JPH1164533A - Earthquake early detecting system having self-learning function by neural network - Google Patents
Earthquake early detecting system having self-learning function by neural networkInfo
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- JPH1164533A JPH1164533A JP9224884A JP22488497A JPH1164533A JP H1164533 A JPH1164533 A JP H1164533A JP 9224884 A JP9224884 A JP 9224884A JP 22488497 A JP22488497 A JP 22488497A JP H1164533 A JPH1164533 A JP H1164533A
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- earthquake
- neural network
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Abstract
Description
【０００１】[0001]
【発明の属する技術分野】地震の早期検知システムにお
ける観測点地震検知装置内の、震源パラメータ（マグニ
チュード、震源距離、震源深さ）の評価プログラムに関
する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a program for evaluating hypocenter parameters (magnitude, hypocenter distance, hypocenter depth) in an observation point earthquake detection apparatus in an early earthquake detection system.
【０００２】[0002]
【従来の技術及び発明が解決しようとする課題】地震早
期検知システムは、震源近傍の検知点でリアルタイムに
地震の揺れを観測、分析を行い、検知した地震波の初動
部分から震源距離、マグニチュード、震源深さ等の震源
パラメータを推定し、観測センターに地震の強い揺れが
来るまでに必要な地震情報を伝達しようとするもので、
それによって地震に備えたり、地震後の緊急対策に有効
な手段として地震被害低減に役立つ。2. Description of the Related Art An early earthquake detection system observes and analyzes the shaking of an earthquake in real time at a detection point near an epicenter, and detects an epicenter distance, a magnitude, an epicenter from an initial part of the detected seismic wave. Estimate source parameters such as depth, and try to transmit necessary earthquake information to the observation center before a strong earthquake tremor comes.
This will help to prepare for an earthquake and reduce earthquake damage as an effective measure for emergency measures after an earthquake.
【０００３】何点かの検知点からの情報から震源パラメ
ータを評価する従来のシステムの場合には、精度は保証
されるが、複数の点の地震波形の情報を電話回線、無線
等で観測センターに電送し、分析を行う必要があり地震
発生後時間を要するために地震の強い揺れが来る前に評
価することは難しい。それに対して、１点の検知点で地
震を検知すると同時にその場で震源パラメータを評価す
る場合、即座に評価することができるが、検知点の地盤
の特性が表れたり、震源と検知点の地震波の伝播経路の
特性が表れたりして、誤差を生じ易い。[0003] In the case of a conventional system for evaluating hypocenter parameters from information from several detection points, accuracy is guaranteed, but information on seismic waveforms at a plurality of points can be obtained from an observation center via a telephone line or radio. Since it is necessary to transmit the data to the station and analyze it, and it takes time after the occurrence of the earthquake, it is difficult to evaluate it before the strong shaking of the earthquake. On the other hand, when an earthquake is detected at a single detection point and the epicenter parameters are evaluated on the spot, the evaluation can be performed immediately, but the characteristics of the ground at the detection point appear and the seismic wave between the epicenter and the detection point Error tends to occur due to the appearance of the characteristics of the propagation path.
【０００４】また、そのような特性を震源パラメータの
評価式の中に考慮するには、通常は、専門家の知見と、
観測データの分析が必要で、それぞれの検知点ごとに、
時間と手間を要する。[0004] In order to consider such characteristics in the evaluation formula of the hypocenter parameter, it is usually necessary to obtain expert knowledge and
Analysis of observation data is necessary, and for each detection point,
It takes time and effort.
【０００５】例えば図１は気象庁などで一般に行われて
いる震源パラメータの評価のフローチャートの図であり
このシステムの特徴は、精度は高いが、複数の観測点の
データを用いるため、その数、データ伝送の状況により
時間が異なるが、現在気象庁では速報値でも地震発生後
10〜30分程度を必要とし、かなり遅い。またこのシステ
ムでは、観測点ではなく観測センターにデータを集め、
観測センターからエンドユーザに情報が伝達されること
も、時間を遅らせる原因となっている。[0005] For example, FIG. 1 is a diagram of a flowchart of the evaluation of hypocenter parameters generally performed by the Japan Meteorological Agency. The feature of this system is that it has high accuracy but uses data of a plurality of observation points. The time varies depending on the transmission situation, but the Meteorological Agency now reports even after the earthquake
It takes about 10-30 minutes and is quite slow. The system also collects data at the observation center, not at the observation point,
The transmission of information from the observation center to the end user is also a cause of the delay.
【０００６】図２は、現在一般に行われている別の震源
パラメータの評価のフローチャートの図である。このシ
ステムでは１つの観測点で全ての震源パラメータの評価
を行う。震源近くの観測点でＳ波を検知したと同時に、
震源パラメータの評価をするため、遠方の観測センター
では大きく揺れる前に地震情報が得られる。また即座に
伝送されるデータは評価したパラメータのみであり、伝
送時間は極めて短い。FIG. 2 is a flowchart of another hypocenter parameter evaluation that is currently generally performed. This system evaluates all hypocenter parameters at one observation point. At the same time as detecting the S wave at the observation point near the epicenter,
In order to evaluate hypocenter parameters, seismic information can be obtained at a distant observation center before it shakes greatly. The data transmitted immediately is only the evaluated parameters, and the transmission time is extremely short.
【０００７】精度は多少落ちるが、即時性を重視したも
ので、緊急対応としては十分な情報となり得る。しかし
回帰式により、震源パラメータ（震源距離、震源深さ、
マグニチュード）を推定するため、回帰式を作成するに
あたって、それなりの経験と専門家の知識が必要にな
る。[0007] Although the accuracy is slightly lowered, the emphasis is on immediacy, and sufficient information can be provided for emergency response. However, according to the regression equation, the source parameters (source distance, source depth,
In order to estimate the magnitude, a considerable amount of experience and expert knowledge is required to create a regression equation.
【０００８】そこで専門家による回帰式を作成すること
なく、容易に震源パラメータを推定し、かつ１観測点の
みでしかも観測センターを経由しないでエンドユーザに
情報が伝達されるシステムを提案する。Therefore, a system is proposed in which the epicenter parameters are easily estimated without creating a regression equation by an expert, and information is transmitted to an end user only at one observation point and without passing through an observation center.
【０００９】[0009]
【課題を解決するための手段】請求項１は、観測点地震
検知装置内で行われる震源パラメータ（マグニチュー
ド、震源距離、震源深さ）の評価に、ニューラルネット
ワークを応用することを特徴とするニューラルネットワ
ークによる自己学習機能を持った地震早期検知システム
を主旨とする。According to a first aspect of the present invention, a neural network is applied to the evaluation of hypocenter parameters (magnitude, hypocenter distance, hypocenter depth) performed in an observation point earthquake detecting apparatus. The main purpose is an earthquake early detection system with a self-learning function using a network.
【００１０】請求項２は、１点の検知点で地震を検知
し、その場で震源パラメータを評価することを特徴とす
る請求項１記載のニューラルネットワークによる自己学
習機能を持った地震早期検知システムを主旨とする。A second aspect of the present invention is an earthquake early detection system having a self-learning function using a neural network, wherein an earthquake is detected at one detection point and an epicenter parameter is evaluated on the spot. The main purpose is.
【００１１】請求項３は、地震情報の通報を、地震検知
装置から直接エンドユーザに通報することを特徴とする
請求項１または２記載のニューラルネットワークによる
自己学習機能を持った地震早期検知システムを主旨とす
る。A third aspect of the present invention is an earthquake early detection system having a self-learning function using a neural network according to the first or second aspect, wherein a report of earthquake information is reported directly from an earthquake detection device to an end user. To the gist.
【００１２】つまり図２のフローチャート中、３番目の
「回帰式により震源距離、震源深さ、マグニチュード推
定」ステップを「ニューラルネットワークにより推定」
に置き換えたシステムとする。In other words, in the flowchart of FIG. 2, the third step of “estimating hypocenter distance, hypocenter depth, and magnitude by regression equation” is “estimated by neural network”.
The system has been replaced with
【００１３】図３は、本発明のニューラルネットワーク
による自己学習機能を持った地震早期検知システムの震
源パラメータの評価フローチャートを示す図である。
（ａ）は地震時のフローチャートであり（ｂ）は学習時
のフローチャートであり地震のないときに時々に行う。FIG. 3 is a diagram showing a flowchart for evaluating hypocenter parameters of an early earthquake detection system having a self-learning function using a neural network according to the present invention.
(A) is a flowchart at the time of an earthquake, and (b) is a flowchart at the time of learning, which is performed from time to time when there is no earthquake.
【００１４】基本的には在来のシステムと同じで、１点
の観測点のみで、Ｓ波検知後即座に全ての評価が終わ
る。相違点は在来のシステムは、影響が極めて大きいと
思われるパラメータのみを用いた回帰式による評価であ
るが、本発明のシステムでは影響があると思われる全て
のパラメータを入力とした、ニューラルネットワークを
用いた評価である。[0014] Basically, the same as in the conventional system, all evaluations are completed immediately after the S-wave detection at only one observation point. The difference is that the conventional system is evaluated by a regression equation using only parameters that are considered to have an extremely large effect, but the neural network uses all the parameters that are considered to be affected in the system of the present invention as inputs. This is an evaluation using.
【００１５】ニューラルネットワークは、人間の神経細
胞をモデル化した解析ツールで、最近工学の分野では、
制御などに盛んに使われるようになってきた。ニューラ
ルネットワークには、学習機能とそこから得られるネッ
トワークを用いる評価機能の２つの機能がある。学習機
能は、地震がない平常時に行い、気象庁から得られる震
源情報（マグニチュード、震源位置）を教師信号とし地
震波の初動部分から得られる様々な情報から検知点や地
震波の伝播経路の特性を学習し、震源パラメータの最適
値を評価するネットワークを導く。従って評価機能は、
地震時に機能し、誤差の少ない震源パラメータが得られ
る。A neural network is an analysis tool that models human nerve cells. Recently, in the field of engineering,
It has been actively used for control and the like. The neural network has two functions, a learning function and an evaluation function using a network obtained from the learning function. The learning function is performed during normal times when there is no earthquake, and uses the epicenter information (magnitude, epicenter location) obtained from the Japan Meteorological Agency as a teacher signal to learn the characteristics of detection points and propagation paths of seismic waves from various information obtained from the initial part of seismic waves. Leads to a network that evaluates the optimal values of hypocenter parameters. Therefore, the evaluation function
It functions during an earthquake and provides source parameters with few errors.
【００１６】また、回帰式を作るときは専門家としての
分析判断が必要であるが、ニューラルネットワークでは
必要が無い。Further, when making a regression equation, it is necessary to make an analytical judgment as an expert, but it is not necessary for a neural network.
【００１７】また１検知点での地震情報を、直接エンド
ユーザに通報することにより、複数の観測点からの情報
を処理する時間が短縮され、地震の早期通報に寄与す
る。Further, by directly reporting the earthquake information at one detection point to the end user, the time for processing information from a plurality of observation points is reduced, which contributes to early notification of an earthquake.
【００１８】また地震検知装置から観測センターを経由
せず直接エンドユーザに情報を通報することはシステム
の簡素化に繋がる。Further, notifying the end user of the information directly from the earthquake detector without passing through the observation center leads to simplification of the system.
【００１９】[0019]
【発明の実施の形態】図４は、実証観測システムの機器
構成ブロック図である。システムの機器構成は在来のも
のと、同様であるが、地震検知装置１４内のソフトウェ
アのみ、ニューラルネットワークを挿入したものと交換
する。FIG. 4 is a block diagram showing the equipment configuration of a demonstration observation system. The system configuration of the system is the same as that of the conventional one, but only the software in the earthquake detecting device 14 is replaced with the one in which the neural network is inserted.
【００２０】図５は全ての評価を地震検知装置１４内で
行い、通報も全て地震検知装置１４から直接出力しシス
テムを簡素化したブロック図である。FIG. 5 is a block diagram showing a simplified system in which all the evaluations are performed in the earthquake detecting device 14 and all the reports are directly output from the earthquake detecting device 14.
【００２１】１検知点の地震検知装置１４内のローカル
パソコン４において、Ｐ波部分の情報を用いたニューラ
ルネットワークによって、震源パラメータを評価する。
震源パラメータ及びＰ波部分の上下動の振動によって、
遠隔地点の震度予測を行う。In the local personal computer 4 in the earthquake detecting device 14 at one detection point, the epicenter parameter is evaluated by a neural network using information on the P wave portion.
By the source parameter and the vibration of the vertical movement of the P wave part,
Predict seismic intensity at remote locations.
【００２２】携帯電話１１、ＰＨＳ１１’、ポケベル１
２に早期検知情報が即座に配信される。また検知点毎の
時刻合わせはＧＰＳ５によって行う。ここにホストコン
ピュータ１３は評価結果の統合、メンテナンス用で、使
用しなくてもよい。またローカルパソコン４の異常時に
は、テレコントローラ７で再立ち上げを行う。なお波形
データの送信は、システムに負荷をかけるため、平常時
にマニュアルで行い自動的には行わない。またマグニチ
ュードの評価は、地震終了後行っていたが、Ｓ波検知時
に即座に行えるようにした。全ての評価は、Ｓ波検知後
１秒以内に終わり、信頼性と即時性が増す。Mobile phone 11, PHS 11 ', Pager 1
2, the early detection information is immediately delivered. The time adjustment for each detection point is performed by GPS5. Here, the host computer 13 is for integration and maintenance of evaluation results, and need not be used. When the local personal computer 4 is abnormal, the telecontroller 7 restarts the operation. The transmission of the waveform data is performed manually in normal times and is not automatically performed in order to put a load on the system. The magnitude evaluation was performed after the end of the earthquake, but can be performed immediately when the S wave is detected. All evaluations are completed within one second after S wave detection, increasing reliability and immediacy.
【００２３】図６は地震検知装置１４の、地震時のフロ
ーチャートを示す図である。FIG. 6 is a diagram showing a flowchart of the earthquake detecting device 14 at the time of an earthquake.
【００２４】[0024]
【発明の効果】この発明は以上のような構成からなり、
その効果は次の通りである。The present invention has the above-described configuration,
The effect is as follows.
【００２５】震源近傍の１点の検知点での情報のみによ
る評価でも、ニューラルネットワークの手法を加えれ
ば、震源パラメータの評価精度が向上でき、地震に備え
たり、地震後の緊急対応に対する情報としてはその評価
誤差は無視できる。Even in the evaluation using only information at one detection point near the epicenter, the accuracy of the evaluation of epicenter parameters can be improved by adding a neural network method. The evaluation error can be ignored.
【００２６】観測された地震波の特性から検知点の設置
された地盤の特性や震源と検知点の地震波の伝播経路の
特性を自動的に学習し、次第に精度が向上する。From the characteristics of the observed seismic wave, the characteristics of the ground where the detection point is installed and the characteristics of the epicenter and the propagation path of the seismic wave at the detection point are automatically learned, and the accuracy is gradually improved.
【００２７】専門家は、システムを設計するときネット
ワークの構成を最初に考えるだけで、後は自動的に行わ
れ、地震を経験する度に評価精度は向上していく。専門
家が後ほど労力や時間をかけて記録波形を分析する必要
がなくなる。ある程度（関東地方のように地震活動の高
いところでは１〜２年程度）の期間稼働させれば、精度
的には問題がない。The expert only considers the configuration of the network at first when designing the system, and thereafter, it is automatically performed, and the evaluation accuracy is improved each time an earthquake is experienced. There is no need for an expert to later analyze the recorded waveform with effort and time. If it is operated for a certain period (about one to two years in places with high seismic activity such as the Kanto region), there is no problem in terms of accuracy.
【００２８】また１検知点での地震情報を、直接エンド
ユーザに通報することにより、複数の観測点からの情報
を処理する時間が短縮され、地震の早期通報に寄与す
る。Further, by directly reporting the earthquake information at one detection point to the end user, the time for processing information from a plurality of observation points is reduced, which contributes to early notification of an earthquake.
【００２９】また地震検知装置から観測センターを経由
せず直接エンドユーザに情報を通報することはシステム
の簡素化に繋がる。Further, notifying the end user of information directly from the earthquake detection device without passing through the observation center leads to simplification of the system.
【図１】気象庁などで一般に行われている震源パラメー
タの評価のフローチャートの図である。FIG. 1 is a flowchart of an evaluation of a hypocenter parameter generally performed by the Meteorological Agency or the like.
【図２】現在一般に行われている別の震源パラメータの
評価のフローチャートの図である。FIG. 2 is a flowchart of another hypocenter parameter evaluation currently generally performed.
【図３】本発明のニューラルネットワークによる自己学
習機能を持った地震早期検知システムの震源パラメータ
の評価フローチャートを示す図である。FIG. 3 is a diagram showing a flowchart for evaluating hypocenter parameters of an earthquake early detection system having a self-learning function using a neural network according to the present invention.
【図４】実証観測システムの機器構成ブロック図であ
る。FIG. 4 is a block diagram of a device configuration of a demonstration observation system.
【図５】全ての評価を地震検知装置内で行い、通報も全
て地震検知装置から直接出力しシステムを簡素化した図
である。FIG. 5 is a diagram showing a simplified system in which all evaluations are performed in the earthquake detection device, and all notifications are directly output from the earthquake detection device.
【図６】地震検知装置の、地震時のフローチャートを示
す図である。FIG. 6 is a diagram showing a flowchart of the earthquake detecting device at the time of an earthquake.
１……地震計、２……アンプ、３……Ａ/ Ｄコンバー
タ、４……ローカルパソコン、５……ＧＳＰ受信機、６
……無停電電源、７……テレコントローラ、８……ＤＳ
Ｕ、９……衛星、１０……電話，１１……携帯電話、１
２……ポケベル、１３……ホストパソコン、１４……地
震検知装置1 ... seismometer, 2 ... amplifier, 3 ... A / D converter, 4 ... local PC, 5 ... GSP receiver, 6
... uninterruptible power supply, 7 ... telecontroller, 8 ... DS
U, 9 satellite, 10 telephone, 11 mobile phone, 1
2… Pager, 13… Host PC, 14… Earthquake detector
Claims (3)
ラメータであるマグニチュード、震源距離、震源深さの
評価に、ニューラルネットワークを応用することを特徴
とするニューラルネットワークによる自己学習機能を持
った地震早期検知システム。1. An earthquake having a self-learning function by a neural network, wherein the neural network is applied to the evaluation of magnitude, hypocenter distance, and epicenter depth, which are hypocenter parameters, performed in an observation point earthquake detection apparatus. Early detection system.
震源パラメータを評価することを特徴とする請求項１記
載のニューラルネットワークによる自己学習機能を持っ
た地震早期検知システム。2. An earthquake early detection system having a self-learning function by a neural network according to claim 1, wherein an earthquake is detected at one detection point and an epicenter parameter is evaluated on the spot.
接エンドユーザに通報することを特徴とする請求項１ま
たは２記載のニューラルネットワークによる自己学習機
能を持った地震早期検知システム。3. The early-earthquake detection system with a self-learning function using a neural network according to claim 1, wherein the notification of the earthquake information is reported directly from the earthquake detection device to the end user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP9224884A JPH1164533A (en) | 1997-08-21 | 1997-08-21 | Earthquake early detecting system having self-learning function by neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP9224884A JPH1164533A (en) | 1997-08-21 | 1997-08-21 | Earthquake early detecting system having self-learning function by neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH1164533A true JPH1164533A (en) | 1999-03-05 |
Family
ID=16820682
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP9224884A Pending JPH1164533A (en) | 1997-08-21 | 1997-08-21 | Earthquake early detecting system having self-learning function by neural network |
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| JP2000258546A (en) * | 1999-03-08 | 2000-09-22 | Takuwa:Kk | Propagation signal processing method and its device |
| KR100911896B1 (en) | 2009-03-11 | 2009-08-13 | (주)대우건설 | Earthquake alarming device based on artificial structural damage learning |
| KR20150035633A (en) * | 2013-09-27 | 2015-04-07 | 한국전력공사 | Apparatus for measuring earthquake intensity and method for the same |
| WO2018008708A1 (en) * | 2016-07-08 | 2018-01-11 | 日本電気株式会社 | Epicentral distance estimating device, epicentral distance estimating method, and computer-readable recording medium |
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| JP2000258546A (en) * | 1999-03-08 | 2000-09-22 | Takuwa:Kk | Propagation signal processing method and its device |
| KR100911896B1 (en) | 2009-03-11 | 2009-08-13 | (주)대우건설 | Earthquake alarming device based on artificial structural damage learning |
| KR20150035633A (en) * | 2013-09-27 | 2015-04-07 | 한국전력공사 | Apparatus for measuring earthquake intensity and method for the same |
| WO2018008708A1 (en) * | 2016-07-08 | 2018-01-11 | 日本電気株式会社 | Epicentral distance estimating device, epicentral distance estimating method, and computer-readable recording medium |
| CN109416408A (en) * | 2016-07-08 | 2019-03-01 | 日本电气株式会社 | Epicentral distance estimation device, epicentral distance estimation method and computer readable recording medium |
| JPWO2018008708A1 (en) * | 2016-07-08 | 2019-04-11 | 日本電気株式会社 | Epicenter distance estimation apparatus, epicenter distance estimation method, and program |
| TWI687711B (en) * | 2016-07-08 | 2020-03-11 | 日商日本電氣股份有限公司 | Epicenter distance estimation device, epicenter distance estimation method, and computer-readable recording medium |
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| JP2021189134A (en) * | 2020-06-04 | 2021-12-13 | 清水建設株式会社 | Earthquake motion evaluation model generation method, earthquake motion evaluation model generation device, earthquake motion evaluation method, and earthquake motion evaluation device |
| JP2021189138A (en) * | 2020-06-04 | 2021-12-13 | 清水建設株式会社 | Earthquake motion evaluation model generation method, earthquake motion evaluation model generation device, earthquake motion evaluation method, and earthquake motion evaluation device |
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