticity （PV） banners over a mesoscale topography （Dabie Mountain, hereafter DM） in eastern China, under an idealized dry adiabatic flow, are studied with a mesoscale numerical model,...Topography-induced potential vorticity （PV） banners over a mesoscale topography （Dabie Mountain, hereafter DM） in eastern China, under an idealized dry adiabatic flow, are studied with a mesoscale numerical model, ARPS. PV banners generate over the leeside of the DM with a maximal intensity of ～1.5 PVU, and extend more than 100 km downstream, while the width varies from several to tens of kilometers, which contrasts with the half-width of the peaks along the ridge of the DM. Wave breaking occurs near the leeside surface of the DM, and leads to a strong PV generation. Combining with the PV generation, due to the friction and the flow splitting upstream, the PV is advected downstream, and then forms the PV banners over the DM. The PV banners are sensitive to the model resolution, Coriolis force, friction, subgrid turbulent mixing, stratification, the upstream wind speed and wind direction. The negative PV banners have a more compact connection with the low level turbulent kinetic energy. The PV banners are built up by the baroclinic and barotropic components. The barotropic-associated PV can identify the distribution of the PV banners, while the baroclinic one only has important contributions on the flanks and on the leeside near the topography. PV fluxes are diagnosed to investigate the influence of friction on the PV banners. Similar patterns are found between the total PV flux and the advective PV flux, except near the surface and inside the dipole of the PV banners, where the nonadvective PV flux associated with the friction has a net negative contribution.展开更多
基金funded by the Inner Mongolia Natural Science Foundation(No.2024MS04021)the Science and Technology Plan of Inner Mongolia Autonomous Region(No.2023YFSH0004)the Director Fund of the Inner Mongolia Autonomous Region Seismological Bureau(No.2023GG01,No.2023GG02,No.2023MS05,No.2023QN13)。
文摘Machine learning(ML)efficiently and accurately processes dense seismic array data,improving earthquake catalog creation,which is crucial for understanding earthquake sequences and fault systems;analyzing its reliability is also essential.An M5.8 earthquake struck Alxa Left Banner,Inner Mongolia,China on April 15,2015,a region with limited CENC monitoring capabilities,making analysis challenging.However,abundant data from ChinArray provided valuable observations for assessing the event.This study leveraged ChinArray data from the 2015 Alxa Left Banner earthquake sequence,employing machine learning(specifically PhaseNet,a deep learning method,and GaMMA,a Bayesian approach)for automated seismic phase picking,association,and location analysis.Our generated catalog,comprising 10,432 phases from 708 events,is roughly ten times larger than the CENC catalog,encompassing all CENC events with strong consistency.A slight magnitude overestimation is observed only at lower magnitudes.Furthermore,the catalog adheres to the Gutenberg-Richter and Omori laws spatially,temporally,and in magnitude distribution,demonstrating its high reliability.Double-difference tomography refined locations for 366 events,yielding a more compact spatial distribution with horizontal errors within 100m,vertical errors within 300m,and travel-time residuals within 0.05s.Depths predominantly range from 10-30km.Aftershocks align primarily NEE,with the mainshock east of the aftershock zone.The near-vertical main fault plane dips northwestward,exhibiting a Y-shaped branching structure,converging at depth and expanding towards the surface.FOCMEC analysis,using first motion and amplitude ratios,yielded focal mechanism solutions for 10 events,including the mainshock.These solutions consistently indicate a strike-slip mechanism with a minor extensional component.Integrating the earthquake sequence's spatial distribution and focal mechanisms suggests the seismogenic structure is a negative flower structure,consistent with the Dengkou-Benjing fault.Comparing the CENC and ML-generated catalogs using the maximum curvature(MAXC)method reveals a 0.6 decrease in completeness magnitude(M_(C)).However,magnitude-frequency distribution discrepancies above the MAXC-estimated M_(C)suggest MAXC may underestimate both M_(C)and the b-value.This study analyzes the 2015 Alxa Left Banner M5.8 earthquake using a reliable,MLgenerated earthquake catalog,revealing detailed information about the sequence,faulting structure,aftershock distribution,and stress characteristics.
基金Under the auspices of the National Natural Science Foundation of China (No. 40301052)the 10th Five-year National Key Technology R&D Program of Ministry of Science and Technology (No. 2001BA606A-01)
文摘The evolvement of a vulnerable ecological region is a dynamic process, which is affected by various factors. During the evolvement process, human activities have a decisive effect. The purpose of studying vulnerable ecological region is to control human economic activities and to develop a negative feedback modulation mechanism.This paper established a model of vulnerable ecological region's evolvement by considering four synthetic variables.These synthetic variables are ecological carrying capacity, ecological resilience, economic development intensity, and economic development velocity. Finally, Ongniud Banner and Aohan Banner in North China were taken as study cases to simulate the evolvement processes of vulnerable ecological regions under different conditions of economic development. The results show that human activities have an important influence on the evolvement trend of vulnerable ecological region.
文摘Radar remote sensing can acquire information of sub-surface covered by sand in arid area, detect surface roughness and vegetation coronet's layer and linear feature such as linear structure and channel sensitively. With sediment facies analysis, this paper studies the features of environmental evolution in mid-late Epipleistocene (60 ka BP-20 ka BP) in northeastern Ejin Banner. The conclusions are listed as follows: (1) The evolution of the three lakes, i.e. Gaxunnur, Sugunur and Tian'e lakes, are dominated by faults and regional climate. (2) By analyzing sedimentary section of old Juyanze Lake, the three lakes used to be a large outflow lake before 50 ka BP in northeastern Ejin Banner, and at 50 ka BP, temperature declined rapidly in northwestern China. The event caused the lake's shrinkage. (3) By fault activity uplift in the northern part of old Juyan Lake and depression in the southern part, the lake's water followed from north to south at around 35 ka BP, old Juyanze fluvial fan was formed. At the same time, Juyan Lake separated from Sugunur Lake and Wentugunr old channel was abandoned. (4) In recent 2000 years, Ruoshui River is a wandering river, sometimes it flows into Juyan Lake and sometimes Sugunur and Gaxunnur lakes. Due to human activities and over exploitation, the oasis ecosystem is rapidly degenerated in 15 years (1986-2000).
基金supported by the National Key Research and Development Program of China (2016YFC0501107)the Project of Ordos Science and Technology Program (2017006)the Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China (2014FY110800)
文摘It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.
基金supported bythe National Key Scientific and Technological Project2006BAC02B03, 2004CB418300, GYHY2000706033 under the FANEDD 200325the Specialized Research Fund for the Doctoral Program of Higher Education (No.20080284019)National Natural Science Foundation of China under Grant Nos. 40705019, 40325014 and 40333031
文摘Topography-induced potential vorticity （PV） banners over a mesoscale topography （Dabie Mountain, hereafter DM） in eastern China, under an idealized dry adiabatic flow, are studied with a mesoscale numerical model, ARPS. PV banners generate over the leeside of the DM with a maximal intensity of ～1.5 PVU, and extend more than 100 km downstream, while the width varies from several to tens of kilometers, which contrasts with the half-width of the peaks along the ridge of the DM. Wave breaking occurs near the leeside surface of the DM, and leads to a strong PV generation. Combining with the PV generation, due to the friction and the flow splitting upstream, the PV is advected downstream, and then forms the PV banners over the DM. The PV banners are sensitive to the model resolution, Coriolis force, friction, subgrid turbulent mixing, stratification, the upstream wind speed and wind direction. The negative PV banners have a more compact connection with the low level turbulent kinetic energy. The PV banners are built up by the baroclinic and barotropic components. The barotropic-associated PV can identify the distribution of the PV banners, while the baroclinic one only has important contributions on the flanks and on the leeside near the topography. PV fluxes are diagnosed to investigate the influence of friction on the PV banners. Similar patterns are found between the total PV flux and the advective PV flux, except near the surface and inside the dipole of the PV banners, where the nonadvective PV flux associated with the friction has a net negative contribution.
