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The conventional transient electromagnetic inversion method has a low calculation speed and precision and is susceptible to falling into local minima, which does not meet the fine detection requirements of urban underground space. In this study, we proposed a novel inversion method based on convolutional bidirectional long short-term memory neural networks for shallow subsurface transient electromagnetic inversion. This network structure possessed strong spatial feature extraction capabilities and a proficient understanding of sequential data, thereby addressing the issues of slow conventional inversion computations and inadequate inversion accuracy. Utilizing the apparent resistivity from a three-layer model as the sample input and the real model as the target, the network was trained using batch normalization and dropout techniques to accelerate the convergence rate. The resulting model achieved real-time inversion speeds and high accuracy, with robust generalization capabilities and adaptability to new data. To assess the inversion performance, we used a novel 1-D inversion error calculation index, the correlation area loss error, for a more accurate measurement. Numerical simulation experiments showed that the proposed method required only 2.121 s to invert data from 100 observation points. The inversion efficiency was significantly superior to the conventional methods, maintaining excellent accuracy while effectively discerning subsurface electrical stratification in geophysics. Applying convolutional bidirectional long short-term memory neural networks to multidimensional and field data yielded results superior to those of conventional inversion, demonstrating the promising applicability and generalization of this approach. This study offers an efficient solution for shallow subsurface transient electromagnetic exploration and holds potential for application in other areas. [ABSTRACT FROM AUTHOR]

The presence of distributed energy sources in integrated energy systems make it difficult to meet the real-time balance between supply and demand, requiring the deployment of energy storage systems. Hydrogen storage can compensate for the lack of electrochemical energy storage in the energy, time and space dimensions. Meanwhile, the cooperative operation of participants within an integrated energy system, compared to individual operation, can help generate additional benefits and reduce carbon emissions. Therefore, this paper proposes a method for optimising the operation of integrated energy systems based on a cooperative game containing hydrogen energy storage systems. Firstly, a model for optimising the operation of an integrated energy system with hydrogen storage energy system considering the revenue from hydrogen sales is constructed. Secondly, a benefit allocation mechanism for the cooperative game based on the shapley value method is proposed. Finally, the BPSO-SA optimisation algorithm is proposed for solution. The total operating cost of the collaborative operation scenario is reduced by 22.96% and the carbon emissions are reduced by 80.11% compared to the independent operation scenario, the operating cost savings and carbon emissions reduction are allocated to the participants according to their contribution, encourage participants to build integrated energy systems through collaboration. • A model for optimising the operation of an IES containing hydrogen energy storage system is developed. • A multi-participant cooperative game benefit allocation mechanism within the IES is developed. • A hybrid particle swarm optimisation-simulated annealing based on beetle antennae search strategy (BPSO-SA) is proposed. [ABSTRACT FROM AUTHOR]