To improve the accuracy of microseismic inversion,seismic anisotropy and moment tensor source should be carefully considered in the forward modelling stage.In this study,3D microseismic anisotropy wave forward modelli...To improve the accuracy of microseismic inversion,seismic anisotropy and moment tensor source should be carefully considered in the forward modelling stage.In this study,3D microseismic anisotropy wave forward modelling with a moment tensor source was proposed.The modelling was carried out based on a rotated-staggered-grid(RSG)scheme.In contrast to staggered-grids,the RSG scheme defines the velocity components and densities at the same grid,as do the stress components and elastic parameters.Therefore,the elastic moduli do not need to be interpolated.In addition,the detailed formulation and implementation of moment-tensor source loaded on the RSG was presented by equating the source to the stress increments.Meanwhile,the RSG-based 3D wave equation forward modelling was performed in parallel using compute unified device architecture(CUDA)programming on a graphics processing unit(GPU)to improve its efficiency.Numerical simulations including homogeneous and anisotropic models were carried out using the method proposed in this paper,and compared with other methods to prove the reliability of this method.Furthermore,the high efficiency of the proposed approach was evaluated.The results show that the computational efficiency of proposed method can be improved by about two orders of magnitude compared with traditional central processing unit(CPU)computing methods.It could not only help the analysis of microseismic full wavefield records,but also provide support for passive source inversion,including location and focal mechanism inversion,and velocities inversion.展开更多
The forecasing of maximum magnitude(M_(max))for fluid-induced seismicity is a critical challenge in risk management for geothermal energy development,unconventional hydrocarbon extraction,and wastewater disposal.This ...The forecasing of maximum magnitude(M_(max))for fluid-induced seismicity is a critical challenge in risk management for geothermal energy development,unconventional hydrocarbon extraction,and wastewater disposal.This review systematically examines eight forecasting models spanning three categories:statistical models(G-R relation,extreme value theory,order statistics),semi-physical models(injection volume/time correlations),and physics-based models(rupture mechanics,rate-state friction,pressure diffusion).Through tripartite analysis of parameter sensitivity,scientific interpretability,and engineering validation,we reveal fundamental constraints:(1)Model performance is strictly governed by parameter coverage.Statistical models relying solely on seismic catalogs neglect fault mechanics parameters.Semi-physical models establish empiricalΔV/T-M_(max) relationships but fail to explain dynamic slip processes and post-injection"trailing effects".Physics-based models integrate fault stress,friction evolution,and fluid diffusion,offering unparalleled mechanistic insights,yet their computational demands pose challenges for real-time field applications(only 7.5%of 228 global cases).(2)Scientific interpretability exhibits hierarchical deficiencies.While physics-based models quantitatively resolve stress transfer and friction evolution(e.g.,rate-state models distinguishing seismic/aseismic slip),forecasting of far-field triggering and multi-fault interactions remain reliant on immature theories like poroelastic coupling.(3)Systemic validation biases emerge:57.5%of cases concentrate in stable North American terrains with underrepresentation of Pacific Rim high-stress zones;79.8%involve geothermal/shale gas operations lacking CO_(2) sequestration validations;M≥4.0 events constitute merely 12.7%of datasets,leaving M≥5.0 predictive capacity unverified.We propose transformative pathways including hybrid“physics-core t machine learning”architectures,standardized multiscenario validation protocols,and dynamic traffic-light systems,aiming to advance cross-disciplinary forecasting paradigms for engineering risk governance.展开更多
Neurotrophins are a family of proteins that regulate neural survival, development, function and plasticity in the central and the peripheral nervous system. There are four neurotrophins： NGF, BDNF, NT-3 and NT-4. Amo...Neurotrophins are a family of proteins that regulate neural survival, development, function and plasticity in the central and the peripheral nervous system. There are four neurotrophins： NGF, BDNF, NT-3 and NT-4. Among them, BDNF is mostly studied in the taste system due to its high expression. Recent studies have shown BDNF play an important role in the developmental and mature taste system, by regulating survival of taste cells and geniculate ganglion neurons, and maintaining and guiding taste nerve innervations. These studies imply BDNF has great potentialities for therapeutic usage to enhance sensory regeneration following nerve injury, with aging, and in some neurodegenerative diseases.展开更多
基金financially supported by the National Natural Science Foundation of China(No.42272204)the National Key Research and Development Program of China(No.2018YFB0605503)the Fundamental Research Funds for the Central Universities(No.2021JCCXDC02)。
文摘To improve the accuracy of microseismic inversion,seismic anisotropy and moment tensor source should be carefully considered in the forward modelling stage.In this study,3D microseismic anisotropy wave forward modelling with a moment tensor source was proposed.The modelling was carried out based on a rotated-staggered-grid(RSG)scheme.In contrast to staggered-grids,the RSG scheme defines the velocity components and densities at the same grid,as do the stress components and elastic parameters.Therefore,the elastic moduli do not need to be interpolated.In addition,the detailed formulation and implementation of moment-tensor source loaded on the RSG was presented by equating the source to the stress increments.Meanwhile,the RSG-based 3D wave equation forward modelling was performed in parallel using compute unified device architecture(CUDA)programming on a graphics processing unit(GPU)to improve its efficiency.Numerical simulations including homogeneous and anisotropic models were carried out using the method proposed in this paper,and compared with other methods to prove the reliability of this method.Furthermore,the high efficiency of the proposed approach was evaluated.The results show that the computational efficiency of proposed method can be improved by about two orders of magnitude compared with traditional central processing unit(CPU)computing methods.It could not only help the analysis of microseismic full wavefield records,but also provide support for passive source inversion,including location and focal mechanism inversion,and velocities inversion.
基金supported by the Fundamental Research Funds from the Institute of Geophysics,China Earthquake Administration(Grant Numbers DQJB24K29)the National Natural Science Foundation of China(Grant Numbers U2039204)。
文摘The forecasing of maximum magnitude(M_(max))for fluid-induced seismicity is a critical challenge in risk management for geothermal energy development,unconventional hydrocarbon extraction,and wastewater disposal.This review systematically examines eight forecasting models spanning three categories:statistical models(G-R relation,extreme value theory,order statistics),semi-physical models(injection volume/time correlations),and physics-based models(rupture mechanics,rate-state friction,pressure diffusion).Through tripartite analysis of parameter sensitivity,scientific interpretability,and engineering validation,we reveal fundamental constraints:(1)Model performance is strictly governed by parameter coverage.Statistical models relying solely on seismic catalogs neglect fault mechanics parameters.Semi-physical models establish empiricalΔV/T-M_(max) relationships but fail to explain dynamic slip processes and post-injection"trailing effects".Physics-based models integrate fault stress,friction evolution,and fluid diffusion,offering unparalleled mechanistic insights,yet their computational demands pose challenges for real-time field applications(only 7.5%of 228 global cases).(2)Scientific interpretability exhibits hierarchical deficiencies.While physics-based models quantitatively resolve stress transfer and friction evolution(e.g.,rate-state models distinguishing seismic/aseismic slip),forecasting of far-field triggering and multi-fault interactions remain reliant on immature theories like poroelastic coupling.(3)Systemic validation biases emerge:57.5%of cases concentrate in stable North American terrains with underrepresentation of Pacific Rim high-stress zones;79.8%involve geothermal/shale gas operations lacking CO_(2) sequestration validations;M≥4.0 events constitute merely 12.7%of datasets,leaving M≥5.0 predictive capacity unverified.We propose transformative pathways including hybrid“physics-core t machine learning”architectures,standardized multiscenario validation protocols,and dynamic traffic-light systems,aiming to advance cross-disciplinary forecasting paradigms for engineering risk governance.
文摘Neurotrophins are a family of proteins that regulate neural survival, development, function and plasticity in the central and the peripheral nervous system. There are four neurotrophins： NGF, BDNF, NT-3 and NT-4. Among them, BDNF is mostly studied in the taste system due to its high expression. Recent studies have shown BDNF play an important role in the developmental and mature taste system, by regulating survival of taste cells and geniculate ganglion neurons, and maintaining and guiding taste nerve innervations. These studies imply BDNF has great potentialities for therapeutic usage to enhance sensory regeneration following nerve injury, with aging, and in some neurodegenerative diseases.
