面向断溶体缝洞储层的智能化散射波地震成像方法

Intelligent diffractions imaging method for fault-karst reservoir

为促进断溶体缝洞型储层的高效勘探开发,在强反射背景下利用绕射波信号实现小尺度地质体的高精度成像,提出一种反射波和绕射波智能化分离及联合成像方法。根据反射波和绕射波在倾角域成像道集中的几何特征差异,首先搭建生成对抗神经网络(GANs),实现反射波和绕射波的运动学识别和分离;其次,根据波形振幅特征,利用自适应相减实现反射和绕射动力学分离;最后,将分离的道集进行叠加成像,获得能够反映连续阻抗界面的反射波成像结果和可以反映小尺度地质体的绕射波成像结果。结果表明,所提出的方法可以有效提高断溶体储层的成像精度,实现小尺度溶洞地质目标体高精度地震成像。

To exhance the efficient exploration and development of fractured reservoirs and achieve high-precision imaging of small-scale geological bodies amidst strong reflections,we propose an intelligent method for separating and imaging reflections and diffractions.Leveraging the geometric differences between reflections and diffractions in dip-angle domain gathers,we initially utilize generative adversarial neural networks(GANs)to kinematically identify and separate reflections and diffractions.Then,based on waveform amplitude characteristics,we adopt an adaptive subtraction method for dynamically separating reflections and diffractions.Ultimately,the separated gathers are stacked to produce reflector images,depicting the subsurface continuous impedance interfaces,and diffractor images,revealing small-scale geological bodies.Numerical experiments conducted on synthetic and field data validate the efficacy of the proposed method in enhancing imaging accuracy of fault karsts and achieving high-resolution seismic imaging of small-scale geological targets.