Underground shallow seismic source positioning method based on deep learning
The invention relates to an underground shallow seismic source positioning method based on deep learning. The underground shallow seismic source positioning method comprises the following steps: arranging a distributed vibration sensor array, generating a learning sample, setting a seismic source bu...
Gespeichert in:
Hauptverfasser: | , , , , , , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | WANG XIAOLIANG LI JIAN HAN YAN WANG YANBO LI MAOJIN SU XINYAN MO BIMING LI YUJIAN |
description | The invention relates to an underground shallow seismic source positioning method based on deep learning. The underground shallow seismic source positioning method comprises the following steps: arranging a distributed vibration sensor array, generating a learning sample, setting a seismic source bullet position corresponding to a three-dimensional energy field image sample as a training label, constructing a deep learning network framework, training a network, and positioning an actual explosion seismic source. According to the invention, the intermediate steps of positioning parameter extraction, positioning model modeling, positioning model calculation and the like in a traditional shallow seismic source positioning process are reduced. The method greatly improves the seismic source positioning efficiency, eliminates the positioning blind area, reduces the dependence of the channel reconstruction precision of a monitoring region on the seismic source positioning precision, and provides a new seismic source |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN110414675A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN110414675A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN110414675A3</originalsourceid><addsrcrecordid>eNqNykEKwjAQRuFsXIh6h_EAgsGqaymKC3Gl6xKbv20gnQmZFK8vggdw9Rbfm5vbkz1yn2ViTzq4GOVNiqBjaEllyi0oiYYShAP3NKIM4unlFJ6EyQOJIlz-6tLMOhcVq18XZn05P-rrBkkaaHItGKWp79ZuK1sdjvvT7p_nA--HNlA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Underground shallow seismic source positioning method based on deep learning</title><source>esp@cenet</source><creator>WANG XIAOLIANG ; LI JIAN ; HAN YAN ; WANG YANBO ; LI MAOJIN ; SU XINYAN ; MO BIMING ; LI YUJIAN</creator><creatorcontrib>WANG XIAOLIANG ; LI JIAN ; HAN YAN ; WANG YANBO ; LI MAOJIN ; SU XINYAN ; MO BIMING ; LI YUJIAN</creatorcontrib><description>The invention relates to an underground shallow seismic source positioning method based on deep learning. The underground shallow seismic source positioning method comprises the following steps: arranging a distributed vibration sensor array, generating a learning sample, setting a seismic source bullet position corresponding to a three-dimensional energy field image sample as a training label, constructing a deep learning network framework, training a network, and positioning an actual explosion seismic source. According to the invention, the intermediate steps of positioning parameter extraction, positioning model modeling, positioning model calculation and the like in a traditional shallow seismic source positioning process are reduced. The method greatly improves the seismic source positioning efficiency, eliminates the positioning blind area, reduces the dependence of the channel reconstruction precision of a monitoring region on the seismic source positioning precision, and provides a new seismic source</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DETECTING MASSES OR OBJECTS ; GEOPHYSICS ; GRAVITATIONAL MEASUREMENTS ; MEASURING ; PHYSICS ; TESTING</subject><creationdate>2019</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20191105&DB=EPODOC&CC=CN&NR=110414675A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20191105&DB=EPODOC&CC=CN&NR=110414675A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG XIAOLIANG</creatorcontrib><creatorcontrib>LI JIAN</creatorcontrib><creatorcontrib>HAN YAN</creatorcontrib><creatorcontrib>WANG YANBO</creatorcontrib><creatorcontrib>LI MAOJIN</creatorcontrib><creatorcontrib>SU XINYAN</creatorcontrib><creatorcontrib>MO BIMING</creatorcontrib><creatorcontrib>LI YUJIAN</creatorcontrib><title>Underground shallow seismic source positioning method based on deep learning</title><description>The invention relates to an underground shallow seismic source positioning method based on deep learning. The underground shallow seismic source positioning method comprises the following steps: arranging a distributed vibration sensor array, generating a learning sample, setting a seismic source bullet position corresponding to a three-dimensional energy field image sample as a training label, constructing a deep learning network framework, training a network, and positioning an actual explosion seismic source. According to the invention, the intermediate steps of positioning parameter extraction, positioning model modeling, positioning model calculation and the like in a traditional shallow seismic source positioning process are reduced. The method greatly improves the seismic source positioning efficiency, eliminates the positioning blind area, reduces the dependence of the channel reconstruction precision of a monitoring region on the seismic source positioning precision, and provides a new seismic source</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DETECTING MASSES OR OBJECTS</subject><subject>GEOPHYSICS</subject><subject>GRAVITATIONAL MEASUREMENTS</subject><subject>MEASURING</subject><subject>PHYSICS</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2019</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNykEKwjAQRuFsXIh6h_EAgsGqaymKC3Gl6xKbv20gnQmZFK8vggdw9Rbfm5vbkz1yn2ViTzq4GOVNiqBjaEllyi0oiYYShAP3NKIM4unlFJ6EyQOJIlz-6tLMOhcVq18XZn05P-rrBkkaaHItGKWp79ZuK1sdjvvT7p_nA--HNlA</recordid><startdate>20191105</startdate><enddate>20191105</enddate><creator>WANG XIAOLIANG</creator><creator>LI JIAN</creator><creator>HAN YAN</creator><creator>WANG YANBO</creator><creator>LI MAOJIN</creator><creator>SU XINYAN</creator><creator>MO BIMING</creator><creator>LI YUJIAN</creator><scope>EVB</scope></search><sort><creationdate>20191105</creationdate><title>Underground shallow seismic source positioning method based on deep learning</title><author>WANG XIAOLIANG ; LI JIAN ; HAN YAN ; WANG YANBO ; LI MAOJIN ; SU XINYAN ; MO BIMING ; LI YUJIAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN110414675A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2019</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DETECTING MASSES OR OBJECTS</topic><topic>GEOPHYSICS</topic><topic>GRAVITATIONAL MEASUREMENTS</topic><topic>MEASURING</topic><topic>PHYSICS</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>WANG XIAOLIANG</creatorcontrib><creatorcontrib>LI JIAN</creatorcontrib><creatorcontrib>HAN YAN</creatorcontrib><creatorcontrib>WANG YANBO</creatorcontrib><creatorcontrib>LI MAOJIN</creatorcontrib><creatorcontrib>SU XINYAN</creatorcontrib><creatorcontrib>MO BIMING</creatorcontrib><creatorcontrib>LI YUJIAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WANG XIAOLIANG</au><au>LI JIAN</au><au>HAN YAN</au><au>WANG YANBO</au><au>LI MAOJIN</au><au>SU XINYAN</au><au>MO BIMING</au><au>LI YUJIAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Underground shallow seismic source positioning method based on deep learning</title><date>2019-11-05</date><risdate>2019</risdate><abstract>The invention relates to an underground shallow seismic source positioning method based on deep learning. The underground shallow seismic source positioning method comprises the following steps: arranging a distributed vibration sensor array, generating a learning sample, setting a seismic source bullet position corresponding to a three-dimensional energy field image sample as a training label, constructing a deep learning network framework, training a network, and positioning an actual explosion seismic source. According to the invention, the intermediate steps of positioning parameter extraction, positioning model modeling, positioning model calculation and the like in a traditional shallow seismic source positioning process are reduced. The method greatly improves the seismic source positioning efficiency, eliminates the positioning blind area, reduces the dependence of the channel reconstruction precision of a monitoring region on the seismic source positioning precision, and provides a new seismic source</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN110414675A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DETECTING MASSES OR OBJECTS GEOPHYSICS GRAVITATIONAL MEASUREMENTS MEASURING PHYSICS TESTING |
title | Underground shallow seismic source positioning method based on deep learning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T08%3A39%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=WANG%20XIAOLIANG&rft.date=2019-11-05&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN110414675A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |