基于神经网络的绕射波速度分析方法  

A neural network-based method for analyzing diffracted wave velocity

作　　者：陶俊宏 赵惊涛 盛同杰 TAO Junhong;ZHAO Jingtao;SHENG Tongjie(College of Geoscience and Surveying Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China)

机构地区：[1]中国矿业大学(北京)地球科学与测绘工程学院,北京100083

出　　处：《煤田地质与勘探》2024年第9期166-175,共10页Coal Geology & Exploration

基　　金：国家自然科学基金创新研究群体项目(42321002)。

摘　　要：【背景】在地震成像领域,精确的速度模型对于细致地刻画地下空间结构至关重要。尤其在煤矿开采中,小尺度地质结构如断层和陷落柱与开采安全事故密切相关,这些结构在地震记录中通常以绕射波的形式出现。【目的和方法】为了有效成像这些小尺度地质体,充分利用绕射波信息进行精细化速度建模显得尤为重要。基于此,提出了一种基于神经网络的绕射场速度分析方法。首先在共虚震源域分离绕射波,利用分离的绕射波道集生成绕射速度谱;其次,在偏移倾角域利用绕射波的拟线性特征生成绕射速度比值γ谱。最终,将传统的反射速度谱与两类绕射速度谱作为输入,基于卷积注意力神经网络进行绕射场智能速度建模。【结果和结论】数值模拟数据测试验证了绕射场速度分析网络能够提高对地层尖灭、岩溶陷落柱等地质体精细化速度建模的准确性,在成像剖面上可有效聚焦绕射波,实现中小尺度构造的精细刻画。对煤矿实际数据的应用进一步证明了该方法在速度建模的效率以及成像精度上均优于传统方法,更适用于复杂地质条件下的小尺度构造成像。Background In seismic imaging,accurate velocity models are crucial for characterizing subsurface structures in a fine-scale manner.Notably,in coal mining,small-scale geological structures like faults and collapse columns are closely associated with mining accidents.These structures typically occur as diffracted waves in seismograms.Objective and Methods To effectively image these small-scale geological bodies,fine-scale velocity modeling using diffracted wave information is particularly important.Hence,this study proposed a neural network-based method for analyzing diffracted-wave velocity.First,diffracted-wave velocity spectra were generated using the gathers of diffracted waves that were separated in the common virtual source domain.Second,a gamma-ray spectrum of diffracted-wave velocity ratios was generated using the quasi-linear characteristics of diffracted waves in the migrated dip-angle domain.Finally,with the conventional reflected-wave velocity spectrum and the two diffracted-wave velocity spectra as inputs,intelligent diffracted-wave velocity modeling was performed based on a convolutional attention neural network.Results and Conclusions The tests using numerical simulation data verified that the diffracted-wave velocity analysis network can enhance the accuracy of fine-scale velocity modeling for geological bodies like stratigraphic pinch-outs and karst collapse columns.This network allows for the effective focusing of diffracted waves in the imaging profiles,thus achieving the fine-scale characterization of small-to medium-scale structures.The application of the proposed method to actual data from a coal mine further demonstrates the method's superiority over conventional methods in terms of velocity modeling efficiency and imaging accuracy.Therefore,the proposed method is more applicable to the imaging of small-scale structures under complex geologic conditions.

关 键 词：绕射波 深度学习 速度分析 智能建模 地震成像 

分 类 号：P631[天文地球—地质矿产勘探]