# US 11,906,678 B2 · Seismic observation device, seismic observation method, and recording medium on which seismic observation program is recorded

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- Number: US 11,906,678 B2
- Document ID: US11906678B2
- Status: Granted
- Kind: B2
- Application number: 17/631,195
- Filed: 2020-07-28
- Published: 2022-09-01
- Granted: 2024-02-20
- Earliest claimed: 2019-08-20
- Assignees: NEC Corporation; Japan Agency for Marine-Earth Science and Technology
- Applicants: NEC Corporation; Japan Agency for Marine-Earth Science and Technology
- Inventors: Naoki Kuwamori; Narumi Takahashi; Masaru Nakano; Kentaro Sueki; Shuichiro Yada; Yoko Kuwamori
- Web page: https://patentgrep.com/patent/US11906678B2

## CPC

- G01V 1/28: Processing seismic data, e.g. for interpretation or for event detection (G01V1/48 takes precedence)
- G01V 1/003: {Seismic data acquisition in general, e.g. survey design (G01V1/3808, G01V1/42 take precedence)}
- G01V 1/01: Measuring or predicting earthquakes
- G01V 1/137: which fluid escapes from the generator in a pulsating manner, e.g. for generating bursts {, airguns}
- G01V 2210/1232: Earthquakes
- H04W 4/38: for collecting sensor information
- H04W 4/90: Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
- G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

A seismic observation device includes an input unit receiving input of time-series data of measurement values of a vibration, a processing target determination unit determining a time period of the time-series data that is a processing target, and a type determination unit acquiring a likelihood of classifying a cause of the vibration indicated in the time-series data in the time period into each of types of cause.

## Claims

### Claim 1

A seismic observation device comprising:
a memory storing instructions; and
one or more processors connected to the memory and configured to execute the instructions to:
receive input of time-series data of measurement values of a vibration;
determine a time period of the time-series data that is a processing target; and
acquire a likelihood of classifying a cause of the vibration indicated in the time-series data in the time period, wherein the cause is classified as at least one of a near-field earthquake, a far-field earthquake, a low frequency earthquake, an artificial earthquake, a micro-earthquake, and a volcanic earthquake.

### Claim 2 (depends on 1)

The seismic observation device according to claim 1,
wherein the one or more processors are configured to further execute the instructions to acquire the likelihood for each type of cause by applying the time-series data in the time period to a trained model provided for each type of cause.

### Claim 3 (depends on 1)

The seismic observation device according to claim 1,
wherein the one or more processors are configured to further execute the instructions to acquire likelihoods of causes of vibrations other than a natural earthquake in addition to a likelihood of a cause of vibration classified as a natural earthquake.

### Claim 4 (depends on 1)

The seismic observation device according to claim 1,
wherein the one or more processors are configured to further execute the instructions to:
receive the input of the time-series data including time-series data of water pressure, and calculate the likelihood on the basis of the time-series data of the water pressure.

### Claim 5 (depends on 1)

The seismic observation device according to claim 1, wherein the one or more processors are configured to further execute the instructions to exclude noise data from a processing target from the time-series data in the time period on the basis of weather data.

### Claim 6

A seismic observation method performed by at least one processor of a seismic observation device, the method comprising:
receiving input of time-series data of measurement values of a vibration from one or more sensors;
retrieving a trained model that is trained according to machine learning;
analyzing, by the trained model, the time-series data to determine a time period of the time-series data that is a processing; and
detecting, during the time period, a trigger indicating a vibration different from a normal vibration; and
acquiring, based upon detection of the trigger, a likelihood of classifying a cause of the vibration indicated in the time-series data in the time period, wherein the cause is classified as at least one of a near-field earthquake, a far-field earthquake, a low frequency earthquake, an artificial earthquake, a micro-earthquake, and a volcanic earthquake.

### Claim 7

A non-transitory computer readable-medium having instructions stored therein, which when executed by a processor in a seismic observation device cause the processor to:
receive input of time-series data of measurement values of a vibration from one or more sensors;
retrieve a trained model that is trained according to machine learning;
analyze, by the trained model, the time-series data to determine a time period of the time-series data that is a processing target;
detect, during the time period, a trigger indicating a vibration different from a normal vibration; and
acquire, based upon detection of the trigger, a likelihood of classifying a cause of the vibration indicated in the time-series data in the time period, wherein the cause is classified as at least one of a near-field earthquake, a far-field earthquake, a low frequency earthquake, an artificial earthquake, a micro-earthquake, and a volcanic earthquake.