Simultaneous Interval-Q Estimation and Attenuation Compensation Based on Fusion Deep Neural Network

Seismic attenuation compensation is a widely used technique for enhancing the resolution of nonstationary seismic data. An accurate and reliable quality factor (
$Q$
) is a prerequisite for attenuation compensation and an important indicator of oil and gas.
$Q$
-factor estimation and attenuation compensation are ill-posed inverse problems, and they are poorly robust when noise is present. Moreover, the process is usually performed in steps, which leads to an accumulation of errors. Deep-learning (DL)-based methods have gained great popularity because of their powerful ability to obtain exact solutions for inverse problems. However, most DL frameworks rely heavily on huge training datasets and lack clear physical meaning. For these reasons, we propose a fusion neural network architecture to build a physically meaningful network to simultaneously implement interval-
$Q$
estimation and seismic attenuation compensation. The fusion neural network consists of two subnetworks based on the spatio-temporal neural network (STNN). And, the method ensures the coupling relationship between
$Q$
-factor and nonattenuated/attenuated seismic data by building a loss function that contains both the
$Q$
loss term and the seismic data loss term. The training dataset is then used to update the subnetworks simultaneously to obtain a fusion network that achieves both interval-
$Q$
estimation and seismic attenuation compensation. We demonstrate the effectiveness of the proposed simultaneous interval-
$Q$
estimation and seismic attenuation compensation algorithm by applying both synthetic and field data examples.