A computer-implemented method and a related system for determining seismic hazard related data. The method includes processing geological/seismological data associated with an area of unknown hazard using a simulation model, and determining simulated ground motion intensity data associated with the area of unknown hazard. The simulation model is determined based on geological/seismological data associated with an area of known hazard and ground motion intensity data associated with the area of known hazard. Alternatively, or additionally, the method includes processing simulated ground motion intensity data associated with the area of unknown hazard with another simulation model. Such simulation model is determined at least partly based on: geological/seismological data associated with an area of unknown hazard, geological/seismological data associated with an area of known hazard, ground motion intensity data associated with the area of known hazard, and simulated ground motion intensity data associated with the area of known hazard.

A method detects seismic events, in particular detects foreshocks for earthquake prediction. The events are detected by a plurality of sensors, wherein at least a part of a water pipe network on which the sensors are arranged is used for detection. An ultrasonic water meter to be connected to a water pipe network and an ultrasonic water meter system connected to a water pipe network are provided to detect seismic events.

The present disclosure relates to a gas and earthquake detector, a gas and earthquake remote monitoring system using the same and a method thereof, and more particularly, to a gas and earthquake detector, a gas and earthquake remote monitoring system using the same and a method thereof, which detect a plurality of gases and earthquakes, detect and autonomously warn gas leakage of a plurality of detection target devices sensitive to the gas and the earthquake, and the earthquake around the detection target device, and easily register the gas leakage and the earthquake in a remote management means through a user mobile terminal of a user to monitor the gas leakage of the detection target device and the earthquake at a detection target device location through the remote management means.

From each of a plurality of cameras, a visual input of a location is received over a network. For each visual input from the plurality of cameras, a coupling correction is performed between a shaking of the camera with respect to the visual input by subtracting velocity vectors of the plurality of cameras from velocity vectors of pixels defining the visual input to provide a processed input. It is determined whether a shaking identified in the processed input is above a predetermined threshold based on the processed input, thereby detecting one or more anomalies. From the one or more anomalies, at least one of a location, magnitude, or depth of an earthquake are inferred based on the shaking identified in the processed input of each of the plurality of cameras.

An earthquake estimation method for more promptly estimating an earthquake on the basis of observation data. The earthquake estimation method includes, by a computer: generating an observation image showing a spatial distribution of seismic wave propagation on a basis of an observation result of seismic waves at a plurality of observation points on a ground; and estimating a parameter of an earthquake with respect to the observation image by using an earthquake estimation model in which a parameter of an earthquake including at least a position of a hypocenter and a magnitude is associated with a simulated observation image showing a spatial distribution of seismic wave propagation on a ground obtained from a result of a numerical simulation of the earthquake, performed with the parameter.

A computer system manages model information for defining a U-Net configured to execute, on the input time-series data, an encoding operation for extracting a feature map relating to the target wave by using downsampling blocks and a decoding operation for outputting data for predicting the first motion time of the target wave by using upsampling blocks, executes the encoding operation and the decoding operation on the input time-series data by using the model information. The downsampling blocks and the upsampling blocks each includes a residual block. The residual block includes a time attention block calculates a time attention for emphasizing a specific time domain in the feature map. The time attention block includes an arithmetic operation for calculating attentions different in time width, and calculates a feature map to which the time attention is added by using the attentions.

The present disclosure provides an earthquake evaluation method based on multi-type geophysical data. The method includes: acquiring natural seismic data about intraplate earthquakes that had occurred, surface elevation data, Bouguer gravity anomaly data, aeromagnetic anomaly data, seismic wave tomography, crustal GPS movement rate and focal mechanism solution in a target region; constructing planar grids of the target region, and respectively performing plane gridding processing on the natural seismic data about the intraplate earthquakes that had occurred, the surface elevation data, the Bouguer gravity anomaly data, and the aeromagnetic anomaly data based on the constructed planar grids of the target region; determining a plane distribution map of a meizoseismal area in the target region based on the data subjected to the plane gridding processing; and superimposing a surface elevation map and the plane distribution map of the meizoseismal area in the target region with the seismic wave tomography, and analyzing the crustal movement on both sides of the meizoseismal area and the matching relationship between the meizoseismal area and the crustal geological structure in combination with the crustal GPS movement rate and the focal mechanism solution so as to determine the geologic origin of a pleistoseismic zone.

The present application discloses an artificial intelligence calculation method and apparatus for monitoring an earthquake in real time based on edge cloud cooperation, and a storage medium. Wherein, the method is applied to a micro-earthquake data processing system. The micro-earthquake data processing system comprises an edge calculation device and a remote server that is in communication connection with the edge calculation device, wherein the remote server deploys a micro-earthquake data analyzing model based on an artificial intelligence to the edge calculation device in advance. Moreover, the method comprises: receiving, by the remote server, effective event data related to the micro-earthquake from the edge calculation device, wherein the effective event data is obtained, by the edge calculation device, by means of calculating and analyzing the micro-earthquake data collected by the edge calculation device by using the micro-earthquake data analyzing model; performing a transfer training to the micro-earthquake data analyzing model by the remote server according to the effective event data; and updating the model after the micro-earthquake data analyzing model that has been transfer-trained is transmitted to the edge calculation device by the remote server.

A network of motion sensors employs sensitive accelerometers to issue time-domain measurements of building movement from multiple locations within and between buildings and other structures. The time-domain measurements from the various motion sensors are synchronized and converted into frequency-domain measurements of building movement. Individual motion sensors can be equipped with the requisite processor and memory to synchronize and covert the time-domain measurements. The motions sensors can classify detected events into various event types, such as earthquakes, wind events, or bipedal locomotion. The sensors can also communicate with one another or other resources to calculate event probabilities. A motion sensor may, for example, receive an earthquake-verification signal responsive to an earthquake-verification request. The network of motion sensors can calculate local soil stiffness and financial loss estimations responsive to their individual or collective frequency-domain measurements.

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.