A system is provided. The system includes a plurality of seismic sensors and a computer device. The computer device is programmed to a) store a plurality of distances between each of the plurality of seismic sensors; b) store one or more fingerprints of a signal to be detected; c) receive a first signal transmitted from a first seismic sensor of the plurality of seismic sensors; d) receive the first signal transmitted from a second seismic sensor of the plurality of seismic sensors; e) compare the first signal to the one or more fingerprints of the signal to be detected; and f) determine a direction of travel of the first signal based on the distance between the first seismic sensor and the second seismic sensor, the first time, and the second time.

A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.

In order to monitor the subsoil of the earth under a target zone, seismic waves coming from an identified mobile noise source are recorded by means of at least one pair of sensors disposed on either side of the target zone, time periods are selected corresponding to the alignments of the pairs of sensors with the noise source, a seismogram of the target zone is reconstructed by interferometry based on the recorded seismic waves and on the selected time periods and an image of the subsoil of the target zone is generated using the seismogram.

Systems and methods for performing focused blind deconvolution of signals received by a plurality of sensors are disclosed. In some embodiments, this may include determining a cross-correlation of first and second signals, obtaining a cross-correlation of a first response function and a second response function based on the cross-correlation of the first and second signals and subject to a first constraint that the first and second response functions are maximally white, and obtaining the first and second response functions based on the cross-correlation of the first and second response functions and subject to a second constraint that the first and second response functions are maximally front-loaded.

The method comprises providing at least one seismic source in a seismic source area and providing a plurality of seismic receivers in said seismic source area, said method comprising measuring a first type of ground vibrations induced in a subsurface of the area of interest by the at least one seismic source with the plurality of seismic receivers. The method further comprises measuring with the plurality of seismic receivers at least one second type of ground vibrations induced by a mechanical source different from the or from each seismic source and analyzing the second type of ground vibrations to determine at least one information among: a physical parameter of the subsurface and/or, a presence of human and/or an animal and/or a vehicle.

Systems and methods for performing focused blind deconvolution of signals received by a plurality of sensors are disclosed. In some embodiments, this may include determining a cross-correlation of first and second signals, obtaining a cross-correlation of a first response function and a second response function based on the cross-correlation of the first and second signals and subject to a first constraint that the first and second response functions are maximally white, and obtaining the first and second response functions based on the cross-correlation of the first and second response functions and subject to a second constraint that the first and second response functions are maximally front-loaded.

A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.

A vibration monitoring system at a work site is provided. The vibration monitoring system includes a vibration detection sensor associated with a structure provided at the work site. The vibration detection sensor is configured to generate a signal indicative of vibrations in an area proximate to the structure. The system includes a stationary alert assembly provided at the work site. The stationary alert assembly is proximate to the structure and the stationary alert assembly is positioned such that an alert provided by the stationary alert assembly is perceivable by users operating at the work site. The system also includes a controller coupled to the vibration detection sensor and the stationary alert assembly. The controller is configured to receive the signal indicative of the vibrations and compare the signal with a predetermined threshold. The controller is configured to provide the alert to the users through the stationary alert assembly based on the comparison.

The method comprises providing at least one seismic source in a seismic source area and providing a plurality of seismic receivers in said seismic source area, said method comprising measuring a first type of ground vibrations induced in a subsurface of the area of interest by the at least one seismic source with the plurality of seismic receivers. The method further comprises measuring with the plurality of seismic receivers at least one second type of ground vibrations induced by a mechanical source different from the or from each seismic source and analyzing the second type of ground vibrations to determine at least one information among: a physical parameter of the subsurface and/or, a presence of human and/or an animal and/or a vehicle.

A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.