A device and method for receiving real-time warning messages about a hazardous situation, for which an alert or a mitigating control action may be warranted, with the means to measure the effects forecasted by the parameters contained in the warning messages, adjust the parameters to more accurately reflect local conditions, and provide feedback about the performance and accuracy of the system sending the warning messages.

The present invention relates to an apparatus and method for detecting an earthquake using an accelerometer. More particularly, the present invention relates to an apparatus and method for detecting an earthquake using an accelerometer, the apparatus and method being capable of improving reliability of acceleration data obtained from the accelerometer and reliably determining whether an earthquake has occurred on the basis of a change between current acceleration data and previous acceleration data.

A wind turbine system and to a method for operating said system is disclosed. The system further comprises a detection device configured for detecting body waves generated by an earthquake. In one aspect, the present disclosure is directed to a system comprising a wind turbine, in particular to an onshore erected wind turbine, a wind turbine controller for controlling the wind turbine, and at least one detection device, which is connected to the wind turbine controller for transmitting signals. The wind turbine includes at least a rotor having at least one rotor blade, wherein the rotor is rotatably mounted to rotation support means of the wind turbine, and a tower having a top end for supporting the rotation support means and a support end. The detection device is configured to detect and measure earthquake generated primary waves (P-waves). The detection device may include at least one sensor or a plurality of sensors, wherein the sender is configured to detect and/or measure earthquake generated P-waves. Such sensor may be further configured to detect an acceleration caused by the earthquake using a built-in accelerometer and then to calculate and output a synthetic acceleration, and to provide an estimated Japan Meteorological Agency seismic intensity scale (shindo scale) value.

Systems and methods provide for the reduction of motion at a ground layer of a site due to a seismic event. A predicted seismic motion is obtained for a geological layer at depth and properties of the layers between the ground layer and layer at depth are determined. A model is created using the layers and the predicted seismic motion. Iteratively for each layer, one or more properties of the layer is changed in the model and a site amplification is performed to determine a change or changes that result in an acceptable response at the ground layer. Subsequently, the in situ layer may be modified according to the modified property or properties to reduce motion at the site due to a seismic event.

In an array-type underwater apparatus for monitoring deformation of a reservoir landslide, an anchor is buried at an underwater monitoring point in a landslide mass, and a floating shell is configured to float on a water surface. A GPS sensor is configured to transmit and receive a GPS signal to obtain a real-time position of the floating shell, a water temperature sensor is used to obtain a water temperature-time relationship, and a gravity wave gauge is used to obtain a wave height-time relationship. An upper end of a pull cord is securely connected to the floating shell via a displacement compensation mechanism, and a lower end of the pull cord is securely connected to the anchor. The displacement compensation mechanism compensates for a displacement after the floating shell floats with a wave. An encoder-type displacement meter measures a real-time distance between the encoder-type displacement meter and the anchor.

A timing alignment method for data acquired by monitoring units of a borehole-surface micro-seismic monitoring system includes acquiring two rock-burst waveform data segments with GPS timestamps; calculating a time difference and a number of sampling points between each pair of adjacent GPS timestamps; adding, on an equal-interval basis, a sampling time to a sampling point missing a timestamp between each pair of adjacent GPS timestamps; calculating average sampling frequencies of the two rock-burst waveform data segments, adding, on an equal-interval basis, a sampling time to a sampling point missing a timestamp except first and last GPS timestamps in each of the two data segments; obtaining sampling times of all sampling points, resampling the sampling times according to a uniform sampling frequency; calculating a rock-burst waveform data segment at a new sampling time with a linear interpolation formula, and aligning the sampling times of the two rock-burst waveform data segments.

A method, system, and computer program product for simulating potential consequences and possible solutions due to a release of stored energy using augmented reality. The method may include aggregating IoT feeds from devices in a surrounding area. The method may also include calculating amounts of stored energy in the surrounding area based on the IoT feeds. The method may also include predicting contextual situations that could result due to a release of the stored energy in the surrounding area. The method may also include determining one or more consequences for each of the contextual situations. The method may also include calculating a degree of severity of the one or more consequences for each contextual situation. The method may also include determining one or more proposed solutions based on the degree of severity. The method may also include transmitting a recommendation of at least one proposed solution for implementation.

The present invention relates to a method and system of warning of estimated time of arrival and expected intensity in a given area resulting from a seismic movement, which comprises a plurality of measurement elements or monitoring stations configuring a network of measurement elements, said method comprising the steps of: arranging the plurality of measurement elements in a specific area; communicating each of the measuring elements with at least one common point or control center; storing in each measuring element an identifier that will uniquely identify the same within the network of monitoring stations; transforming by means of the measuring element the measurement of the movement to a scalar or set of scalars representing the intensity of the movement; transmitting periodically and in real time the measurement and the unique identifier thereof to the control center for the duration of the movement; recording through the control center the individualized measurements from each of the monitoring stations; verifying through the control center if the received measurement corresponds to an actual earthquake or a mechanical noise; designating a destination point; determining the expected intensity and expected arrival time; automatically dispatching an earthquake early warning to the destination point.

Disclosed herein are earthquake detection devices capable of initiating a safety response in the event of an earthquake. The earthquake detection devices comprise a plurality of three-component accelerometers for measuring acceleration in three directions; and a processing unit for: receiving acceleration measurements from each of the plurality of accelerometers, determining if the acceleration measurements meet or exceed a predetermined threshold value and sending a signal to one or more transducers.

A liquefaction evaluation model generation device includes a data acquisition unit configured to acquire training data in which learning vibration data indicating a physical quantity associated with vibrations observed in the ground is defined as a question and learning liquefaction data indicating the degree of liquefaction occurring in the ground where the vibrations associated with the learning vibration data have been observed is defined as an answer, and a machine learning execution unit configured to execute machine learning using the training data and generate a liquefaction evaluation model that is a machine learning model. A liquefaction evaluation device includes a data acquisition unit configured to acquire inference vibration data indicating a physical quantity associated with vibrations observed in the ground and an inference execution unit configured to input the inference vibration data to the above-described machine learning model and cause the machine learning model to output inference liquefaction data indicating the degree of liquefaction occurring in the ground where the vibrations associated with the inference vibration data have been observed.