A novel method for automatic identification of rock fracture signals in microseismic monitoring

Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to ident...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jiang, Ruochen [verfasserIn] Dai, Feng [verfasserIn] Liu, Yi [verfasserIn] Li, Ang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation

Übergeordnetes Werk:	Enthalten in: Measurement - Amsterdam [u.a.] : Elsevier Science, 1983, 175
Übergeordnetes Werk:	volume:175

DOI / URN:	10.1016/j.measurement.2021.109129

Katalog-ID:	ELV005793203

Internformat


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100	1		\|a Jiang, Ruochen \|e verfasserin \|4 aut
245	1	0	\|a A novel method for automatic identification of rock fracture signals in microseismic monitoring
264		1	\|c 2021
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520			\|a Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques.
650		4	\|a Automatic identification
650		4	\|a Microseismic monitoring
650		4	\|a Microseismic signals
650		4	\|a Blasting signals
650		4	\|a Underground rock excavation
700	1		\|a Dai, Feng \|e verfasserin \|4 aut
700	1		\|a Liu, Yi \|e verfasserin \|4 aut
700	1		\|a Li, Ang \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Measurement \|d Amsterdam [u.a.] : Elsevier Science, 1983 \|g 175 \|h Online-Ressource \|w (DE-627)320404927 \|w (DE-600)2000550-7 \|w (DE-576)259484342 \|7 nnns
773	1	8	\|g volume:175
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936	b	k	\|a 50.21 \|j Messtechnik
951			\|a AR
952			\|d 175

Indexfelder

author_variant	r j rj f d fd y l yl a l al
matchkey_str	jiangruochendaifengliuyiliang:2021----:nvlehdoatmtcdniiainfokrcueinli
hierarchy_sort_str	2021
bklnumber	50.21
publishDate	2021
allfields	10.1016/j.measurement.2021.109129 doi (DE-627)ELV005793203 (ELSEVIER)S0263-2241(21)00157-3 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Jiang, Ruochen verfasserin aut A novel method for automatic identification of rock fracture signals in microseismic monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques. Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation Dai, Feng verfasserin aut Liu, Yi verfasserin aut Li, Ang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 175 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:175 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 175
spelling	10.1016/j.measurement.2021.109129 doi (DE-627)ELV005793203 (ELSEVIER)S0263-2241(21)00157-3 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Jiang, Ruochen verfasserin aut A novel method for automatic identification of rock fracture signals in microseismic monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques. Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation Dai, Feng verfasserin aut Liu, Yi verfasserin aut Li, Ang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 175 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:175 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 175
allfields_unstemmed	10.1016/j.measurement.2021.109129 doi (DE-627)ELV005793203 (ELSEVIER)S0263-2241(21)00157-3 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Jiang, Ruochen verfasserin aut A novel method for automatic identification of rock fracture signals in microseismic monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques. Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation Dai, Feng verfasserin aut Liu, Yi verfasserin aut Li, Ang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 175 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:175 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 175
allfieldsGer	10.1016/j.measurement.2021.109129 doi (DE-627)ELV005793203 (ELSEVIER)S0263-2241(21)00157-3 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Jiang, Ruochen verfasserin aut A novel method for automatic identification of rock fracture signals in microseismic monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques. Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation Dai, Feng verfasserin aut Liu, Yi verfasserin aut Li, Ang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 175 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:175 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 175
allfieldsSound	10.1016/j.measurement.2021.109129 doi (DE-627)ELV005793203 (ELSEVIER)S0263-2241(21)00157-3 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Jiang, Ruochen verfasserin aut A novel method for automatic identification of rock fracture signals in microseismic monitoring 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques. Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation Dai, Feng verfasserin aut Liu, Yi verfasserin aut Li, Ang verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 175 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:175 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.21 Messtechnik AR 175
language	English
source	Enthalten in Measurement 175 volume:175
sourceStr	Enthalten in Measurement 175 volume:175
format_phy_str_mv	Article
bklname	Messtechnik
institution	findex.gbv.de
topic_facet	Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation
dewey-raw	660
isfreeaccess_bool	false
container_title	Measurement
authorswithroles_txt_mv	Jiang, Ruochen @@aut@@ Dai, Feng @@aut@@ Liu, Yi @@aut@@ Li, Ang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320404927
dewey-sort	3660
id	ELV005793203
language_de	englisch
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author	Jiang, Ruochen
spellingShingle	Jiang, Ruochen ddc 660 bkl 50.21 misc Automatic identification misc Microseismic monitoring misc Microseismic signals misc Blasting signals misc Underground rock excavation A novel method for automatic identification of rock fracture signals in microseismic monitoring
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topic_title	660 DE-600 50.21 bkl A novel method for automatic identification of rock fracture signals in microseismic monitoring Automatic identification Microseismic monitoring Microseismic signals Blasting signals Underground rock excavation
topic	ddc 660 bkl 50.21 misc Automatic identification misc Microseismic monitoring misc Microseismic signals misc Blasting signals misc Underground rock excavation
topic_unstemmed	ddc 660 bkl 50.21 misc Automatic identification misc Microseismic monitoring misc Microseismic signals misc Blasting signals misc Underground rock excavation
topic_browse	ddc 660 bkl 50.21 misc Automatic identification misc Microseismic monitoring misc Microseismic signals misc Blasting signals misc Underground rock excavation
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title	A novel method for automatic identification of rock fracture signals in microseismic monitoring
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title_full	A novel method for automatic identification of rock fracture signals in microseismic monitoring
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author_browse	Jiang, Ruochen Dai, Feng Liu, Yi Li, Ang
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title_sort	a novel method for automatic identification of rock fracture signals in microseismic monitoring
title_auth	A novel method for automatic identification of rock fracture signals in microseismic monitoring
abstract	Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques.
abstractGer	Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques.
abstract_unstemmed	Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques.
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title_short	A novel method for automatic identification of rock fracture signals in microseismic monitoring
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doi_str	10.1016/j.measurement.2021.109129
up_date	2024-07-06T19:10:56.291Z
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A novel method for automatic identification of rock fracture signals in microseismic monitoring

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