䌌⹂巵⛙ᇬ䂻⦿ⷵ䤓䪣䴅₰䞷ኔኖኣኽSeismic + Oceanic Sensors (SOS)2019㄃7㦗30㡴峂᧶⺞㨦 ⮹捝

Seismic + Oceanic Sensors (SOS)
Paroscientific ♫ࡣ⅏ᐖ㆙࿌ࠊ ᆅᏛࠊࡑࡋ࡚Ẽೃኚືࡢィ ࡜࠸ࡗࡓᙺ๭ࢆ඲࡚‶ࡓࡍ஦ࡀฟ᮶ࡿࠕᆅ
ື࣭ᾏὒ⌧㇟ィ ࢭࣥࢧ࣮⥲ྜࢩࢫࢸ࣒㸦Seismic + Oceanic Sensors = SOS㸧ࠖࢆ⪃᱌ࠊ㛤Ⓨࡋࠊࡑࡢࣃࣇ࢛࣮࣐ࣥࢫホ౯࡟ྲྀࡾ⤌ࢇ࡛ࡁࡓࠋSOS ࡟࠾࠸࡚ࡣࠊᆅ㟈ࡸὠἼ࡞࡝ࡢ▷ᮇⓗ࡞⌧㇟ࡣ㧗ศゎ⬟ࢆ᭷ࡍࡿ⤯ᑐᅽຊࢤ࣮ࢪ࡜ 3 ㍈ຍ㏿ᗘィ࡛ィ ྍ⬟࡛࠶ࡿࠋࡲࡓ⌧ሙᰯṇ᪉ἲ࡛࠶ࡿࠕA-0-Aࠖࢆ⏝࠸࡚ࢭࣥࢧ࣮ࢻࣜࣇࢺࢆ㝖ཤࡍࡿ஦࡛ࠊ ᆅᏛⓗ࡞ィ ࡸᾏỈ㠃ኚ໬࡜࠸ࡗࡓ㛗࿘ᮇⓗ࡞⌧㇟ࡢほ ࡶྍ⬟࡛
࠶ࡿࠋSOS ࣔࢪ࣮ࣗࣝࡣᑠᆺࡢ⥲ྜࢩࢫࢸ࣒࡛࠶ࡾࠊࢣ࣮ࣈࣝࢩࢫࢸ࣒ࠊᾏᗏほ ࢩࢫࢸ࣒㸦OBR㸧ࠊࡑࡋ࡚ ROV ࡟࡞࡝࡟ᐜ᫆࡟ᦚ㍕ྍ⬟࡛࠶ࡿࠋ⅏ᐖ㆙࿌ࢩࢫࢸ࣒࡜ࡋ࡚ࡢᙺ๭
SOS ࣔࢪ࣮ࣗࣝ࡟ྵࡲࢀࡿ 3 ㍈ຍ㏿ᗘィ࡜⤯ᑐᅽຊࢤ࣮ࢪࡣᆅ㟈ືࡸὠἼ࡜࠸ࡗࡓࢲ࢖ࢼ࣑ࢵࢡ࡞ᆅືࡸ
ᾏὒ⌧㇟࡟ࡼࡗ࡚ᘬࡁ㉳ࡇࡉࢀࡿỈᅽኚືࡸຍ㏿ᗘኚືࢆࡑࢀࡒࢀྠ᫬࡟ィ ࡍࡿࡇ࡜ࡀฟ᮶ࡿࠋ㸦ཧ⪃㈨ᩱࡣࡇࡕࡽ http://paroscientific.com/pdf/10_Oceans_2012_Disaster_Warning_Systems.pdf)⤯ᑐᅽຊࢤ࣮ࢪ࡜ຍ㏿ᗘィࡣࠊࡓ࡜࠼㉸ᕧ኱ᆅ㟈ࡸࡑࢀ࡟క࠺ὠἼࡀⓎ⏕ࡋ࡚ࡶィ ್ࡀ᣺ࡾษࢀ࡞࠸
ࣞࣥࢪࢆ᭷ࡋ࡚࠸ࡿࠋࡑࢀ࡟ຍ࠼ࠊࣃࣟ♫ࡢࢼࣀศゎ⬟ฎ⌮࢚ࣞࢡࢺࣟࢽࢡࢫ࡟ࡼࡗ࡚ࣇࣝࢫࢣ࣮ࣝ
1ppb㸦1 ඙ศࡢ 1㸧ࡢศゎ⬟ࢆᣢࡓࡏࡿ஦ࡀྍ⬟࡛࠶ࡾࠊỈᅽィ࡛ࡣࣇࣝࢫࢣ࣮ࣝ 1000m ༢఩ࡢࡶࡢ࡛
ᩘ࣐࢖ࢡ࣓࣮ࣟࢺࣝࠊ3g ࣇࣝࢫࢣ࣮ࣝࡢຍ㏿ᗘィ࡛ࡣᩘࢼࣀ g ࡢ್ࡲ࡛ィ ྍ⬟࡛࠶ࡿࠋᆅẆኚື࡞࡝ࡢᆅᏛⓗ࡞ィࢩࢫࢸ࣒࡜ࡋ࡚ࡢᙺ๭

⌧ᅾࠊᾏᇦ࡟࠾࠸࡚ࡼࡾ⢭ᐦ࡟ࢫ࣮ࣟࢫࣜࢵࣉ⌧㇟ࡸ ᬮ໬࡟క࠺ᾏୖỈ఩ኚ໬ࢆ ᐃ࡛ࡁࡿ ᆅᏛⓗ⿦⨨࣭ᡭἲࡀᚲせ࡜ࡉࢀ࡚࠸ࡿ㸦ヲࡋࡃࡣᮏ᭩ࡢ᭱ᚋ࡟㍕ࡏ࡚࠶ࡿࠊ᭱᪂ࡢᆅ⌫Ꮫⓗ◊✲ㄽᩥࢆཧ⪃ࡢࡇ࡜㸧ࠋࡲࡓࠊᆅẆኚື࡞࡝ࡢ㛗ᮇⓗ࡞⌧㇟ࡣࢭࣥࢧ࣮⮬యࡢ㛗ᮇⓗࢻࣜࣇࢺ࡟ࡼࡗ࡚㞃ࡉࢀ࡚ࡋࡲ࠺
஦ࡀ࠶ࡿࡀࠊ⌧ᅾ࡛ࡣࠕA-0-Aࠖ࡜࿧ࡤࢀࡿ⌧ሙᰯṇ᪉ἲࡀ⪃᱌ࡉࢀ࡚࠾ࡾࠊ⤯ᑐᅽࢤ࣮ࢪࡢ㛗ᮇⓗࢻࣜࣇࢺࢆྲྀࡾ㝖ࡃࡢ࡟ຠᯝⓗ࡛࠶ࡿࠋู㈨ᩱ㸦࢔ࣉࣜࢣ࣮ࢩࣙࣥࣀ࣮ࢺ㸧࡟࡚ヲ⣽࡟ゎㄝࡋ࡚࠶ࡿࡼ࠺࡟ࠊࢭࣥࢧ࣮ࡢࢻࣜࣇࢺࡢཎᅉࡣ୺࡟
஧ࡘ᭷ࡿ࡜ᛮࢃࢀࡿࠋ1 ࡘ┠ࡣࢭࣥࢧ࣮࡟ᅽຊࡀ᥃࠿ࡗ࡚࠸࡞࠸ሙྜ࡟㢧ⴭ࡟࡞ࡿỈᬗࡢ࢞ࢫ๤㞳࡟ࡼࡿୖྥࡁࡢ㸦ฟຊ್ࡀୖ᪼ࡍࡿ㸧ࢻࣜࣇࢺࠊࡑࡋ࡚ 2 ࡘ┠ࡣࢭࣥࢧ࣮ࡀຍᅽࡉࢀ࡚࠸ࡿሙྜ࡟㢧ⴭ࡟࡞ࡿࢡ࣮ࣜࣉ⌧㇟࡟ࡼࡿୗྥࡁࡢ㸦ฟຊ್ࡀῶᑡࡍࡿ㸧ࢻࣜࣇࢺ࡛࠶ࡿࠋࡇࡢ 2 ࡘࡢ⌧㇟࡟ࡼࡿࢻࣜࣇࢺ
㔞ࡣ᫬㛫⤒㐣࡜ඹ࡟ᣦᩘ㛵ᩘⓗ㸦e ≧㸧࡟ῶᑡࡍࡿࠋᐇ㝿࡟ࡣࢭࣥࢧ࣮ࡢ⥲ྜⓗ࡞ࢻࣜࣇࢺࡣࡇࡢ஧ࡘ
ࡢ⌧㇟ࡀ㔜࡞ࡗ࡚࠸ࡿࡶࡢ࡜ᛮࢃࢀࡿࡓࡵࠊࢭࣥࢧ࣮ࡢࢻࣜࣇࢺࡣຍᅽᒚṔ࡞࡝࡟ࡶᕥྑࡉࢀࡿ࡜⪃࠼ࡽࢀࡿࠋࢻࣜࣇࢺࡣ୺࡟ࢭࣥࢧ࣮ࡢࢫࣃࣥࡼࡾ(0)Ⅼࡢ࢜ࣇࢭࢵࢺࡢࡳ࡟ᙳ㡪ࡍࡿࡓࡵࠊA-0-A ᘧ⌧ሙᰯṇἲ࡛ࡣࢻࣜࣇࢺࡀ࡝ࡢ⌧㇟࡟㉳ᅉࡋ࡚࠸ࡿ࠿࡟㛵ࢃࡽࡎࢻࣜࣇࢺ㝖ཤࡀྍ⬟࡛࠶ࡿࠋ

SOS ࣔࢪ࣮ࣗࣝࡢᵓ㐀
SOS ࣔࢪ࣮ࣗࣝ࡟ྵࡲࢀࡿࢭࣥࢧ࣮ࡣḟࡢ㏻ࡾ࡛࠶ࡿࠋ⤯ᑐᅽຊࢤ࣮ࢪ 2 ྎ㸦ࣇࣝࢫࢣ࣮ࣝࣞࣥࢪࡣỈ఩ 700,1400,2000,3000,4000,7000m ࠿ࡽ㑅ᢥྍ㸧ࠊ3 ㍈ຍ㏿ᗘィ㸦࢔ࣛ࢖࣓ࣥࢺ⿵ṇ௜ࡁ㸧1 ྎࠊ
Digiquartz ࣂ࣓࣮ࣟࢱ࣮㸦ᚤẼᅽィ㸧1 ྎࠊࡑࡋ࡚ྛࢭࣥࢧ࣮⏝ࡢࢼࣀศゎ⬟ฎ⌮࢚ࣞࢡࢺࣟࢽࢡࢫ 3 ࡘ࡛࠶ࡿࠋᅽຊఏ᧛ࣛ࢖ࣥࡣ┿✵࡟ࡼࡗ࡚࢜࢖࡛ࣝ඘‶ࡉࢀ࡚࠾ࡾࠊ3 ᪉ྥࡢ࣮࣎ࣝࣂࣝࣈ࡟⧅ࡆࡿࡇ࡜
࡛ᾏỈᅽຊ್㸦A㸧࡜⟂యෆࡢ኱Ẽᅽ್(0)ࡢ㛫࡛ࡢษࡾ᭰࠼ࡀྍ⬟࡟࡞ࡿࠋᗘ⿵ṇࡉࢀࠊࣜࢽ࢔ࣛ࢖ࢬࡉࢀࡓ RS-232 ฟຊࡀࠊḟࡢࡼ࠺࡟ྛࢭࣥࢧ࣮࠿ࡽᚓࡽࢀࡿࠋ
xxxx

2 ࡘࡢᅽຊィ࡟ࡼࡿᾏỈᅽຊ್㸦A㸧࡜ྛࢭࣥࢧ࣮ෆࡢ ᗘ㸦1 ࣏࣮ࢺ㸧
3 ㍈ຍ㏿ᗘィ࡟ࡼࡿ X,Y,Z ྛ㍈࠿ࡽࡢຍ㏿ᗘཬࡧ 1G ࡢ࣋ࢡࢺ್ࣝ࡜ຍ㏿ᗘィෆࡢ ᗘ(1 ࣏࣮ࢺ)ࣂ࣓࣮ࣟࢱ࣮㸦ᚤẼᅽィ㸧࡟ࡼࡿ⟂యෆࡢ኱Ẽᅽ್(0)࡜ᚤẼᅽィෆࡢ ᗘ(1 ࣏࣮ࢺ)PPS ࢖ࣥࣉࢵࢺ࡟ࡼࡿࠊ඲ࢭࣥࢧ࣮ࡢྠᮇ໬࡜ࢱ࢖࣒ࢫࢱࣥࣉ

SOS Block Diagram㝣ୖࣛ࣎ヨ㦂࡟࠾࠸࡚ࡢ A-0-A ࢝ࣜࣈ࣮ࣞࢩࣙࣥᡭἲホ౯ࡢ⤖ᯝA-0-A ᪉ᘧ࡛ࡣࣂࣝࣈࢆ⏝࠸ࠊ⤯ᑐᅽຊࢤ࣮ࢪࡀࡉࡽࡉࢀ࡚࠸ࡿᅽຊ್ࢆᾏỈᅽຊ್(A)࡜ࢩࢫࢸ࣒⟂యෆࡢᅽຊ್(0)ࡢ㛫࡛ษࡾ᭰࠼ࡽࢀࡿࡼ࠺࡟ࡍࡿࠋᅽຊ್(A)ࡢฟຊࡣࠊྛᰯṇⅬ࡛⟂యෆࡢᚤẼᅽィࢆᶆ‽࡟⏝࠸࡚ ᐃࡋࡓ(0)Ⅼࡢ࢜ࣇࢭࢵࢺࢻࣜࣇࢺ࡟ࡼࡗ࡚⿵ṇࡉࢀࡿࠋ࠾ࡼࡑ㸱ᖺ㛫ࡢ㝣ୖヨ㦂࡛ࡣࠊࢭࣥࢧ࣮ࡢࢫࣃࣥ(A)ࢻࣜࣇࢺࡣ୍ḟᶆ‽ࣆࢫࢺࣥࢤ࣮ࢪ DWT ࢆ⏝࠸࡚ィ ࡉࢀࡓࠋ୍ḟᶆ‽ࣆࢫࢺࣥࢤ࣮ࢪࢆ⏝࠸࡚ࡢ A-0-A ࢝ࣜࣈ࣮ࣞࢩࣙࣥࢸࢡࢽࢵࢡࡢ᭱ึࡢヨ㦂ࡣ⏘⥲◊㸦AIST㸧ࡢィ㔞ᶆ‽⥲ྜࢭࣥࢱ࣮㸦NMIJ㸧࡟࡚⾜ࢃࢀࡓࠋࡑࡇ࡛ࡣᅜ㝿ᦠᖏᶆ‽ᶵჾ㸦transfer standard㸧࡜ࡋ࡚ࡢᐇ⦼ࡶ࠶ࡿࣇࣝࢫࢣ࣮ࣝ 100MPa㸦Ỉ఩ 10,000m㸧ࡢ㧗ᅽィ 6 ྎࡢ 0 ್ࡢࢻࣜࣇࢺࢆ 7 ᖺ㛫࡯࡝グ㘓ࡋ࡚࠾ࡾࠊ0 ್࡛ࡢ㛗ᮇࢻࣜࣇࢺࡣୖྥࡁ࡛࠶ࡗࡓࠋࡑࡢࢭࣥࢧ࣮ࢆ 100MPa ࡢᅽຊ࡛ຍᅽࡋ⥆ࡅ࡚㛗ᮇࢻࣜࣇࢺ㸦4 ࣨ᭶࡯࡝㸧ࢆィ ࡍࡿ࡜ࠊ100MPa ࡢᅽຊୗ࡛ࡢࢭࣥࢧ࣮ࡢࢻࣜࣇࢺࡣୗྥࡁ࡛࠶ࡗ
ࡓࠋࡋ࠿ࡋࠊຍᅽ≧ែ᫬ࡢࢭࣥࢧ࣮ࡢࢻࣜࣇࢺࡣ 0 ್࡜ A ್㸦ࣇࣝࢫࢣ್࣮ࣝ㸧࡛࡯ࡰ୍⮴ࡍࡿ್ࢆ♧ࡋࡓࡢ࡛ࠊDWT ࡛ィ ࡍࡿ A㸦ࣇࣝࢫࢣ࣮ࣝ㸧್ࡢࢻࣜࣇࢺ࠿ࡽ 0 ್࡛ࡢࢻࣜࣇࢺࢆᕪࡋᘬࡃ࡜ࠊA ್ࡢࢻࣜࣇࢺࡣࣂࣛࡘࡁ 1ppm ⛬ࡢᖹࡽ࡞ࢢࣛࣇ࡟࡞ࡗࡓࠋࡘࡲࡾࠊA0A ᡭἲ࡛ࢫࣃࣥࢻࣜࣇࢺࡣ 1ppm
ࡲ࡛㝖ཤฟ᮶ࡓ࡜ゝ࠼ࡿࠋୗࡢᅗ࡛ࡣࣆࣥࢡࡢ⥺ࢢࣛࣇࡀ 0 ್࡛ࡢࢻࣜࣇࢺࠊ㟷ࡢ⥺ࢢࣛࣇࡀࣇࣝࢫࢣ
್࣮࡛ࣝࡢࢻࣜࣇࢺࠊࡑࡋ࡚⥳ࡢ⥺ࢢࣛࣇࡀ 2 ࡘࡢᕪࢆྲྀࡗࡓ≀࡛࠶ࡿࠋ
Drift at Full Scale (A = 100 MPa), Drift at 0 (8 points linearly connected) & Residuals
5

1.5

0

1
0.5

-10
0
-15
-0.5
-20
-1

-25

-1.5

-30
-35
11/17/2014

Residuals [ppm]Drift relative to first A-0-A [ppm]-512/7/201412/27/2014

1/16/2015

2/5/2015

2/25/2015

3/17/2015

4/6/2015-2
4/26/2015

Time [Date]ࡑࡢᚋ 2 ᖺ㛫࡯࡝ࠊParoscientific ᮏ♫࡛ࡶ 80 ྎ࡯࡝ࡢᅽຊィ㸦ࣇࣝࢫࢣ࣮ࣝỈ఩ 3000m㸧ࢆ⏝࠸࡚
A-0-A ࢸࢫࢺࡀ⾜ࢃࢀࡓࠋ౛࠼ࡤ 2015 ᖺ 11 ᭶ 3 ᪥࠿ࡽ 2015 ᖺ 12 ᭶ 29 ᪥ࡲ࡛ 20 ྎࡢࢭࣥࢧ࣮ࡀ
ࣇࣝࢫࢣ࣮ࣝᅽຊୗ࡛ಖᣢࡉࢀࠊᐃᮇⓗ࡞ A-0-A ᡭἲ࡟ࡼࡗ࡚ࣇࣝࢫࢣ࣮ࣝ A ್࡜ 0 ್ࡢࢻࣜࣇࢺࢆィ ࡋࡓࠋࡑࡢෆࡢ 19 ྎࡢࢭࣥࢧ࣮ࡣࡑࡢᚋᩘࣨ᭶࡯࡝኱Ẽᅽ࡟㛤ᨺࡉࢀࡓࡀࠊࡲࡓ 2016 ᖺ 4 ᭶
19 ᪥࠿ࡽ 2016 ᖺ 8 ᭶ 19 ᪥ࡲ࡛ࣇࣝࢫࢣ࣮ࣝᅽຊୗ࡛ A-0-A ᐃࡀ⾜ࢃࢀࡓࠋࡇࡢ 2 ࡘࡢヨ㦂ࡢ⤖ᯝࠊ඲ࢭࣥࢧ࣮ࡢᖹᆒⓗࢫࣃࣥࢻࣜࣇࢺ㸦A ್ࡢࢻࣜࣇࢺ࠿ࡽ 0 ್ࡢࢻࣜࣇࢺࢆᕪࡋᘬ࠸ࡓ≀㸧ࡣࣂࣛࡘࡁࡀỈ఩+/-1.4cm ࡛ᖹࡽ࡞ࢢࣛࣇ࡟࡞ࡗࡓ㸦ࡇࡢ್ࡣᶆ‽࡜ࡋ࡚౑ࢃࢀࡓ DWT ࡢỈ఩ 3000m
࡜࠸࠺ᅽຊ್࡟࠾ࡅࡿࠊ+/-5ppm ࡢ୙☜ᐃࡉ࡛ࡶ࠶ࡿ㸧ࠋୗࡢᅗ࡛ࡣࠊ㟷ࡢࢢࣛࣇࡀࢮࣟⅬ࡛ࡢᖹᆒࢻࣜࣇࢺࠊࣆࣥࢡࡢࢢࣛࣇࡀࣇࣝࢫࢣ್࣮࡛ࣝࡢᖹᆒࢻࣜࣇࢺࠊࡑࡋ࡚⥳ࡢࢢࣛࣇࡀࡑࡢ 2 ࡘࡢᕪ࡛࠶ࡿࠋ㉥ࡢࢢࣛࣇࡣヨ㦂୰ࡢ 20 ྎࡢࢭࣥࢧ࣮ࡢ ᗘ࡛࠶ࡿࠋDrift of FS, 0,Span (FS - 0), & Temp, average of 20 sensors
22

4

2

21.5

-4

21

Temp [C]

-2

-6

-8

20.5

-10

-12
11/03/15

11/10/15

11/17/15

11/24/15

12/01/15

12/08/15

12/15/15

12/22/15

20
12/29/15

Date

Drift of FS, 0, Span (FS - 0), & Temp, average of 19 sensors
4
25

2
0

24

-2
-4

23

-6
-8
-10

22

-12
21

-14
-16

-18
20
4/19/16 4/26/16 5/3/16 5/10/16 5/17/16 5/24/16 5/31/16 6/7/16 6/14/16 6/21/16 6/28/16 7/5/16 7/12/16 7/19/16 7/26/16 8/2/16 8/9/16 8/16/16 8/23/16
Date

Temp [C]

Drift relative [cm H2O]

Drift relative [cm H2O]

0SOS ࢩ ࢫࢸ࣒ࢆ⏝࠸ࡓᐇᾏᇦ࡛ࡢヨ㦂ࠊࣔࣥࢺ࣮ࣞ‴ Monterey Accelerated Research
System (MARS)࡛ࡢ⤖ᯝ࣡ࢩࣥࢺࣥ኱Ꮫ㸦University of Washington㸧ࡢ◊✲ࢳ࣮࣒ࡣ 2017 ᖺ 6 ᭶୰᪪࡟ୖグࡢ SOS ࣔࢪ࣮ࣗࣝ
ࢆ࢝ࣜࣇ࢛ࣝࢽ࢔ᕞࣔࣥࢺ࣮ࣞ‴ࡢ Monterey Accelerated Research System (MARS)࡟ᒎ㛤ࡋࠊᾏᗏ࡛
ࡢヨ㦂ࢆጞࡵࡓࠋ
ḟࡢࢢࣛࣇࡣ 2017 ᖺ 9 ᭶ 7 ᪥࡟࣓࢟ࢩࢥ࣭ࢳ࢔ࣃࢫᕞ࡛㉳ࡁࡓ M8.1 ࡢᆅ㟈ࡼࡗ࡚Ⓨ⏕ࡋࡓᆶ┤࣭ᖹ⾜᪉ྥࡢຍ㏿ᗘ್ࠊࡑࡋ࡚Ỉᅽኚ໬࡛࠶ࡿࠋPlots Courtesy of the University of Washingtonࡉࡽ࡟ࠊ2017 ᖺ 9 ᭶㸱᪥࡟ࡣ໭ᮅ㩭ࡀ᰾ᐇ㦂ࢆ⾜ࡗࡓ࡜ᛮࢃࢀࡿ᣺ືࡀ 9000km 㞳ࢀࡓࣔࣥࢺ࣮ࣞ‴ࡢ
SOS ࢩࢫࢸ࣒࠿ࡽࡶ᳨ฟࡉࢀࡓࠋḟࡢࢢࣛࣇ࡟㍕ࡏࡿࡼ࠺࡟ࠊࡇࡢ⌧㇟ࡣ 3 ㍈ຍ㏿ᗘィ࡛ࡣྛ㍈࡛ 1 ࣐
࢖ࢡࣟ g ༢఩ࡢ᣺ື࡜ࡋ࡚⌧ࢀࠊ⤯ᑐᅽຊࢤ࣮ࢪ࡛ࡣ࠾ࡼࡑ᣺ᖜ 0.01hPa㸦Ỉ఩ 0.1mm㸧ࡢỈᅽኚ໬࡜ࡋ࡚⌧ࢀࡓࠋPlots Courtesy of the University of Washington

MARS ヨ㦂ࡢ୺࡞┠ⓗࡣࠊA-0-A ⌧ሙᰯṇ᪉ἲࡀᐇᾏᇦ࡛࡝ࢀ⛬ຠᯝⓗ࡟ᅽຊࢭࣥࢧ࣮ࡢࢻࣜࣇࢺࢆ㝖ཤฟ᮶ࡿ࠿ࢆ☜ㄆࡍࡿ஦࡛࠶ࡗࡓࠋ3 ᖺ㛫ࡢ㝣ୖヨ㦂࡛ࡣ୍ḟᶆ‽࡛࠶ࡿࣆࢫࢺࣥࢤ࣮ࢪ DWT ࢆ⏝࠸࡚
ࢫࣃࣥ㸦A㸧ࡢࢻࣜࣇࢺࢆᐃࡵࡓࡀࠊMARS ࡛ࡢ࣡ࢩࣥࢺࣥ኱Ꮫࡢ SOS ヨ㦂࡟࠾࠸࡚ࡣྠᮇࡋࡓ 2 ྎࡢ⤯ᑐᅽຊࢤ࣮ࢪࡢᕪࢆྲྀࡿ஦࡛ࢫࣃࣥࡢᏳᐃᛶࢆồࡵࡓࠋMARS ヨ㦂࡛ࡣࠊ2 ྎࡢ APG ࡣᐃᮇⓗ࡟ᾏᗏᅽຊ್(A)࠿ࡽ⟂యෆ㒊ᅽຊ(0)࡟ษࡾ᭰࠼ࡽࢀࠊ(0)Ⅼ࡛ 5ศ㛫⤒㐣ࡋࡓᚋࡲࡓ(A)࡟ษࡾ᭰࠼ࡽࢀࡓࠋࡇࡢ᭱୰⣙ 4 ศᚋࡢ(0)್ࢆ 1 ศ㛫㸦180-240 ⛊㸧ᖹᆒࡋࡓࢹ
࣮ࢱ࠿ࡽᅽຊィ P1ࠊP2 ࢆᚤẼᅽィ࡛ᰯṇࡋࡓ㸦P1-Barometric), (P2-Barometric)࡜ P1 ࡜ P2 ࡢᕪࢆྲྀࡗࡓࡶࡢ࡛࠶ࡿ(P1-P2)ࢆồࡵࡓࠋࡇࡢ㝿ࠊP1 ࡜ P2 ࡢ 0 Ⅼ࢜ࣇࢭࢵࢺࡣ␗࡞ࡿ᪉ྥ࡟ࢻࣜࣇࢺࡋ࡚࠸ࡓ
㸦2 ྎࡢࢭࣥࢧ࣮㛫ࡢ┦ᑐⓗ࡞(0)Ⅼࢻࣜࣇࢺᕪ 㸻 4.2 hPa ࡛࠶ࡿ㸧ࠋࡇࡢᵝ࡟ồࡵࡓ 0 Ⅼ࡛ࡢ 2 ྎࡢࢭࣥࢧ࣮㛫ࡢ┦ᑐⓗࢻࣜࣇࢺࢆࠊྠࡌࡼ࠺࡟ᾏᗏᅽຊ್(A)࡛ 2 ྎࡢᕪࢆྲྀࡿ஦࡟ࡼࡗ࡚ồࡵࡓ┦ᑐⓗࢻࣜࣇࢺ࠿ࡽᕪࡋᘬࡃ஦࡛ࠊࢫࣃࣥࡢᏳᐃᛶࢆồࡵࡓࠋ⤖ᯝ࡜ࡋ࡚ḟࡢࢢࣛࣇ࡟㍕ࡏࡿࡼ࠺࡟ࠊᾏᗏᅽຊ್㸦A㸧࡛ࡢ 2 ྎࡢࢭࣥࢧ࣮㛫ࡢ┦ᑐⓗࢻࣜࣇࢺࡣ 0್࡛ࡢࢻࣜࣇࢺࢹ࣮ࢱࢆ⏝࠸ࡿ஦࡛ࠊỈ఩ 0.1mm ༢఩ࡲ࡛㝖ཤࡉࢀࡓࠋ2 ᖺ㛫࡯࡝ࡢヨ㦂࡛ࠊᶆ‽೫ᕪ
ࡣ 0.046 hPa㸦Ỉ఩ 0.46 mm㸧࡛࠶ࡿࠋ㟷ࡢࢢࣛࣇࡀ 2 ྎࡢࢭࣥࢧ࣮㛫ࡢࣇࣝࢫࢣ್࣮ࣝ(A)࡛ࡢ┦ᑐⓗ࡞ࢻࣜࣇࢺࠊࡑࡋ࡚⥳ࡢࢢࣛࣇࡀࡑࢀ࠿ࡽ 2 ྎࡢࢭࣥࢧ࣮㛫ࡢ(0)್࡛ࡢ┦ᑐⓗ࡞ࢻࣜࣇࢺࢆᕪࡋᘬ࠸ࡓ
≀࡛࠶ࡿࠋ

ࡲ࡜ࡵ
SOS ࢩࢫࢸ࣒ࡣ㧗ศゎ⬟⤯ᑐᅽຊࢤ࣮ࢪ࡜ 3 ㍈ຍ㏿ᗘィ࡟ࡼࡗ࡚ࠊᾏᗏ࡛ࡢᆅ㟈ືࡸὠἼ࡜࠸ࡗࡓ▷ᮇⓗ࡞⌧㇟ࡢࡼࡾ⢭ᐦ࡞ィ ࢆྍ⬟࡟ࡍࡿࠋࡲࡓࠊࣂ࣓࣮ࣟࢱ࣮㸦ᚤẼᅽィ㸧ࡶᦚ㍕ࡋࠊࢩࢫࢸ࣒⟂యෆࡢ኱Ẽᅽ್㸦㸮㸧ࢆࣜࣇ࢓ࣞࣥࢫ࡟⤯ᑐᅽຊࢤ࣮ࢪࡢࢻࣜࣇࢺࢆ⢭ᐦ࡟ ᐃྍ⬟࡟ࡍࡿ⌧ሙᵓᡂἲ࡛࠶
ࡿ A-0-A ࡶྍ⬟࡟ࡍࡿ஦࡛ࠊࡇࢀࡲ࡛ᅔ㞴ࡔࡗࡓ㛗ᮇⓗ࡞ᆅẆኚືࡸᾏ㠃Ỉ఩ኚ໬࡜࠸ࡗࡓ ᆅᏛⓗ࡞ィ ࡶᐇ⌧ࡍࡿࠋSOS ࣔࢪ࣮ࣗࣝࡣᑠᆺ࡞⥲ྜࢩࢫࢸ࣒࡛࠶ࡾࠊࢣ࣮ࣈࣝࢩࢫࢸ࣒ࡸᾏᗏほ ࢩࢫࢸ࣒
㸦OBR㸧ࠊࡑࡋ࡚ ROV ➼࡟ࡶᦚ㍕ྍ⬟࡛࠶ࡿࠋ⌧ᅾ࡛ࡣ MARS ࢧ࢖ࢺ࡟࡚ࢣ࣮ࣈࣝࢩࢫࢸ࣒࡜ࡋ࡚ᶵ
⬟ࡋ࡚࠾ࡾࠊᡂᯝࡶฟ࡚࠸ࡿࡼ࠺࡞ࡢ࡛ࠊParoscientific ࡣࡇࢀ࠿ࡽࡶ SOS ࣔࢪ࣮ࣗࣝ࡟ࡼࡿඃࢀࡓᆅ
⌫Ꮫⓗィ ࢭࣥࢧ࣮ࢩࢫࢸ࣒ࡢᥦ౪࡟ᑾຊࡋ࡚࠸ࡃḟ➨࡛࠶ࡿࠋࡑࡢ௚ࡢ࢔ࣉࣜࢣ࣮ࢩࣙࣥࣀ࣮ࢺ࡬ࡢࣜࣥࢡ
x ࢻࣜࣇࢺ᰿※࡟㛵ࡍࡿ࣏࣮ࣞࢺ
http://www.paroscientific.com/pdf/G8101_Root_Causes_of_Quartz_Sensors_Drift.pdf
x ࢻࣜࣇࢺ࡬ࡢᩘᘧࣔࢹࣝ࡟㛵ࡍࡿ࣏࣮ࣞࢺ
http://www.paroscientific.com/pdf/G8095_Mathematical_Models.pdf
x ࢭࣥࢧ࣮ࢻࣜࣇࢺ㝖ཤࡢࡓࡵ࡟⪃᱌ࡉࢀࡓ⌧ሙᰯṇ᪉ἲ࡟㛵ࡍࡿ࣏࣮ࣞࢺ
http://www.paroscientific.com/pdf/G8097_Calibration_Methods_to_Eliminate_Sensor_Drift.pdf

⮬↛⅏ᐖ㆙࿌ࢩࢫࢸ࣒ࠊ

ᆅᏛⓗ◊✲࡟㛵ࡍࡿㄽᩥ࠿ࡽࡢᢤ᧻

2016 ᖺᗘ࡟ࡣ⅏ᐖ㆙࿌ࢩࢫࢸ࣒ࠊ࠾ࡼࡧ㛗ᮇⓗ࡞ ᆅᏛⓗほ ࡟౑ࢃࢀࡿ⌧ᅾࡢᾏᗏほ ᢏ⾡ࢆグ㏙ࡋࡓㄽᩥࡀከࡃⓎ⾲ࡉࢀࡓࠋ௨ୗ࡟㍕ࡏࡿᢤ᧻ࡉࢀࡓㄽᩥ࡟ࡣࠊ⤯ᑐᅽຊࢤ࣮ࢪࢆ౑ࡗࡓὠἼ㆙࿌ࢩࢫࢸ࣒ࡸࢫ࣮ࣟࢫࣜࢵࣉ⌧㇟ィ ࠊཬࡧ 3 ㍈ຍ㏿ᗘィࢆ⏝࠸ࡓᆅ㟈ືィ ࡸഴࡁࡢィ ࢆグ㏙ࡋ࡚࠸ࡿࡶࡢ
ࡶ࠶ࡿࠋࣃࣟࢧ࢖࢚ࣥࢸ࢕ࣇ࢕ࢵࢡ࡛ࡣࡇࢀࡲ࡛࡟⤯ᑐᅽຊࢤ࣮ࢪࠊ3 ㍈ຍ㏿ᗘィࠊຍ㏿ᗘィࠊXY ഴᩳィࠊࡑࡋ࡚ࢭࣥࢧ࣮ࢻࣜࣇࢺࢆຠᯝⓗ࡟ྲྀࡾ㝖ࡃ⌧ሙᰯṇ᪉ἲࢆ㛤Ⓨࡋ࡚࠸ࡿࠋࡇࢀࡽࢆ඲࡚ྜࢃࡏࢀࡤࠊࢲ࢖
ࢼ࣑ࢵࢡ࡞ᆅ㟈ື࡜Ỉᅽኚ໬ࡔࡅ࡛࡞ࡃࠊࢭࣥࢧ࣮ࡢࢻࣜࣇࢺࢆྲྀࡾ㝖࠸ࡓ㛗ᮇⓗ࡞ᆅẆኚື㸦῝ᗘኚ໬࡜ഴᩳ㸧ࢆ୍ᗘ࡟ほᐹࡍࡿࢭࣥࢧ࣮ࢩࢫࢸ࣒ࡀᐇ⌧ྍ⬟࡛࠶ࡿࠋㄽᩥ࠿ࡽࡢᢤ᧻㸸
Measuring slow slip offshore
Anne M. Tréhu Science 06 May 2016: http://science.sciencemag.org/content/352/6286/654
This has led to development of alternative techniques for obtaining seafloor geodetic data such as
differencing of swath bathymetric data, seafloor and borehole strain meters, and arrays of absolutepressure gauges (APGs), which measure changes in the mass of overlying ocean, from which seafloor
uplift or subsidence can be determined. Because of their relatively low unit cost, APGs can achieve thespatial coverage needed to understand complex environments, although there are a number ofchallenges to be overcome, including long-term sensor drift and separation of tectonic signals fromoceanographic effects.
Slow slip near the trench at the Hikurangi subduction zone, New Zealand
Laura M. Wallace, Spahr C. Webb, Yoshihiro Ito, Kimihiro Mochizuki, Ryota Hino, Stuart Henrys, Susan
Y. Schwartz, Anne F. Sheehan Science 06 May 2016:
http://science.sciencemag.org/content/352/6286/701
The range of fault slip behaviors near the trench at subduction plate boundaries is critical to know, as thisis where the world’s largest, most damaging tsunamis are generated. Our knowledge of these behaviors
has remained largely incomplete, partially due to the challenging nature of crustal deformation
measurements at offshore plate boundaries. Here we present detailed seafloor deformation observations
made during an offshore slow-slip event (SSE) in September and October 2014, using a network ofabsolute pressure gauges deployed at the Hikurangi subduction margin offshore New Zealand. These
data show the distribution of vertical seafloor deformation during the SSE and reveal direct evidence for
SSEs occurring close to the trench (within 2 kilometers of the seafloor), where very low temperatures andpressures exist.
Seafloor geodetic constraints on interplate coupling of the Nankai Trough megathrust zone
Yusuke Yokota, Tadashi Ishikawa, Shun-ichi Watanabe, Toshiharu Tashiro & Akira Asada Nature (2016)
doi:10.1038/nature17632: http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17632.html

Interplate megathrust earthquakes have inflicted catastrophic damage on human society. Such anearthquake is predicted to occur in the near future along the Nankai Trough off southwestern Japan—an
economically active and densely populated area in which megathrust earthquakes have already occurred.
Megathrust earthquakes are the result of a plate-subduction mechanism and occur at slip-deficit regions(also known as ‘coupling’ regions where friction prevents plates from slipping against each other and theaccumulated energy is eventually released forcefully. Many studies have attempted to capture
distributions of slip-deficit rates (SDRs) in order to predict earthquakes. However, these studies could not
obtain a complete view of the earthquake source region, because they had no seafloor geodetic data.
The Hydrographic and Oceanographic Department of the Japan Coast Guard (JHOD) has beendeveloping a precise and sustainable seafloor geodetic observation network in this subduction zone toobtain information related to offshore SDRs. Here, we present seafloor geodetic observation data and an
offshore interplate SDR-distribution model. Our data suggest that most offshore regions in this subduction
zone have positive SDRs. Specifically, our observations indicate previously unknown regions of highSDR that will be important for tsunami disaster mitigation, and regions of low SDR that are consistent
with distributions of shallow slow earthquakes and subducting seamounts. This is the first direct evidence
that coupling conditions might be related to these seismological and geological phenomena. Our findings
provide information for inferring megathrust earthquake scenarios and interpreting research on the
Nankai Trough subduction zone.
Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida
Gwaii earthquake
Gusman, A. R., A. F. Sheehan, K. Satake, M. Heidarzadeh, I. E. Mulia, and T. Maeda (2016), Geophys.
Res. Lett., 43, doi: 10.1002/2016GL068368.
Recent addition of pressure gauges to ocean bottom seismometers (OBSs) provides alternative data fortsunami forecast simulation. Because OBSs are usually deployed in a dense array, they provide highdensity tsunami observations as well.
Here we use the tsunami waveforms recorded on the Cascadia OBS array to demonstrate two different
approaches for tsunami forecast: (1) estimation of the fault slip distribution of the 2012 Haida Gwaii
earthquake by tsunami waveform inversion and then forecasting the coastal tsunami heights bynumerical forward modeling and (2) progressive assimilation of the tsunami waveforms recorded in thearray, reproduction of wavefields in the vicinity of the array, and then forecasting of wavefields bynumerical modeling.
The tsunami amplitudes recorded by the DPGs are less reliable than those recorded by the DARTs and
APGs. Therefore, only 27 tsunami waveforms at DART and APG stations are used for waveform
inversion.
The accuracy of the tsunami forecast strongly depends on the spatial distribution of the stations. A
denser array would predict the wavefield between the stations both accurately and quickly, which in turnwidens the lead time of an accurate forecast. Although the APG and DPG data during the 2012 Haida
Gwaii tsunami were not transmitted in real time, our retrospective data assimilation demonstrates thecapability of such a dense tsunami array to forecast an incoming tsunami. Real-time tsunami observation
technologies, such as the cabled offshore dense tsunami array of S-net (about 150 stations spaced at
30–50 km intervals) that is being deployed in the Japan subduction zone, would provide data required forreal-time tsunami forecasts using the methods presented in this paper.
The tsunami forecast from the tsunami data assimilation method produces similar results as those fromthe traditional tsunami-forecasting method which starts from a fault model. The tsunami data assimilation
method that we present can be run continuously in real time and does not require a tsunami sourcemodel. The method can be tested further for various configurations of tsunami source and coast to be
implemented for future tsunami warning systems.
Connecting slow earthquakes to huge earthquakes
Kazushige Obara* and Aitaro Kato, Science 15 Jul 2016: Vol. 353, Issue 6296, pp. 253-257
DOI: 10.1126/science.aaf1512, http://science.sciencemag.org/content/353/6296/253 .
Slow earthquakes are characterized by a wide spectrum of fault slip behaviors and seismic radiation
patterns that differ from those of traditional earthquakes. However, slow earthquakes and huge
megathrust earthquakes can have common slip mechanisms and are located in neighboring regions of
the seismogenic zone. The frequent occurrence of slow earthquakes may help to reveal the physics
underlying megathrust events as useful analogs. Slow earthquakes may function as stress meters
because of their high sensitivity to stress changes in the seismogenic zone. Episodic stress transfer tomegathrust source faults leads to an increased probability of triggering huge earthquakes if the adjacent
locked region is critically loaded. Careful and precise monitoring of slow earthquakes may provide new
information on the likelihood of impending huge earthquakes.
If we can retrieve a precursory signal prior to a megathrust earthquake, it would be useful for disastermitigation.
Slow earthquakes provide phenomenological evidence for the existence of a transition zone between
locked and creeping zones proposed by thermal modeling studies and the partial release of slip deficit. In
the past two decades, slow earthquakes have been detected in many subduction zones along the PacificRim. Among the various slow earthquake types observed along the Pacific Rim, ETS behavior in
Cascadia is similar to that seen in Nankai; however, the scale of activity is much larger in Cascadia,
where the ETS zone extends for 1200 km along the strike of the subducting plate and is divided into
several segments.
Recently, seafloor pressure gauge observations revealed that the slip area of the SSE extends close tothe (Hikurangi) trench.
Slow earthquakes with various time scales in and around the rupture area of the Tohoku earthquake had
loaded the mainshock fault. A decade-long SSE took place in the deepest part of the Tohoku mainshock
rupture area and accelerated over time. Near the mainshock rupture initiation point, SSEs were detected
geodetically by seafloor pressure gauges in 2008 and 2011 in the vicinity of the high-slip area of themainshock rupture zone.
Long-term monitoring of slow earthquakes is required so that a reliable picture of these phenomena canbe built over all time scales, and so that physics-based numerical simulations that reproduce theobserved plate boundary faulting behavior can be developed.
Sensing of upslope passages of frontal bores across the trench slope break of the Japan Trench
Fukao, Y., H. Sugioka, A. Ito, H. Shiobara, J. M. Paros, and R. Furue (2016), J. Geophys. Res. Oceans,
121, doi:10.1002/2015JC011432.
The original purpose of our observations is to detect postseismic seafloor disturbances, including
submarine landslides, in the epicentral region of the 2011 Tohoku-Oki. The observations are made with afree-fall/pop-up ocean bottom accelerometer (AOBS) and a free-fall/pop-up broadband ocean bottom
seismometer (BBOBS). The AOBS contains a triaxial accelerometer developed by Quartz Seismic

Sensors, Inc., which uses quartz crystal resonators to convert the analog force inputs to digital outputs.
The accelerometers have ranges of 20 m/s2, sensitivity in ppb, and a good long-term stability. They canmeasure strong earthquakes without clipping and can use the invariance of the Earth’s 1 g gravity vector
as a reference. The triaxial accelerometer senses both the acceleration change due to grounddisplacement and the component change of the 1 g gravity vector due to ground tilt. The former isdominant in the frequency range related to strong seismic waves, and the latter is dominant well belowthat range. The latter is the frequency range of our interest, where the root-mean-square (RMS) noise
floor is below 10–8 g. The three axes of the accelerometer are designed to be mutually orthogonal, andany small internal mechanical misalignments are corrected in the retrieved data using the alignment
matrix table prepared by the factory. Using the alignment matrix allows accurate measurements ofseismic signals and tilts on the decoupled orthogonal axes. The sampling rate is set at 1 Hz. In practice,the measured total acceleration value is not time invariant but decreases quasi exponentially through theobservational period of 10 months. This decrease is due to the long-term drifts of the component sensors,
but it is slow enough to ignore for the analysis of the transient events of our interest. The accelerometercontains an internal quartz crystal temperature sensor to compensate for thermal effects on acceleration.
Even though there is a thermal time lag, the internal temperature is a good measure of external
temperature changes.
Excerpt from: An, C., Cai, C., Zheng, Y., Meng, L., & Liu, P. (2017). Theoretical solution andapplications of ocean bottom pressure induced by seismic seafloor motion. GeophysicalResearch Letters, 44. https://doi.org/10.1002/2017GL075137
Seismic signals captured by ocean bottom pressure sensors, which are designed to record tsunami
waves, are largely ignored. In this paper, we derive a simple theoretical solution of the ocean bottompressure as a function of prescribed seafloor motion. All the assumptions are clearly stated and analyzed.
The solution is checked by comparing the seafloor displacement and pressure from three M7+
earthquakes, recorded by ocean bottom seismometers and pressure gauges located off the Japanese
coast. We then show two applications. First, using the seafloor displacement data recorded by an ocean
bottom seismometer, the pressure amplitude recorded by the associated pressure gauge is corrected,
and vice versa. Second, pressure recordings from Deep Ocean Assessment and Reporting of Tsunamis
during the 2011 Tohoku earthquake are converted to seafloor displacements, which are then utilized toestimate the earthquake focal mechanism. Thus, we demonstrate that seismic signals recorded bypressure sensors have great potential for fast estimate of earthquake source parameters.

Excerpt from: Tolkova, E., Nicolsky, D., Wang, D. A Response Function Approach for Rapid FarField Tsunami Forecasting Pure Appl. Geophys. (2017) 174: 3249. https://doi.org/10.1007/s00024017-1612-0
Tsunami forecasting, in particular in application to ocean-wide seismically generated tsunamis, relies on
numerical modeling for predicting the tsunami wave height and currents before the actual tsunami arrives
at the coasts. Conventionally, the modeling solves an initial value problem with an estimated tsunami
source function. At the same time, deducing the tsunami source presents a major challenge. Existing
instruments do not directly measure the tsunami source—an initial displacement of the sea surface in theearthquake (EQ) area. Instead, the source has to be inferred from related data. The first-available landbased seismic and geodetic observations can provide for rapid warning for nearby coasts within a few
minutes after the EQ, with more elaborated source products to follow (Melgar et al. 2016). The next
available source estimate, produced by a technology termed W phase inversion of centroid moment
tensor, uses measurements of long period (100–1000 s) seismic waves in the far field of the EQ. Since
2009, the Pacific Tsunami Warning Center (PTWC) routinely makes realtime tsunami forecasts taking thefocal mechanism from the W phase inversion and assuming a uniform slip on a rectangular fault (Foster
et al. 2012; Wang et al. 2012). Seismogeodesy with near-source realtime GPS measurements (Crowell et
al. 2012; Melgar et al. 2013; Titov et al. 2016a) can improve characterization of the fault geometry, but its
applicability is limited to EQs occurring in the vicinity of dense GPS arrays. This sequence of early torelatively early available source models is used for estimating expected tsunami heights by coarsely
defined ranges assigned to coastal zones (Melgar et al. 2016 and references therein).
Once a tsunami reaches open-ocean tsunameter stations, sea level measurements offer an attractive
option to bypass the complexity of fault motions, and to reconstruct the tsunami source directly.
Commonly, the source is assembled from pre-defined basis functions. It is assumed that hypotheticaltsunamis radiated by these basis sources linearly superimpose in deep water. Then the basis
sources/tsunamis are combined to achieve the best fit to the observations—a procedure termed source
inversion. The resulting source is expected to further advance the accuracy of the forecast, which can
now aim at predicting location-specific time-histories of wave elevation and currents.

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