An Unsupervised Inversion Method for Seismic Brittleness Parameters Driven by the Physical Equation

Brittleness is an important parameter characterizing the fracturing properties of shale reservoir, which can be predicted by the prestack seismic inversion. In order to overcome the low efficiency and ill-posed problems of the traditional prestack brittleness inversion, we propose a new unsupervised deep learning (DL) inversion method for seismic brittleness parameters based on the physical equation. This method integrates DL framework and the physical equation and provides a DL inversion strategy without actual labels. We first input the original seismic data into the Fastformer network and use the low-frequency model as the physical constraint to predict the brittle parameters. Then, the prediction results of brittleness parameters are sent to the forward modeling module (a linear approximation equation) to calculate the synthetic seismic data. Next, the error between the calculated seismic data and original seismic data is used to update the network prediction results. The network parameters are iteratively optimized to minimize the error, and the brittle prediction parameters are finally output. In the whole training process, it is not necessary to use the real brittle parameters as the labels. Through this method, the effect of approximate unsupervised learning is obtained. Finally, we apply the proposed method to the synthetic data and field data and compared with the results inverted by the traditional L1 method. The experimental results show that the proposed method has higher inversion accuracy and efficiency than the traditional L1 method, which has a great potential in the practical application.