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.

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.

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.

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.

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.

Combined migration velocity model-building and its application in tunnel seismic prediction

We propose a combined migration velocity analysis and imaging method based on Kirchhoff integral migration and reverse time migration,using the residual curvature analysis and layer stripping strategy to build the velocity model.This method improves the image resolution of Kirchhoff integral migration and reduces the computations of the reverse time migration.It combines the advantages of efficiency and accuracy of the two migration methods.Its application in tunnel seismic prediction shows good results.Numerical experiments show that the imaging results of reverse time migration are better than the imaging results of Kirchhoff integral migration in many aspects of tunnel prediction.Field data show that this method has efficient computations and can establish a reasonable velocity model and a high quality imaging section.Combination with geological information can make an accurate prediction of the front of the tunnel geological structure.

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.

A New Model for Prediction of Mean Liquid Circulating Velocity in Bubble Columns

A new model without any fitting parameter for estimating the mean liquid recirculating velocity has been derived from previous work directly. The prediction agrees with experimental data reasonably well. Accurency of prediction from the new model is comparable with the models reported in the literature. However, the new model has a potential capability to predict the average liquid recirculation velocity at elevated pressure bubble columns since n and c is developed under pressure. However this needs to be further tested experimentally.

Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.

Estimation of the unfrozen water content of saturated sandstones by ultrasonic velocity

The unfrozen water content(UWC)of rocks at low temperature is an important index for evaluating the stability of the rock engineering in cold regions and artificial freezing engineering.This study addresses a new method to estimate the UWC of saturated sandstones at low temperature by using the ultrasonic velocity.Ultrasonic velocity variations can be divided into the normal temperature stage(20 to 0℃),quick phase transition stage(0 to-5℃)and slow phase transition stage(-5 to-25℃).Most increment of ultrasonic velocity is completed in the quick phase transition stage and then turns to be almost a constant in the slow phase transition stage.In addition,the UWC is also measured by using nuclear magnetic resonance(NMR)technology.It is validated that the ultrasonic velocity and UWC have a similar change law against freezing and thawing temperatures.The WE(weighted equation)model is appropriate to estimate the UWC of saturated sandstones,in which the parameters have been accurately determined rather than by data fitting.In addition,a linear relationship between UWC and ultrasonic velocity is built based on pore ice crystallization theory.It is evidenced that this linear function can be adopted to estimate the UWC at any freezing temperature by using P-wave velocity,which is simple,practical,and accurate enough compared with the WE model.

A model for predicting bubble rise velocity in a pulsed gas solid fluidized bed

Bed stability, and especially the bed density distribution, is affected by the behavior of bubbles in a gas solid fluidized bed. Bubble rise velocity in a pulsed gas-solid fluidized bed was studied using photographic and computational fluid dynamics methods. The variation in bubble rise velocity was investigated as a function of the periodic pulsed air flow. A predictive model of bubble rise velocity was derived: ub=ψ(Ut+Up-Umf)+kp(gdb)(1/2). The software of Origin was used to fit the empirical coefficients to give ψ = 0.4807 and kp = 0.1305. Experimental verification of the simulations shows that the regular change in bubble rise velocity is accurately described by the model. The correlation coefficient was 0.9905 for the simulations and 0.9706 for the experiments.

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.

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.

Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.

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 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.

The influence of pore structure on P-& S-wave velocities in complex carbonate reservoirs with secondary storage space

Secondary storage spaces with very complex geometries are well developed in Ordovician carbonate reservoirs in the Tarim Basin,which is taken as a study case in this paper.It is still not clear how the secondary storage space shape influences the P-＆ S-wave velocities （or elastic properties） in complex carbonate reservoirs.In this paper,three classical rock physics models （Wyllie timeaverage equation,Gassmann equation and the Kuster-Toks z model） are comparably analyzed for their construction principles and actual velocity prediction results,aiming at determining the most favourable rock physics model to consider the influence of secondary storage space shape.Then relationships between the P-＆ S-wave velocities in carbonate reservoirs and geometric shapes of secondary storage spaces are discussed from different aspects based on actual well data by employing the favourable rock physics model.To explain the influence of secondary storage space shape on V P-V S relationship,it is analyzed for the differences of S-wave velocities between derived from common empirical relationships （including Castagna＇s mud rock line and Greenberg-Castagna V P-V S relationship） and predicted by the rock physics model.We advocate that V P-V S relationship for complex carbonate reservoirs should be built for different storage space types.For the carbonate reservoirs in the Tarim Basin,the V P-V S relationships for fractured,fractured-cavernous,and fractured-hole-vuggy reservoirs are respectively built on the basis of velocity prediction and secondary storage space type determination.Through the discussion above,it is expected that the velocity prediction and the V P-V S relationships for complex carbonate reservoirs should fully consider the influence of secondary storage space shape,thus providing more reasonable constraints for prestack inversion,further building a foundation for realizing carbonate reservoir prediction and fluid prediction.

Tunnel seismic tomography method for geological prediction and its application

Typical existing methods of tunnel geological prediction include negative apparent velocity, horizontal seismic profile, and the Tunnel Seismic Prediction （TSP） method as this technology is under development at home and abroad. Considering simpler observational methods and data processing, it is hard to accurately determine the seismic velocity of the wall rock in the front of the tunnel face. Therefore, applying these defective methods may result in inaccurate geological inferences which will not provide sufficient evidence for classifying the wall rock characteristics. This paper proposes the Tunnel Seismic Tomography （TST） method using a spatial observation arrangement and migration and travel time inversion image processing to solve the problem of analyzing the velocity structure of wall rock in the front of the tunnel face and realize accurate imaging of the geological framework of the tunnel wall rock. This method is very appropriate for geological prediction under complex geological conditions.