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Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic wavelet, observed data band limitation and noise, but it also requires a forward operator that characterizes physical relation between measured data and model parameters. Deep learning methods have been successfully applied to solve geophysical inversion problems recently. It can obtain results with higher resolution compared to traditional inversion methods, but its performance often not fully explored for the lack of adequate labeled data (i.e., well logs) in training process. To alleviate this problem, we propose a semi-supervised learning workflow based on generative adversarial network (GAN) for acoustic impedance inversion. The workflow contains three networks: a generator, a discriminator and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. The benchmark models Marmousi2, SEAM and a field data are used to demonstrate the performance of our method. Results show that impedance predicted by the presented method, due to making use of both labeled and unlabeled data, are better consistent with ground truth than that of conventional deep learning methods.

As a non-productive activity, environmental information disclosure is not only a prerequisite for environmental governance and sustainable development of listed companies, but also an effective means for executives to relieve pressure on business performance. Taking Shanghai and Shenzhen A-share listed companies as research samples, the authors have carried out an empirical study to test the relationship between subsidiary performance pressure and environmental information disclosure in enterprise groups, and examines the moderating effect of the parent company's shareholding on the main effect, as well as the differentiation of the moderating effect between high and low degree of executives' synergy allocation level in parent-subsidiary corporations. The results show that: firstly, the performance pressure of listed companies has a positive impact on environmental information disclosure; secondly, the parent company's shareholding will weaken the positive impact of listed company's performance pressure on environmental information disclosure. The higher the parent company's shareholding ratio, the weaker the positive impact of subsidiary company's performance pressure on environmental information disclosure. Thirdly, when the degree of executives' synergy allocation level in parent-subsidiary corporations is low, the negative moderating effect of parent's shareholding ratio is stronger.

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