Calculations for the transient electromagnetic (TEM) method are frequently performed by using the Gaver‐Stehfest algorithm to compute the inverse Laplace transform (G‐S transform). It is a pure real number operation and only needs implementing the calculation for the values of a small number of Laplace transform variable s (usually, 12s values for one sample time). So it is a rather rapid algorithm. However, when we need to calculate TEM responses for much sampling time, the computation quantity of this algorithm will be quite large. Based on the delay theorem of Laplace transform, a new algorithm for the G‐S transform is proposed in this paper. This method can reduce the computation quantity by orders of magnitude, and therefore increase markedly the speed of computation for TEM responses with dense samples.

As a kind of electromagnetic exploration method in frequency domain, spectral induced polarization (SIP) technique is a branch of electromagnetic exploration methods in geophysics exploration, which is widely used in environmental and engineering geophysical prospecting, as well as mineral exploration, oil & gas and coal exploration. In the theory of SIP data interpretation, inversion of three spectral induced polarization parameters (m, τ and c) in Cole-Cole model is a basic job. Due to their respective special character in the function expression, the inversion of m, τ and c from frequency spectral response data based on linear inversion theory is usually instable and even sometimes not convergent when data have a little errors. In this paper, we adopt a kind of improved genetic algorithm to implement inversion. Inversion on one Cole-Cole model shows that the algorithm converges fast, has very good stability and high precision, and even can permits the observed data random error up to 15%. Inversion on two Cole-Cole models, which simulates the fact that the effects of electromagnetic and induced polarization exist simultaneously, show the algorithm is still fast convergence and high precision, and permits a few observed data error (say, 5% random error). Only when random errors add up to 10%, the results of inversion begin to become obvious error.

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With the development of the society, the education and the construction of the campus in recent years, the designing of Gymnasium in Universities is facing many new problems, which has aroused public attention. The planning and design for gymnasium in universities are integrated and studied as a whole. People start to explore the possible direction of development from its own characteristics, so that the original intention of the design are implemented and reflected in the project construction and utilization, thus helping the university campus to create a harmonious atmosphere, it is also conducive to integrate the university campus organically into the urban environment, so that the college stadiums can achieve the goal of sustainable development through the sharing of resources.

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