Surface Rupture and Co-seismic Displacement Produced by the Ms 8.0 Wenchuan Earthquake of May ^(12)th,2008,Sichuan,China:Eastwards Growth of the Qinghai-Tibet Plateau

An earthquake of Ms 8 struck Wenchuan County, western Sichuan, China, on May 12^th, 2008 and resulted in long surface ruptures （〉300 km）. The first-hand observations about the surface ruptures produced by the earthquake in the worst-hit areas of Yingxiu, Beichuan and Qingchuan, ascertained that the causative structure of the earthquake was in the central fault zones of the Longmenshan tectonic belt. Average co-seismic vertical displacements along the individual fault of the Yingxiu-Beiehuan rupture zone reach 2.514 m and the cumulative vertical displacements across the central and frontal Longmenshan fault belt is about 5-6 m. The surface rupture strength was reduced from north of Beichuan to Qingchuan County and shows 2-3 m dextral strike-slip component. The Wenchuan thrust-faulting earthquake is a manifestation of eastward growth of the Tibetan Plateau under the action of continuous convergence of the Indian and Eurasian continents.

Research on Co-seismic Displacement of the Yutian MS7.3 Earthquake Based on the High Frequency Data of GNSS

After the Yutian M_S7.3 earthquake,the authors instantly collected 1Hz high frequency data of the 4 reference stations within 350 km around the epicenter,and calculated the GNSS data with the TRACK module. The results showed that:( 1) The co-seismic displacement of Yutian station,about 54 km from the epicenter,is the most obvious,particularly in the EW component,with a change of about 52.5 ± 11mm,which is more than three times the mean-square error of calculating precision.( 2) In the Yutian reference station,the biggest variation in the EW component appeared within 1 minute after the earthquake.( 3) The change in the NS component is not great.

Co-seismic displacements of 2011 Japan Mw9.0 earthquake recorded by far-field GPS stations

Co-seismic displacements of the 2011 Mw9.0 Japan earthquake recorded by GPS stations in China and surrounding areas showed a movement toward the epicenter. The horizontal displacements were up to 1 - 3 cm in northeastern China, 3 -8 mm in the North China, and 2 cm in the Korean peninsula. The vertical movements in China were small uplifts.

Co-seismic surface displacement of the June 21, 2022 M_(W)6 Khōst,Afghanistan earthquake from InSAR observations

A robust estimation of the earthquake location, seismic moment, and fault geometry is essential for objective seismic hazard assessment. Seismic events in a remote location, specifically in the absence of seismic and GNSS networks, can be investigated effectively using the In SAR-based technique. This study adopts the Differential Interferometric SAR(DIn SAR) technique to quantify the co-seismic surface displacement caused by the June 21, 2022, Khōst M_(W)6 earthquake that occurred along the western plate boundary between the Indian and Eurasian plate. The interferograms show that the maximum surface deformation occurred on the northwest and southwest of the fault line. From coherence, the Line of Sight(LOS) displacement, and the co-seismic surface displacement analysis, it has been observed that surface deformation was most pronounced in the southwest region of the fault line, and the surface has moved to the opposite direction along the fault line, which indicates a sinistral slightly oblique strike-slip movement. This In SAR-based observation appears consistent with the seismic waveforms derived from co-seismic surface displacements. Further, it has been argued that the slip deficit accumulated during the period of the last about 48 years along the frontal region of the northward extension of the Suleiman range and associated fault zone is qualitatively estimated at about 1.5 m, which is consistent with the seismic waveforms derived finite slip model.

Analysis of the Wenchuan Ms8.0 Earthquake's Co-seismic Stress and Displacement Change by Using the Finite Element Method

The variation of in situ stress before and after earthquakes is an issue studied by geologists. In this paper, on the basis of the fault slip dislocation model of Wenchuan Ms8.0 earthquake, the changes of co-seismic displacement and the distribution functions of stress tensor around the Longmen Shan fault zone are calculated. The results show that the co-seismic maximum surface displacement is 4.9 m in the horizontal direction and 6.5 m in the vertical direction, which is almost consistent with the on-site survey and GPS observations. The co-seismic maximum horizontal stress in the hanging wall and footwall decreased sharply as the distance from the Longmen Shan fault zone increased. However, the vertical stress and minimum horizontal stress increased in the footwall and in some areas of the hanging wall. The study of the co-seismic displacement and stress was mainly focused on the long and narrow region along the Longmen Shan fault zone, which coincides with the distribution of the earthquake aftershocks. Therefore, the co-seismic stress only affects the aftershocks, and does not affect distant faults and seismic activities. The results are almost consistent with in situ stress measurements at the two sites before and after Wenchuan Ms8.0 earthquake. Along the fault plane, the co-seismic shear stress in the dip direction is larger than that in the strike direction, which indicates that the faulting mechanism of the Longmen Shan fault zone is a dominant thrust with minor strike-slipping. The results can be used as a reference value for future studies of earthquake mechanisms.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

A Deep Learning Estimation Method for Temperature-Induced Girder End Displacements of Suspension Bridges

To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R2 increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R2.Compared to ANN,with an MSE of 4.6371 and an R2 of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R2 of 13.23%,demonstrating a significant enhancement in predictive performance.

Decomposing InSAR LOS displacement into co-seismic dislocation with a linear in-terpolation model: A case study of the Kunlun Mountain M_s=8.1 earthquake

It has always been a difficult problem to extract horizontal and vertical displacement components from the InSAR LOS （Line of Sight） displacement since the advent of monitoring ground surface deformation with InSAR technique. Having tried to fit the firsthand field investigation data with a least squares model and obtained a preliminary result, this paper, based on the previous field data and the InSAR data, presents a linear cubic interpolation model which well fits the feature of earthquake fracture zone. This model inherits the precision of investigation data; moreover make use of some advantages of the InSAR technique, such as quasi-real time observation, continuous recording and all-weather measurement. Accordingly, by means of the model this paper presents a method to decompose the InSAR slant range co-seismic displacement （i.e. LOS change） into horizontal and vertical displacement components. Approaching the real motion step by step, finally a serial of curves representing the co-seismic horizontal and vertical displacement component along the main earthquake fracture zone are approximately obtained.

Comparison of displacement damage effects on the dark signal in CMOS image sensors induced by CSNS back-n and XAPR neutrons

This study investigates the effects of displacement damage on the dark signal of a pinned photodiode CMOS image sensor(CIS)following irradiation with back-streaming white neutrons from white neutron sources at the China spallation neutron source(CSNS)and Xi'an pulsed reactor(XAPR).The mean dark signal,dark signal non-uniformity(DSNU),dark signal distribution,and hot pixels of the CIS were compared between the CSNS back-n and XAPR neutron irradiations.The nonionizing energy loss and energy distribution of primary knock-on atoms in silicon,induced by neutrons,were calculated using the open-source package Geant4.An analysis combining experimental and simulation results showed a noticeable proportionality between the increase in the mean dark signal and the displacement damage dose(DDD).Additionally,neutron energies influence DSNU,dark signal distribution,and hot pixels.High neutron energies at the same DDD level may lead to pronounced dark signal non-uniformity and elevated hot pixel values.

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models,such as state-of-the-art machine learning(ML)models.To address these challenges,this study proposes a data augmentation framework that uses generative adversarial networks(GANs),a recent advance in generative artificial intelligence(AI),to improve the accuracy of landslide displacement prediction.The framework provides effective data augmentation to enhance limited datasets.A recurrent GAN model,RGAN-LS,is proposed,specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data.A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data.Then,the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory(LSTM)networks and particle swarm optimization-support vector machine(PSO-SVM)models for landslide displacement prediction tasks.Results on two landslides in the Three Gorges Reservoir(TGR)region show a significant improvement in LSTM model prediction performance when trained on augmented data.For instance,in the case of the Baishuihe landslide,the average root mean square error(RMSE)increases by 16.11%,and the mean absolute error(MAE)by 17.59%.More importantly,the model’s responsiveness during mutational stages is enhanced for early warning purposes.However,the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM.Further analysis indicates that an optimal synthetic-to-real data ratio(50%on the illustration cases)maximizes the improvements.This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results.By using the powerful generative AI approach,RGAN-LS can generate high-fidelity synthetic landslide data.This is critical for improving the performance of advanced ML models in predicting landslide displacement,particularly when there are limited training data.Additionally,this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Simulation of Rotating Asymmetric Sideways Forces during Vertical Displacement Events in CFETR

Tokamak plasmas with elongated cross sections are susceptible to vertical displacement events(VDEs),which can damage the first wall via heat flux or electromagnetic(EM)forces.We present a 3D nonlinear reduced magnetohydrodynamic(MHD)simulation of CFETR plasma during a cold VDE following the thermal quench,focusing on the relationship among the EM force,plasma displacement,and the n=1 mode.The dominant mode,identified as m/n=2/1,becomes destabilized when most of the current is contracted within the q=2 surface.The displacement of the plasma current centroid is less than that of the magnetic axis due to the presence of SOL current in the open field line region.Hence,the symmetric component of the induced vacuum vessel current is significantly mitigated.The direction of the sideways force keeps a constant phase approximately compared to the asymmetric component of the vacuum vessel current and the SOL current,which in turn keep in-phase with the dominant 2/1 mode.Their amplitudes are also closely associated with the growth of the dominant mode.These findings provide insights into potential methods for controlling the phase and amplitude of sideways forces during VDEs in the future.

Effects of Fluorescent Pair on the Kinetics of DNA Strand Displacement Reaction

Fluorescent labels are widely used in the characterizations of DNA-based reaction network operations.We systematically studied the effects of commonly used fluorescent pairs on thermal stabilities of signal-substrate duplex and the strand displacement kinetics.It is demonstrated that the modifications of duplex with fluorescent pairs stabilize DNA duplex by up to 3.5℃,and the kinetics of DNA strand displacement circuit is also evidently slowed down.These results highlight the importance of fluorescent pairs towards the kinetic modulation in designing nucleic acid probes and complex DNA dynamic circuits.

Shear mechanical properties and frictional sliding responses of rough joint surfaces under dynamic normal displacement conditions

A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.

Estimation of Displacement and Extension due to Reverse Drag of Normal Faults: Forward Method

In the case of reverse drag of normal faulting, the displacement and horizontal extension are determined based on the established equations for the three mechanisms: rigid body, vertical shear and inclined shear. There are three sub-cases of basal detachment for the rigid body model: horizontal detachment, antithetic detachment and synthetic detachment. For the rigid body model, the established equations indicate that the total displacement on the synthetic base (Dt2) is the largest, that on the horizontal base (Dt1) is moderate, and that on the antithetic base (Dt3) is the smallest. On the other hand, the value of (Dt1) is larger than the displacement for the vertical shear (Dt4). The value of (Dt1) is larger than or less than the displacement for the inclined shear (Dt5) depending on the original fault dip δ0, bedding angle θ, and the angle of shear direction β. For all original parameters, the value of Dt5 is less than the value of Dt4. Also, by comparing three rotation mechanisms, we find that the inclined shear produces largest extension, the rigid body model with horizontal detachment produces the smallest extension, and the vertical shear model produces moderate extension.

Retinal displacement after surgery for idiopathic macular hole

AIM:To review and summarize the mechanism hypothesis,influencing factors and possible consequences of macular retinal displacement after idiopathic macular hole(IMH)surgery.METHODS:PubMed and Web of Science database was searched for studies published before April 2023 on“Retinal displacement”,“Idiopathic macular holes”,and“Macular displacement”.RESULTS:Recently,more academics have begun to focus on retinal displacement following idiopathic macular holes.They found that internal limiting membrane(ILM)peeling was the main cause of inducing postoperative position shift in the macular region.Moreover,several studies have revealed that the macular hole itself,as well as ILM peeling method,will have an impact on the result.In addition,this phenomenon is related to postoperative changes in macular retinal thickness,cone outer segment tips line recovery,the occurrence of dissociated optic nerve fiber layer(DONFL)and the degree of metamorphopsia.CONCLUSION:As a subclinical phenomenon,the clinical significance of postoperative macular displacement cannot be underestimated as it may affect the recovery of anatomy and function.

Effect of high-multiple water injection on rock pore structure and oil displacement efficiency

Experimental methods,including mercury pressure,nuclear magnetic resonance(NMR)and core(wateroil)displacement,are used to examine the effects of high-multiple water injection(i.e.water injection with high injected pore volume)on rock properties,pore structure and oil displacement efficiency of an oilfield in the western South China Sea.The results show an increase in the permeability of rocks along with particle migration,an increase in the pore volume and the average pore throat radius,and enhanced heterogeneity after high-multiple water injection.Compared with normal water injection methods,a high-multiple water injection is more effective in improving the oil displacement efficiency.The degree of recovery increases faster in the early stage due to the expansion of the swept area,and the transition from oil-wet to water-wet.The degree of recovery increases less in the late stage due to various factors,including the enhancement of heterogeneity in the rocks.Considering both the economic aspect and the production limit of water flooding,it is recommended to adopt other technologies to further enhance oil recovery after 300 PV water injection.

Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging(GPI)diagnostic has been developed and operated on EAST since 2012,and the time-delay estimation(TDE)method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data.However,with the TDE method it is difficult to analyze the data with fast transient events,such as edge-localized mode(ELM).Consequently,a method called the spatial displacement estimation(SDE)algorithm is developed to estimate the turbulence velocity with high temporal resolution.Based on the SDE algorithm,we make some improvements,including an adaptive median filter and super-resolution technology.After the development of the algorithm,a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm,and the calculated speed agrees well with preset speed.This SDE algorithm is applied to the EAST GPI data analysis,and the derived propagation velocity of turbulence is consistent with that from the TDE method,but with much higher temporal resolution.

Direct scaling of residual displacements for bilinear and pinching oscillators

The estimation of residual displacements in a structure due to an anticipated earthquake event has increasingly become an important component of performance-based earthquake engineering because controlling these displacements plays an important role in ensuring cost-feasible or cost-effective repairs in a damaged structure after the event.An attempt is made in this study to obtain statistical estimates of constant-ductility residual displacement spectra for bilinear and pinching oscillators with 5%initial damping,directly in terms of easily available seismological,site,and model parameters.None of the available models for the bilinear and pinching oscillators are useful when design spectra for a seismic hazard at a site are not available.The statistical estimates of a residual displacement spectrum are proposed in terms of earthquake magnitude,epicentral distance,site geology parameter,and three model parameters for a given set of ductility demand and a hysteretic energy capacity coefficient in the case of bilinear and pinching models,as well as for a given set of pinching parameters for displacement and strength at the breakpoint in the case of pinching model alone.The proposed scaling model is applicable to horizontal ground motions in the western U.S.for earthquake magnitudes less than 7 or epicentral distances greater than 20 km.

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).