Rock physics and seismic reflectivity parameterization and amplitude variation with offsets inversion in terms of total organic carbon indicator

Total organic carbon (TOC) prediction with elastic parameter inversions has been widely used in the identification and evaluation of source rocks. However, the elastic parameters used to predict TOC are not only determined by TOC but also depend on the other physical properties of source rocks. Besides, the TOC prediction with the elastic parameters inversion is an indirect method based on the statistical relationship obtained from well logs and experiment data. Therefore, we propose a rock physics model and define a TOC indicator mainly affected by TOC to predict TOC directly. The proposed rock physics model makes the equivalent elastic moduli of source rocks parameterized by the TOC indicator. Combining the equivalent elastic moduli of source rocks and Gray’s approximation leads to a novel linearized approximation of the P-wave reflection coefficient incorporating the TOC indicator. Model examples illustrate that the novel reflectivity approximation well agrees with the exact Zoeppritz equation until incident angles reach 40°. Convoluting the novel P-wave reflection approximation with seismic wavelets as the forward solver, an AVO inversion method based on the Bayesian theory is proposed to invert the TOC indicator with seismic data. The synthetic examples and field tests validate the feasibility and stability of the proposed AVO inversion approach. Using the inversion results of the TOC indicator, TOC is directly and accurately estimated in the target area.

Key parameter optimization and analysis of stochastic seismic inversion

Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.

Influence of inaccurate wavelet phase estimation on seismic inversion

On the assumption that the seismic wavelet amplitude spectrum is estimated accurately, a group of wavelets with different phase spectra, regarded as estimated wavelets, are used to implement linear least-squares inversion. During inversion, except for the wavelet phase, all other factors affecting inversion results are not taken into account. The inversion results of a sparse reflectivity model （or blocky impedance model） show that： （1） although the synthetic data using inversion results matches well with the original seismic data, the inverted reflectivity and acoustic impedance are different from that of the real model. （2） the inversion result reliability is dependent on the estimated wavelet Z transform root distribution. When the estimated wavelet Z transform roots only differ from that of the real wavelet near the unit circle, the inverted reflectivity and impedance are usually consistent with the real model; （3） although the synthetic data matches well with the original data and the Cauchy norm （or modified Cauchy norm） with a constant damping parameter has been optimized, the inverted results are still greatly different from the real model. Finally, we suggest using the L1 norm, Kurtosis, variation, Cauchy norm with adaptive damping parameter or/and modified Cauchy norm with adaptive damping parameter as evaluation criteria to reduce the bad influence of inaccurate wavelet phase estimation and obtain good results in theory.

Analysis of the ambiguity of log-constrained seismic impedance inversion

Although the ambiguity of seismic inversion is widely recognized in both theory and practice, so far as a concrete inversion example is concerned, there is not any objective, controllable method or any standard for how to evaluate and determine its ambiguity and reliability, especially for the high frequency components beyond the effective seismic frequency band. Taking log-constrained impedance inversion as an example, a new appraisal method is proposed on the basis of analyzing a simple geological model. Firstly, the inverted impedance model is transformed to a reflection coefficient series. Secondly, the maximum effective frequency of the real seismic data is chosen as a cutoff point and the reflection coefficient series is decomposed into two components by low-pass and high-pass filters. Thirdly, the geometrical reflection characteristics of the high-frequency components and that of the real seismic data are compared and analyzed. Then, the reliability of the inverted impedance model is appraised according to the similarity of geometrical characteristics between the high-frequency components and the real seismic data. The new method avoids some subjectivity in appraising the inverted result, and helps to enhance the reliability of reservoir prediction by impedance inversion technology.

Seismic impedance inversion based on cycle-consistent generative adversarial network

Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.

A Method of Improving Seismic Data Resolution：Comprehensive Inversion of Well logging and Seismic Data

Comprehensive inversion of logging and seismic data presented in this paper is a method to improve seismic data resolution. It involves using ample high-frequency information and complete low-frequency information of known logging to make up for the lack of limited bandwidth of practical seismic recording, obtaining an approximate reflection coefficient sequence (or wave impedance) of high resolution by iterative inversion and providing more reliable seismic evidence for further lithologic interpretation and lateral tracking, correlation and prediction of thin reservoir. The comprehensive inversion can be realized in the following steps: (1) to establish an initial model of higher resolution; (2) to obtain wavelets, and (3) to constrain iterative inversion. The key to this inversion lies in building an initial model. It is assumed from our experience that when the initial model is properly given, iterative inversion can be quickly converged to the ideal result.

Seismic AVO statistical inversion incorporating poroelasticity

Seismic amplitude variation with offset(AVO) inversion is an important approach for quantitative prediction of rock elasticity,lithology and fluid properties.With Biot-Gassmann's poroelasticity,an improved statistical AVO inversion approach is proposed.To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients,the AVO equation of reflection coefficients parameterized by porosity,rock-matrix moduli,density and fluid modulus is initially derived from Gassmann equation and critical porosity model.From the analysis of the influences of model parameters on the proposed AVO equation,rock porosity has the greatest influences,followed by rock-matrix moduli and density,and fluid modulus has the least influences among these model parameters.Furthermore,a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity,rock-matrix modulus,density and fluid modulus.Besides,the Laplace probability model and differential evolution,Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework.Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters,which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

An evaluation of deep thin coal seams and water-bearing/resisting layers in the quaternary system using seismic inversion

Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.

Joint inversion of gravity and seismic data along a profile across the seismogenic fault of 2010 Yushu Ms7. 1 earthquake

Yushu Ms7.1 earthquake occurred on the Ganzi-Yushu fault zone, across which we carried out a joint relative-gravity and seismic-reflection survey, and then performed a gravity inversion constrained by the seismic-reflection result. Based on the data of complete Bouguer gravity anomaly and seismic reflection, we obtained a layered interface structure in deep crust down to Moho. Our study showed that the inversion could reveal the interfaces of strata along the survey profile and the directions of regional faults in two-dimension. From the characteristics of the observed topography of the Moho basement, we tentatively confirmed that the uplift of eastern edge of Qinghai-Tibet plateau was caused by the subduetion of the Indian plate.

Development and application of iterative facies-constrained seismic inversion

To improve the accuracy of inversion results,geological facies distributions are considered as additional constraints in the inversion process.However,the geological facies itself also has its own uncertainty.In this paper,the initial sedimentary facies maps are obtained by integrated geological analysis from well data,seismic attributes,and deterministic inversion results.Then the fi rst iteration of facies-constrained seismic inversion is performed.According to that result and other data such as geological information,the facies distribution can be updated using cluster analysis.The next round of facies-constrained inversion can then be performed.This process will be repeated until the facies inconsistency or error before and after the inversion is minimized.It forms a new iterative facies-constrained seismic inversion technique.Compared with conventional facies-constrained seismic inversion,the proposed method not only can reduces the non-uniqueness of seismic inversion results but also can improves its resolution.As a consequence,the sedimentary facies will be more consistent with the geology.A practical application demonstrated that the superposition relationship of sand bodies could be better delineated based on this new seismic inversion technique.The result highly increases the understanding of reservoir connectivity and its accuracy,which can be used to guide further development.

Applications of seismic moment tensor inversion in fast response to earthquakes

Using the technique of seismic moment tensor inversion, the source mechanisms of 10 earthquakes with Ms5.2that occurred in China from November 1996 to January 1998 were determined rapidly. The determined resultswere sent as 'Bulletins of Source Mechanism Parameters of Earthquakes' to the Seismic Regime Guards' Office,China Seismological Bureau, and the relevant provincial seismological bureaus. These bulletins have played rolein the fast response to large earthquakes.

Moment tensor and stress inversion solutions of acoustic emissions during compression and tensile fracturing in crystalline rocks

We investigate the accuracy and robustness of moment tensor(MT)and stress inversion solutions derived from acoustic emissions(AEs)during the laboratory fracturing of prismatic Barre granite specimens.Pre-cut flaws in the specimens introduce a complex stress field,resulting in a spatial and temporal variation of focal mechanisms.Specifically,we consider two experimental setups:(1)where the rock is loaded in compression to generate primarily shear-type fractures and(2)where the material is loaded in indirect tension to generate predominantly tensile-type fractures.In each test,we first decompose AE moment tensors into double-couple(DC)and non-DC terms and then derive unambiguous normal and slip vectors using k-means clustering and an unstructured damped stress inversion algorithm.We explore temporal and spatial distributions of DC and non-DC events at different loading levels.The majority of the DC and the tensile non-DC events cluster around the pre-cut flaws,where macro-cracks later develop.Results of stress inversion are verified against the stress field from finite element(FE)modeling.A good agreement is found between the experimentally derived and numerically simulated stress orientations.To the best of the authors’knowledge,this work presents the first case where stress inversion methodologies are validated by numerical simulations at laboratory scale and under highly heterogeneous stress distributions.

A comparison of deep learning methods for seismic impedance inversion

Deep learning is widely used for seismic impedance inversion,but few work provides in-depth research and analysis on designing the architectures of deep neural networks and choosing the network hyperparameters.This paper is dedicated to comprehensively studying on the significant aspects of deep neural networks that affect the inversion results.We experimentally reveal how network hyperparameters and architectures affect the inversion performance,and develop a series of methods which are proven to be effective in reconstructing high-frequency information in the estimated impedance model.Experiments demonstrate that the proposed multi-scale architecture is helpful to reconstruct more high-frequency details than a conventional network.Besides,the reconstruction of high-frequency information can be further promoted by introducing a perceptual loss and a generative adversarial network from the computer vision perspective.More importantly,the experimental results provide valuable references for designing proper network architectures in the seismic inversion problem.

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.

Impedance inversion of pre-stack seismic data in the depth domain

The extensive application of pre-stack depth migration has produced huge volumes of seismic data,which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth domain.It is difficult to estimate seismic wavelets directly from seismic data due to the nonstationarity of the data in the depth domain.We conduct a velocity transformation of seismic data to make the seismic data stationary and then apply the ridge regression method to estimate a constant seismic wavelet.The estimated constant seismic wavelet is constructed as a set of space-variant seismic wavelets dominated by velocities at different spatial locations.Incorporating the weighted superposition principle,a synthetic seismogram is generated by directly employing the space-variant seismic wavelets in the depth domain.An inversion workflow based on the model-driven method is developed in the depth domain by incorporating the nonlinear conjugate gradient algorithm,which avoids additional data conversions between the time and depth domains.The impedance inversions of the synthetic and field seismic data in the depth domain show good results,which demonstrates that seismic inversion in the depth domain is feasible.The approach provides an alternative for forward numerical analyses and elastic property inversions of depth-domain seismic data.It is advantageous for further studies concerning the stability,accuracy,and efficiency of seismic inversions in the depth domain.

Genetic algorithm in seismic waveform inversion and its application in deep seismic sounding data interpretation

A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthetic seismogram algorithm, which is capable of calculating the response of thin alternating high and low velocity layers, is applied as a solution for forward modeling, and the genetic algorithm is used to find the optimal solution of the inverse problem. Numerical tests suggest that the method has the capability of resolving low-velocity layers, thin alternating high and low velocity layers, and noise suppression. Waveform inversion using P-wave records from Zeku, Xiahe and Lintao shots in the seismic wide-angle reflection/refraction survey along northeastern Qinghai-Xizang （Tibeteau） Plateau has revealed fine structures of the bottom of the upper crust and alternating layers in the middle/lower crust and topmost upper mantle.

Application of well log normalization in coalfield seismic inversion

During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be normalized in the mining area. By studying well-logging normalization methods, and focusing on the characteristics of the coalfield, the frequency histogram method was used in accordance with the condition of the Guqiao Coal Mine. In this way, the density and sonic velocity at marker bed in the non-key well were made to close to those in the key well, and were eventually equal. Well log normalization was completed when this method was applied to the entire logging curves. The results show that the scales of logging data were unified by normalizing coal logging curves, and the logging data were consistent with wave impedance inversion data. A satisfactory inversion effect was obtained.

A method of seismic meme inversion and its application

Under the condition of thin interbeds with great lateral changes in terrestrial basins,a seismic meme inversion method is established based on the analysis of seismic sedimentology technology.The relationship between seismic waveform and high-frequency well logs is established through dynamic clustering of seismic waveform to improve the vertical and horizontal resolution of inversion results;meanwhile,by constructing the Bayesian inversion framework of different seismic facies,the real facies controlled inversion is realized.The forward model verification results show that the seismic meme inversion can realize precise prediction of 3 m thick thin interbeds,proving the rationality and high precision of the method.The application in the Daqing placanticline shows that the seismic meme inversion could identify 2 m thin interbeds,and the coincidence rates of inversion results and drilling data were more than 80%.The seismic meme inversion method can improve the accuracy of reservoir prediction and provides a useful mean for thin interbeds prediction in terrestrial basins.

2-D crustal structure of Changbaishan-Tianchi volcanic region determined by seismic traveltime inversion

2-D velocity structure and tectonics of the crust and upper mantle is revealed by inversion of seismic refraction and wide-angle reflection traveltimes acquired along the profile L1 in the Changbaishan-Tianchi volcanic region. It is used in this study that seismic traveltime inversion for simultaneous determination of 2-D velocity and interface structure of the crust and upper mantle. The result shows that, under Changbaishan-Tianchi crater, there exists a low-velocity body in the shape of an inverted triangle, and the crustal reflecting boundaries and Moho all become lower by a varying margin of 2-6 km, forming a crustal root which is assumed to be the Changbaishan-Tianchi volcanic system. Finally, we make a comparison between our 2-D velocity model and the result from the studies by using trial-and-error forward modeling with SEIS83.

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.