Estimating effective Q parameters from reflection seismic data using BPNN

The viscoelasticity of an underground medium will cause absorption and attenuation of seismic waves, resulting in energy attenuation and phase distortion. This absorption and attenuation is often quantified by the quality Factor Q. The strong attenuation effect resulting from geology is a challenging problem for high-resolution imaging. To compensate for the attenuation effect, it is necessary to estimate the attenuation parameters accurately. However, it is difficult to directly derive a heterogeneous attenuation Q model. This research letter proposes a method to derive a Q model from reflection seismic data using a backpropagation neural network (BPNN), one of the most widely used neural network models. We treated the Q detection problem as a pattern recognition task and train a network to assign the correct Q classes to a set of input patterns. The proposed method uses synthetic data for network training and validation. Finally, we used a set of model data and a set of field data to demonstrate the effectiveness of this method, and the high-resolution imaging results in the time domain with appropriate compensation are obtained.