Water abundance prediction of sandstone aquifers based on the distance function
Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteri...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tan, Fei [verfasserIn] Cheng, Xiaozhi [verfasserIn] Xie, Daolei [verfasserIn] Man, Xiaoquan [verfasserIn] Wei, Jiuchuan [verfasserIn] Xu, Jianguo [verfasserIn] Han, Jie [verfasserIn] Zhang, Guangxue [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© Saudi Society for Geosciences 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 14(2021), 10 vom: Mai |
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| Übergeordnetes Werk: |
volume:14 ; year:2021 ; number:10 ; month:05 |
| Links: |
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| DOI / URN: |
10.1007/s12517-021-07195-z |
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| Katalog-ID: |
SPR043998925 |
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| 520 | |a Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. | ||
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| 650 | 4 | |a Combination weighting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a The analytic hierarchy process |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a The entropy weight method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Geophysical exploration |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cheng, Xiaozhi |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Daolei |e verfasserin |4 aut | |
| 700 | 1 | |a Man, Xiaoquan |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Jiuchuan |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Jianguo |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Guangxue |e verfasserin |4 aut | |
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10.1007/s12517-021-07195-z doi (DE-627)SPR043998925 (SPR)s12517-021-07195-z-e DE-627 ger DE-627 rakwb eng 550 ASE Tan, Fei verfasserin aut Water abundance prediction of sandstone aquifers based on the distance function 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2021 Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. Water abundance partitioning (dpeaa)DE-He213 Combination weighting (dpeaa)DE-He213 The analytic hierarchy process (dpeaa)DE-He213 The entropy weight method (dpeaa)DE-He213 Geophysical exploration (dpeaa)DE-He213 Cheng, Xiaozhi verfasserin aut Xie, Daolei verfasserin aut Man, Xiaoquan verfasserin aut Wei, Jiuchuan verfasserin aut Xu, Jianguo verfasserin aut Han, Jie verfasserin aut Zhang, Guangxue verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 10 vom: Mai (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:10 month:05 https://dx.doi.org/10.1007/s12517-021-07195-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 10 05 |
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10.1007/s12517-021-07195-z doi (DE-627)SPR043998925 (SPR)s12517-021-07195-z-e DE-627 ger DE-627 rakwb eng 550 ASE Tan, Fei verfasserin aut Water abundance prediction of sandstone aquifers based on the distance function 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2021 Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. Water abundance partitioning (dpeaa)DE-He213 Combination weighting (dpeaa)DE-He213 The analytic hierarchy process (dpeaa)DE-He213 The entropy weight method (dpeaa)DE-He213 Geophysical exploration (dpeaa)DE-He213 Cheng, Xiaozhi verfasserin aut Xie, Daolei verfasserin aut Man, Xiaoquan verfasserin aut Wei, Jiuchuan verfasserin aut Xu, Jianguo verfasserin aut Han, Jie verfasserin aut Zhang, Guangxue verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 10 vom: Mai (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:10 month:05 https://dx.doi.org/10.1007/s12517-021-07195-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 10 05 |
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10.1007/s12517-021-07195-z doi (DE-627)SPR043998925 (SPR)s12517-021-07195-z-e DE-627 ger DE-627 rakwb eng 550 ASE Tan, Fei verfasserin aut Water abundance prediction of sandstone aquifers based on the distance function 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2021 Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. Water abundance partitioning (dpeaa)DE-He213 Combination weighting (dpeaa)DE-He213 The analytic hierarchy process (dpeaa)DE-He213 The entropy weight method (dpeaa)DE-He213 Geophysical exploration (dpeaa)DE-He213 Cheng, Xiaozhi verfasserin aut Xie, Daolei verfasserin aut Man, Xiaoquan verfasserin aut Wei, Jiuchuan verfasserin aut Xu, Jianguo verfasserin aut Han, Jie verfasserin aut Zhang, Guangxue verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 10 vom: Mai (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:10 month:05 https://dx.doi.org/10.1007/s12517-021-07195-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 10 05 |
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10.1007/s12517-021-07195-z doi (DE-627)SPR043998925 (SPR)s12517-021-07195-z-e DE-627 ger DE-627 rakwb eng 550 ASE Tan, Fei verfasserin aut Water abundance prediction of sandstone aquifers based on the distance function 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2021 Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. Water abundance partitioning (dpeaa)DE-He213 Combination weighting (dpeaa)DE-He213 The analytic hierarchy process (dpeaa)DE-He213 The entropy weight method (dpeaa)DE-He213 Geophysical exploration (dpeaa)DE-He213 Cheng, Xiaozhi verfasserin aut Xie, Daolei verfasserin aut Man, Xiaoquan verfasserin aut Wei, Jiuchuan verfasserin aut Xu, Jianguo verfasserin aut Han, Jie verfasserin aut Zhang, Guangxue verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 10 vom: Mai (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:10 month:05 https://dx.doi.org/10.1007/s12517-021-07195-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 10 05 |
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10.1007/s12517-021-07195-z doi (DE-627)SPR043998925 (SPR)s12517-021-07195-z-e DE-627 ger DE-627 rakwb eng 550 ASE Tan, Fei verfasserin aut Water abundance prediction of sandstone aquifers based on the distance function 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2021 Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. Water abundance partitioning (dpeaa)DE-He213 Combination weighting (dpeaa)DE-He213 The analytic hierarchy process (dpeaa)DE-He213 The entropy weight method (dpeaa)DE-He213 Geophysical exploration (dpeaa)DE-He213 Cheng, Xiaozhi verfasserin aut Xie, Daolei verfasserin aut Man, Xiaoquan verfasserin aut Wei, Jiuchuan verfasserin aut Xu, Jianguo verfasserin aut Han, Jie verfasserin aut Zhang, Guangxue verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 14(2021), 10 vom: Mai (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:14 year:2021 number:10 month:05 https://dx.doi.org/10.1007/s12517-021-07195-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 10 05 |
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Tan, Fei @@aut@@ Cheng, Xiaozhi @@aut@@ Xie, Daolei @@aut@@ Man, Xiaoquan @@aut@@ Wei, Jiuchuan @@aut@@ Xu, Jianguo @@aut@@ Han, Jie @@aut@@ Zhang, Guangxue @@aut@@ |
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Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. 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| author |
Tan, Fei |
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Tan, Fei ddc 550 misc Water abundance partitioning misc Combination weighting misc The analytic hierarchy process misc The entropy weight method misc Geophysical exploration Water abundance prediction of sandstone aquifers based on the distance function |
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550 ASE Water abundance prediction of sandstone aquifers based on the distance function Water abundance partitioning (dpeaa)DE-He213 Combination weighting (dpeaa)DE-He213 The analytic hierarchy process (dpeaa)DE-He213 The entropy weight method (dpeaa)DE-He213 Geophysical exploration (dpeaa)DE-He213 |
| topic |
ddc 550 misc Water abundance partitioning misc Combination weighting misc The analytic hierarchy process misc The entropy weight method misc Geophysical exploration |
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ddc 550 misc Water abundance partitioning misc Combination weighting misc The analytic hierarchy process misc The entropy weight method misc Geophysical exploration |
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Arabian journal of geosciences |
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Arabian journal of geosciences |
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Water abundance prediction of sandstone aquifers based on the distance function |
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Water abundance prediction of sandstone aquifers based on the distance function |
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Tan, Fei |
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Arabian journal of geosciences |
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Arabian journal of geosciences |
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Tan, Fei Cheng, Xiaozhi Xie, Daolei Man, Xiaoquan Wei, Jiuchuan Xu, Jianguo Han, Jie Zhang, Guangxue |
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Tan, Fei |
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10.1007/s12517-021-07195-z |
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verfasserin |
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water abundance prediction of sandstone aquifers based on the distance function |
| title_auth |
Water abundance prediction of sandstone aquifers based on the distance function |
| abstract |
Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. © Saudi Society for Geosciences 2021 |
| abstractGer |
Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. © Saudi Society for Geosciences 2021 |
| abstract_unstemmed |
Abstract To establish a more accurate roof water abundance evaluation model and mitigate the difficulties in roof water abundance prediction and evaluation in the coal mine production process, the 2101 working face of the Yingpanhao coal mine was studied. Combined with the hydrogeological characteristics and drilling data of the study area and based on the lithology and structure, four controlling factors, including the equivalent thickness of sandstone, the lithological coefficient of sandstone, the degree of fracture development, and the sand-mud interlayer coefficient, were selected as evaluation indexes for the prediction of roof water abundance zones of sandstone aquifers. In the case of limited borehole data, the distance function was used to couple the improved analytic hierarchy process with the entropy weight method. The errors caused by subjective and objective factors were effectively reduced, and on this basis, a water abundance prediction model of roof sandstone was built. With the use of geophysical exploration results, the model predictions were combined with geophysical survey data to generate a comprehensive water abundance zone map, which divided the study area into three zones according to the water abundance. The results show that the high water abundance areas were mainly concentrated in the west of the working face, and the water abundance throughout the study area was generally low. The prediction results were verified by the data from drainage holes exposed by drilling, which provided a scientific and practical method for the prediction of the water abundance of coal seam roofs. © Saudi Society for Geosciences 2021 |
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| title_short |
Water abundance prediction of sandstone aquifers based on the distance function |
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https://dx.doi.org/10.1007/s12517-021-07195-z |
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Cheng, Xiaozhi Xie, Daolei Man, Xiaoquan Wei, Jiuchuan Xu, Jianguo Han, Jie Zhang, Guangxue |
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Cheng, Xiaozhi Xie, Daolei Man, Xiaoquan Wei, Jiuchuan Xu, Jianguo Han, Jie Zhang, Guangxue |
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| doi_str |
10.1007/s12517-021-07195-z |
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2024-07-03T22:16:28.571Z |
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| score |
7.399906 |