A method and system for analyzing a seismically active field based on expansion of an empirical orthogonal function is provided. The research region of the seismic active field is gridded at equal intervals for the preset research region of a seismic active field; a seismic active field function matrix correlated with the research region of the seismic active field spatially and temporally is constructed according to the gridding of the research region of the seismic active field; and the seismic active field function matrix is expanded with an empirical orthogonal function to obtain a main typical field and a temporal factor thereof, and an anomaly on the temporal factor of the seismic active field is analyzed with a method index, a parameter index and an anomaly index.

Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.

A performance-level seismic motion hazard analysis method includes: (1) extracting seismic motion data and denoising the data; (2) extracting feature parameters from the data, and carrying out initialization; (3) generating a training set, an interval set and a test set; (4) training a multi-layer neural network based on the training set; (5) training output values of the neural network based on the interval set, and calculating a mean and a standard deviation of relative errors of the output values; (6) training the neural network based on the test set to determine output values, and calculating a magnitude interval based on an interval confidence; (7) carrying out probabilistic seismic hazard analysis to determine an annual exceeding probability and a return period of a performance-level seismic motion; and (8) determining a magnitude and an epicentral distance that reach the performance-level seismic motion based on the performance-level seismic motion and consistent probability.

Systems and methods are provided for generating a model for detection of seismic events. In this regard, one or more processors may receive from one or more stations located along an underwater optical route, one or more time series of polarization states of a detected light signal during a time period. The one or more processors may transform the one or more time series of polarization states into one or more spectrums in a frequency domain. Seismic activity data for the time period may be received by the one or more processors, where the seismic activity data include one or more seismic events detected in a region at least partially overlapping the underwater optical route. The one or more processors then generate a model for detecting seismic events based on the one or more spectrums and the seismic activity data.

A stimulation includes an injection of a volume of fluid into a formation. A method includes obtaining a mechanical earth model of the formation, modeling a hydraulic fracture growth pattern in the formation from a stimulation of the formation, determining an increase in pressure in the formation resulting from the stimulation, and predicting whether a seismic event will occur in the formation based on the increase in pressure.

In certain embodiments, an assembly has intermediate pods magnetically, but not galvanically, coupled along an electrically conductive cable. Each pod has a magnetic core surrounding and inductively coupled to the cable and one or more coils inductively coupled to the magnetic core. The pod transmits, for example, outgoing current pulses on the cable by inducing electrical signals in the cable using a transmitter coil and the magnetic core. In addition, the pod repeats, for example, incoming current pulses on the cable by inducing electrical signals in the cable using the transmitter coil and the magnetic core, based on electrical signals induced in a receiver coil via the magnetic core by the incoming current pulses. Such an assembly can function as a data collection system for scientific research and/or as an early-warning system for events, such as earthquakes and tsunamis, without having to modify the cable itself.

Disclosed is a sensor for receiving power from the outside and measuring data on a current state. A smart safety management sensor for measuring safety-related data of a structure includes a detection module installed in a structure and configured to detect a state of the structure at a preset interval, a control module operatively associated with the detection module and configured to calculate a result value based on data received by the detection module, and an output module operatively associated with the control module and configured to receive a result value calculated by the control module and to provide information to a supervisor.

A system for collecting and managing parametric data via an external communications network comprises one or more parametric stations operatively connected via the external network to a certification server and a payout server. Each parametric station is configured to receive parametric data from a remote source, determine that the parametric data satisfies a predetermined condition, and transmit the parametric data over the external network to the certification server in response to the parametric data satisfying the predetermined condition. The certification server is configured to generate a certification report based on the parametric data and a data model related to the remote source and transmit the generated certification report to the payout server. The payout server is configured to determine that terms of an associated contract are satisfied based on the certification report, and trigger a payout based on the terms that are satisfied based on the certification report.

An inundation depth prediction device includes: a flow speed value acquiring unit that acquires a flow speed value on the sea surface; and an inundation depth predicting unit that predicts an inundation depth on the ground by inputting the flow speed value acquired by the flow speed value acquiring unit to a learned inundation depth prediction model used for predicting the inundation depth on the ground from the flow speed value on the sea surface.

A seismic sensor 10 comprises an acceleration acquisition unit 21, an acceleration waveform generation unit 22, a frequency sensing unit 24, and an earthquake determination unit 25. The acceleration acquisition unit 21 detects vibration and measures the acceleration of the vibration. The acceleration waveform generation unit 22 generates an acceleration waveform that indicates the relation between the elapsed time and the acceleration measured by the acceleration acquisition unit 21. The frequency sensing unit 24 senses the frequency of the acceleration waveform generated by the acceleration waveform generation unit 22. The earthquake determination unit 25 determines whether or not there is an earthquake by classifying acceleration waveforms into those in which the frequency sensed by the frequency sensing unit 24 is above a specific threshold and those in which the frequency is below the specific threshold, and excluding any acceleration waveforms in which the frequency is above the specific threshold.