基于小样本数据的模型-数据驱动地震反演方法

Model-data-driven seismic inversion method based on small sample data

针对薄互层砂体识别难度大、常规模型驱动和数据驱动等地震预测方法精度较低的难题,提出一种基于空变目标函数的模型-数据驱动地震AVO反演新方法。该方法利用零延迟互相关函数和F范数(Frobenius范数)构建目标函数,以反距离加权理论根据反演目标道所在的位置控制目标函数的变化,进而改变训练样本、初始低频模型和地震数据对反演的约束权重,能够基于小样本数据反演得到较高精度、较高分辨率的速度和密度参数,适用于薄互层砂体的精细识别。薄互层地质模型测试结果表明,针对小样本数据,新方法的反演结果具有较高的精度和分辨率,能够识别约1/30波长厚度的砂岩薄层。丽水凹陷实际应用表明,新方法反演结果与测井数据的相对误差较小,且能够识别约1/15波长厚度的薄互层砂体。

As sandstone layers in thin interbedded section are difficult to identify, conventional model-driven seismic inversion and data-driven seismic methods have low precision in predicting them. To solve this problem, a model-data-driven seismic AVO(amplitude variation with offset) inversion method based on a space-variant objective function has been worked out. In this method, zero delay cross-correlation function and F norm are used to establish objective function. Based on inverse distance weighting theory, change of the objective function is controlled according to the location of the target CDP(common depth point), to change the constraint weights of training samples, initial low-frequency models, and seismic data on the inversion. Hence, the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data, and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data, and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick. Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data, and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.