Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio.With improved seismometers and better global coverage,a sharp increase i...Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio.With improved seismometers and better global coverage,a sharp increase in the volume of recorded seismic data has been achieved.This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods.In this study,we develop 1D deep Residual Neural Network(ResNet),for tackling the problem of seismic signal detection and phase identification.This method is trained and tested on the dataset recorded by the Southern California Seismic Network.Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases.Compared to previously proposed deep learning methods,the introduced framework achieves around 4%improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center.The model generalizability is also tested further on the STanford EArthquake Dataset.In addition,the experimental result on the same subset of the STanford EArthquake Dataset,when masked by different noise levels,demonstrates the model’s robustness in identifying the seismic phases of small magnitude.展开更多
Large earthquakes are global natural disasters,which accompany intense ground shaking that can cause damages to buildings and infrastructure and sometimes lead to secondary disasters such as tsunamis and landslides.Th...Large earthquakes are global natural disasters,which accompany intense ground shaking that can cause damages to buildings and infrastructure and sometimes lead to secondary disasters such as tsunamis and landslides.The severity and spatial distribution of seismic hazards highly depend on the magnitude and rupture process of the earthquake.展开更多
Selective area electron diffraction(SAED)patterns can provide valuable insight into the structure of a material.However,the manual identification of collected patterns can be a significant bottleneck in the overall ph...Selective area electron diffraction(SAED)patterns can provide valuable insight into the structure of a material.However,the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow.In this work,we utilize the recent advances in computer vision and machine learning(ML)to automate the indexing of SAED patterns.The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys.The most successful approach trained a neural network(NN)to make a classification of the phase and zone axis,and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification.The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.展开更多
In unit cell simulations,identification of ordered phases in block copolymers(BCPs)is a tedious and time-consuming task,impeding the advancement of more streamlined and potentially automated research workflows.In this...In unit cell simulations,identification of ordered phases in block copolymers(BCPs)is a tedious and time-consuming task,impeding the advancement of more streamlined and potentially automated research workflows.In this study,we propose a scattering-based automated identification strategy(SAIS)for characterization and identification of ordered phases of BCPs based on their computed scattering patterns.Our approach leverages the scattering theory of perfect crystals to efficiently compute the scattering patterns of periodic morphologies in a unit cell.In the first stage of the SAIS,phases are identified by comparing reflection conditions at a sequence of Miller indices.To confirm or refine the identification results of the first stage,the second stage of the SAIS introduces a tailored residual between the test phase and each of the known candidate phases.Furthermore,our strategy incorporates a variance-like criterion to distinguish background species,enabling its extension to multi-species BCP systems.It has been demonstrated that our strategy achieves exceptional accuracy and robustness while requiring minimal computational resources.Additionally,the approach allows for real-time expansion and improvement to the candidate phase library,facilitating the development of automated research workflows for designing specific ordered structures and discovering new ordered phases in BCPs.展开更多
Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters pr...Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.展开更多
This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave loc...This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.展开更多
The abrasive crystalline phases in Colgate Regular, Aquafresh , and Macleans tcothpaste were identifiedby X-ray powder diffractometry to be calcium ortho-phaphate hydrate (CaHPO_4 . 2H_2O),calcite (CaCO_3) ,and aragon...The abrasive crystalline phases in Colgate Regular, Aquafresh , and Macleans tcothpaste were identifiedby X-ray powder diffractometry to be calcium ortho-phaphate hydrate (CaHPO_4 . 2H_2O),calcite (CaCO_3) ,and aragonite and calcite (beth CaCO_3), respectively.展开更多
Precipitation-hardenable commercial Mg alloy QE22(Mg-2.5Ag-2.ONd-0.7Zr,wt.%)has excellent mechan-ical properties,but precipitates in this alloy have not been well understood.In this work,precipitate phasesγ",γ,...Precipitation-hardenable commercial Mg alloy QE22(Mg-2.5Ag-2.ONd-0.7Zr,wt.%)has excellent mechan-ical properties,but precipitates in this alloy have not been well understood.In this work,precipitate phasesγ",γ,andδformed during the isothermal ageing process at 150,200,250,and 300℃have been characterized using atomic-resolution high-angle annular dark-field scanning transmission electron mi-croscopy and atomic-scale energy-dispersive X-ray spectroscopy.The morphology,crystal structure,and orientation relationship of these precipitate phases have been determined.Domain boundaries usually exist in a singleγparticle,which can be characterized by a separation vector of[1(1)01]_(α).Theδphase forms in situ from its precursorγphase,consequently leading to the formation of three different variants within a single 8 particle.The nucleation of theδphase is strongly related to the domain boundaries of the y phase.The formation of theγphase may be promoted by its precursorγ"phase.The similarities in atomic structures of theγ",γ,andδphases are described and discussed,indicating that transfor-mations between these precipitate phases can be accomplished through the diffusion of added alloying elements.展开更多
The micro-area characterization experiments like scanning Kelvin probe force microscope(SKPFM)and Kernel average misorientation have the defects of complex sample preparation and occasional errors in test results,whic...The micro-area characterization experiments like scanning Kelvin probe force microscope(SKPFM)and Kernel average misorientation have the defects of complex sample preparation and occasional errors in test results,which makes it impossible to accurately and quickly analyze the pitting behavior induced by inclusions in some cases,prompting attempts to turn to simulation calculation research.The method of calculating band structure and work function can be used to replace current-sensing atomic force microscopy and SKPFM to detect the potential and conductivity of the sample.The band structure results show that Al_(2)O_(3) inclusion is an insulator and non-conductive,and it will not form galvanic corrosion with the matrix.Al_(2)O_(3) inclusion does not dissolve because its work function is higher than that of the matrix.Moreover,the stress concentration of the matrix around the inclusion can be characterized by first-principles calculation coupled with finite element simulation.The results show that the stress concentration degree of the matrix around Al_(2)O_(3) inclusion is serious,and the galvanic corrosion is formed between the high and the low stress concentration areas,which can be used to explain the reason of the pitting induced by Al_(2)O_(3) inclusions.展开更多
The phase composition, phase transition and phase structure transformation of the wire-cut section of functionally graded WC-Co cemented carbide with dual phase structure were investigated by XRD phase analysis. It is...The phase composition, phase transition and phase structure transformation of the wire-cut section of functionally graded WC-Co cemented carbide with dual phase structure were investigated by XRD phase analysis. It is shown that the composition of η phase in the core zone is Co_3W_3C (M_6 C type). The structure of cobalt based solid solution binder phase is fcc type. At the cooling stage of the sintering process, the phase transition of η phase, i.e. M_6C→M_12C and the martensitic phase transition of the cobalt based solid solution binder phase, i.e. fcc→hcp are suppressed, which facilitates the strengthening of the alloy. Because the instantaneous temperature of the discharge channel is as high as 10 000 ℃ during the wire cutting process, the processed surface is oxidized. Nevertheless, the oxide layer thickness is in micro grade. In the oxide film, η phase is decomposed into W_2C and CoO, and cobalt based solid solution binder is selectively oxidized, while WC remains stable due to the existence of carbon containing liquid organic cutting medium.展开更多
Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i...Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i.e.,pre-trained)deep-learning detector/picker models,there are still some cases where the direct applications of such models do not generalize well.In such cases,substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one.To address this challenge,we present Blockly Earthquake Transformer(BET),a deep-learning platform for efficient customization of deep-learning phase pickers.BET implements Earthquake Transformer as its baseline model,and offers transfer learning and fine-tuning extensions.BET provides an interactive dashboard to customize a model based on a particular dataset.Once the parameters are specified,BET executes the corresponding phase-picking task without direct user interaction with the base code.Within the transfer-learning module,BET extends the application of a deep-learning P and S phase picker to more specific phases(e.g.,Pn,Pg,Sn and Sg phases).In the fine-tuning module,the model performance is enhanced by customizing the model architecture.This no-code platform is designed to quickly deploy reusable workflows,build customized models,visualize training processes,and produce publishable figures in a lightweight,interactive,and open-source Python toolbox.展开更多
In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and a...In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.展开更多
The isothermal section of the Ce-Co-Al ternary system at 573 K was investigated by X-ray powder diffraction （XRD）, scanning electron microscopy （SEM） and energy dispersive X-ray spectroscopy （EDS） techniques. It...The isothermal section of the Ce-Co-Al ternary system at 573 K was investigated by X-ray powder diffraction （XRD）, scanning electron microscopy （SEM） and energy dispersive X-ray spectroscopy （EDS） techniques. It consisted of 19 single-phase regions, 46 two-phase regions and 25 three-phase regions. Four ternary compounds, namely CeCoAl, Ce2Co15Al2, CeCoAl4, CeCo2Al8, were confirmed in this system. At 573 K, the maximum solid solubilities of Co in CeAl2 and Al in CeCo2 were about 10.4 at.% and 10.0 at.%, respectively. The homogeneity range of CoAl phase extended from about 46.0 to 56.0 at.% Al.展开更多
The 4th Member of Shahejie Formation in Yanjia Area is located in the north wing of the central uplift zone of Dongying Depression, facing Chenjiazhuang Uplift in the north and Minfeng subsag in the south. Large-scale...The 4th Member of Shahejie Formation in Yanjia Area is located in the north wing of the central uplift zone of Dongying Depression, facing Chenjiazhuang Uplift in the north and Minfeng subsag in the south. Large-scale glutenite bodies developed during the 4th Member of Shahejie Formation, forming a distribution pattern of "canal and segment correspondence" with relatively low degree of exploration. Based on the study of Yanjia deep paleogeomorphology, sand body distribution characteristics and seismic facies characteristics, combined with the drilling situation, the source system and sedimentary evolution of glutenite are studied to determine the source direction and sedimentary evolution characteristics of deep glutenite in Yanjia area. Based on the detailed description of the glutenite reservoir by various geophysical methods, the main controlling factors for the formation of deep oil and gas reservoirs in this area are determined by means of oil source correlation, source rock analysis, oil and gas thermal evolution law, reservoir anatomy, matching relationship between oil and gas phase state and lower limit of oil and gas-bearing physical properties, etc.展开更多
文摘Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio.With improved seismometers and better global coverage,a sharp increase in the volume of recorded seismic data has been achieved.This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods.In this study,we develop 1D deep Residual Neural Network(ResNet),for tackling the problem of seismic signal detection and phase identification.This method is trained and tested on the dataset recorded by the Southern California Seismic Network.Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases.Compared to previously proposed deep learning methods,the introduced framework achieves around 4%improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center.The model generalizability is also tested further on the STanford EArthquake Dataset.In addition,the experimental result on the same subset of the STanford EArthquake Dataset,when masked by different noise levels,demonstrates the model’s robustness in identifying the seismic phases of small magnitude.
基金supported by the National Natural Science Foundation of China(Grant No.42488301)。
文摘Large earthquakes are global natural disasters,which accompany intense ground shaking that can cause damages to buildings and infrastructure and sometimes lead to secondary disasters such as tsunamis and landslides.The severity and spatial distribution of seismic hazards highly depend on the magnitude and rupture process of the earthquake.
基金The funding for this work was provided by the U.S.Department of Energy,Office of Nuclear Energy Contract DEAC07-051D14517The CNN work was partially supported by the National Science Foundation(award number 1552716).
文摘Selective area electron diffraction(SAED)patterns can provide valuable insight into the structure of a material.However,the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow.In this work,we utilize the recent advances in computer vision and machine learning(ML)to automate the indexing of SAED patterns.The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys.The most successful approach trained a neural network(NN)to make a classification of the phase and zone axis,and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification.The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.
基金This work was supported by the National Natural Science Foundation of China(Grants No.21873021).
文摘In unit cell simulations,identification of ordered phases in block copolymers(BCPs)is a tedious and time-consuming task,impeding the advancement of more streamlined and potentially automated research workflows.In this study,we propose a scattering-based automated identification strategy(SAIS)for characterization and identification of ordered phases of BCPs based on their computed scattering patterns.Our approach leverages the scattering theory of perfect crystals to efficiently compute the scattering patterns of periodic morphologies in a unit cell.In the first stage of the SAIS,phases are identified by comparing reflection conditions at a sequence of Miller indices.To confirm or refine the identification results of the first stage,the second stage of the SAIS introduces a tailored residual between the test phase and each of the known candidate phases.Furthermore,our strategy incorporates a variance-like criterion to distinguish background species,enabling its extension to multi-species BCP systems.It has been demonstrated that our strategy achieves exceptional accuracy and robustness while requiring minimal computational resources.Additionally,the approach allows for real-time expansion and improvement to the candidate phase library,facilitating the development of automated research workflows for designing specific ordered structures and discovering new ordered phases in BCPs.
基金supported in part by the National Natural Science Foundation of China(No.52177085)Science and Technology Planning Project of Guangzhou(No.202102021208)。
文摘Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.
文摘This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.
基金HWC acknowledges the support from the National Key Research and Development Program of China(No.2021YFB03702101)National Natural Science Foundation of China(Nos.52071033,52101150)+3 种基金Sichuan Science and Technology Program(No.2022YFG0287)Fundamental Research Funds for the Central Universities(No.2682021CX114)Project on Function Development of Large-scale Instruments of Chongqing University(No.gnkf2022017)JFN acknowledges the financial support from the Australian Research Council.
文摘Precipitation-hardenable commercial Mg alloy QE22(Mg-2.5Ag-2.ONd-0.7Zr,wt.%)has excellent mechan-ical properties,but precipitates in this alloy have not been well understood.In this work,precipitate phasesγ",γ,andδformed during the isothermal ageing process at 150,200,250,and 300℃have been characterized using atomic-resolution high-angle annular dark-field scanning transmission electron mi-croscopy and atomic-scale energy-dispersive X-ray spectroscopy.The morphology,crystal structure,and orientation relationship of these precipitate phases have been determined.Domain boundaries usually exist in a singleγparticle,which can be characterized by a separation vector of[1(1)01]_(α).Theδphase forms in situ from its precursorγphase,consequently leading to the formation of three different variants within a single 8 particle.The nucleation of theδphase is strongly related to the domain boundaries of the y phase.The formation of theγphase may be promoted by its precursorγ"phase.The similarities in atomic structures of theγ",γ,andδphases are described and discussed,indicating that transfor-mations between these precipitate phases can be accomplished through the diffusion of added alloying elements.
基金supported by the National Natural Science Foundation of China(Nos.52364044 and 52204364)Central Guidance on Local Science and Technology Development Fund Projects of Inner Mongolia Autonomous Region(No.2022ZY0090)Basic Scientific Research Business Expenses of Colleges and Universities in Inner Mongolia Autonomous Region(Nos.2023QNJS011 and 0406082226).
文摘The micro-area characterization experiments like scanning Kelvin probe force microscope(SKPFM)and Kernel average misorientation have the defects of complex sample preparation and occasional errors in test results,which makes it impossible to accurately and quickly analyze the pitting behavior induced by inclusions in some cases,prompting attempts to turn to simulation calculation research.The method of calculating band structure and work function can be used to replace current-sensing atomic force microscopy and SKPFM to detect the potential and conductivity of the sample.The band structure results show that Al_(2)O_(3) inclusion is an insulator and non-conductive,and it will not form galvanic corrosion with the matrix.Al_(2)O_(3) inclusion does not dissolve because its work function is higher than that of the matrix.Moreover,the stress concentration of the matrix around the inclusion can be characterized by first-principles calculation coupled with finite element simulation.The results show that the stress concentration degree of the matrix around Al_(2)O_(3) inclusion is serious,and the galvanic corrosion is formed between the high and the low stress concentration areas,which can be used to explain the reason of the pitting induced by Al_(2)O_(3) inclusions.
基金Projects(50323008, 50574104) supported by the National Natural Science Foundation of ChinaProject (04JJ3084) supported by the Natural Science Foundation of Hunan Province, China
文摘The phase composition, phase transition and phase structure transformation of the wire-cut section of functionally graded WC-Co cemented carbide with dual phase structure were investigated by XRD phase analysis. It is shown that the composition of η phase in the core zone is Co_3W_3C (M_6 C type). The structure of cobalt based solid solution binder phase is fcc type. At the cooling stage of the sintering process, the phase transition of η phase, i.e. M_6C→M_12C and the martensitic phase transition of the cobalt based solid solution binder phase, i.e. fcc→hcp are suppressed, which facilitates the strengthening of the alloy. Because the instantaneous temperature of the discharge channel is as high as 10 000 ℃ during the wire cutting process, the processed surface is oxidized. Nevertheless, the oxide layer thickness is in micro grade. In the oxide film, η phase is decomposed into W_2C and CoO, and cobalt based solid solution binder is selectively oxidized, while WC remains stable due to the existence of carbon containing liquid organic cutting medium.
基金funded by a Discovery Grant(RGPIN-2018-03752)from the Natural Science and Engineering Research Council of Canada(PA)This is NRCan publication number 20220610.
文摘Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i.e.,pre-trained)deep-learning detector/picker models,there are still some cases where the direct applications of such models do not generalize well.In such cases,substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one.To address this challenge,we present Blockly Earthquake Transformer(BET),a deep-learning platform for efficient customization of deep-learning phase pickers.BET implements Earthquake Transformer as its baseline model,and offers transfer learning and fine-tuning extensions.BET provides an interactive dashboard to customize a model based on a particular dataset.Once the parameters are specified,BET executes the corresponding phase-picking task without direct user interaction with the base code.Within the transfer-learning module,BET extends the application of a deep-learning P and S phase picker to more specific phases(e.g.,Pn,Pg,Sn and Sg phases).In the fine-tuning module,the model performance is enhanced by customizing the model architecture.This no-code platform is designed to quickly deploy reusable workflows,build customized models,visualize training processes,and produce publishable figures in a lightweight,interactive,and open-source Python toolbox.
基金the National Natural Science Foundation of China(Nos.41804047 and 42111540260)Fundamental Research Funds of the Institute of Geophysics,China Earthquake Administration(NO.DQJB19A0114)the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(No.IGGCAS-201904).
文摘In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.
基金supported by the National Natural Science Foundation of China (50631040, 50861006, 50961005)
文摘The isothermal section of the Ce-Co-Al ternary system at 573 K was investigated by X-ray powder diffraction （XRD）, scanning electron microscopy （SEM） and energy dispersive X-ray spectroscopy （EDS） techniques. It consisted of 19 single-phase regions, 46 two-phase regions and 25 three-phase regions. Four ternary compounds, namely CeCoAl, Ce2Co15Al2, CeCoAl4, CeCo2Al8, were confirmed in this system. At 573 K, the maximum solid solubilities of Co in CeAl2 and Al in CeCo2 were about 10.4 at.% and 10.0 at.%, respectively. The homogeneity range of CoAl phase extended from about 46.0 to 56.0 at.% Al.
文摘The 4th Member of Shahejie Formation in Yanjia Area is located in the north wing of the central uplift zone of Dongying Depression, facing Chenjiazhuang Uplift in the north and Minfeng subsag in the south. Large-scale glutenite bodies developed during the 4th Member of Shahejie Formation, forming a distribution pattern of "canal and segment correspondence" with relatively low degree of exploration. Based on the study of Yanjia deep paleogeomorphology, sand body distribution characteristics and seismic facies characteristics, combined with the drilling situation, the source system and sedimentary evolution of glutenite are studied to determine the source direction and sedimentary evolution characteristics of deep glutenite in Yanjia area. Based on the detailed description of the glutenite reservoir by various geophysical methods, the main controlling factors for the formation of deep oil and gas reservoirs in this area are determined by means of oil source correlation, source rock analysis, oil and gas thermal evolution law, reservoir anatomy, matching relationship between oil and gas phase state and lower limit of oil and gas-bearing physical properties, etc.
