This invention relates to devices and processes for geophysical prospecting, subsurface fluid monitoring and, more particular, to the use of interferometric techniques using Control Source Electromagnetic (CSEM) and Magnetoturelic (MT) signals to create images of sub-surface structures and fluids.

Disclosed are methods and systems for producing bipole source modeling with reduced computational loads. A method may comprise receiving first electromagnetic data and second electromagnetic data from a first shotpoint and a second of a marine electromagnetic survey, modelling a first electromagnetic field and second electromagnetic field for one or more dipole sources of a bipole source and combining a plurality of data points to provide an approximation of an electromagnetic field for the bipole source. A system may comprise electromagnetic sensors, a bipole source, wherein the bipole source comprise a pair of electrodes that are separated by a distance, wherein the bipole source is configured to generate an electromagnetic field, and a data processing system configured to receive electromagnetic data from a plurality of shotpoints of the bipole source and model electromagnetic fields for one or more dipole sources of the bipole source from the electromagnetic data.

Methods and systems for determining and removing swell noise from electric field data collected from streamers towed at different are disclosed. In one aspect, a first set of streamers called upper streamers is towed at a shallow depth, and the second set of streamers called lower streamers is towed below the upper streamers. Receivers located along the streamers measure surrounding electric fields and produce electric field signals. A proportionality parameter is calculated as a function of the electric field signals generated by vertically aligned receivers. The proportionality parameter can be used to calculate an approximate swell noise that is used to remove swell noise from electric field data measured by the receivers.

Determining parameters associated with a hydrocarbon bearing formation beneath a sea bed. At least some of the illustrative embodiments are methods including: obtaining data gathered regarding a plurality of distinct readings by sensors, the readings responsive to a source of electrical energy towed in water above the hydrocarbon bearing formation, the sensors sense an electrical parameter associated with the source; combining a first datum associated with a first path of travel of the source with a second datum associated with a second path of travel of the source, the second path of travel distinct from the first path of travel, and the combining creates a first combined datum; and determining the parameter associated with the hydrocarbon bearing formation by evaluating the first combined datum.

A resistivity profile can be generated directly from measured electromagnetic field data from a marine survey. A series of transformations can be applied to remove a conductivity dependency from a boundary value problem such that an inversion method may no longer be required to generate the resistivity profile.

Provided is a method for positioning and predicting concealed orebody based on parallel double-tunnel transient electromagnetic exploration, and belongs to the applied geophysics exploration technology. The method can locate and predict concealed orebodies with low resistivity around the tunnel in a full spatial domain based on a parallel double-tunnel transient electromagnetic method. The implementation of the method mainly includes measuring an electrical parameter, observing a tunnel transient electromagnet, calculating an apparent resistivity, determining an upper limit of an apparent resistivity abnormity, delineating the apparent resistivity abnormity and positioning and predicting concealed orebodies. The method can effectively solve the double-solution problem of positioning concealed orebodies in the full spatial domain of the tunnel.

A noise suppression method for extracting a target frequency signal from a CSAMT time series, and aims at solving the problems that an existing CSAMT data denoising method is long in denoising time, small in application range and poor in robustness. The method includes the following steps: acquiring CSAMT data to be subjected to noise suppression as input data, the CSAMT data being the CSAMT data with noise; preprocessing the input data to obtain preprocessed data; performing noise suppression on the preprocessed data through a trained denoising network to obtain noise-suppressed CSAMT data; wherein the denoising network is constructed based on an improved temporal convolutional network, a bidirectional long short-term memory network and a fully connected layer which are connected in sequence. According to the method, the denoising time of the CSAMT data is shortened, the application range is expanded, and the robustness is improved.