Abstract: Provided herein is a tunnel electromagnetic joint scanning detection method and a system thereof. It introduces a new tunnel electromagnetic detection system called TEJS, realizes three-dimensional joint inversion of multi-component, time domain and frequency domain signals, and forms tunnel joint scanning imaging. This method adopts the mode of surface transmission, underground reception, multi-source transmission and multi-component reception. Based on an observation system, a large number of stochastic models are constructed and numerically simulated, and a large number of training data sets are constructed by using the simulated data to complete the training of UNet model. This model can realize real-time and fast imaging of the position of the low-resistance anomalous body in three-dimensional space. An algorithm forms a dual checking mechanism through surface imaging and underground imaging, which constrains the three-dimensional spatial position of anomalous body together to prevent misjudgment.

Abstract: The present disclosure belongs to the field of geophysical exploration, and discloses a method, a system, a medium, a device and a terminal for predicting the probing depth of transient electromagnetic. Based on the existing published resistivity model database, the transient electromagnetic field in layered medium is calculated, and the probing depth is calculated based on Jacobian matrix to establish a training data set. The simulated induced electromotive force is used as the input of the neural network, and the calculated probing depth is used as the output of the network. A rapid mapping between observation data and probing depth is established by using residual neural network.

Abstract: The present disclosure discloses a three-dimensional inversion method of airborne transient electromagnetics based on deep learning. The method of the present disclosure proposes two strategies that focus on training datasets, to improve the performance of deep learning models, including divide and conquer strategy and random models generating. Through a large of reasonable structural models, appropriate network setups, a more generalized result can be obtained through our proposed U-Net framework, which has been demonstrated to be effective on both synthetic and field data. This scheme can realize the rapid prospecting of three-dimensional resistivity structure in large-area target region, and solve the problem of low efficiency of traditional three-dimensional inversion calculation of ATEM and poor migration ability of three-dimensional inversion based on deep learning developed by predecessors.

Abstract: Methods of XRF quantitative analysis of heavy metal elements based on LLE-SVR, such as may include: establishing a relationship between peak information and element content by using Local Linear Embedding Dimensionality For Reduction and Support Vector Regression Predictive Algorithms based on machine learning, to quantitatively analyze the content information of elements contained in substances.