An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China

Zhang, Kai; Lin, Niantian; Wan, Xiaolei; Yang, Jiuqiang; Wang, Xiaodong; Tian, Gaopeng

doi:10.1007/s40948-022-00468-1

Cited by 5 publications

(1 citation statement)

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“…or 1 sec. Applications of CSAMT include explorations for geothermal (Zhao et al, 2019;Zhang et al, 2022), groundwater petroleum (Younis et al, 2015;Zhang et al, 2021) and mineral deposits (Liu et al, 2020;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Junian,

Grandis

2023

MGPB

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The Controlled Source Audio-frequency Magnetotellurics (CSAMT) is a geophysical method utilizing artificial electromagnetic signal source to estimate subsurface resistivity structures. One-dimensional (1D) inversion modelling of CSAMT data is non-linear and the solution can be estimated by using global optimization algorithms. Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) are well-known population-based algorithms having relatively simple mathematical formulation and implementation. Hybridization of PSO and GWO algorithms (called hybrid PSO-GWO) can improve the convergence capability to the global solution. This study applied the hybrid PSO-GWO algorithm for 1D CSAMT inversion modelling. Tests were conducted with synthetic CSAMT data associated with 3-layer, 4-layer and 5-layer earth models to determine the performance of the algorithm. The results show that the hybrid PSO-GWO algorithm has a good performance in obtaining the minimum misfit compared to the original PSO and GWO algorithms. The hybrid PSO-GWO algorithm was also applied to invert CSAMT field data for gold mineralization exploration in the Cibaliung area, Banten Province, Indonesia. The algorithm was able to reconstruct the resistivity model very well which is confirmed by the results from inversion of the data using standard 2D MT inversion software. The model also agrees well with the geological information of the study area.

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Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Junian,

Grandis

2023

MGPB

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An Intelligent Approach for Gas Reservoir Identification and Structural Evaluation by ANN and Viterbi Algorithm—A Case Study From the Xujiahe Formation, Western Sichuan Depression, China

Zhang

Lin

Yang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Deep learning-based near-field effect correction method for Controlled Source Electromagnetic Method and application

Luo,

Chen,

Wang

et al. 2024

PLoS ONE

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Addressing the impact of near-field effects in the Controlled Source Electromagnetic Method(CSEM) has long been a focal point in the realm of geophysical exploration. Therefore, we propose a deep learning-based near-field correction method for controlled-source electromagnetic methods. Initially, diverse datasets for a layered geologic model are generated through forward simulation. Building upon the characteristics of near-field effects, a deep learning network utilizing LSTM-CNN is meticulously constructed. Multiple experiments are executed to scrutinize the network’s effectiveness in mitigating near-field effects and its resilience against noise. Following this, the proposed method is applied to actual CSEM data to validate its applicability in practice. The method is subsequently tested on measured CSEM data, confirming its practical efficacy. Results from experiments indicate that, for theoretical data, the LSTM-CNN network-trained data closely aligns with simulated data, showcasing a significant improvement. Moreover, when applied to measured data, the method eradicates false high-resistance anomalies at lower frequencies. In conclusion, this deep learning-based correction method proficiently eliminates the influence of near-field effects in the CSEM, delivering practical application benefits that more accurately reflect the authentic geologic structure.

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An approach for predicting geothermal reservoirs distribution using wavelet transform and self-organizing neural network: a case study of radon and CSAMT data from Northern Jinan, China

Cited by 5 publications

References 50 publications

Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

Hybrid Particle Swarm Optimization and Grey Wolf Optimizer Algorithm for Controlled Source Audio-Frequency Magnetotellurics (Csamt) One-Dimensional Inversion Modelling

An Intelligent Approach for Gas Reservoir Identification and Structural Evaluation by ANN and Viterbi Algorithm—A Case Study From the Xujiahe Formation, Western Sichuan Depression, China

Deep learning-based near-field effect correction method for Controlled Source Electromagnetic Method and application

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