3D inversion of DC data using artificial neural networks

Neyamadpour, Ahmad; Abdullah, Wan Ahmad Tajuddin Wan; Taib, Samsudin; Niamadpour, Danesh

doi:10.1007/s11200-010-0027-5

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…According to Figure 11, the FBNN model with a value of 0.001 for η and 0.9 for α clearly enabled efficient training of the networks based on the statistical criteria. Moreover, by comparing with the previous study by Signh et al, 38 Neyamadpour et al, 39 Karmarkar et al, 40 and Winiczenko et al, 41 it was noted that this suggested value provides the better result. 18,20,42 Theoretically, more training datasets are not usually more accurate models due to the interaction between synthetic data and the model.…”

Section: Trainingmentioning

confidence: 53%

Inversion of 2D cross-hole electrical resistivity tomography data using artificial neural network

2022

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Geophysical inversion is often ill-posed because of its nonlinearity and the ordinary measured data of measured data. To deal with these problems, an artificial neural network (ANN) has been introduced with the capability of a nonlinear and complex problem for geophysical inversion. This study aims to invert 2D cross-hole electrical resistivity tomography data using a feedforward back-propagation neural network (FBNN) approach. To generate the synthetic data to train the model, eighteen forward models (100 to 600 Ω.m homogeneous medium and three different locations of 10 Ω.m of the grouted bulb) with a dipole-dipole array configuration were adopted. The effect of the hyperparameter on the performance of the proposed FBNN model was examined. Various datasets from the laboratory testing result were also tested using the suggested FBNN model and then the error between the actual and predicted area in each model was determined. The results show that our suggested FBNN model, with the trainrp training function, 4 hidden layers, 75 neurons in each hidden layer, 0.8 learning rate, 1 of momentum coefficient, and 54,000 training data points, has higher performance and better accuracy than other models. It was found that the error value of the FBNN model was about 15% to 18% lower compared to the conventional inversion model.

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Section: Trainingmentioning

confidence: 53%

Inversion of 2D cross-hole electrical resistivity tomography data using artificial neural network

2022

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“…Method Traditional machine learning converts real-world problems into mathematical models, trains historical data models, and finally converts the new data to real-world solutions. Among them, the artificial neural network is suitable for solving this kind of structure is complex changes of the underground geological inversion problem, not to need for data analysis and modeling, able to recognize and deal with the background and the law is not clear information [27][28][29], however, it is very hard to complex geological region has certain limitation, for example, hard training large networks and depth, In the inversion process, there are problems such as over-fitting, slow convergence rate and low accuracy [7,[30][31][32]. Based on this, domestic and foreign geophysicists have carried out in-depth research on this issue and put forward a series of improved methods, such as: Jiang et al proposed COSFLA optimization based on wavelet packet denoising and ANFIS network [33] and resistivity imaging inversion based on kernel principal component wavelet neural network based on ISFLA training [34], which achieved good results by using kernel principal component wavelet neural network.…”

Section: Based On Traditional Machine Learning Inversionmentioning

confidence: 99%

Review on Resistivity Inversion of Underground Abnormal Bodies

Liu¹,

Kong²,

Du³

et al. 2022

JENR

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With the development of petroleum in China, most oilfields have entered the stage of high water cut development.Well ground resistivity method as a new type of electric prospecting method, which have influence on the formation of small, measuring low cost advantages, gradually become one of the key technology of remaining oil distribution in the detection of resistivity inversion is through observation of the underground space apparent resistivity data reconstruction of the underground resistivity distribution, can realize the resistivity imaging of the underground space. In order to achieve the morphological characterization and spatial location of the abnormal area of underground resistivity, and then to carry out geological interpretation, the definition and properties of resistivity inversion are summarized, and the bottleneck problems encountered in practical engineering application are re-recognized and analyzed. On this basis, the theoretical method, numerical method and inversion method based on machine learning are introduced to solve the inverse resistivity problem of underground abnormal body. The inversion method based on deep learning is emphatically introduced, and its advantages and disadvantages and applicability are evaluated. It is pointed out that inversion is an ideal tool for data analysis. Then, it is pointed out that the development direction of resistivity inversion of underground abnormal body is to propose an optimized inversion network architecture based on deep learning.

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“…Jiang [27] use the 2D closed structure model to describe complex 2D soil structure, Onier et al [28] develop a 2D numerical model based on a numerical solver to analyse soil structure volumetric parameter. Also, more stereoscopic and accurate 3D soil stratified structures are more commonly used in geology [29][30][31], which could visualise the soil's electrical properties better.…”

Section: Introductionmentioning

confidence: 99%