Shear wave velocity model using HVSR inversion beneath Bandar Lampung City

The horizontal-to-vertical spectral ratio(HVSR)method has been used to characterize site-effect parameters that are indispensable in seismic hazard and risk-reduction studies in urban areas and rapid land-use planning.This method is widely used because it is the cheapest and simplest geophysical method for the acquisition and processing stages.In subsequent developments,the HVSR method has been widely used to determine elastic rock parameters,particularly shear wave velocity(v_(S)),through the HVSR curve inversion process.Furthermore,the v_(S)structural model can be used to delineate the presence of complex geological structures,particularly faults and sedimentary basins.Bandar Lampung is a city in Lampung Province with many fault structures and groundwater basins to the south.There are 83 HVSR measurement points around Bandar Lampung for delineating the presence of fault structures and groundwater basins.We produced the HVSR curve from the measurement results and then performed an inversion process using the particle swarm optimization algorithm to obtain v_(S)for the depth profile.Subsequently,from this profile,we produced a two-dimensional(2D)lateral and vertical model.The mean v_(S)value was calculated from all the measurement points,and we found stiff soil layers reaching depths of approximately 5 m,with a value of v_(S)<330 m/s.A bedrock layer with a velocity exceeding 1250 m/s was visible at a depth of 100 m.Based on the 2D model,the v_(S)structure shows that the city of Bandar Lampung is divided into two zones,with a NW-SE boundary.The north-middle-eastern part of the city consists of harder rocks.This harder rock is characterized by extremely high v_(S)values,starting from a depth of 50 m.In contrast,the south-middle-west exhibits a low-moderate v_(S)anomaly associated with groundwater basins SW of the city.From the 2D v_(S)structural model,fault structures can be found along the city,characterized by a contrast of v_(S)values from low to medium and from medium to high.

Inversion of walkaway VSP data in the presence of lateral velocity heterogeneity

Multi-azimuth walkaway vertical seismic profiling is an established technique for the estimation of in situ slowness surfaces and inferring anisotropy parameters.Normally,this technique requires the assumption of lateral homogeneity,which makes the horizontal slowness components at depths of downhole receivers equal to those measured at the surface.Any violations of this assumption,such as lateral heterogeneity or nonzero dip of intermediate interfaces,lead to distortions in reconstructed slowness surfaces and,consequently,to errors in estimated anisotropic parameters.In this work,we relax the assumption of lateral homogeneity and discuss how to correct vertical seismic profile data for weak lateral heterogeneity.We describe a procedure of downward continuation of recorded traveltimes that accounts for the presence of both vertical inhomogeneity and weak lateral heterogeneity,which produces correct slowness surfaces at depths of downhole receivers,noticing that sufficiently dense receiver coverage along a borehole is required to separate influences of vertical and lateral heterogeneity on measured traveltimes and obtain accurate estimates of the slowness surfaces.Once the slowness surfaces are found and a desired type of anisotropic model to be inverted is selected,the corresponding anisotropic parameters,providing the best fit to the estimated slownesses,can be obtained.We invert the slowness surfaces of P-waves for parameters of the simplest anisotropic model describing dipping fractures(transversely isotropic medium with a tilted symmetry axis).Five parameters of this model,namely,the P-wave velocity V0 in the direction of the symmetry axis,Thomsen's anisotropic coefficients e and d,the tilt n,and the azimuth b of the symmetry axis,can be estimated in a stable manner when maximum source offset is greater than half of receiver depth.

Crustal and uppermost mantle structure of the northeastern Qinghai-Xizang Plateau from joint inversion of surface wave dispersions and receiver functions with P velocity constraints

Lithospheric structure beneath the northeastern Qinghai-Xizang Plateau is of vital significance for studying the geodynamic processes of crustal thickening and expansion of the Qinghai-Xizang Plateau. We conducted a joint inversion of receiver functions and surface wave dispersions with P-wave velocity constraints using data from the Chin Array Ⅱ temporary stations deployed across the Qinghai-Xizang Plateau. Prior to joint inversion, we applied the H-κ-c method(Li JT et al., 2019) to the receiver function data in order to correct for the back-azimuthal variations in the arrival times of Ps phases and crustal multiples caused by crustal anisotropy and dipping interfaces. High-resolution images of vS, crustal thickness, and vP/vSstructures in the Qinghai-Xizang Plateau were simultaneously derived from the joint inversion. The seismic images reveal that crustal thickness decreases outward from the Qinghai-Xizang Plateau. The stable interiors of the Ordos and Alxa blocks exhibited higher velocities and lower crustal vP/vSratios. While, lower velocities and higher vP/vSratios were observed beneath the Qilian Orogen and Songpan-Ganzi terrane(SPGZ), which are geologically active and mechanically weak, especially in the mid-lower crust.Delamination or thermal erosion of the lithosphere triggered by hot asthenospheric flow contributes to the observed uppermost mantle low-velocity zones(LVZs) in the SPGZ. The crustal thickness, vS, and vP/vSratios suggest that whole lithospheric shortening is a plausible mechanism for crustal thickening in the Qinghai-Xizang Plateau, supporting the idea of coupled lithospheric-scale deformation in this region.

Joint inversion of Rayleigh group and phase velocities for S-wave velocity structure of the 2021 M_(S)6.0 Luxian earthquake source area,China

On September 16,2021,a MS6.0 earthquake struck Luxian County,one of the shale gas blocks in the Southeastern Sichuan Basin,China.To understand the seismogenic environment and its mechanism,we inverted a fine three-dimensional S-wave velocity model from ambient noise tomography using data from a newly deployed dense seismic array around the epicenter,by extracting and jointly inverting the Rayleigh phase and group velocities in the period of 1.6–7.2 s.The results showed that the velocity model varied significantly beneath different geological units.The Yujiasi syncline is characterized by low velocity at depths of~3.0–4.0 km,corresponding to the stable sedimentary layer in the Sichuan Basin.The eastern and western branches of the Huayingshan fault belt generally exhibit high velocities in the NE-SW direction,with a few local low-velocity zones.The Luxian MS6.0 earthquake epicenter is located at the boundary between the high-and low-velocity zones,and the earthquake sequences expand eastward from the epicenter at depths of 3.0–5.0 km.Integrated with the velocity variations around the epicenter,distribution of aftershock sequences,and focal mechanism solution,it is speculated that the seismogenic mechanism of the main shock might be interpreted as the reactivation of pre-existing faults by hydraulic fracturing.

Inversion techniques to obtain local rotation velocity and ion temperature profiles for the x-ray crystal spectrometer on EAST

Inversion techniques are conducted based on the tangential x-ray crystal spectroscopy(TXCS)geometry on EAST to obtain the local profiles of ion temperature(Ti)and toroidal rotation velocity(vt).Firstly,local emissivity profiles of the impurity argon are obtained using the asymmetrical Abel inversion.Then,the local vt and Ti profiles are calculated by considering the local emissivity profiles and the TXCS detailed geometry.In addition,how the changes in the vt profiles affect the accuracy in the Ti profiles is discussed in detail.It is also found that the lineintegrated Ti profiles are becoming less accurate with the increase in the radial gradient in the local vt profiles.Nonetheless,accurate Ti radial profiles are reconstructed after considering the effects of the emissivity and velocity,which are verified by comparing the inverted vt and Ti profiles with those local profile measurements from the Charge eXchange Recombination Spectroscopy(CXRS)on EAST.

Converted wave AVO inversion for average velocity ratio and shear wave reflection coefficient

Based on the empirical Gardner equation describing the relationship between density and compressional wave velocity, the converted wave reflection coefficient extrema attributes for AVO analysis are proposed and the relations between the extrema position and amplitude, average velocity ratio across the interface, and shear wave reflection coefficient are derived. The extrema position is a monotonically decreasing function of average velocity ratio, and the extrema amplitude is a function of average velocity ratio and shear wave reflection coefficient. For theoretical models, the average velocity ratio and shear wave reflection coefficient are inverted from the extrema position and amplitude obtained from fitting a power function to converted wave AVO curves. Shear wave reflection coefficient sections have clearer physical meaning than conventional converted wave stacked sections and establish the theoretical foundation for geological structural interpretation and event correlation. ＂The method of inverting average velocity ratio and shear wave reflection coefficient from the extrema position and amplitude obtained from fitting a power function is applied to real CCP gathers. The inverted average velocity ratios are consistent with those computed from compressional and shear wave well logs.

Tomographic velocity inversion for ADCIGs in areas with a rugged surface

Pre-stack depth migration velocity analysis is one of the key techniques influencing image quality. As for areas with a rugged surface and complex subsurface, conventional prestack depth migration velocity analysis corrects the rugged surface to a known datum or designed surface velocity model on which to perform migration and update the velocity. We propose a rugged surface tomographic velocity inversion method based on angle-domain common image gathers by which the velocity field can be updated directly from the rugged surface without static correction for pre-stack data and improve inversion precision and efficiency. First, we introduce a method to acquire angle-domain common image gathers （ADCIGs） in rugged surface areas and then perform rugged surface tornographic velocity inversion. Tests with model and field data prove the method to be correct and effective.

Three dimensional shear wave velocity structure of crust and upper mantle in South China Sea and its adjacent regions by surface waveform inversion

We assembled approximately 328 seismic records. The data set was from 4 digitally recording long-period and broadband stations of CDSN. We carried out the inversion based on the partitioned waveform inversion (PWI). It partitions the large-scale optimization problem into a number of independent small-scale problems. We adopted surface waveform inversion with an equal block (2((2() discretization in order to acquire the images of shear velocity structure at different depths (from surface to 430 km) in the crust and upper-mantle. The resolution of all these anomalies has been established with (check-board( resolution tests. These results show significant difference in velocity, lithosphere and asthenosphere structure between South China Sea and its adjacent regions.

用Velocity Verlet积分器改进HMC抽样方法

Hamilton Monte Carlo (HMC)方法是一种常用的快速抽样方法.在对哈密顿方程进行抽样时,HMC方法使用Leapfrog积分器,这可能造成方程的位置及动量的迭代值在时间上不同步,其产生的误差会降低抽样效率及抽样结果的稳定性.为此,本文提出了IHMC(Improved HMC)方法,该方法用Velocity Verlet积分器替代Leapfrog积分器,每次迭代时都计算两变量在同一时刻的值.为验证方法的效果,本文进行了两个实验,一个是将该方法应用于非对称随机波动率模型(RASV模型)的参数估计,另一个是将方法应用于方差伽马分布的抽样,结果显示:IHMC方法比HMC方法的效率更高、结果更稳定.

Wave equation tomographic velocity inversion method based on the Born/Rytov approximation

This paper discusses Born/Rytov approximation tomographic velocity inversion methods constrained by the Fresnel zone. Calculations of the sensitivity kernel function and traveltime residuals are critical in tomographic velocity inversion. Based on the Bom/Rytov approximation of the frequency-domain wave equation, we derive the traveltime sensitivity kemels of the wave equation on the band-limited wave field and simultaneously obtain the traveltime residuals based on the Rytov approximation. In contrast to single-ray tomography, the modified velocity inversion method improves the inversion stability. Tests of the near- surface velocity model and field data prove that the proposed method has higher accuracy and Computational efficiency than ray theory tomography and full waveform inversion methods.

ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

In vivo label-free measurement of blood flow velocity symmetry based on dual line scanning third-harmonic generation microscopy excited at the 1700 nm window

Measurement of bloodflow velocity is key to understanding physiology and pathology in vivo.While most measurements are performed at the middle of the blood vessel,little research has been done on characterizing the instantaneous bloodflow velocity distribution.This is mainly due to the lack of measurement technology with high spatial and temporal resolution.Here,we tackle this problem with our recently developed dual-wavelength line-scan third-harmonic generation(THG)imaging technology.Simultaneous acquisition of dual-wavelength THG line-scanning signals enables measurement of bloodflow velocities at two radially symmetric positions in both venules and arterioles in mouse brain in vivo.Our results clearly show that the instantaneous bloodflow velocity is not symmetric under general conditions.

Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir

Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.

Rock critical porosity inversion and S-wave velocity prediction

A critical porosity model is often used to calculate the dry frame elastic modulus by the rock critical porosity value which is affected by many factors. In practice it is hard for us to obtain an accurate critical porosity value and we can generally take only an empirical critical porosity value which often causes errors. In this paper, we propose a method to obtain the rock critical porosity value by inverting P-wave velocity and applying it to predict S-wave velocity. The applications of experiment and log data both show that the critical porosity inversion method can reduce the uncertainty resulting from using an empirical value in the past and provide the accurate critical porosity value for predicting S-wave velocity which significantly improves the prediction accuracy.

Flood Velocity Prediction Using Deep Learning Approach

Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.

Layer-Valley Hall Effect under Inversion and Time-Reversal Symmetries

Hall effects have been the central paradigms in modern physics,materials science and practical applications,and have led to many exciting breakthroughs,including the discovery of topological Chern invariants and the revolution of metrological resistance standard.To date,the Hall effects have mainly focused on a single degree of freedom(Do F),and most of them require the breaking of spatial-inversion and/or time-reversal symmetries.Here we demonstrate a new type of Hall effect,i.e.,layer-valley Hall effect,based on a combined layer-valley Do F characterized by the product of layer and valley indices.The layer-valley Hall effect has a quantum origin arising from the layer-valley contrasting Berry curvature,and can occur in nonmagnetic centrosymmetric crystals with both spatial-inversion and time-reversal symmetries,transcending the symmetry constraints of single Do F Hall effect based on the constituent layer or valley index.Moreover,the layer-valley Hall effect is highly tunable and shows a W-shaped pattern in response to the out-of-plane electric fields.Additionally,we discuss the potential detection approaches and material-specific design principles of layer-valley Hall effect.Our results demonstrate novel Hall physics and open up exotic paradigms for new research direction of layer-valleytronics that exploits the quantum nature of the coupled layer-valley DoF.

Migration images guided high-resolution velocity modeling based on fully convolutional neural network

Current data-driven deep learning(DL)methods typically reconstruct subsurface velocity models directly from pre-stack seismic records.However,these purely data-driven methods are often less robust and produce results that are less physically interpretative.Here,the authors propose a new method that uses migration images as input,combined with convolutional neural networks to construct high-resolution velocity models.Compared to directly using pre-stack seismic records as input,the nonlinearity between migration images and velocity models is significantly reduced.Additionally,the advantage of using migration images lies in its ability to more comprehensively capture the reflective properties of the subsurface medium,including amplitude and phase information,thereby to provide richer physical information in guiding the reconstruction of the velocity model.This approach not only improves the accuracy and resolution of the reconstructed velocity models,but also enhances the physical interpretability and robustness.Numerical experiments on synthetic data show that the proposed method has superior reconstruction performance and strong generalization capability when dealing with complex geological structures,and shows great potential in providing efficient solutions for the task of reconstructing high-wavenumber components.

Origin of tradeoff between movement velocity and attachment duration of kinesin motor on a microtubule

Kinesin-1 motor protein is a homodimer containing two identical motor domains connected by a common long coiledcoil stalk via two flexible neck linkers. The motor can step on a microtubule with a velocity of about 1 μm·s-1and an attachment duration of about 1 s under physiological conditions. The available experimental data indicate a tradeoff between velocity and attachment duration under various experimental conditions, such as variation of the solution temperature,variation of the strain between the two motor domains, and so on. However, the underlying mechanism of the tradeoff is unknown. Here, the mechanism is explained by a theoretical study of the dynamics of the motor under various experimental conditions, reproducing quantitatively the available experimental data and providing additional predictions. How the various experimental conditions lead to different decreasing rates of attachment duration versus velocity is also explained.

Linearized waveform inversion for vertical transversely isotropic elastic media:Methodology and multi-parameter crosstalk analysis

Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.