A building integrity assessment system includes: an earthquake detector including: a building bottom sensor at a bottom of a building and that detects acceleration and an earthquake early-warning receiver that receives an earthquake early warning; a cloud computer; and sensors disposed at a plurality of positions in the building and that measures an influence of an earthquake on the building at each of the positions and wirelessly transmits measurement results to the cloud computer. The cloud computer estimates and evaluates the integrity of the building based on the measurement results. In response to the building bottom sensor detecting preliminary tremors or the earthquake early-warning receiver receiving the earthquake early warning, the plurality of sensors measures the influence of the earthquake on the building from a time before a major motion arrives at the building to a time after the arrival.

A processor 204 of a sensor device performs measurement processing by one or a plurality of sensors 203 and transmission processing of sensor data generated by the measurement processing. The sensor device includes a processing routine table 211 that stores a processing routine configured to include, corresponding to an identifier for identifying processing performed by a processor, a type of the processing, an execution trigger of the processing, and trigger information that prescribes a trigger for transmitting the sensor data. The processor controls processing in a processing routine of the processing routine table, based on trigger information, so that the sensor data subjected to measurement processing is immediately transmitted, or temporarily stored in a buffer 212 and transmitted after a predetermined time.

A seismic warning system comprises: a plurality of sensors, each sensor sensitive to a physical phenomenon associated with seismic events and operative to output an electronic signal representative of the sensed physical phenomenon; a data acquisition unit communicatively coupled to receive the electronic signal from each of the plurality of sensors, the data acquisition unit comprising a processor configured to estimate characteristics of a seismic event based on the electronic signal associated with a P-wave from each of the plurality of sensors; and a local device communicatively coupled to the data acquisition unit. The plurality of sensors, the data acquisition unit and the local device are local to one another.

According to one embodiment, there is provided a method of correcting recorded seismic data where each receiver clock is potentially inaccurate. Since the seismic wave field is not random, and contains coherent events that are recorded by all receivers in a local area, it is possible to estimate the differences in the time reference by comparing the recordings of different receivers in a local area. With no external time reference, time signal, or pilot trace, an entire seismic data itself can be used to determine how each receiver's clock is drifting from true time.

A seismic monitoring system includes a plurality of seismic monitors and a processing device operatively coupled to the plurality of seismic monitors. The processing device receives recordings of waveforms of motion detected at the plurality of seismic detectors in a geographic area. The processing device applies the respective recordings to corresponding positions of the seismic detectors in a three-dimensional geological model that describes its elastic attributes and tests a plurality of moment tensors at a plurality of locations. Based on the testing, the processing device determines a globally convergent source location and moment tensor in the three-dimensional model based on the testing.

The present invention discloses a submarine seismic monitoring apparatus and system based on the submarine Internet of things. A sea surface buoy network device and a submarine network device in the monitoring apparatus are connected by using an anchor system; the submarine network device and a submarine seismic detection device are connected by using a submarine photoelectric composite cable; there are one or more submarine seismic detection devices; the sea surface buoy network device includes a satellite transceiver apparatus, an Internet of things platform server, a network time server, and an autonomous energy supply apparatus; the submarine network device includes a photoelectric separation cabin, a submarine server, a bottom anchor weight block, and a mechanical releaser; and the submarine seismic detection device includes multiple submarine seismometer network nodes, where the multiple submarine seismometer network nodes are successively connected in series end to end by using the submarine photoelectric composite cable. The apparatus and system in the present invention not only can be used for submarine structure detection, but also can be used for earthquake disaster and tsunami warning, and can implement autonomous energy supply, long timing, and unattended operation.

A seismic sensor that suppresses power consumption operates in a power-saving mode and a measurement mode in which the power consumption is larger than that in the power-saving mode. The seismic sensor includes a measurement unit that measures an acceleration, a filtering unit that, if the acceleration measured by the measurement unit exceeds a predetermined threshold, causes a shift from the power-saving mode to the measurement mode to be performed, and performs filtering on the measured acceleration, an earthquake determination unit that determines whether or not an earthquake has occurred based on the filtered acceleration, and an index calculation unit that, if where the earthquake determination unit determined that an earthquake has occurred, calculates an index value indicating the scale of the earthquake. A shift from the measurement mode to the power-saving mode is performed if the earthquake determination unit determined that no earthquake has occurred.

Methods, systems, and computer programs are presented for predicting the scale and scope of damage after an earthquake. One method includes an operation for identifying a plurality of features, each feature being correlated to an indication of structural damage caused to a structure by an earthquake. The method further includes performing machine learning, using one or more hardware processors, to analyze destruction caused by one or more earthquakes to obtain a damage-estimation algorithm. The machine learning is based on the identified plurality of features. Further, the method includes operations for accessing shaking data for a new earthquake, and for estimating, using the one or more hardware processors, earthquake damage at a block level for a geographical region utilizing the damage-estimation algorithm and the shaking data. Further, the earthquake damage at the block level is presented, on a display screen, in a map of at least part of the geographical region.

A computer calculates a change amount of a total number of electrons from an observation start time in the ionosphere between an observation station and a satellite based on observation data of a signal received from the satellite by the observation station on the ground. The computer estimates the change amount of the total number of electrons to be calculated next based on the time change of the change amount of the total number of electrons from the observation start time in the ionosphere and calculates a difference (estimation error) between the estimated change amount of the total number of electrons and the actually calculated change amount of the total number of electrons. The computer calculates a correlation value between the estimation error calculated for each observation station and the estimation error calculated for a predetermined number of the observation stations in the vicinity of each observation station. In a case where the correlation value calculated for each observation station is a predetermined threshold value or more, when the correlation value is also the predetermined threshold value or more for the predetermined number of observation stations in the vicinity of the observation station, the computer determines that an abnormality has occurred in the ionosphere between the observation station and the satellite.

A model learning unit of an anomaly detection device learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration sensor placed on a monitoring target. The anomaly detection unit learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration sensor. Then, the anomaly detection unit determines whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period.