A system comprising: a magnetic transmitter configured to generate magnetic fields; a magnetic sensor configured to generate signals based on characteristics of the magnetic fields; and one or more computer systems configured to: cause the magnetic transmitter to generate a first plurality of magnetic fields at a first frequency; receive a first plurality of signals from the magnetic sensor; determine data indicative of a position and orientation of the magnetic sensor at a first position of the magnetic sensor; determine a distortion term that corresponds to a first position of the magnetic sensor; cause the magnetic transmitter to generate a third plurality of magnetic fields at the first frequency; receive a third plurality of signals from the magnetic sensor; and determine a second position and orientation of the magnetic sensor relative to the magnetic transmitter, wherein the first frequency is greater than the second frequency.

Disclosed is a magnetotelluric inversion method based on a fully convolutional neural network. The magnetotelluric inversion method includes: constructing a multi-dimensional geoelectric model; constructing a fully convolutional neural network structure model to obtain initialized fully convolutional neural network model parameters; training and testing the fully convolutional neural network structure model based on the training sets and the test sets to obtain optimized fully convolutional neural network structure model parameters; determining whether training of the fully convolutional neural network structure model is completed according to fitting error changes corresponding to the training sets and the test sets; and finally, inputting measured apparent resistivity into a trained fully convolutional neural network structure model for inversion, and further optimizing the fully convolutional neural network structure model by analyzing precision of an inversion result until an inversion fitting error satisfies a set error requirement.

A near magnetic field variation detection method comprises following steps of: measuring magnetic field by a first magnetic field sensor and a second magnetic field sensor respectively; and calculating a magnetic field measurement difference, wherein the magnetic field measurement difference is (1) a magnitude of a difference of a first-magnetic-field-measurement measured by the first magnetic field sensor and a second-magnetic-field-measurement measured by the second magnetic field sensor, or (2) a magnitude of a difference of a first-magnetic-field-measurement-component measured by the first magnetic field sensor along a characteristic direction and a second-magnetic-field-measurement-component measured by the second magnetic field sensor along the characteristic direction; wherein a near magnetic field variation is occurred when (a) the magnetic field measurement difference is continuously greater than a characteristic-threshold within a characteristic-time-period, or (b) an average value of the magnetic field measurement difference is greater than a characteristic-average-threshold within a characteristic-average-time-period.

A metal detector that uses a verity of sensors as input for a neural network to optimize discrimination and detection of known or unknown objects. The detector will also have employ an user defined frequencies that will help to create custom settings. The detector will be able to use the sweeping motion of the detector head to create many frames of reference for understanding the composition, depth, size, type, approximant length of time it had remained, and to some extent orientation of known/unknown objects. This will be fed back to a user who can help identify the object once it is found. If it is different or new, the user can enter what it was into a database that can be used to train signals for future use, such as through using the data for training and/or updating machine learning models which can be used to accurately identify object.

A security portal includes magnetic field sensors in a sensor assessment network (SAN) for tracking a magnetic dipole target. Each sensor includes a sensor transducer, a sensor coil, and a digitally controlled potentiometer. A sensor controller applies a stepped voltage, samples an output frequency at each stepped voltage value, generates a magnetic sensor response curve, and converts a non-linear response of the sensor transducer to a magnetic field value for each x, y and z channel as a function of frequency for a specific potentiometer setting based upon the sensed magnetic dipole that is tracked in the security portal. A SAN controller receives the magnetic field values from each channel and determines the magnetic field vectors of the target over each sample.

A method for scanning artificial structure, wherein a scanning artificial structure apparatus comprises four magnetic-field sensors, the four magnetic-field sensors are non-coplanar configured, the method comprises following steps of: moving the scanning artificial structure apparatus along a scanning path within a to-be-tested area, in the meantime, measuring magnetic field by the four magnetic-field sensors, and recording a position sequence when measuring magnetic field, wherein four magnetic-field measurement sequences are measured by the four magnetic-field sensors; and calculating a magnetic-field variation distribution from the four magnetic-field measurement sequences and the position sequence, wherein the magnetic-field variation distribution is corresponding to at least one artificial structure distribution.

Disclosed are methods and apparatus for identifying non-metallic subterranean structures using amorphous metal markers associated with the structures. Some examples will include the amorphous metal in the form of one or more sections of an amorphous metal foil within a protective enclosure sufficient to physically isolate the amorphous metal foil from the surrounding Earth. The amorphous metal foil and enclosure may be in the form of a tape which either will be secured to, or placed proximate the subterranean structure, which may be, for example, a pipe or conduit, or other non-metallic structure.

The present invention, thanks to the horizontal positional tracking unit (20)—mounted to a hand-held metal detector (10)—consisting of optical flow sensor lens (22), an optical flow sensor camera (21), an optical flow sensor processor (23), a height sensor (24) and an IMU sensor (25); allows the calculation of the depth of the target (60) by tracking the horizontal position while the user freely sweeps the search head (11) of the metal detector (10) with the “optical flow” method and using the metal detection signals received from many point positions around the detected target center with this position; so it relates to a method of measuring a target depth and a metal detector using this method, which allow calculation to be made independently of the type and practical the size of the metal.

A method for determining an occupancy state of a parking space of a parking facility, including the following method steps: a. detecting magnetic field measured values in the surroundings of the parking space along an axis with the aid of a magnetic field sensor within a predefined duration; b. forming a Poincaré diagram as a function of the magnetic field measured values detected in method step a; c. determining a line of best fit through the points of the Poincaré diagram formed in method step b, with the aid of linear regression; d. determining differences in the vertical direction between the line of best fit and the respective points; e. calculating a mean value of the differences determined in method step d; and f. determining the occupancy state of the parking space as a function of the mean value calculated in method step e.

The present invention discloses a method and system for processing gravity and magnetic data in geological resource exploration. The method includes: acquiring first (i) potential field data and (ii) gradient data of an observation surface, performing upward continuation of the acquired data using a wave-number domain conversion method to obtain second and third gradient data and second potential field data, and determining third potential field data using a fourth-order explicit scheme Milne method according to the first, second, and third gradient data, and the second potential field data; calculating fourth gradient data using an ISVD method according to the third potential field data; and correcting the third potential field data using a fourth-order implicit scheme Simpson method according to the fourth gradient data, the first potential field data, and the first and second gradient data to obtain corrected third potential field data.