Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity
Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical technique...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Si, Guangyao [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag GmbH Austria, part of Springer Nature 2020 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Rock mechanics and rock engineering - Springer Vienna, 1983, 53(2020), 11 vom: 30. Juli, Seite 5111-5132 |
|---|---|
| Übergeordnetes Werk: |
volume:53 ; year:2020 ; number:11 ; day:30 ; month:07 ; pages:5111-5132 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00603-020-02210-3 |
|---|
| Katalog-ID: |
OLC2120816964 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2120816964 | ||
| 003 | DE-627 | ||
| 005 | 20230504181702.0 | ||
| 007 | tu | ||
| 008 | 230504s2020 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00603-020-02210-3 |2 doi | |
| 035 | |a (DE-627)OLC2120816964 | ||
| 035 | |a (DE-He213)s00603-020-02210-3-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 690 |q VZ |
| 084 | |a 16,13 |a 19,1 |2 ssgn | ||
| 100 | 1 | |a Si, Guangyao |e verfasserin |0 (orcid)0000-0003-0090-8484 |4 aut | |
| 245 | 1 | 0 | |a Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag GmbH Austria, part of Springer Nature 2020 | ||
| 520 | |a Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. | ||
| 650 | 4 | |a Spatial–temporal | |
| 650 | 4 | |a Clustering | |
| 650 | 4 | |a Seismic event | |
| 650 | 4 | |a Mining-induced seismicity | |
| 700 | 1 | |a Cai, Wu |4 aut | |
| 700 | 1 | |a Wang, Shuyu |4 aut | |
| 700 | 1 | |a Li, Xu |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Rock mechanics and rock engineering |d Springer Vienna, 1983 |g 53(2020), 11 vom: 30. Juli, Seite 5111-5132 |w (DE-627)129620696 |w (DE-600)246075-0 |w (DE-576)015126897 |x 0723-2632 |7 nnns |
| 773 | 1 | 8 | |g volume:53 |g year:2020 |g number:11 |g day:30 |g month:07 |g pages:5111-5132 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00603-020-02210-3 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-UMW | ||
| 912 | |a SSG-OLC-ARC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-GEO | ||
| 912 | |a SSG-OPC-GGO | ||
| 912 | |a SSG-OPC-GEO | ||
| 912 | |a GBV_ILN_30 | ||
| 912 | |a GBV_ILN_2004 | ||
| 951 | |a AR | ||
| 952 | |d 53 |j 2020 |e 11 |b 30 |c 07 |h 5111-5132 | ||
| author_variant |
g s gs w c wc s w sw x l xl |
|---|---|
| matchkey_str |
article:07232632:2020----::rdcinfeaieyihnryesieetuigptatmoaprmtiai |
| hierarchy_sort_str |
2020 |
| publishDate |
2020 |
| allfields |
10.1007/s00603-020-02210-3 doi (DE-627)OLC2120816964 (DE-He213)s00603-020-02210-3-p DE-627 ger DE-627 rakwb eng 690 VZ 16,13 19,1 ssgn Si, Guangyao verfasserin (orcid)0000-0003-0090-8484 aut Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2020 Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. Spatial–temporal Clustering Seismic event Mining-induced seismicity Cai, Wu aut Wang, Shuyu aut Li, Xu aut Enthalten in Rock mechanics and rock engineering Springer Vienna, 1983 53(2020), 11 vom: 30. Juli, Seite 5111-5132 (DE-627)129620696 (DE-600)246075-0 (DE-576)015126897 0723-2632 nnns volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 https://doi.org/10.1007/s00603-020-02210-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_30 GBV_ILN_2004 AR 53 2020 11 30 07 5111-5132 |
| spelling |
10.1007/s00603-020-02210-3 doi (DE-627)OLC2120816964 (DE-He213)s00603-020-02210-3-p DE-627 ger DE-627 rakwb eng 690 VZ 16,13 19,1 ssgn Si, Guangyao verfasserin (orcid)0000-0003-0090-8484 aut Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2020 Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. Spatial–temporal Clustering Seismic event Mining-induced seismicity Cai, Wu aut Wang, Shuyu aut Li, Xu aut Enthalten in Rock mechanics and rock engineering Springer Vienna, 1983 53(2020), 11 vom: 30. Juli, Seite 5111-5132 (DE-627)129620696 (DE-600)246075-0 (DE-576)015126897 0723-2632 nnns volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 https://doi.org/10.1007/s00603-020-02210-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_30 GBV_ILN_2004 AR 53 2020 11 30 07 5111-5132 |
| allfields_unstemmed |
10.1007/s00603-020-02210-3 doi (DE-627)OLC2120816964 (DE-He213)s00603-020-02210-3-p DE-627 ger DE-627 rakwb eng 690 VZ 16,13 19,1 ssgn Si, Guangyao verfasserin (orcid)0000-0003-0090-8484 aut Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2020 Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. Spatial–temporal Clustering Seismic event Mining-induced seismicity Cai, Wu aut Wang, Shuyu aut Li, Xu aut Enthalten in Rock mechanics and rock engineering Springer Vienna, 1983 53(2020), 11 vom: 30. Juli, Seite 5111-5132 (DE-627)129620696 (DE-600)246075-0 (DE-576)015126897 0723-2632 nnns volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 https://doi.org/10.1007/s00603-020-02210-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_30 GBV_ILN_2004 AR 53 2020 11 30 07 5111-5132 |
| allfieldsGer |
10.1007/s00603-020-02210-3 doi (DE-627)OLC2120816964 (DE-He213)s00603-020-02210-3-p DE-627 ger DE-627 rakwb eng 690 VZ 16,13 19,1 ssgn Si, Guangyao verfasserin (orcid)0000-0003-0090-8484 aut Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2020 Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. Spatial–temporal Clustering Seismic event Mining-induced seismicity Cai, Wu aut Wang, Shuyu aut Li, Xu aut Enthalten in Rock mechanics and rock engineering Springer Vienna, 1983 53(2020), 11 vom: 30. Juli, Seite 5111-5132 (DE-627)129620696 (DE-600)246075-0 (DE-576)015126897 0723-2632 nnns volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 https://doi.org/10.1007/s00603-020-02210-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_30 GBV_ILN_2004 AR 53 2020 11 30 07 5111-5132 |
| allfieldsSound |
10.1007/s00603-020-02210-3 doi (DE-627)OLC2120816964 (DE-He213)s00603-020-02210-3-p DE-627 ger DE-627 rakwb eng 690 VZ 16,13 19,1 ssgn Si, Guangyao verfasserin (orcid)0000-0003-0090-8484 aut Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2020 Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. Spatial–temporal Clustering Seismic event Mining-induced seismicity Cai, Wu aut Wang, Shuyu aut Li, Xu aut Enthalten in Rock mechanics and rock engineering Springer Vienna, 1983 53(2020), 11 vom: 30. Juli, Seite 5111-5132 (DE-627)129620696 (DE-600)246075-0 (DE-576)015126897 0723-2632 nnns volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 https://doi.org/10.1007/s00603-020-02210-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_30 GBV_ILN_2004 AR 53 2020 11 30 07 5111-5132 |
| language |
English |
| source |
Enthalten in Rock mechanics and rock engineering 53(2020), 11 vom: 30. Juli, Seite 5111-5132 volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 |
| sourceStr |
Enthalten in Rock mechanics and rock engineering 53(2020), 11 vom: 30. Juli, Seite 5111-5132 volume:53 year:2020 number:11 day:30 month:07 pages:5111-5132 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Spatial–temporal Clustering Seismic event Mining-induced seismicity |
| dewey-raw |
690 |
| isfreeaccess_bool |
false |
| container_title |
Rock mechanics and rock engineering |
| authorswithroles_txt_mv |
Si, Guangyao @@aut@@ Cai, Wu @@aut@@ Wang, Shuyu @@aut@@ Li, Xu @@aut@@ |
| publishDateDaySort_date |
2020-07-30T00:00:00Z |
| hierarchy_top_id |
129620696 |
| dewey-sort |
3690 |
| id |
OLC2120816964 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2120816964</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504181702.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230504s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00603-020-02210-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2120816964</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00603-020-02210-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">16,13</subfield><subfield code="a">19,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Si, Guangyao</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0090-8484</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Austria, part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial–temporal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Seismic event</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mining-induced seismicity</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, Wu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Shuyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Xu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Rock mechanics and rock engineering</subfield><subfield code="d">Springer Vienna, 1983</subfield><subfield code="g">53(2020), 11 vom: 30. Juli, Seite 5111-5132</subfield><subfield code="w">(DE-627)129620696</subfield><subfield code="w">(DE-600)246075-0</subfield><subfield code="w">(DE-576)015126897</subfield><subfield code="x">0723-2632</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:11</subfield><subfield code="g">day:30</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:5111-5132</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00603-020-02210-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-UMW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-ARC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">53</subfield><subfield code="j">2020</subfield><subfield code="e">11</subfield><subfield code="b">30</subfield><subfield code="c">07</subfield><subfield code="h">5111-5132</subfield></datafield></record></collection>
|
| author |
Si, Guangyao |
| spellingShingle |
Si, Guangyao ddc 690 ssgn 16,13 misc Spatial–temporal misc Clustering misc Seismic event misc Mining-induced seismicity Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity |
| authorStr |
Si, Guangyao |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129620696 |
| format |
Article |
| dewey-ones |
690 - Buildings |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0723-2632 |
| topic_title |
690 VZ 16,13 19,1 ssgn Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity Spatial–temporal Clustering Seismic event Mining-induced seismicity |
| topic |
ddc 690 ssgn 16,13 misc Spatial–temporal misc Clustering misc Seismic event misc Mining-induced seismicity |
| topic_unstemmed |
ddc 690 ssgn 16,13 misc Spatial–temporal misc Clustering misc Seismic event misc Mining-induced seismicity |
| topic_browse |
ddc 690 ssgn 16,13 misc Spatial–temporal misc Clustering misc Seismic event misc Mining-induced seismicity |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Rock mechanics and rock engineering |
| hierarchy_parent_id |
129620696 |
| dewey-tens |
690 - Building & construction |
| hierarchy_top_title |
Rock mechanics and rock engineering |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129620696 (DE-600)246075-0 (DE-576)015126897 |
| title |
Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity |
| ctrlnum |
(DE-627)OLC2120816964 (DE-He213)s00603-020-02210-3-p |
| title_full |
Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity |
| author_sort |
Si, Guangyao |
| journal |
Rock mechanics and rock engineering |
| journalStr |
Rock mechanics and rock engineering |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
txt |
| container_start_page |
5111 |
| author_browse |
Si, Guangyao Cai, Wu Wang, Shuyu Li, Xu |
| container_volume |
53 |
| class |
690 VZ 16,13 19,1 ssgn |
| format_se |
Aufsätze |
| author-letter |
Si, Guangyao |
| doi_str_mv |
10.1007/s00603-020-02210-3 |
| normlink |
(ORCID)0000-0003-0090-8484 |
| normlink_prefix_str_mv |
(orcid)0000-0003-0090-8484 |
| dewey-full |
690 |
| title_sort |
prediction of relatively high-energy seismic events using spatial–temporal parametrisation of mining-induced seismicity |
| title_auth |
Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity |
| abstract |
Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. © Springer-Verlag GmbH Austria, part of Springer Nature 2020 |
| abstractGer |
Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. © Springer-Verlag GmbH Austria, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine. © Springer-Verlag GmbH Austria, part of Springer Nature 2020 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_30 GBV_ILN_2004 |
| container_issue |
11 |
| title_short |
Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity |
| url |
https://doi.org/10.1007/s00603-020-02210-3 |
| remote_bool |
false |
| author2 |
Cai, Wu Wang, Shuyu Li, Xu |
| author2Str |
Cai, Wu Wang, Shuyu Li, Xu |
| ppnlink |
129620696 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00603-020-02210-3 |
| up_date |
2024-07-04T05:12:26.497Z |
| _version_ |
1803624064207552512 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2120816964</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504181702.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230504s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00603-020-02210-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2120816964</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00603-020-02210-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">16,13</subfield><subfield code="a">19,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Si, Guangyao</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0090-8484</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Austria, part of Springer Nature 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ(x)max, was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ(x)max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ(x)max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial–temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial–temporal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Seismic event</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mining-induced seismicity</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, Wu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Shuyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Xu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Rock mechanics and rock engineering</subfield><subfield code="d">Springer Vienna, 1983</subfield><subfield code="g">53(2020), 11 vom: 30. Juli, Seite 5111-5132</subfield><subfield code="w">(DE-627)129620696</subfield><subfield code="w">(DE-600)246075-0</subfield><subfield code="w">(DE-576)015126897</subfield><subfield code="x">0723-2632</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:11</subfield><subfield code="g">day:30</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:5111-5132</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00603-020-02210-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-UMW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-ARC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">53</subfield><subfield code="j">2020</subfield><subfield code="e">11</subfield><subfield code="b">30</subfield><subfield code="c">07</subfield><subfield code="h">5111-5132</subfield></datafield></record></collection>
|
| score |
7.4024096 |