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.

3D S-wave velocity structure of the Ningdu basin in Jiangxi province inferred from ambient noise tomography with dense array

The Ningdu basin,located in southern Jiangxi province of southwest China,is one of the Mesozoic basin groups which has exploration prospects for geothermal energy.A study on the detailed velocity structure of the Ningdu basin can provide important information for geothermal resource exploration.In this study,we deployed a dense seismic array in the Ningdu basin to investigate the 3D velocity structure and discuss implications for geothermal exploration and geological evolution.Based on the dense seismic array including 35 short-period(5 s-100 Hz)seismometers with an average interstation distance of~5 km,Rayleigh surface wave dispersion curves were extracted from the continuous ambient noise data for surface wave tomographic inversion.Group velocity tomography was conducted and the 3D S-wave velocity structure was inverted by the neighborhood algorithm.The results revealed obvious low-velocity anomalies in the center of the basin,consistent with the low-velocity Cretaceous sedimentary rocks.The basement and basin-controlling fault can also be depicted by the S-wave velocity anomalies.The obvious seismic interface is about 2 km depth in the basin center and decreases to 700 m depth near the basin boundary,suggesting spatial thickness variations of the Cretaceous sediment.The fault features of the S-wave velocity profile coincide with the geological cognition of the western boundary basincontrolling fault,which may provide possible upwelling channels for geothermal fluid.This study suggests that seismic tomography with a dense array is an effective method and can play an important role in the detailed investigations of sedimentary basins.

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.

The bound weighted average method(BWAM)for predicting S-wave velocity

The shear-wave velocity is a very important parameter in oil and gas seismic exploration, and vital in prestack elastic-parameters inversion and seismic attribute analysis. However, sheafing-velocity logging is seldom carried out because it is expensive. This paper presents a simple method for predicting S-wave velocity which covers the basic factors that influence seismic wave propagation velocity in rocks. The elastic modulus of a rock is expressed here as a weighted arithmetic average between Voigt and Reuss bounds, where the weighting factor, w, is a measurement of the geometric details of the pore space and mineral grains. The S-wave velocity can be estimated from w, which is derived from the P-wave modulus. The method is applied to process well-logging data for a carbonate reservoir in Sichuan Basin, and shows the predicted S-wave velocities agree well with the measured S-wave velocities.

Estimation of S-wave Velocity for Gas Hydrate Reservoir in the Shenhu Area,North South China Sea

Estimation of S-wave velocity using logging data has mainly been performed for sandstone, mudstone and oil and gas strata, while its application to hydrate reservoirs has been largely overlooked. In this paper we present petxophysical methods to estimate the S-wave velocity of hydrate reservoirs with the P-wave velocity and the density as constraints. The three models used in this paper are an equivalent model （MBGL）, a three-phase model （TPBE）, and a thermo-elasticity model （TEM）. The MBGL model can effectively describe the internal relationship among the components of the rock, and the estimated P-wave velocities are in good agreement with the measured data （2.8% error）. However, in the TPBE model, the solid, liquid and gas phases axe considered to be independent of each other, and the estimation results are relatively low （46.6% error）. The TEM model is based on the sensitivity of the gas hydrate to temperature and pressure, and the accuracy of the estimation results is also high （3.6% error）. Before the estimation, the occurrence patterns of hydrates in the Shenhu area were examined, and occurrence state one （the hydrate is in solid form in the reservoir） was selected for analysis. By using the known P-wave velocity and density as constraints, a reasonable S-wave velocity value （ranging from 400 to 1100 m s 1 and for a hydrate layer of 1100 m s 1） can be obtained through multiple iterations. These methods and results provide new data and technical support for further research on hydrates and other geological features in the Shenhu area.

Mapping crustal S-wave velocity structure with SV-component receiver function method

In this article, we analyze the characters of SV-component receiver function of teleseismic body waves and its advantages in mapping the S-wave velocity structure of crust in detail. Similar to radial receiver function, SV-component receiver function can be obtained by directly deconvolving the P-component from the SV-component of teleseismic recordings. Our analyses indicate that the change of amplitude of SV-component receiver function against the change of epicentral distance is less than that of radial receiver function. Moreover, the waveform of SV-component receiver function is simpler than the radial receiver function and gives prominence to the PS converted phases that are the most sensitive to the shear wave velocity structure in the inversion. The synthetic tests show that the convergence of SV-component receiver function inversion is faster than that of the radial receiver function inversion. As an example, we investigate the S-wave velocity structure beneath HIA sta-tion by using the SV-component receiver function inversion method.

Estimation of Shallow S-Wave Velocity Structure of Two Practical Sites from Microtremors Array Observation in Tangshan Area

Microtremors array observation for estimating S-wave velocity structure from phase velocities of Rayleigh and Love wave on two practical sites in Tangshan area by a China-US joint group are researched.The phase velocities of Rayleigh wave are estimated from vertical component records and those of Love wave are estimated from three-component records of microtremors array using modified spatial auto-correlation method.Haskell matrix method is used in calculating Rayleigh and Love wave phase velocities,and the shallow S-wave velocity structure of two practical sites are estimated by means of a hybrid approach of Genetic Algorithm and Simplex.The results are compared with the PS logging data of the two sites,showing it is feasible to estimate the shallow S-wave velocity structure of practical site from the observation of microtremor array.

S-wave velocity structure in Tangshan earthquake region and its adjacent areas from joint inversion of receiver functions and surface wave dispersion

Using the seismic records of 83 temporary and 17 permanent broadband seismic stations deployed in Tangshan earthquake region and its adjacent areas(39°N–41.5°N,115.5°E–119.5°E),we conducted a nonlinear joint inversion of receiver functions and surface wave dispersion.We obtained some detailed information about the Tangshan earthquake region and its adjacent areas,including sedimentary thickness,Moho depth,and crustal and upper mantle S-wave velocity.Meanwhile,we also obtained the vP/vS structure along two sections across the Tangshan region.The results show that:(1)the Moho depth ranges from 30 km to 38 km,and it becomes shallower from Yanshan uplift area to North China basin;(2)the thickness of sedimentary layer ranges from 0 km to 3 km,and it thickens from Yanshan uplift region to North China basin;(3)the S-wave velocity structure shows that the velocity distribution of the upper crust has obvious correlation with the surface geological structure,while the velocity characteristics of the middle and lower crust are opposite to that of the upper crust.Compared with the upper crust,the heterogeneity of the middle and lower crust is more obvious;(4)the discontinuity of Moho on the two sides of Tangshan fault suggests that Tangshan fault cut the whole crust,and the low vS and high vP/vS beneath the Tangshan earthquake region may reflect the invasion of mantle thermal material through Tangshan fault.

Double-difference tomography of P- and S-wave velocity structure beneath the western part of Java, Indonesia

West Java in the western part of the Sunda Arc has a relatively high seismicity due to subduction activity and faults.In this study,double-difference tomography was used to obtain the 3D velocity tomograms of P and S waves beneath the western part of Java.To infer the geometry of the structure beneath the study area,precise earthquake hypo・center determination was first performed before tomographic imaging.For this,earthquake waveform data were extracted from the regional Meteorological,Climatological,Geophysical Agency(BMKG)network of Indonesia from South Sumatra to Central Java.The P and S arrival times for about 1,000 events in the period April 2009 to July 2016 were selected,the key features being events of magnitude>3,azimuthal gap<210°and number of phases>8.A nonlinear method using the oct-tree sampling algorithm from the NonLinLoc program was employed to determine the earthquake hypocenters.The hypocenter locations were then relocated using double-difference tomography(tomoDD).A significant reduction of travel-time(root mean square basis)and a better clustering of earthquakes were achieved which correlated well with the geological structure in West Java.Double-difference tomography was found to give a clear velocity structure,especially beneath the volcanic arc area,i.e.,under Mt Anak Krakatau,Mt Salak and the mountains complex in the southern part of West Java.Low velocity anomalies for the P and S waves as well as the vp/vs ratio below the volcanoes indicated possible partial melting of the upper mantle which ascended from the subducted slab beneath the volcanic arc.

S-wave velocity structure beneath Changbaishan volcano inferred from receiver function

The S wave velocity structure in Changbaishan volcanic region was obtained from teleseismic receiver function modeling. The results show that there exist distinct low velocity layers in crust in volcano area. Beneath WQD station near to the Tianchi caldera the low velocity layer at 8 km depth is 20 km thick with the lowest S-wave velocity about 2.2 km/s At EDO station located 50 km north of Tianchi caldera, no obvious crustal low velocity layer is detected. In the volcanic region, the thickness of crustal low velocity layer is greater and the lowest velocity is more obvious with the distance shorter to the caldera. It indicates the existence of the high temperature material or magma reservoir in crust near the Tianchi caldera. The receiver functions and inversion result from different back azimuths at CBS permanent seismic station show that the thickness of near surface low velocity layer and Moho depth change with directions. The near surface low velocity layer is obviously thicker in south direction. The Moho depth shows slight uplifting in the direction of the caldera located. We con- sider that the special near surface velocity structure is the main cause of relatively lower prominent frequency of volcanic earthquake waveforms recorded by CBS station. The slight uplifting of Moho beneath Tianchi caldera indicates there is a material exchanging channel between upper mantle and magma reservoir in crust.

Crustal S-wave velocity structure across the northeastern South China Sea continental margin: implications for lithology and mantle exhumation

The northeastern margin of the South China Sea (SCS), developed from continental rifting and breakup, is usually thought of as a non-volcanic margin. However, post-spreading volcanism is massive and lower crustal high-velocity anomalies are widespread, which complicate the nature of the margin here. To better understand crustal seismic velocities, lithology, and geophysical properties, we present an S-wave velocity (VS) model and a VP/VS model for the northeastern margin by using an existing P-wave velocity (VP) model as the starting model for 2-D kinematic S-wave forward ray tracing. The Mesozoic sedimentary sequence has lower VP/VS ratios than the Cenozoic sequence;in between is a main interface of P-S conversion. Two isolated high-velocity zones (HVZ) are found in the lower crust of the continental slope, showing S-wave velocities of 4.0–4.2 km/s and VP/VS ratios of 1.73–1.78. These values indicate a mafic composition, most likely of amphibolite facies. Also, a VP/VS versus VP plot indicates a magnesium-rich gabbro facies from post-spreading mantle melting at temperatures higher than normal. A third high-velocity zone (VP : 7.0–7.8 km/s;VP/VS: 1.85–1.96), 70-km wide and 4-km thick in the continent-ocean transition zone, is most likely to be a consequence of serpentinization of upwelled upper mantle. Seismic velocity structures and also gravity anomalies indicate that mantle upwelling/ serpentinization could be the most severe in the northeasternmost continent-ocean boundary of the SCS. Empirical relationships between seismic velocity and degree of serpentinization suggest that serpentinite content decreases with depth, from 43% in the lower crust to 37% into the mantle.

S-wave velocity structure in the SE Tibetan plateau

We use observations recorded by 23 permanent and 99 temporary stations in the SE Tibetan plateau to obtain the S-wave velocity structure along two profiles by applying joint inversion with receiver functions and surface waves. The two profiles cross West Yunnan block （WYB）, the Central Yunnan sub-block （CYB）, South China block （SCB）, and Nanpanjiang basin （NPB）. The profile at -25°N shows that the Moho interface in the CYB is deeper than those in the WYB and the NPB, and the topography and Moho depth have clear correspondence. Beneath the Xiaojiang fault zone （XJF）, there exists a crustal low-velocity zone （LYZ）, crossing the XJF and expanding eastward into the SCB. The NPB is shown to be of relatively high velocity. We speculate that the eastward extrusion of the Tibetan plateau may pass through the XJF and affect its eastern region, and is resisted by the rigid NPB, which has high velocity. This may be the main cause of the crustal thickening and uplift of the topography. In the Tengchong volcanic area, the crust is shown to have alternate high- and low-velocity layers, and the upper mantle is shown to be of low velocity. We consider that the magma which exists in the crust is from the upper mantle and that the complex crustal velocity structure is related to magmatic differentiation. Between the Tengchong volcanic area and the XJF, the crustal velocity is relatively high. Combining these observations with other geophysical evi- dence, it is indicated that rock strength is high and defor- mation is weak in this area, which is why the level of seismicity is quite low. The profile at ~ 23~N shows that the variation of the Moho depth is small from the eastern rigid block to the western active block with a wide range of LVZs. We consider that deformation to the south of the SE Tibetan Plateau is weak.

S-wave velocity structure inferred from receiver function inversion in Tengchong volcanic area

Tengchong volcanic area is located near the impinging and underthrust margin of India and Eurasia plates. The volcanic activity is closely related to the tectonic environment. The deep structure characteristics are inferred from the receiver function inversion with the teleseismic records in the paper. The results show that the low velocity zone is influenced by the NE-trending Dayingjiang fault. The S-wave low velocity structure occurs obviously in the southern part of the fault, but unobviously in its northern part. There are low velocity zones in the shallow po-sition, which coincides with the seismicity. It also demonstrates that the low velocity zone is directly related to the thermal activity in the volcanic area. Therefore, we consider that the volcano may be alive again.

Crustal S-wave velocity structure of the Yellowstone region using a seismic ambient noise method

The Yellowstone volcano is one of the largest active volcanoes in the world, and its potential hazards demand detailed seismological and geodetic studies. Previous studies with travel time tomography and receiver functions have revealed a low-velocity layer in the crust beneath the Yellowstone volcano, suggesting the presence of a magma chamber at depth. We use ambient seismic noise from regional seismic stations to retrieve short-period surface waves and then study the shallow shear velocity structure of the Yellowstone region by surface wave dispersion analysis. We first obtained a crustal model of the area outside of the Yellowstone volcano and then constructed an absolute shear wave velocity structure in combination with receiver function results for the crust beneath the Yellowstone volcano. The velocity model shows a low-velocity layer with shear velocity at around 1.3 km/s, suggesting that a large-scale magma chamber exists at shallow levels within the crust of the Yellowstone volcanic region.

A two-step multi-frequency receiver function inversion method for shallow crustal S-wave velocity structure and its application across the basin-mountain range belts in Northeast China

A shallow crustal velocity structure(above 10 km depth) is essential for understanding the crustal structures and deformation and assessing the exploration prospect of natural resources, and also provides priori information for imaging deeper crustal and mantle structure. Passive-source seismic methods are cost-effective and advantageous for regional-scale imaging of shallow crustal structures compared to active-source methods. Among these passive methods, techniques utilizing receiver function waveforms and/or body-wave amplitude ratios have recently gained prominence due to their relatively high spatial resolution. However, in basin regions, reverberations caused by near-surface unconsolidated sedimentary layers often introduce strong non-uniqueness and uncertainty, limiting the applicability of such methods. To address these challenges, we propose a two-step inversion method that uses multi-frequency P-RF waveforms and P-RF horizontal-to-vertical amplitude ratios. Synthetic tests indicate that our two-step inversion method can mitigate the non-uniqueness of the inversion and enhance the stability of the results. Applying this method to teleseismic data from a linear seismic array across the sedimentary basins in Northeast China, we obtain a high-resolution image of the shallow crustal S-wave velocity structure along the array. Our results reveal significant differences between the basins and mountains. The identification of low-velocity anomalies(<2.8 km s^(-1)) at depths less than 1.0 km beneath the Erlian Basin and less than 2.5 km beneath the Songliao Basin suggests the existence of sedimentary layers. Moreover, the high-velocity anomalies(~3.4–3.8 km s^(-1)) occurring at depths greater than 7 km in the Songliao Basin may reflect mafic intrusions emplaced during the Early Cretaceous. Velocity anomaly distribution in our imaging result is consistent with the location of the major faults, uplifts, and sedimentary depressions, as well as active-source seismic results. This application further validates the effectiveness of our method in constraining the depth-dependent characteristics of the S-wave velocity in basins with unconsolidated sedimentary cover.

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.

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.

Shear wave velocity prediction:A review of recent progress and future opportunities

Shear logs,also known as shear velocity logs,are used for various types of seismic analysis,such as determining the relationship between amplitude variation with offset(AVO)and interpreting multiple types of seismic data.This log is an important tool for analyzing the properties of rocks and interpreting seismic data to identify potential areas of oil and gas reserves.However,these logs are often not collected due to cost constraints or poor borehole conditions possibly leading to poor data quality,though there are various approaches in practice for estimating shear wave velocity.In this study,a detailed review of the recent advances in the various techniques used to measure shear wave(S-wave)velocity is carried out.These techniques include direct and indirect measurement,determination of empirical relationships between S-wave velocity and other parameters,machine learning,and rock physics models.Therefore,this study creates a collection of employed techniques,enhancing the existing knowledge of this significant topic and offering a progressive approach for practical implementation in the field.

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.

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.