recent advances in prediction of earthquakesdinesh/gnss_train_files/...recent advances in prediction...

Recent Advances in Prediction of EarthquakesShunji Murai, Professor Emeritus, Univ. of Tokyo &

CTO, Japan Earthquake Science Exploration Agency

GNSS Training Course T151-30

GIC/AIT

17 January 2019

+ Introduction

+ Part 1: Review of prediction

+ Part 2: Flow of prediction with

GNSS data

+ Part 3: Additional RS &GIS

techniques for prediction

+ Part 4: Mini-plate theory

+ Conclusions

ⒸJESEA2019

Introduction

+ I started research on the prediction of earthquakes

since 2002 with my partner; Dr. Harumi Araki.

+ The basic prediction method depends on

multi-temporal GNSS data in which abnormal signals

or pre-cursors must be involved.

+ In Japan free access is available to daily GNSS

data of 1,300 CORSs all over Japan.

+ I did realized very abnormal signals just before the

East Japan Great EQ occurred on the 11th March

2011 with 18,000 victims mainly due to Tsunami.

+ In order to rescue human lives I invested to

establish a private company namely JESEA in 2013.

ⒸJESEA2019

Part 1: Review of Prediction of Earthquakes

+ No 1: Nobody has succeeded the prediction of earthquakes in

the human history. It is a challenging theme in the latter half

of my life.

+ No 2: Though so many people died in the past history of

Japan due to huge earthquakes; 30 times with more

than one thousand victims in the past 400 years, Japanese

government and seismic scientists gave up the prediction of

earthquakes in 2012 after many unreliable trials spending

huge budget.

+ No 3: Conventional prediction method in Japan only relies on

information about seismographs, active faults and the past

record of giant earthquakes, which are analyzed with

statistical and provability model. The method is unreliable!

ⒸJESEA2019

ⒸJESEA 2019ⒸGoogle

Large EQ’s since1923

and distribution of

Nuclear Power Plants

Japan is EQ prone

country under risky

condition everywhere

and every time

Fukushima NP

X

What should be the key for prediction?

+ No.1: Remote sensing method should be applied to detect

macroscopic anomalies or pre-signals in advance to

occurrence of earthquakes. We need time series of scientific

observation data covering whole area. The existence of the

pre-signals has been verified by my research in 2007.

+ No.2: Quantitative correlation between anomalies of

observation data and the occurrence of earthquakes should

be developed.

+ No.3: The anomalies should be analyzed and visualized using

GIS techniques and artificial intelligence for better

understanding for risky areas.

ⒸJESEA2019

Macroscopic Phenomena before earthquakes

Epicenter

GNSS

Infrasound

sensor

1. Crustal Change

2. Infrasound anomaly

3. ELF emission anomaly

4. Temperature anomaly

5. Ionospheric anomaly

ⒸJESEA2019

Part 2: Flow of prediction with GNSS data

Download of GNSS data from GSI database

Data filing in EXCEL and graphic

representation

Detection of anomalies

Visualization of anomalies

Judgement and mapping of risky areas

Preparation of documents

Dissemination of MEGA EQ Prediction ⒸJESEA2019

East Japan Great EQ: 2011/3/11 M9.0, SI=7

Miyagi AreaYamagata Area

M9.0, SI=7

Damage due to Tsunami

All Japan Islands quaked

ⒸJESEA2019 ⒸTenki.jp

What were the movements at 2011/3/11 EQ？

©海上保安庁 ©NASA

1.1m sank

Sea Bottom

3m rose

Just after

ⒸJESEA2019

Ojika

5.3m moved

-120

-100

-80

-60

-40

-20

0

20

2010/1/9 2011/1/9 2012/1/9 2013/1/9 2014/1/9 2015/1/9 2016/1/9 2017/1/9

Miyagi Area 胆沢

栗駒２

宮城東和

高清水

南方

小野田

河北

丸森

女川

利府

気仙沼

栗駒

鳴子

志津川

涌谷

宮城川崎

亘理

七ヶ宿

牡鹿

白石

Ojika

2011/3/11EQ

Height Change in Miyagi Area, Tsunami hit area: 2010-2017

110cm sank!

-30

-25

-20

-15

-10

-5

0

5

10

2010/1/9 2011/1/9 2012/1/9 2013/1/9 2014/1/9 2015/1/9 2016/1/9 2017/1/9

Yamagata Area 真室川

鶴岡

大蔵

山形

飯豊

酒田

山形新庄

天童

飛島

立川

朝日

山形小国

米沢

白鷹

遊佐

村山

上山

西川

山辺

2011/3/11EQ

Height Change in Yamagata Area near Japan Sea: 2010-2017

25cm sank!

Keeping sunk after EQ

Scientific findings for prediction with GNSS data

+No.1: The Earth is moving always about 5mm to 1cm vertically

and horizontally in normal condition but moves abnormally in

advance to large earthquake without knowing the reason.

+No.2: Abnormal movement and variation will not be only one

pattern but vary case by case. Earthquake is very complicate

phenomenon.

+No.3: Sinking tendency would be more risky than rising

tendency to induce earthquakes.

ⒸJESEA2019

+No.4: The time span between pre-signal abnormality and

actual occurrence of earthquake ranges a few weeks to few

months.

Prediction method by crustal change with GNSS data

GSI’s CORS: Daily XYZH

+ Short term anomaly# Weekly vertical change

# Monthly horizontal change

+ Long term anomaly

# Two year up-down tendency

# Accumulated stress

GSI CORSDistribution of CORS

JESEA’s own CORS

+ Real time anomaly

# hourly XYZH change

ⒸJESEA2019

ⒸGSI

Prediction examples with correct judges

Example 1: West Shimane Pref. 2018/4/9 M6.1 SI=5+

X

X

2 months before: Weekly vertical anomaly

1 month before: Weekly vertical anomaly

Osaka

OsakaShimane

Shimane

ⒸJESEA2019

Prediction examples with correct judges

Example 2: North Nagano Pref. 2018/5/25 M5.2 SI=5+

Japanese SI= 5+ 3 months before: Weekly vertical anomaly with

more than 6cm

XX

NaganoNagano

ⒸJESEA2019

ⒸTenki.jp

Prediction examples with incorrect judges

Example 3: North Osaka 2018/6/18 M6.1 SI=6-

Japanese SI= 6- 2 weeks before: Weekly vertical anomaly with

more than 4cm

X

Weekly vertical anomaly

with more than 4cm was

recognized but data

were released just on

the day of occurrence.

As the result the

prediction was unable in

time.

ⒸJESEA2019

ⒸTenki.jp

Prediction examples with correct judges

Example 4: East Chiba 2018/7/7 M6.0 SI=5-

Japanese SI= 5- 10 days before: Weekly horizontal anomalies

were concentrated

X

Tokyo

ⒸJESEA2019

ⒸTenki.jp

Prediction examples with correct judges

Example 5: Hokkaido 2018/9/6 M6.7 SI=7

Japanese SI= 7 1 month before: Monthly vertical anomalies

of down tendency

Sapporo

Hokkaido

Sendai

X

6 months before 1 month before

Sapporo

Down

ⒸJESEA2019

Erimo

X

ⒸTenki.jp

Anomaly appeared on the 5th September, 14 hours before EQ!

3:08 2018/9/6

Hokkaido EQ occurred

13:00 2018/9/5

Anomaly appeared

at Erimo, Hokkaido

ⒸJESEA2019

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

20

16

/1/9

20

16

/1/1

62

01

6/1

/23

20

16

/1/3

02

01

6/2

/62

01

6/2

/13

20

16

/2/2

02

01

6/2

/27

20

16

/3/5

20

16

/3/1

22

01

6/3

/19

20

16

/3/2

62

01

6/4

/22

01

6/4

/92

01

6/4

/16

20

16

/4/2

32

01

6/4

/30

20

16

/5/7

201

6/5

/14

20

16

/5/2

12

01

6/5

/28

20

16

/6/4

20

16

/6/1

12

01

6/6

/18

20

16

/6/2

52

01

6/7

/22

01

6/7

/92

01

6/7

/16

20

16

/7/2

32

01

6/7

/30

201

6/8

/62

01

6/8

/13

20

16

/8/2

02

01

6/8

/27

20

16

/9/3

20

16

/9/1

02

01

6/9

/17

20

16

/9/2

42

01

6/1

0/1

20

16

/10

/82

01

6/1

0/1

52

01

6/1

0/2

22

01

6/1

0/2

92

01

6/1

1/5

20

16

/11

/12

20

16

/11

/19

20

16

/11

/26

20

16

/12

/32

01

6/1

2/1

02

01

6/1

2/1

72

01

6/1

2/2

42

01

6/1

2/3

1

3.9cm

6.3cm

B

A

A

B

Prediction examples with correct judges

Example 6: North Ibaragi 2016/12/28 M6.3 SI=6-

It is risky if height difference exceeds 6cm

ⒸJESEA2019

ⒸTenki.jp

Sample of AI based risk map

As of 2018/9/1

X

Hokkaido East

Iburi EQ occurred

on 2018/9/6

(M6.7, SI=7)

in the risky level 4

of AI risk map

East Iburi EQ

ⒸJESEA2019

Ratio of correct prediction of earthquakes

2013-2018

Correct ＝ 29/54= 53.7%

Almost ＝ 17/54= 31.5%

Not correct＝ 8/54= 14.8%©JESEA2019

Definition

Correct: Within 3 months from anomality, EQ occurred

Almost: Within 6 months from anomaly, EQ occurred

Incorrect: In spite of anomality, EQ did not occur

2013年 2014年 2015年 2016年 2017年 2018年 Total

Correct ○ 3/9=33.3% 5/8=62.5% 5/10=50.0% 6/10=60.0% 5/8=62.5% 5/9=55.5% 29/54=53.7%

Almost △ 5/9=55.5% 2/8=15.0% 4/10=40.0% 4/10=40.0% 2/8=25.0% 0/9=0% 17/54=31.5%

Incorrect × 1/9=11.1% 1/8=12.5% 1/10=10.0% 0/10=0.0% 1/8=12.5% 4/9=44.4% 8/54=14.8%

Part 3: Additional RS &GIS techniques

for prediction

+ New findings for prediction with RS data

# Infrasound sensor: abnormal wave pattern

# Thermometer: pseudo temperature change

# Disturbance of ionosphere: time delay of GNSS

# Atmospheric low pressure: sudden fall

+ My policy

All possible geosphere phenomena should be

scientifically validated on correlation between the

phenomena and occurrence of earthquakes.

Above phenomena have been already validated and

can be used as supplemental tools for prediction. ⒸJESEA2019

Prediction method by infrasound anomaly

Ultra low frequency sound: 0.0004～0.001Hz

Infrasound sensor made in China

Anomaly was recognized

2 days before Kobe EQ

1995/1/17 M7.3, 6400 victims

ⒸBeijing University of Technology

ⒸJESEA2019

Prediction example: Hokkaido EQ with provable judges

About 1 month before About 20 days beforeⒸJESEA2019

Hokkaido EQ: 2018/9/6 M6.7

Prediction example: Kuichi-no-Erabu Volcano

2019/12/18

Volcanic Eruption at

Kuchi-no-Erabu Island Infrasound Anomaly 4 days before

GNSS H Anomaly: 10 days before

Kyushu

H>4cm

ⒸJESEA2019

Prediction method by temperature anomaly

Temperature is measured with platinum resistance sensor

by detecting electric resistivity from weak electric current

Platinum resistance thermometerAnomaly will be recognized

about within a month

before EQ due to EM emissionR

esis

tivit

y

Temperature (K)

Pt

ⒸJESEA2019

0

5

10

15

20

25

30

0:1

0

1:1

0

2:1

0

3:1

0

4:1

0

5:1

0

6:1

0

7:1

0

8:1

0

9:1

0

10

:10

11

:10

12

:10

13

:10

14

:10

15

:10

16

:10

17

:10

18

:10

19:1

0

20

:10

21:1

0

22

:10

23

:10

20180905

日高 日高門別

0

5

10

15

20

25

20

160

411

0

:10

1:3

0

2:5

0

4:1

0

5:3

0

6:5

0

8:1

0

9:3

0

10

:50

12

:10

13

:30

14

:50

16

:10

17

:30

18

:50

20

:10

21

:30

22

:50

阿蘇山 熊本 菊池 甲佐

ⒸJESEA2019

Kumamoto EQ（2016/04/16）

Anomaly detected 5 days before

Hokkaido EQ（2018/9/6）

Anomaly detected 1 day before

Validation example: Kumamoto and Hokkaido EQ

Asosan

HidakaMombetsu

20180411

Japanese patent approved

2016/4/15

1 day before2016/4/11

5 days before2016/4/7

9 days before

2016/4/3

13 days before

MashikiAsosan

X

Kumamoto EQ

（2016/04/16: M7.3）

From 2 weeks before

anomaly is observed

at Mashiki & Asosan

near the epi-center

X X

X

Mashiki

Hokkaido EQ

2018/9/6：M6.7, SI=7

2018/9/5

1 day before

X

Monbetsu

Hidaka

Very new innovation Japanese patent approved

Prediction method by ionospheric anomaly

GNSS

GNSS antenna

EM wave

ⒸJESEA2019

Comparison between the exist and new AI method

ここが赤色曲線からのズレなのか、緑色の曲線の一部なのかを見分けることができるか？

02120700_obs_#15.xlsx

It is hard to detect anomaly

from high order curve

TEC Method AI New Method

with peak curve

This prediction method would be my final goal

to enable early warning a few hours in advance

to the occurrence of huge earthquake.

ⒸJESEA2019

East Japan EQ（2011/03/11）

Anomaly just about 4 hours before

EQ

5 4 3 2 16

Validation example: East Japan Great EQ

0

5 GPS data

at 2 CORS

Kumamoto EQ, Fore shock

M6.5, SI=7 2016.04.14

Kumamoto major EQM7.3, SI=7 2016.04.16

Anomaly before 2

hours and 15min.

Anomaly before 2

hours and 25 minutes

Validation example: Kumamoto EQ

ⒸJESEA2019

Validation of low pressure: East Japan Great EQ

Fore shock(M7.3):2011/3/9: Main shock(M9.0):2011/3/11

Fore shock

Main shock

Atmospheric pressure at near epi-center

Pre

ssure

Fall

Fore

Main

Pre-slip

ⒸJESEA2019

ⒸTenki.jp

Validation of low pressure

Kobe Great EQ

1995/1/17(M7.3)

Pre

ssure

Fall

Pre

ssure

Fall

Atmospheric pressure at near epi-center

January 8-17

Validation of low pressure

Kumamoto EQ

2016/4/16(M7.3)

990

995

1000

1005

1010

1015

1020

1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721

Kumamoto

12 16151413

Pre

ssure

Fall

2 days before

4 days before

ⒸJESEA2019

Part 4: Mini-plate theory

+ New findings of mini-plates

# The hint of mini-plate theory was obtained from

the change analysis of Kumamoto EQ occurred

2016/4/16

# Mini-plates was clustered with time sequence

GNSS data variation in 2016

# Mini-plates are more related to occurrence of

EQs rather than faults or geotectonic map

# About 70% of large Eqs are located near

the boundary of mini-plates

# Mini-plates are useful for understanding

geodynamic characteristics of the moving Earth

ⒸJESEA2019

The hint of mini-plate theory from Kumamoto EQ

M7.3 SI=7

・Green/red: rose

・Blue: sank・Arrows: Horizontal

changeⒸJESEA2019

2016.4.16

ⒸTenki.jp

Clustering of mini-plates with GNSS data

GNSS data (XYZ) in 2016

Convert to (NEU)

PCA analysis （PC1,PC2)

Eliminate errors/noises

Select cluster number

Nearest neighbor interpolation for image map

Comparison with geotectonic map

ⒸJESEA2019

Correlation with earthquakes

PCA analysis （PC1,PC2)

Pointwise cluster map

Determine Cluster Number

Clustering of PC1 and PC2 domain

ⒸJESEA2019

Comparison between

Mini-plate map and Faults

ⒸJESEA2019

a) National Atlas: Geotectonic Map b) Clustering Map

Comparison between geotectonic map and mini-plate map

ⒸJESEA2019

Comparison between geological map and mini-plate map

ⒸJESEA2019

Ⓒ地質調査総合センター

Distribution of larger Eqs than SI=5

10 years from 2008 to 2018

Depth of

Epi-center

Less than 15km

15km-30km

More than 30km

Size of marker

SI=7

SI=6+ SI=5-

SI=5+ SI=5-

ⒸJESEA2019ⒸGoogle

Buffering radius= 25km

Boundary check

Near boundary

Not boundary

Does EQ occur near mini-plate boundary?

71% of larger EQs occurred

near mini-plate boundary

ⒸJESEA2019ⒸGoogle

No.dN

N-S

dE

E-W

dU

H

dNEU

Move

azNEU

Azimuth

Hori./Vert.

move

1 -4.01 3.966 0.815 5.698 135.32 SE+: Down - -

2 -2.137 1.361 0.311 2.553 147.5 SE+: Down -

3 1.378 -2.117 0.152 2.531 303.06 NW-: UP/Down -

4 -6.044 6.927 -0.301 9.199 131.1 SE ++: Down -

5 -8.802 9.17 -0.052 12.711 133.83 SE++: UP/Down-

6 -13.336 15.733 0.193 20.626 130.28 SE++:Up/Down -

7 -26.764 27.556 -0.487 38.417 134.17 SE+++* Down - -

8 -25.265 32.97 11.144 43.006 127.46 SE+++: UP+++

Geo-dynamism of mini-plates with GNSS data

ⒸJESEA2019

ⒸGoogle

Naming of Mini-Plates of Japan Name of Mini-Plate

1： Central belt MP

with SE moves/rising

2： Southern central belt MP

with SE moves/little up/down

3： South coast MP

with NW moves/little sinking

4： Northern central belt MP

with SE moves/little up/down

5： Wet to East central belt MP

with SE moves/little up/down

6： West to East northern belt MP

with SE moves/little up/down

7： West Tohoku MP

with big SE moves/sinking

8： East Tohoku MP

with SE big moves /rising

❶

❷

❸

➍

❺

❻

❼

❻❺

➍❶

❷❸

❺

❽

➍❶

❷❺

➍

❸

❸ ❸

❶➍❺

❷

ⒸJESEA2019

Comparison between existing geo-tectonics map

and mini-plate map with GNSS data

Existing

geo-tectonics map

Mini-plate map

Information

Analog

and

Qualitative

Digital

and

Quantitative

Status

Static

and

Time independent

Dynamic

and

Time dependent

Productivity

Professional

and

Not reproductive(geological survey needed)

Reproductive

and

Repeatable

(clustering needed)

Relation

to

Earthquakes

Weakly related Strongly related

ⒸJESEA2019

Conclusions

+ I realized that the main stream of the prediction of

earthquakes should be along with remote sensing but not

seismic science.

+ Geospatial techniques and artificial intelligence are strong

tools to support advanced data analysis and prediction.

+ All possible macroscopic phenomena should be used or

supplemented in order to increase the accuracy.

+ Alert level prediction would be possible with ionospheric

anomalies in a few years after validation and verification are

to be implemented.

+ Mini-plate theory would be an innovation principle for

better understanding and predicting earthquakes.

+ I hope that collaboration with GIC/AIT and UN will contribute

to the reduction of earthquake disasters in Asian region.

ⒸJESEA2019

Thank you for your attentionⒸJESEA2019