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.

地下介质的粘弹性会引起地震波的吸收和衰减，导致能量衰减和相位畸变。这种吸收和衰减通常通过品质因数 Q 来量化。地质引起的强衰减效应对于高分辨率成像来说是一个具有挑战性的问题。为了补偿衰减效应，需要准确估计衰减参数。然而，直接推导异构衰减Q模型是很困难的。本研究信件提出了一种使用反向传播神经网络 (BPNN)（最广泛使用的神经网络模型之一）从反射地震数据导出 Q 模型的方法。我们将 Q 检测问题视为模式识别任务，并训练网络将正确的 Q 类分配给一组输入模式。所提出的方法使用合成数据进行网络训练和验证。最后，我们使用一组模型数据和一组现场数据证明了该方法的有效性，并获得了适当补偿的时域高分辨率成像结果。