Recent Advances in Prediction of Earthquakes
Shunji 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 theEast Japan Great EQ occurred on the 11th March2011 with 18,000 victims mainly due to Tsunami.
+ In order to rescue human lives I invested toestablish 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 halfof my life.
+ No 2: Though so many people died in the past history ofJapan due to huge earthquakes; 30 times with morethan one thousand victims in the past 400 years, Japanese
government and seismic scientists gave up the prediction ofearthquakes 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 pastrecord of giant earthquakes, which are analyzed withstatistical and provability model. The method is unreliable!
ⒸJESEA2019

Large EQ’s since1923
and distribution of
Nuclear Power Plants
Japan is EQ prone
country under risky
condition everywhereand every timeX
Fukushima NPⒸGoogleⒸJESEA 2019

What should be the key for prediction?
+ No.1: Remote sensing method should be applied to detectmacroscopic anomalies or pre-signals in advance tooccurrence of earthquakes. We need time series of scientificobservation data covering whole area. The existence of the
pre-signals has been verified by my research in 2007.
+ No.2: Quantitative correlation between anomalies ofobservation 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
1.
2.
3.
4.
5.

GNSS

Crustal Change
Infrasound anomaly
ELF emission anomaly
Temperature anomaly
Ionospheric anomaly

InfrasoundsensorEpicenterⒸ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

M9.0, SI=7
Yamagata Area

Miyagi Area

Damage due to Tsunami
All Japan Islands quakedⒸJESEA2019ⒸTenki.jp

What were the movements at 2011/3/11 EQ？
Just afterOjika
5.3m moved

1.1m sank

Sea Bottom
3m rose©海上保安庁©NASAⒸJESEA2019

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

胆沢

2011/3/11EQ

20

栗駒２
宮城東和
高清水

0

南方
小野田

-20

河北
丸森
女川

-40

利府
気仙沼

-60

栗駒
鳴子
志津川

-80

涌谷

Ojika

-100

亘理
七ヶ宿

110cm sank!

-120
2010/1/9

宮城川崎

2011/1/9

2012/1/9

2013/1/9

2014/1/9

牡鹿

2015/1/9

2016/1/9

2017/1/9

白石Height Change in Yamagata Area near Japan Sea: 2010-2017
2011/3/11EQ

10

Yamagata Area

真室川

鶴岡
大蔵

5

山形
飯豊
酒田

0

山形新庄
天童

-5

飛島
立川

-10

朝日

山形小国

-15

米沢
白鷹

-20

遊佐

Keeping sunk after EQ

-25

上山

25cm sank!

西川

-30

2010/1/9

2011/1/9

2012/1/9

2013/1/9

2014/1/9

村山

2015/1/9

2016/1/9

2017/1/9

山辺

Scientific findings for prediction with GNSS data
+No.1: The Earth is moving always about 5mm to 1cm verticallyand horizontally in normal condition but moves abnormally inadvance 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 risingtendency to induce earthquakes.

+No.4: The time span between pre-signal abnormality andactual occurrence of earthquake ranges a few weeks to few
months.
ⒸJESEA2019

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

JESEA’s own CORS
+ Real time anomaly# hourly XYZH changeⒸGSI

GSI CORS

Distribution of CORSⒸJESEA2019

Prediction examples with correct judges
Example 1: West Shimane Pref. 2018/4/9 M6.1 SI=5+

ShimaneShimane

X

X
Osaka

Osaka

2 months before:

Weekly vertical anomaly

1 month before:

Weekly vertical anomalyⒸJESEA2019

Prediction examples with correct judges
Example 2: North Nagano Pref. 2018/5/25 M5.2 SI=5+

X

X

NaganoNaganoⒸTenki.jp

Japanese SI= 5+

3 months before:

Weekly vertical anomaly withmore than 6cmⒸJESEA2019

Prediction examples with incorrect judges
Example 3: North Osaka 2018/6/18 M6.1 SI=6Weekly vertical anomaly
with more than 4cm wasrecognized but data
were released just on
the day of occurrence.
As the result theprediction was unable in
time.

X

ⒸTenki.jp

Japanese SI= 6-

2 weeks before:

Weekly vertical anomaly withmore than 4cmⒸJESEA2019

Prediction examples with correct judges
Example 4: East Chiba 2018/7/7 M6.0 SI=5-

X
Tokyo

ⒸTenki.jp

Japanese SI= 5-

10 days before:

Weekly horizontal anomalies
were concentratedⒸJESEA2019

Prediction examples with correct judges
Example 5: Hokkaido 2018/9/6 M6.7 SI=7

SapporoHokkaido

Sapporo
X

X
Down

Erimo
SendaiⒸTenki.jp

Japanese SI= 7

6 months before

1 month before

1 month before:

Monthly vertical anomaliesof down tendencyⒸJESEA2019

Anomaly appeared on the 5th September, 14 hours before EQ!
13:00 2018/9/5
Anomaly appearedat Erimo, Hokkaido

3:08 2018/9/6
Hokkaido EQ occurredⒸJESEA2019

A

BⒸTenki.jp

3.0

2.0

2016/1/9
2016/1/16
2016/1/23
2016/1/30
2016/2/6
2016/2/13
2016/2/20
2016/2/27
2016/3/5
2016/3/12
2016/3/19
2016/3/26
2016/4/2
2016/4/9
2016/4/16
2016/4/23
2016/4/30
2016/5/7
2016/5/14
2016/5/21
2016/5/28
2016/6/4
2016/6/11
2016/6/18
2016/6/25
2016/7/2
2016/7/9
2016/7/16
2016/7/23
2016/7/30
2016/8/6
2016/8/13
2016/8/20
2016/8/27
2016/9/3
2016/9/10
2016/9/17
2016/9/24
2016/10/1
2016/10/8
2016/10/15
2016/10/22
2016/10/29
2016/11/5
2016/11/12
2016/11/19
2016/11/26
2016/12/3
2016/12/10
2016/12/17
2016/12/24
2016/12/31

Prediction examples with correct judges

Example 6: North Ibaragi 2016/12/28 M6.3 SI=6It is risky if height difference exceeds 6cm
9.0

8.0

7.0

A

6.0

5.0

6.3cm

4.0

3.9cm
B

1.0

0.0

ⒸJESEA2019

Sample of AI based risk map
As of 2018/9/1
East Iburi EQ

Hokkaido East
Iburi EQ occurred
on 2018/9/6(M6.7, SI=7)
in the risky level 4of AI risk map

XⒸJESEA2019

Ratio of correct prediction of earthquakes
2013-2018
2013年

2014年

2015年

2017年

2018年

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%

2016年

0/10=0.0%

1/8=12.5%

4/9=44.4%

Total

8/54=14.8%

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

Correct＝
Almost＝Not correct＝

29/54=
17/54=
8/54=

53.7%
31.5%
14.8%
©JESEA2019

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 bescientifically validated on correlation between thephenomena and occurrence of earthquakes.
Above phenomena have been already validated andcan be used as supplemental tools for prediction.
ⒸJESEA2019

Prediction method by infrasound anomaly
Ultra low frequency sound: 0.0004～0.001Hz
Anomaly was recognized
2 days before Kobe EQ
1995/1/17 M7.3, 6400 victims

Infrasound sensor made in ChinaⒸJESEA2019ⒸBeijing University of Technology

Prediction example: Hokkaido EQ with provable judges
Hokkaido EQ: 2018/9/6 M6.7

About 1 month before

About 20 days beforeⒸJESEA2019

Prediction example: Kuichi-no-Erabu Volcano
2019/12/18
GNSS H Anomaly: 10 days before

Kyushu
H>4cm

Volcanic Eruption at
Kuchi-no-Erabu IslandⒸJESEA2019

Infrasound Anomaly 4 days before

Prediction method by temperature anomaly
Temperature is measured with platinum resistance sensor
by detecting electric resistivity from weak electric current

Resistivity

Pt

Temperature (K)Platinum resistance thermometerⒸJESEA2019

Anomaly will be recognizedabout within a month
before EQ due to EM emission

Validation example: Kumamoto and Hokkaido EQ
Hokkaido EQ（2018/9/6）

Kumamoto EQ（2016/04/16）
20180411

25

20180905
30

20

HidakaMombetsu

25

15
20

10
15

5

22:50

21:30

20:10

18:50

16:10

14:50

13:30

12:10

10:50

9:30

8:10

6:50

5:30

4:10

2:50

10

5

Japanese patent approved

阿蘇山

熊本

菊池

甲佐

Anomaly detected 5 days before

日高

23:10

22:10

21:10

20:10

19:10

18:10

17:10

16:10

15:10

14:10

13:10

12:10

11:10

9:10

10:10

8:10

7:10

6:10

5:10

4:10

3:10

2:10

1:10

0
0:10

1:30

20160411 0:10

0

17:30

Asosan

日高門別

Anomaly detected 1 day beforeⒸJESEA2019

AsosanX

MashikiX

2016/4/15
1 day before

Kumamoto EQ（2016/04/16: M7.3）

From 2 weeks before
anomaly is observed
at Mashiki & Asosannear the epi-center

2016/4/11
5 days before

MashikiX

2016/4/3
13 days beforeX

2016/4/7
9 days before

Hokkaido EQ

2018/9/6：M6.7, SI=7
2018/9/5
1 day before

HidakaX
Monbetsu

Prediction method by ionospheric anomaly
Very new innovation

Japanese patent approved

GNSS

EM wave

GNSS antennaⒸJESEA2019

Comparison between the exist and new AI method
02120700_obs_#15.xlsx

TEC Method

AI New Methodwith peak curve

It is hard to detect anomaly
ここが赤色曲線からのズレなのか、緑色の曲線
from high order curveの一部なのかを見分けることができるか？

This prediction method would be my final goal
to enable early warning a few hours in advanceto the occurrence of huge earthquake.

Validation example: East Japan Great EQ
East Japan EQ（2011/03/11）
5 GPS data
at 2 CORS

6

5

4

3

2

1

0

EQ

Anomaly just about 4 hours beforeⒸJESEA2019

Validation example: Kumamoto EQ
Anomaly before 2hours and 15min.

Kumamoto EQ, Fore shock
M6.5, SI=7 2016.04.14

Anomaly before 2hours and 25 minutes

Kumamoto major EQ
M7.3, SI=7 2016.04.16Ⓒ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

Fore

Pressure Fall

Atmospheric pressure at near epi-centerPre-slipⒸTenki.jp

Main shockⒸJESEA2019

Validation of low pressure
Kobe Great EQ
1995/1/17(M7.3)

Validation of low pressure
Kumamoto EQ
2016/4/16(M7.3)

Atmospheric pressure at near epi-center
January 8-17

Kumamoto
2 days before

Pressure Fall

4 days before

Pressure Fall

Pressure Fall

1020
1015
1010
1005
1000
995
990 12

13

14

15

16

1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721 1 5 9 131721ⒸJESEA2019

Part 4: Mini-plate theory+ New findings of mini-plates# The hint of mini-plate theory was obtained fromthe 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
2016.4.16
M7.3 SI=7ⒸTenki.jpⒸJESEA2019

・Green/red: rose
・Blue: sank・Arrows: Horizontal
change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
PCA analysis （PC1,PC2)

Pointwise cluster map
Nearest neighbor interpolation for image map
Comparison with geotectonic map
Correlation with earthquakes

Determine Cluster NumberⒸJESEA2019

Clustering of PC1 and PC2 domainⒸJESEA2019

Comparison between
Mini-plate map and FaultsⒸJESEA2019

Comparison between geotectonic map and mini-plate mapa) National Atlas: Geotectonic Mapb) Clustering MapⒸJESEA2019

Comparison between geological map and mini-plate mapⒸ地質調査総合センターⒸJESEA2019

Distribution of larger Eqs than SI=510 years from 2008 to 2018
Size of marker
SI=7
SI=6+
SI=5SI=5+
SI=5-

Depth ofEpi-center
Less than 15km
15km-30km
More than 30km

ⒸGoogleⒸJESEA2019

Does EQ occur near mini-plate boundary?
Boundary check
Near boundary

Not boundary

71% of larger EQs occurred
near mini-plate boundary
Buffering radius= 25km

ⒸGoogleⒸJESEA2019

Geo-dynamism of mini-plates with GNSS data

ⒸGoogleHori./Vert.
move

No.

dN
N-S

dE
E-W

dU
H

dNEU
Move

azNEU
Azimuth

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 -4-6.0446.927-0.301

9.199

131.1 SE ++: Down -5-8.8029.17-0.05212.711133.83 SE++: UP/Down-

6-13.336

15.7330.193

20.626130.28 SE++:Up/Down -7-26.764

27.556-0.48738.417134.17 SE+++* Down - -8-25.265

32.97

11.14443.006127.46 SE+++: UP+++ⒸJESEA2019

Name of Mini-Plate

Naming of Mini-Plates of Japan
❷

❶

➍

❸

1： Central belt MPwith SE moves/rising

2： Southern central belt MPwith SE moves/little up/down❺❻

3： South coast MP

with NW moves/little sinking

❼

❺

➍
❺

❶
❷
❺ ➍

❸

➍
❶
❷
❸

❽

4： Northern central belt MPwith SE moves/little up/down

5： Wet to East central belt MP❻❺with SE moves/little up/down

6： West to East northern belt MP

➍
❶
❷
❸ ❸

with SE moves/little up/down
7： West Tohoku MPwith big SE moves/sinking
8： East Tohoku MPwith SE big moves /risingⒸJESEA2019

Comparison between existing geo-tectonics map
and mini-plate map with GNSS data
Existing
geo-tectonics map

Mini-plate map

Information

Analogand
Qualitative

Digitaland
Quantitative

StatusStaticandTime independent

Dynamic
andTime dependent

Productivity

Professionaland
Not reproductive

Reproductive
andRepeatable(clustering needed)(geological survey needed)

Relation
Weakly relatedtoEarthquakes

Strongly relatedⒸJESEA2019

Conclusions+ I realized that the main stream of the prediction ofearthquakes should be along with remote sensing but notseismic 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 ionosphericanomalies in a few years after validation and verification are
to be implemented.
+ Mini-plate theory would be an innovation principle forbetter understanding and predicting earthquakes.
+ I hope that collaboration with GIC/AIT and UN will contributeto the reduction of earthquake disasters in Asian region.
ⒸJESEA2019

Thank you for your attentionⒸJESEA2019