Characteristics of dispersion curves for Love channel waves in transversely isotropic media

Coal seams have a pronounced bedding structure with developed cracks and exhibit signifi cant anisotropy.However,few studies have examined the frequency dispersion properties of channel waves in anisotropic coal seams.In this study,numerical solutions are calculated using the generalized reflection–transmission coefficient method for the dispersion curves of Love channel waves in vertical transversely isotropic(VTI)and horizontal transversely isotropic(HTI)medium models.Moreover,the frequency dispersion characteristics of Love channel waves in several typical transversely isotropic models are analyzed.We fi nd that the dispersion curves for isotropic and VTI media diff er signifi cantly.In addition,the phase and Airy-phase velocities in VTI media are higher than those in isotropic media.Thus,neglecting this difference in practical channel wave detection will result in large detection errors.The dispersion curves for the isotropic and HTI media do not differ signifi cantly,and the Airy-phase velocities of various modes are similar.The group-velocity curve for a coal seam model containing a dirt band is found to be extremely irregular.The fundamental-mode Airy phase is not pronounced,but the fi rst-mode Airy phase can be clearly observed.Hence,fi rst-mode channel waves are suitable for detecting dirt bands.

基于Qt编程的实时二维地震波有限差分模拟

将Qt编程与二维有限差分正演模拟相结合,开发一种能够进行实时正演模拟以及将正演结果用png图片形式或segy文件格式输出的软件。首先,将二维声波方程、二阶Clayton_Engquist_majda吸收边界条件以及子波函数进行离散后进行软件设计和编程;编程过程中,试验发现全波形和半波形子波对正演结果的影响,并提出解决方法;给出实时显示波场的实现思路并进行验证,通过增益参数的添加提高了波场对比度和正演效果。

Instantaneous phase inversion based on an unwrapping algorithm

The full-waveform inversion method is a high-precision inversion method based on the minimization of the misfit between the synthetic seismograms and the observed data.However,this method suffers from cycle skipping in the time domain or phase wrapping in the frequency because of the inaccurate initial velocity or the lack of low-frequency information.furthermore,the object scale of inversion is affected by the observation system and wavelet bandwidth,the inversion for large-scale structures is a strongly nonlinear problem that is considerably difficult to solve.In this study,we modify the unwrapping algorithm to obtain accurate unwrapped instantaneous phase,then using this phase conducts the inversion for reducing the strong nonlinearity.The normal instantaneous phases are measured as modulo 2π,leading the loss of true phase information.The path integral algorithm can be used to unwrap the instantaneous phase of the seismograms having time series and onedimensional(1 D)signal characteristics.However,the unwrapped phase is easily affected by the numerical simulation and phase calculations,resulting in the low resolution of inversion parameters.To increase the noise resistance and ensure the inversion accuracy,we present an improved unwrapping method by adding an envelope into the path integral unwrapping algorithm for restricting the phase mutation points,getting accurate instantaneous phase.The objective function constructed by unwrapping instantaneous phase is less affected by the local minimum,thereby making it suitable for full-waveform inversion.Further,the corresponding instantaneous phase inversion formulas are provided.Using the improved algorithm,we can invert the low-wavenumber components of the underneath structure and ensure the accuracy of the inverted velocity.Finally,the numerical tests of the 2 D Marmousi model and 3 D SEG/EAGE salt model prove the accuracy of the proposed algorithm and the ability to restore largescale low-wavenumber structures,respectively.

Intelligent identification method and application of seismic faults based on a balanced classification network

This study combined fault identification with a deep learning algorithm and applied a convolutional neural network(CNN)design based on an improved balanced crossentropy(BCE)loss function to address the low accuracy in the intelligent identification of seismic faults and the slow training speed of convolutional neural networks caused by unbalanced training sample sets.The network structure and optimal hyperparameters were determined by extracting feature maps layer by layer and by analyzing the results of seismic feature extraction.The BCE loss function was used to add the parameter which is the ratio of nonfaults to the total sample sets,thereby changing the loss function to find the reference of the minimum weight parameter and adjusting the ratio of fault to nonfault data.The method overcame the unbalanced number of sample sets and improved the iteration speed.After a brief training,the accuracy could reach more than 95%,and gradient descent was evident.The proposed method was applied to fault identification in an oilfield area.The trained model can predict faults clearly,and the prediction results are basically consistent with an actual case,verifying the effectiveness and adaptability of the method.

Ground roll separation method via threshold fi ltering and constraint of seismic wavelet support in curvelet domain

Most traditional ground roll separation methods utilize only the difference in geometric characteristics between the ground roll and the refl ection wave to separate them.When the geometric characteristics of data are complex,these methods often lead to damage of the reflection wave or incompletely suppress the ground roll.To solve this problem,we proposed a novel ground roll separation method via threshold filtering and constraint of seismic wavelet support in the curvelet domain;this method is called the TFWS method.First,curvelet threshold fi ltering(CTF)is performed by using the diff erence of the curvelet coeffi cient of the refl ection wave and the ground roll in the location,scale,and slope of their events to eliminate most of the ground roll.Second,the degree of the local damaged signal or the local residual noise is estimated as the local weighting coeffi cient.Under the constraints of seismic wavelet and local weighting coeffi cient,the L1 norm of the refl ection coeffi cient is minimized in the curvelet domain to recover the damaged refl ection wave and attenuate the residual noise.The local weighting coeffi cient in this paper is obtained by calculating the local correlation coeffi cient between the high-pass fi ltering result and the CFT result.We applied the TFWS method to simulate and fi eld data and compared its performance with that of frequency and wavenumber filtering and the CFT method.Results show that the TFWS method can attenuate not only linear ground roll,aliased ground roll,and nonlinear noise but also strong noise with a slope close to the refl ection events.