Acoustic Velocity-Based Inversion of the Physical Properties of Sea Ice in the Central Arctic Region

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.

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.

Stepwise joint inversion of surface wave dispersion,Rayleigh wave ZH ratio,and receiver function data for 1D crustal shear wave velocity structure

Accurate determination of seismic velocity of the crust is important for understanding regional tectonics and crustal evolution of the Earth. We propose a stepwise joint linearized inversion method using surface wave dispersion, Rayleigh wave ZH ratio （i.e., ellipticity）, and receiver function data to better resolve 1D crustal shear wave velocity （Vs） structure. Surface wave dispersion and Rayleigh wave ZH ratio data are more sensitive to absolute variations of shear wave speed at depths, but their sensi- tivity kernels to shear wave speeds are different and complimentary. However, receiver function data are more sensitive to sharp velocity contrast （e.g., due to the existence of crustal interfaces） and Vp/Vs ratios. The stepwise inversion method takes advantages of the complementary sensitivities of each dataset to better constrain the Vs model in the crust. We firstly invert surface wave dispersion and ZH ratio data to obtain a 1D smooth absolute vs model and then incorporate receiver function data in the joint inver- sion to obtain a finer Vs model with better constraints on interface structures. Through synthetic tests, Monte Carlo error analyses, and application to real data, we demonstrate that the proposed joint inversion method can resolve robust crustal Vs structures and with little initial model dependency.

Seismic velocity inversion based on CNN-LSTM fusion deep neural network

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.

Inversion of shear wave interval velocity on prestack converted wave data

Seismic velocity is important to migration of seismic data, interpretation of lithology and lithofacies as well as prediction of reservoir. The information of shear wave velocity is required to reduce the uncertainty for discriminating lithology, identifying fluid type in porous material and calculating gas saturation in reservoir prediction. Based on Zoeppritz equations, a numeral and scanning method was proposed in this paper. Shear wave velocity can be calculated with prestack converted wave data. The effects were demonstrated by inversion of theoretical and real seismic data.

Crustal velocity structure of central Gansu Province from regional seismic waveform inversion using firework algorithm

The firework algorithm（FWA） is a novel swarm intelligence-based method recently proposed for the optimization of multi-parameter, nonlinear functions. Numerical waveform inversion experiments using a synthetic model show that the FWA performs well in both solution quality and efficiency. We apply the FWA in this study to crustal velocity structure inversion using regional seismic waveform data of central Gansu on the northeastern margin of the Qinghai-Tibet plateau. Seismograms recorded from the moment magnitude（MW） 5.4 Minxian earthquake enable obtaining an average crustal velocity model for this region. We initially carried out a series of FWA robustness tests in regional waveform inversion at the same earthquake and station positions across the study region,inverting two velocity structure models, with and without a low-velocity crustal layer; the accuracy of our average inversion results and their standard deviations reveal the advantages of the FWA for the inversion of regional seismic waveforms. We applied the FWA across our study area using three component waveform data recorded by nine broadband permanent seismic stations with epicentral distances ranging between 146 and 437 km. These inversion results show that the average thickness of the crust in this region is 46.75 km, while thicknesses of the sedimentary layer, and the upper, middle, and lower crust are 3.15,15.69, 13.08, and 14.83 km, respectively. Results also show that the P-wave velocities of these layers and the upper mantle are 4.47, 6.07, 6.12, 6.87, and 8.18 km/s,respectively.

Simultaneous Inversion of Earthquake Relocation and Velocity Structure in the Shanxi-reservoir,Wenzhou

Using the data of P-wave network and Zhejiang and travel time recorded at the Shanxi-reservoir seismological Fujian local networks, we implemented a simultaneous inversion of earthquake relocation and velocity structure and determined the new locations of earthquakes in the Shanxi-reservoir. The results show that： （1） the overall epicenter distribution is NW directed, and the Shanxi reservoir induced seismicity has a close relationship to the Shuangxi-Jiaoxiyang fault; （2） the focal depth of the Shanxi reservoir induced seismicity is 5.4km in average, less than the average focal depth in the South China earthquake zone; （3） the focal depth is shallower on the reservoir shore and deeper in the reservoir inundation area. At the beginning of the reservoir induced seismicity, the focal depth increased gradually. This may be due to the gradual penetration of water into a larger depth that induced deeper earthquakes; and （4） there is a low P-wave velocity anomaly in the study area, located at the intersection of multiple faults in the reservoir inundation area. The Shanxi reservoir induced seismicity mostly occurred in this lowvelocity anomaly zone. This may be related to water penetration.

Inversion Method for Sound Velocity Profile of Eddy in Deep Ocean

The modal wave number tomography approach is used to obtain sound speed profile of water column in deep ocean. The approach consists of estimation of the local modal eigenvalues from complex pressure field and use of these data as input to modal perturbative inversion method for obtaining the local sound speed profile. The empirical orthonormal function (EOF) is applied to reduce the parameter search space. The ocean environment used for numerical simulations includes the Munk profile as the unperturbed background speed profile and a weak Gaussian eddy as the sound speed profile perturbation. The results of numerical simulations show the method is capable of monitoring the oceanic interior structure.

Two-step interface and velocity inversion—— Study o e of the Tangshan seismic region

This paper studies the computation method of two step inversion of interface and velocity in a region. The 3 D interface is described by a segmented incomplete polynomial; while the reconstruction of 3 D velocity is accomplished by the principle of least squares in functional space. The computation is carried out in two steps. The first step is to inverse the shape of 3 D interface; while the second step is to do 3 D velocity inversion by distributing the remaining residual errors of travel time in accordance with their weights. The data of seismic sounding in the Tangshan Luanxian seismic region are processed, from which the 3 D structural form in depth of the Tangshan seismic region and the 3 D velocity distribution in the crust below the Tangshan Luanxian seismic region are obtained. The result shows that the deep 3 D structure in the Tangshan seismic region trends NE on the whole and the structure sandwiched between the NE trending Fengtai Yejituo fault and the NE trending Tangshan fault is an uplifted zone of the Moho. In the 3 D velocity structure of middle lower crust below that region, there is an obvious belt of low velocity anomaly to exist along the NE trending Tangshan fault, the position of which tallies with that of the Tangshan seismicity belt. The larger block of low velocity anomaly near Shaheyi corresponds to a denser earthquake distribution. In that region, there is an NW trending belt of high velocity anomaly, probably a buried fault zone. The lower crust below the epicentral region of the Tangshan M S=7.8 earthquake is a place where the NE trending belt of low velocity anomaly meets the NW trending belt of high velocity anomaly. The two sets of structures had played an important role in controlling the preparation and occurrence of the M S=7.8 Tangshan earthquake.

A Quadratic precision generalized nonlinear global optimization migration velocity inversion method

An important research topic for prospecting seismology is to provide a fast accurate velocity model from pre-stack depth migration. Aiming at such a problem, we propose a quadratic precision generalized nonlinear global optimization migration velocity inversion. First we discard the assumption that there is a linear relationship between residual depth and residual velocity and propose a velocity model correction equation with quadratic precision which enables the velocity model from each iteration to approach the real model as quickly as possible. Second, we use a generalized nonlinear inversion to get the global optimal velocity perturbation model to all traces. This method can expedite the convergence speed and also can decrease the probability of falling into a local minimum during inversion. The synthetic data and Mamlousi data examples show that our method has a higher precision and needs only a few iterations and consequently enhances the practicability and accuracy of migration velocity analysis （MVA） in complex areas.

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.

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.

Assessing the effects of model parameter assumptions on surface-wave inversion results

Surface-wave inversion is a powerful tool for revealing the Earth's internal structure.However,aside from shear-wave velocity(v_(S)),other parameters can influence the inversion outcomes,yet these have not been systematically discussed.This study investigates the influence of various parameter assumptions on the results of surface-wave inversion,including the compressional and shear velocity ratio(v_(P)/v_(S)),shear-wave attenuation(Q_(S)),density(ρ),Moho interface,and sedimentary layer.We constructed synthetic models to generate dispersion data and compared the obtained results with different parameter assumptions with those of the true model.The results indicate that the v_(P)/v_(S) ratio,Q_(S),and density(ρ) have minimal effects on absolute velocity values and perturbation patterns in the inversion.Conversely,assumptions about the Moho interface and sedimentary layer significantly influenced absolute velocity values and perturbation patterns.Introducing an erroneous Mohointerface depth in the initial model of the inversion significantly affected the v_(S) model near that depth,while using a smooth initial model results in relatively minor deviations.The assumption on the sedimentary layer not only affects shallow structure results but also impacts the result at greater depths.Non-linear inversion methods outperform linear inversion methods,particularly for the assumptions of the Moho interface and sedimentary layer.Joint inversion with other data types,such as receiver functions or Rayleigh wave ellipticity,and using data from a broader period range or higher-mode surface waves,can mitigate these deviations.Furthermore,incorporating more accurate prior information can improve inversion results.

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.

Tomographic inversion of OBS converted shear waves:case study of profile EW6 in the Dongsha area

Studies of converted S-wave data recorded on the ocean bottom seismometer(OBS)allow for the estimation of crustal S-wave velocity,from which is further derived the Vp/Vs ratio to constrain the crustal lithology and geophysical properties.Constructing a precise S-wave velocity model is important for deep structural research,and inversion of converted S-waves provides a potential solution.However,the inversion of the converted S-wave remains a weakness because of the complexity of the seismic ray path and the inconsistent conversion interface.In this study,we introduced two travel time correction methods for the S-wave velocity inversion and imaged different S-wave velocity structures in accordance with the corresponding corrected S-wave phases using seismic data of profile EW6 in the northeastern South China Sea(SCS).The two inversion models show a similar trend in velocities,and the velocity difference is<0.15 km/s(mostly in the range of 0–0.1 km/s),indicating the accuracy of the two travel time correction methods and the reliability of the inversion results.According to simulations of seismic ray tracing based on different models,the velocity of sediments is the primary influencing factor in ray tracing for S-wave phases.If the sedimentary layer has high velocities,the near offset crustal S-wave refractions cannot be traced.In contrast,the ray tracing of Moho S-wave reflections was not significantly impacted by the velocity of the sediments.The two travel time correction methods have their own advantages,and the application of different approaches is based on additional requirements.These works provide an important reference for future improvements in converted S-wave research.

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.

Research on Multi-Wave Pore Pressure Prediction Method Based on Three Field Velocity Fusion

The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction velocity field, which has a long research period and low resolution and restricts the accuracy of seismic pressure prediction;This paper proposed for the first time the use of machine learning algorithms, based on the feasibility analysis of wellbore logging pressure prediction, to integrate the CVI velocity inversion field, velocity sensitive post stack attribute field, and AVO P-wave and S-wave velocity reflectivity to obtain high-precision seismic P and S wave velocities. On this basis, high-resolution formation pore pressure and other parameters prediction based on multi waves is carried out. The pressure prediction accuracy is improved by more than 50% compared to the P-wave resolution of pore pressure prediction using only root mean square velocity. Practice has proven that the research method has certain reference significance for reservoir pore pressure prediction.

A THEORY FOR WAVE EQUATION INVERSE PROBLEM:THE UNION METHOD FOR SCATTERED WAVEEXTRAPOLATION AND VELOCITY IMAGING

A new theory for inverse problem of wave equation, that is, the union method for scattered wave extrapolation and velocity imaging, is proposed in this paper. This method is very different from the classical wave extrapolation for migration, because we relate directly the scattered wave extrapolation to velocity inversion. And also this method is different from any linearized inverse method of wave equation, because we needn′t use linearized approximation. Because of this, the method can be applied to strong scattering case effectively (i.e. the value of scattered wave is not small, which can not be neglected). This method, of course, is different from nonlinearized optimum inverse method, because in this paper, the nonlinear inverse problem is turned into two steps inverse problem, i.e. scattered wave extrapolated and velocity imaging, which can be solved easily. Hence, the problem how to get the global optimum solution by using the nonlinearized optimum inverse method doesn′t bother us by using the method in this paper.