Transient electromagnetic characteristics of coal seams intruded by magmatic rocks

Yang, Yang; Xiong, Bin; Peng, Sanxi; Liu, Siqin; Chen, Hanbo; Zhang, Tianyu

doi:10.1371/journal.pone.0263293

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…The runoff series has significant non-stationarity, especially in flood season, which fluctuates violently and cause some uncertainty to the time series prediction. Therefore, in order to eliminate the adverse effects caused by singular sample data as much as possible, the maximum and minimum normalization method [44][45][46] was used to normalize the runoff, rainfall and temperature data.…”

Section: Study Area and Datamentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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Section: Study Area and Datamentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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“…Before model construction, the 3σ principle was used to analyze and eliminate the abnormal data in the input data. In addition, to eliminating the impact of data dimension and singular sample data on model prediction performance, the maximum and minimum normalization method [ 46 , 47 ] was used to normalize the input data. In the process of model construction, the sample data of 2016 was selected to calibrate the parameters of candidate models.…”

Section: Resultsmentioning

confidence: 99%

Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria

Ruan,

Cui,

Meng

et al. 2023

PLoS ONE

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In recent years, with the rapid development of economy and society, river water environmental pollution incidents occur frequently, which seriously threaten the ecological health of the river and the safety of water supply. Water pollution prediction is an important basis for understanding development trends of the aquatic environment, preventing water pollution incidents and improving river water quality. However, due to the large uncertainty of hydrological, meteorological and water environment systems, it is challenging to accurately predict water environment quality using single model. In order to improve the accuracy and stability of water pollution prediction, this study proposed an integrated learning criterion that integrated dynamic model average and model selection (DMA-MS) and used this criterion to construct the integrated learning model for water pollution prediction. Finally, based on the prediction results of the integrated learning model, the connectivity risk of the connectivity project was evaluated. The results demonstrate that the integrated model based on the DMA-MS criterion effectively integrated the characteristics of a single model and could provide more accurate and stable predictions. The mean absolute percentage error (MAPE) of the integrated model was only 11.1%, which was 24.5%–45% lower than that of the single model. In addition, this study indicates that the nearest station was the most important factor affecting the performance of the prediction station, and managers should pay increased attention to the water environment of the control section that is close to their area. The results of the connectivity risk assessment indicate that although the water environment risks were not obvious, the connectivity project may still bring some risks to the crossed water system, especially in the non-flood season.

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“…3 coal seam. Yang et al, 2018;Zhou et al, 2018;Yang et al, 2022). Essentially, the ground TEM can accurately delineate the horizontal distribution range of the water-bearing coal goaf, thereby ensuring the safe production of coal mines.…”

Section: Figure 10mentioning

confidence: 99%

Numerical simulation of water-bearing coal goaf using ground-borehole TEM—a case study of Tongxin Coal Mine, Shanxi Provice, China

Yang,

Peng,

Shan

et al. 2024

Front. Earth Sci.

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Transient electromagnetic method (TEM) is widely used in coalfield hydrogeological exploration and goaf investigation. However, given the limitations of the method, the vertical resolution of ground TEM is somewhat low for effectively detecting water-bearing coal goaf. Compared with ground TEM, ground-borehole TEM is characterized by a relatively better ability for detecting low-resistivity target bodies. In this study, a low-resistivity thin plate model was established, and the ground-borehole TEM electromagnetic responses of different points (within and without of the plate) were numerically simulated. We considered an exploration project in Datong City (Shanxi Province, China) as a study case, to conduct the field investigation, examine the characteristics of ground-borehole TEM electromagnetic responses under different situations, and validate the numerical model as well, including (a) water-bearing coal goaf and (b) solid coal. The result showed that, (a) the longer the sampling time is, the weaker the electromagnetic response of ground-borehole TEM will be; (2) ground-borehole TEM stands out with a high vertical resolution for water-bearing coal goaf; (c) the extreme points of the Z component and the polarity changing points of the X and Y components can indicate the interface where the physical properties alternated.

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Transient electromagnetic characteristics of coal seams intruded by magmatic rocks

Cited by 8 publications

References 13 publications

Improved runoff forecasting based on time-varying model averaging method and deep learning

Improved runoff forecasting based on time-varying model averaging method and deep learning

Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria

Numerical simulation of water-bearing coal goaf using ground-borehole TEM—a case study of Tongxin Coal Mine, Shanxi Provice, China

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