基于机器学习的时间反转散射体检测方法

地下小尺度散射体的检测和识别对于提高地震勘探的分辨率具有重要意义,目前业界普遍采用绕射波分离及成像方法检测地下散射体,而绕射波成像的质量主要取决于绕射波和反射波波场分离的程度.本文将被动源震源定位问题中常用的时间反转原理引入到地下散射体检测中,首先通过分析被动源和主动源模型反传波场的聚焦状态,验证了时间反转原理应用于地...

Time-reversal scatterer detection method using machine learning

The detection and identification of subsurface small-scale scatterers is of great significance to improve the resolution of seismic exploration. At present, diffraction wave separation and imaging methods are widely used to detect subsurface scatterers, and the quality of diffraction imaging mainly depends on the degree of separation of diffraction wave and reflection wave. In this paper, the time-reversal principle, which is commonly used in passive source location, is introduced into the subsurface scatterer detection. Firstly, by analyzing the focusing status of the back propagation wavefield of the passive source and the active source, the feasibility of applying the time-reversal principle to the subsurface scatterer detection is verified. Then the naive Bayes classification algorithm in machine learning is introduced, and the classification algorithm framework suitable for time-reversal scatterer detection is given. The probability of each point in the model to become a scatterer is calculated, and finally the most likely position of subsurface scatterer is detected. The experimental results of scatterer point model and Sigsbee2a model show that the proposed method can detect and locate subsurface scatterers without separating reflection wave and diffraction wave. Meanwhile, considering the influence of multiple scattering, the detection results can accurately reflect the location of subsurface scatterers.