Systems and methods for exploring for subterranean structures using electromagnetism

Abstract

The present disclosure provides systems and methods for detecting and mapping a subsurface object. The system includes a set of transmitters configured to emit a primary electromagnetic field, a set of receivers spatially separated from the transmitter and configured to detect a secondary magnetic field generated by the subsurface object in response to the primary electromagnetic field, and a processor operably connected to the transmitter and the receivers. The processor is configured to control the transmitter to generate the primary electromagnetic field, generate a data signal based on the secondary magnetic field detected by the receivers, and analyze the data signal to determine characteristics of the subsurface object based on the processed signal.

Description

SYSTEMS AND METHODS FOR EXPLORING FOR SUBTERRANEAN STRUCTURESUSING ELECTROMAGNETISMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Application No. 63 / 559,313, titled Systems and Methods for Exploring for Subterranean Structures using Electromagnetism, filed February 29, 2024, which is hereby incorporated by reference in its entirety.STATEMENT OF GOVERNMENT SUPPORT

[0002] This invention was made with government support under W913E518C0013 awarded by the Department of Defense (DOD) and N00014-20- 1-2341 awarded by the Office of Naval Research (ONR). The government has certain rights in the invention.BACKGROUND

[0003] Detection, location, and identification (classification) of underground objects and geological features presents a challenge for various applications. A variety of sensing technologies have been developed and tested to detect and locate subsurface objects, including radio frequency tomography, ground penetrating radar (GPR), seismic methods, synthetic aperture radar, microgravity methods, electromagnetic gradiometer (EMG), and low frequency and transient electromagnetic induction (EMI) sensing.

[0004] Some of these methods are used for construction of highways, water supplies, underground prospecting, and hazard reduction. For example, vibro-acoustic (VA) methods have been used for pipe detection, and electrical cable detection has been accomplished with passive magnetic fields, low-frequency electromagnetic sensors, and GPR. However, in general, these methods are limited to shallow, subsurface targets. That is, such methods are generally capable of detection and identification for targets buried only up to 3-4 ft. Other sensors and techniques have their own advantages and limitations for buried targets in different environment conditions. For example, VA methods using ground excitation are better suited for detecting assets under grass-covered areas than assets under bare soil areas. GPR works better on bare dry ground than on grass-covered areas because the wet ground or grass has a higher conductivity, which reduces the transmissionof radar waves. Because noninvasive geophysical methods are cost-effective, efforts in recent years have focused on the development of innovative standoff sensing techniques for detecting and locating subsurface linear conductors, such as wires and pipes.

[0005] Regardless of the particular technology or system employed, each is limited in at least one, and often multiple, important ways. Thus, there is a continuing need for new systems and methods for exploring for subterranean targets or structures.SUMMARY

[0006] The present disclosure overcomes the aforementioned drawbacks by providing systems and methods not only for detecting and mapping subsurface objects using electromagnetic sensing techniques, but also for identifying target size, type and material properties, distinguishing materials. One non-limiting example would be distinguishing materials like lead from copper. One non-limiting example system utilizes a combination of transmitters to generate electromagnetic fields that interact with subsurface structures, and vector (tri-axial) receivers to detect signals from the structures. The systems and methods provided herein can be used to locate and identify various underground objects, including pipes, wires, and geological features, with improved accuracy and efficiency compared to traditional methods.

[0007] According to an aspect of the present disclosure, a system for detecting and mapping a subsurface object is provided. The system includes a transmitter configured to emit a primary electromagnetic field, a set of receivers spatially separated from the transmitter and configured to detect a secondary magnetic field generated by the subsurface object in response to the primary electromagnetic field, and a processor operably connected to the transmitter and the receivers. The processor is configured to control the transmitter to generate the primary electromagnetic field, generate a data signal based on the secondary magnetic field detected by the receivers, and analyze the data signal to determine characteristics of the subsurface object based on the processed signal.

[0008] According to another aspect of the present disclosure, a method for detecting and mapping a subsurface object is provided. The method includes steps comprising emitting a primary electromagnetic field from a transmitter, detecting, with a set of receivers spatially separated from the transmitter, a secondary magnetic field generated by the subsurface object in response to the primary electromagnetic field, processing a signal generated by detecting secondary magnetic field, and determining characteristics of the subsurface object from signal. Some non-limitingexamples of characteristics may include depth, geolocation, orientation and classification features, such as size, or wall thickness.

[0009] According to a further aspect of the present disclosure, a system of detecting and mapping a subsurface structure is provided. The system comprises a transmitter coil, two receiver coils, and a processor. The processor is configured to carry out steps including emitting a primary electric field from the transmitter coil in a range of the subsurface structure to induce a current in the subsurface structure to generate a secondary magnetic field, detecting the secondary magnetic field using the two receiver coils as a first signal and a second signal, subtracting the first signal from the second signal, and processing the subtracted signal using forward and inverse electromagnetic induction (EMI) models to determine a parameter of the subsurface structure or crossings of a plurality of subsurface structures. Parameters, as non-limiting examples, may include length, depth, orientation, or the like.

[0010] According to yet another aspect of the present disclosure, a system of detecting and mapping a subsurface object is provided. The system includes two concentric transmitter loops, a concentric receiver coil aligned with and within the two concentric transmitter loops, a set of triaxial gradiometers, and a processor. The processor is configured to emit a primary electric field from the two transmitter coils in a range of the subsurface object to induce a current in the subsurface object to generate a secondary magnetic field, detect a signal of the secondary magnetic field using the receiver coil, process the signal using forward and inverse electromagnetic induction (EMI) models, and extract parameters of the signal and compare to a library of signals to identify the subsurface object.

[0011] According to an additional aspect of the present disclosure, a system of detecting and mapping a subsurface device is provided. The system comprises one or more transmitter coils, one or more receiver coils, and a processor. The processor is configured to emit a primary electric field from the one or more transmitter coils in a range of the subsurface device to induce a current in the subsurface device to generate a secondary magnetic field, detect a signal of the secondary magnetic field using the one or more receiver coils, process the signal using forward and inverse electromagnetic induction (EMI) models, and extract parameters of the signal and compare to a library of signals to identify the subsurface device.

[0012] According to a further aspect of the present disclosure, a system is provided for detecting and mapping a subsurface irregularity, such as permafrost. The system includes a primarytransmitter coil, a bucking transmitter coil, a receiver coil, and a processor. The processor is configured to emit a primary electromagnetic field from the transmitter coil in a range of the subsurface irregularity to induce a current in the subsurface irregularity to generate a secondary magnetic field, emit a bucking field to oppose the magnetic field of the of the primary transmitter coil at the receiver coil, detect a signal of the secondary magnetic field using the receiver coil, and process the signal using forward and inverse electromagnetic induction (EMI) models.

[0013] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive. That is, this summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.BRIEF DESCRIPTION OF FIGURES

[0014] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0015] Fig. 1 is a schematic of a non-liming example EMG system for detecting and mapping a subsurface object, according to aspects of the present disclosure.

[0016] Fig. 2 is a flowchart of the processing performed by an EMG set-up, according to aspects of the present disclosure.

[0017] Fig. 3 is a flow chart of the EMI forward and inverse modeling, according to aspects of the present disclosure.

[0018] Fig. 4 is a schematic illustration of a system in accordance with the present disclosure being used to analyze an area.DETAILED DESCRIPTION

[0019] The present disclosure recognizes that, when conductive structures are excited by alternating electromagnetic fields generated by the transmitter currents and / or radio fields, they reradiate electromagnetic fields that include an electric component and a magnetic component. At short ranges, e.g., less than several hundred feet, the concentric lines of the secondary magneticfield emitted by wires and other conductive structures can be measured and compared using two magnetic dipole antennas (i.e. loop receiver coils) with a short separation distance between them.

[0020] Attempts to create underground infrastructure detectors have used a transmitter loop and a triaxial gradiometer receiver. Placed on the ground, the transmitter can induce linear currents along subsurface structure (wires, pipe, etc ), which in return produce secondary electromagnetic fields, and the triaxial gradiometer sensor is moved to detect and locate the secondary signals for the infrastructure (i.e., induced currents). The magnetic field strength from the transmitter loop and its resulted eddy currents diminishes with distance and therefore the secondary signals, due to eddy currents, from the structure diminish as well, as a result underground structures become undetectable using the standard EMI detectors. The system requires the electric field to be coupled with the elongated structure (such as wire, pipe, underground railroads, etc.) for detection — meaning the electric field lines should align tangentially to the wire. While this technology has demonstrated effective subsurface wire detection and localization, it relies heavily on prior knowledge of wire location and direction or involves a time-consuming process of scanning areas repeatedly by moving the transmitter to different locations. Furthermore, when transmitters are directly above the wire, (i.e. when the electric field is perpendicular to the structure's elongation direction) the signals become undetectable.

[0021] The present disclosure provides systems and methods that overcome the aforementioned drawbacks by providing systems and methods that use a multi-axis practical electromagnetic gradiometer (PEG). In one non-limiting example, systems and methods are provided for detection and mapping of underground wires. In another non-limiting example, systems and methods are provided for detection, mapping, and identification or subsurface metallic targets using an ultralight electromagnetic array (ULEMA). In further non-limiting examples, systems and methods are provided for detecting and mapping permafrost using a frequency domain, electromagnetic induction (EMI) system. In one further non-limiting example, systems and methods are provided for detecting and identifying non-metallic targets or voids using high-frequency (HFEMI).

[0022] The systems and methods described herein can utilize the underlying physics of broadband electromagnetic induction (EMI) sensing phenomena for detecting and characterizing subterranean structures. When conductive structures are excited by alternating electromagnetic fields generated by the transmitter currents and / or radio fields, these structures re-radiate electromagnetic fields that include both electric and magnetic components. At short ranges,typically less than several hundred feet, the concentric lines of the magnetic field emitted by conductive structures can be measured and analyzed using two magnetic dipole antennas (i.e., loop receiver coils) with a short separation distance between them.

[0023] In some cases, an alternating primary electromagnetic field may be generated by an external source placed above the soil. The primary electromagnetic field may penetrate the soil and impinge on a target. As a result, eddy currents (V- J = 0-solenoidal) and / or linear currents (V- J t 0-non-solenoidal) J may be created inside conducting soil and in (or on) the conductor. These currents may produce a detectable secondary electromagnetic field outside the soil given a suitable sensor.

[0024] The subsurface structures that may be detected using these electromagnetic sensing techniques can vary widely. In some cases, the subsurface object may be a pipe, wire, cable, rail, tunnel, or any man-made or natural subsurface structure. The composition of these structures may also vary. In some cases, the subsurface object may be metallic, non-metallic, or comprise metallic and non-metallic components in varying degrees.

[0025] In other cases, the subsurface object may be an improvised explosive device (IED) composed of varying amounts of metallic and non-metallic components. The ability to detect such objects is particularly valuable in security and defense applications, as well as many others. For example, the electromagnetic sensing techniques described herein may also be applied to detect geological features. In some cases, the subsurface object may be a natural or man-made void. In other cases, the subsurface object may be a ground layer such as permafrost. The detection of these geological features can be crucial for various applications, including construction, environmental monitoring, and resource exploration.

[0026] Current underground infrastructure detection methods face several challenges and limitations. Many existing systems rely heavily on prior knowledge of the structure's location and direction, or involve time-consuming processes of scanning areas repeatedly by moving the transmitter to different locations. Additionally, when horizontal loop transmitters are positioned directly above an elongated structure such that the electric field is perpendicular to the structure's elongation direction, the signals may become undetectable.

[0027] The systems and methods described herein are not similarly limited and may use multiaxis practical electromagnetic gradiometers and advanced signal processing techniques. These approaches and others, as will be described, allow for more efficient and effective detection,mapping, and characterization of a wide range of subterranean structures, from man-made infrastructure to geological features.

[0028] Referring to FIG. 1, a non-liming example of an electromagnetic sensing system 100 may be used for detecting and mapping a subsurface object 102 buried in a ground surface 104. The subsurface object 104 may be, but is not limited to, a pipe, wire, cable, rail, tunnel, or any manmade subsurface structure. The structure may be metallic, non-metallic, or comprise metallic and non-metallic components in varying degree. Alternatively, the subsurface object 102 may be an IED composed of varying amounts of metallic and non-metallic components. Further, the subsurface object 102 may be a geological feature such as a natural or man-made void or ground layer such as permafrost.

[0029] The electromagnetic sensing system 100 can include a transmitter 106 configured to emit a primary electromagnetic field 108 that penetrates the ground surface 104 and interacts with the subsurface object 102. As a result of this interaction, a secondary magnetic field 110 may be generated by the subsurface object 102. The electromagnetic sensing system 100 also includes a receiver 112 configured to detect the secondary magnetic field 110. The transmitter 106 and the receiver 112 are spatially separated. The transmitter 106 and the receiver 112 may be located in different devices or housings. In some cases, the transmitter 106 and the receiver 112 may be integrated in a single housing. The transmitter 106 may include a transmitter coil. The receiver 112 may include a receiver coil. In some cases, the receiver 112 may be a single axis, bi-axial, or tri-axial receiver. In some cases, the receiver 112 may include a global positioning system (GPS) receiver and inertial measurement unit (IMU) for determining the geospatial location of the receiver 112. This geospatial information may be used in processing the detected signals to accurately map the location of the subsurface object 102.

[0030] The electromagnetic sensing system 100 may also include a processor 114 operably connected to the transmitter 106 and the receiver 112. The processor 114 may be configured to control the transmission of the primary electromagnetic field 108 by the transmitter 106 and process the detected signal of the secondary magnetic field 110 by the receiver 112. The electromagnetic sensing system 100 allows for the detection and mapping of the subsurface object 102 through the interaction between the primary electromagnetic field 108 and the secondary magnetic field 110, with the signals being processed by the processor 114 to determine characteristics of the subsurface object 102.

[0031] Referring to Fig. 2, the electromagnetic sensing system of Fig. 1 may be used to perform a process 200 for detecting and mapping the subsurface object. The process 200 includes several steps that utilize the components of the electromagnetic sensing system to gather and analyze data about the subsurface object. In particular, at step 202, a primary electromagnetic field is emitted in a range of the subsurface object. The transmission of the primary electromagnetic field, including its frequency or other parameters may be controlled or adjusted. In some cases, the primary electromagnetic field may be emitted at a range of frequencies. The specific frequency or frequencies and / or other signal parameters may be selected based. For example, frequencies may be advantageously selected from a non-limiting range of about 10 kHz - 10 MHz, for example for objects that are or include metal. Also, frequencies may be advantageously selected form a nonlimiting range of about 0 - 10 kHz for objects that include or are formed of non-metallic materials. These or other parameters may be selected for the detection of various types of subsurface objects at different depths within the ground surface.

[0032] The primary electromagnetic field can be designed to penetrate the ground surface and interacts with the subsurface object. This interaction induces a current in the subsurface object, which in turn generates the secondary magnetic field. Thus, at step 204, the receiver detects the signal of the secondary magnetic field. The receiver may be positioned above the ground surface and spatially separated from the transmitter to detect the secondary magnetic field.

[0033] Following the detection of the secondary magnetic field, the process 200 proceeds to step 206. At step 206, the detected signal is processed. In one non-limiting example, the processing may use forward and / or inverse electromagnetic induction (EMI) models. These models, as will be described, can be designed for the analysis of the signal characteristics to determine various properties of the subsurface object.

[0034] Finally, at step 208, the results of the signal processing are utilized to locate, map, and / or identify the subsurface object. This step may involve comparing the processed signal data to known signatures of various materials and objects to determine the nature of the subsurface object. Additionally or alternatively, this step may include correlating information to generate a report, map, or other communication, which may be transmitted or displayed.

[0035] The process 200 may be repeated multiple times, with the electromagnetic sensing system moved to different positions, to create a comprehensive map of subsurface objects in a given area. In some cases, the process 200 may be performed continuously as the electromagnetic sensingsystem is moved across an area, allowing for real-time detection and mapping of subsurface objects.

[0036] Referring now to Fig. 3, one non-limiting example of a process for performing signal processing, such as may be performed at step 206 of Fig. 2 is provided. The signal processing begins at step 210, where the signal of the secondary magnetic field detected by the receiver may be denoised. Though optional, in some cases, denoising the signal may include using a median filter and / or wavelet denoising. These denoising techniques help to remove noise and improve the quality of the signal for further processing.

[0037] Following denoising, the process moves to step 212, whereby the signal may be coregistered and interpolated with geolocation information from the receiver. This geolocation information may be obtained from a GPS receiver and / or IMU integrated with the receiver. Additionally, initial values of a location and orientation of the subsurface object may be generated using the inverse EMI model.

[0038] At step 214, the initial values of the location and orientation of the subsurface object can be applied to the forward EMI model to generate model data. The forward EMI model represents the signal of the secondary magnetic field as a set of orthogonal electric (or magnetic) sources distributed along a line. In some cases, the magnitude of each of the set of electrical (magnetic) sources may be scaled with the primary electromagnetic field.

[0039] The process then proceeds to step 216, where an objective function can be computed to determine an error between the initial values of the location and orientation and the model data. This step allows for the assessment of how well the initial estimates match the actual detected signal.

[0040] At step 218, the initial values of the location and orientation of the subsurface object can be updated to reduce the error determined in step 216. This update aims to refine the estimates and bring them closer to the actual characteristics of the subsurface object.

[0041] After step 218, steps 214-218 can be repeated until the error between the calculated and measured data is less than an expected error or reaches a maximum number of iterations. This iterative process allows for continuous refinement of the location and orientation estimates, improving the accuracy of the subsurface object detection and characterization.

[0042] By employing these advanced signal processing techniques, the electromagnetic sensing system 100 of Fig. 1 may provide more accurate and reliable detection, mapping, and identification of subsurface objects 102 in various applications and environments.

[0043] The systems and methods described herein can utilize mathematical models to analyze the electromagnetic fields generated and detected during sensing of subterranean structures. These models can allow for the estimation of electromagnetic properties of both the soil and target objects. In particular, during electromagnetic sensing, electromagnetic fields are generated using electric currents and / or voltage sources. The primary electromagnetic field penetrates conducting, dielectric, and magnetic materials, carrying electromagnetic energy and forces. This field interacts with charged particles, such as electrons and ions, causing their movement or displacement. At low frequencies, typically generated by current loops as an electromagnetic field source, the primary magnetic field is significantly stronger than the electric field. As a result, according to Faraday’s Law, the time-varying primary magnetic field induces eddy currents within conductors, forming closed loops expressed as J = oE or magnetic dipole M=mB. As the frequency increases, the electric field also increases, influencing bound charges, such as those in water. The electric field displaces these charges, reorients bound charges, and induces electric dipoles, described by P = sE. Consequently, the detected signals comprise contributions from both freely moving charges (i.e., eddy currents) and bound charge dipoles (i.e., dielectric and magnetic materials). These combined responses are characterized by complex conductivity, which is a combination eddy currents and magnetic dipoles or complex dielectric permittivity, which combines induced currents and bounded electric dipoles.

[0044] However, the systems and methods of the present disclosure recognize that soil's electromagnetic properties, such as conductivity, permittivity, and permeability, significantly influence high-frequency EMI responses. These EMI signals are highly dependent on the spatial distribution of these parameters, which can vary over very short distances. As a result, a measurement taken at one location may not accurately represent the electromagnetic properties just a few meters away or at a different depth.

[0045] For effective detection, mapping, and identification of subsurface materials, such as rare earth elements, it is valuable to accurately map the EM properties of soils and / or underwater sediments. While existing methods can measure soil conductivity, many require direct contact with the ground using probes, limiting their practicality for large-scale surveys. To address thislimitation, a two-step approach may be used for mapping soil and / or sediment electromagnetic properties from standoff electromagnetic data set. This enables non-contact, high-resolution mapping, improving the accuracy and efficiency of subsurface material identification.

[0046] The systems and methods described above can use a current carrying loop that produces time varying primary electromagnetic field with time dependence e jwt . The primary EM field impinges objects having conducting (s), dielectric (e), and magnetic (m) properties. The primary electromagnetic field penetrates the object to some degree and induces conduction (linear and eddy) currents, electric and magnetic dipoles within it. In return the induced sources produce a secondary or scattered field outside the object.

[0047] Referring to Fig. 4, the modeling and processing can be illustrated. A system 400 includes transmitter loop 402 is placed at or near a surface of the ground 404. A time-varying current in the transmitter loop produces the primary electromagnetic field(s) 406. The primary EM field 404 induces volume currents, magnetic, and electric dipoles in the subsurface. These induced sources generate electromagnetic fields, which are recorded by the receivers 408. The system 400 can be moved point to point 410 to collect high-fidelity EMI data set of the overall area of the ground 404.

[0048] To estimate near-subsurface EM parameters, a volume of the ground 404 can be divided into N sub volumes, and an electric dipole and an electric dipole Pn is placed at center rnof each sub-volume and the secondary magnetic field at rmpoint:

[0052] In this equation, km=where f is frequency. The complex permittivity and permeability are given by:ctric conductivity, relative dielectric-permittivity and magnetic-permeability of the mthsub-volume, respectively. Further, e0and jJ.orepresent the permittivity and permeability of free space.

[0055] The magnitudes of the responding electric dipoles Pnand electromagnetic parametersmay be determined by minimizing differences between the modeled and measured magnetic field:

[0057] Once the magnitudes of the responding sources are determined, the electric field at the center of each sub-volume may be calculated as:

[0059] Using the relation between electric dipole Pmpolarizability and the local electric field E(rm), the complex electrical permittivitymay be re-estimated and validated:

[0061] In some cases, this model may be applied iteratively to refine estimates of soil and target electromagnetic properties. The model may account for variations in soil conductivity, permittivity, and permeability, as well as the geometry and composition of subsurface objects.

[0062] By applying this framework to the data collected by the electromagnetic sensing system, detailed information about subsurface structures may be obtained. This may include the location, orientation, size, and material properties of buried objects, as well as characteristics of the surrounding soil.

[0063] The accuracy and resolution of the electromagnetic property estimates may depend on factors such as the frequency range of the primary electromagnetic field, the spatial distribution of measurements, and the signal-to-noise ratio of the detected secondary fields. In some cases, multiple measurements at different frequencies or spatial positions may be combined to improve the robustness of the estimates. Though non-limiting, this model provides a foundation for interpreting the electromagnetic responses detected by the sensing system, enabling the characterization of a wide range of subsurface structures and geological features.

[0064] The systems and methods described herein may utilize advanced electromagnetic induction (EMI) sensors and systems for detecting, locating, and identifying subsurface objects. These advanced systems may include the Ultra-Light Electromagnetic Array (ULEMA) and the Linear Current Sensing (LCS) system.

[0065] The ULEMA may be designed as a lightweight, portable EMI system capable of detecting and characterizing subsurface metallic objects. In some cases, the ULEMA may comprise multipletransmitter coils and receiver coils arranged in a specific configuration to optimize detection capabilities. The transmitter coils may generate primary electromagnetic fields at various frequencies, while the receiver coils may detect the secondary magnetic fields produced by subsurface objects in response to the primary fields.

[0066] In some implementations, the ULEMA may operate in both time-domain and frequencydomain modes. Time-domain operation may involve transmitting pulsed electromagnetic fields and measuring the decay of induced currents in subsurface objects over time. Frequency-domain operation may involve transmitting continuous-wave electromagnetic fields at multiple frequencies and measuring the amplitude and phase of the secondary fields.

[0067] The ULEMA may be capable of detecting and characterizing a wide range of subsurface metallic objects, including pipes, wires, and other infrastructure components. In some cases, the system may be able to differentiate between different types of metals based on their electromagnetic responses.

[0068] The Linear Current Sensing (LCS) system may be designed specifically for detecting and mapping elongated conductive objects such as pipes and wires. The LCS may utilize a different approach compared to traditional EMI systems. In some implementations, the LCS may generate a primary electric field that induces linear currents along the length of subsurface conductors.

[0069] The LCS may comprise a transmitter loop that generates the primary electric field and a set of receiver coils that detect the secondary magnetic fields produced by the induced linear currents. In some cases, the receiver coils may be arranged in a gradiometer configuration to enhance sensitivity and reduce interference from the primary field.

[0070] One potential advantage of the LCS system may be its ability to detect and map long, continuous conductors over extended distances. This capability may be particularly useful for mapping underground utility networks or locating buried cables.

[0071] Both the ULEMA and LCS systems may incorporate advanced signal processing techniques to enhance detection capabilities and reduce false alarms. These techniques may include noise reduction algorithms, multi-channel data fusion, and inversion methods for estimating target properties.

[0072] In some implementations, the ULEMA and LCS systems may be integrated with positioning systems such as GPS or inertial measurement units (IMUs) to provide accurategeolocation of detected objects. This integration may enable the creation of detailed subsurface maps showing the locations and characteristics of buried infrastructure.

[0073] The advanced EMI sensors and systems described herein may offer improved detection sensitivity, depth penetration, and target discrimination compared to conventional metal detectors or utility locators. These capabilities may enable more efficient and accurate mapping of subsurface infrastructure in various applications, including urban planning, construction, and environmental assessment.

[0074] The electromagnetic sensing technology described herein may be applied to a wide range of applications for detecting, mapping, and identifying subsurface objects and structures. These applications may span various industries and sectors, including infrastructure management, environmental monitoring, and defense.

[0075] In some cases, the electromagnetic sensing technology may be used for detecting and mapping underground utilities such as water pipes, gas lines, electrical conduits, and telecommunication cables. This capability may be particularly valuable for urban planning, construction projects, and maintenance of existing infrastructure. By accurately locating underground utilities, the technology may help prevent accidental damage during excavation activities and facilitate more efficient repair and upgrade operations.

[0076] The technology may also be applied to the detection and mapping of underground storage tanks, which may be important for environmental assessment and remediation efforts. In some cases, the electromagnetic sensing methods may be able to detect leaks or corrosion in buried tanks, potentially preventing soil and groundwater contamination.

[0077] For military installations, the electromagnetic sensing technology may offer several benefits. In some cases, the technology may be used to create detailed maps of underground infrastructure on military bases, including power and communication networks, water and sewage systems, and fuel storage facilities. This information may be crucial for base operations, maintenance planning, and security assessments.

[0078] The technology may also be applied to detect and identify buried unexploded ordnance (UXO) on military training grounds or former conflict zones. In some cases, the advanced signal processing techniques employed by the electromagnetic sensing systems may allow for discrimination between harmless metal debris and potentially dangerous UXO, improving the efficiency and safety of clearance operations.

[0079] In archaeological applications, the electromagnetic sensing technology may be used to locate and map buried structures, artifacts, or ancient infrastructure without the need for invasive excavation. This non-destructive approach may be particularly valuable for preserving sensitive historical sites while still gathering important information about subsurface features.

[0080] The technology may also find applications in geological exploration. In some cases, the electromagnetic sensing methods may be used to detect and map subsurface mineral deposits, groundwater resources, or geological formations. This information may be valuable for resource exploration, water management, and geotechnical assessments.

[0081] In environmental monitoring applications, the technology may be used to detect and map contamination plumes in soil or groundwater. The ability to characterize subsurface conditions without extensive drilling or sampling may provide a more cost-effective and less invasive approach to environmental assessment and remediation planning.

[0082] For transportation infrastructure, the electromagnetic sensing technology may be applied to assess the condition of roads, bridges, and tunnels. In some cases, the technology may be able to detect subsurface voids, moisture intrusion, or deterioration of reinforcing structures, potentially enabling more proactive maintenance and reducing the risk of structural failures.

[0083] In agricultural applications, the technology may be used to map subsurface drainage systems, detect buried irrigation lines, or characterize soil properties. This information may help farmers optimize irrigation practices, improve crop yields, and manage water resources more effectively.

[0084] The electromagnetic sensing technology may also have applications in border security and law enforcement. In some cases, the technology may be used to detect underground tunnels or hidden caches, potentially aiding in efforts to prevent smuggling or unauthorized border crossings.

[0085] For disaster response and search and rescue operations, the electromagnetic sensing technology may be applied to locate survivors trapped under debris or to assess the stability of damaged structures. The ability to rapidly map subsurface conditions may provide valuable information to first responders and emergency management teams.

[0086] In some cases, the technology may be adapted for marine applications, such as mapping underwater pipelines, cables, or archaeological sites. The electromagnetic sensing methods may provide an alternative or complement to traditional sonar-based techniques for underwater surveying.

[0087] The versatility of the electromagnetic sensing technology may allow for its application in various challenging environments, including arctic regions for permafrost mapping, desert areas for locating buried oases or ancient riverbeds, and urban environments for complex infrastructure mapping.

[0088] Example 1

[0089] Forward and Inverse Electromagnetic Models for Analyzing Electromagnetic Signals to Detect, Map, and Characterize Subterranean Elongated Object

[0090] The Practical Electromagnetic Gradiometer (PEG) described in U.S. Patent No. US 9,568,632 and fully incorporated herein by reference, has been used to develop both forward and inverse electromagnetic models for the detection and mapping of underground wires. The PEG comprises a transmitter coil and a single-axis gradiometer receiver, predominantly operating at a frequency of 200 kHz. The transmitter is positioned on the surface in proximity to the target. The transmitter emits an electric field that penetrates the ground, reaching the elongated target and inducing linear current along the wire. This induced current, in turn, generates a secondary magnetic field detected by two spatially separated single-axis receiver coils. The received signal from one coil is subtracted from the signal from the second receiver coil. Subsequently, the acquired signals are processed using forward and inverse EMI models.

[0091] The forward model represents the target's electromagnetic signals as a set of orthogonal electric sources distributed along a line. The magnitude of these orthogonal sources is scaled with respect to the primary electric field. The targets' depth and orientations are determined from the data through a two-step inversion process. In the first step, the magnitude of the orthogonal electric dipoles is computed by solving a linear set of equations for the target's specified depth and orientation. In the second step, the target's depth and orientation are refined by minimizing an objective function, which quantifies the mismatch between the modeled and measured data. These steps are iterated until a desirable mismatch error is achieved, or a maximum number of iterations is reached.

[0092] The system is designed to identify, map, and profile underground targets, including wires, pipes, and subterranean railways. It can also be adapted for detecting and identifying underwater cables and pipes. The software utilizes physics-based models with robust noise tolerance, considering the interactions of electromagnetic fields with soil, underground targets, and theinterplay between soil and targets. The algorithm derives the depth, orientation, and length of the targets from multi-frequency and tri-axial sensor data.

[0093] A non-limiting example can be as follows:

[0094] 1. The measured data from the first sensor is subtracted from the measured data from the second sensor.

[0095] 2. The subtracted data is denoised using median filters and wavelet denoising techniques.

[0096] 3. The preprocessed data is co-registered and interpolated with geolocation information.

[0097] 4. The pre-processed data is passed to the inversion code.

[0098] 5. The inversion code generates initial values for the target's location and orientation.

[0099] 6. The forward model assumes that the target is placed at the initial depth and oriented along the initial orientation, which is generated in the step 5.

[0100] 7 The primary electric field produced by the transmitter coil is calculated at the target, and the magnitudes of the induced orthogonal electric dipoles are scaled based on the electric field.

[0101] 8. The secondary magnetic field from the target is calculated at the receiver's geolocation.

[0102] 9 A linear system of equations is solved to determine the magnitude of the responded orthogonal sources.

[0103] 10. Using the responded orthogonal sources, the secondary magnetic field is calculated, and an objective function (measuring the mismatch between modeled and measured data) is computed.

[0104] 11 The target's location and orientation are updated based on the mismatch between the model and actual data.

[0105] 12. The updated location and orientation are passed to the forward model, and steps 6 through 12 are repeated until the mismatch error between the model and actual data is less than the expected error or reaches the maximum number of iterations.

[0106] While studies have demonstrated the model's ability to accurately estimate the target's depth using a single-axis receiver, the extracted orientation was found to be inconsistent. To enhance the accuracy of orientation estimation, our team developed a new tri-axial gradiometer system.

[0107] The tri-axial system consists of two sets of tri-axial receivers, spaced 1.75 meters apart. These receivers measure magnetic field and voltage in three orthogonal directions. The process involves amplifying the measured analog signals, digitizing them using an FPGA board, applyingfast Fourier transforms, and extracting signals above the sensor threshold for each frequency. The system also leverages signals from nearby transmitters, including signals of opportunity. The measured signals are processed for each frequency using the steps outlined from 1 to 12. The tri- axial and multi-frequency data provide comprehensive information for estimating the target's depth, orientation, and length.

[0108] Example 2

[0109] Methods and Systems for Detection, Mapping, and Identification Subsurface Metallic Targets

[0110] The process of detecting, mapping, localizing, and remediating unexploded ordnance (UXO) is both time-consuming and expensive. Cleanup costs for UXO are significantly higher for challenging sites, such as those in wooded, rocky, wet, marshy, and rough terrain, which make up more than 50% of the 11 million acres of UXO-contaminated sites in the USA. Over the past three decades, extensive research into understanding electromagnetic induction (EMI) phenomena for detecting subsurface metallic targets with high conductivity and permeability has led to the development, construction, and implementation of advanced bistatic EMI systems. These advanced systems incorporate multiple transmitters and multiple triaxial receivers, along with sophisticated EMI forward, inverse, and classification models. They have provided new capabilities for UXO cleanup efforts by distinguishing potentially hazardous munitions and UXO from non-hazardous metal debris. However, most, if not all, of the presently available advanced EMI sensors for UXO detection and classification are large, cumbersome pushcart-based systems with substantial weight (ranging between 120 and 300 pounds) and limited detection capabilities (generating Tx currents of less than lOAmperes). These attributes render the current COTS systems unsuitable for integration into small-scale (<25 kg) Unmanned Aerial Systems (UAS). They often require a two-step process: initial dynamic anomaly detection / selection, followed by cued (static) EMI data acquisition for each detected anomaly. Here, we introduce the Ultra-Light Electromagnetic Array (ULEMA) system, which we have designed, built, and validated for the detection and classification of subsurface targets. The ULEMA system comprises three small and one large transmitter loops, along with four tri-axial receivers. This configuration enables the detection and classification of targets in a single pass, eliminating the need for cued data acquisition. Three smaller transmitters are strategically positioned to illuminate targets from various sides, while one larger transmitter is designed for detecting and classifying deep targets.The instrument is compact and lightweight, making it suitable for deployment by hand, on unmanned ground vehicles, or aerial systems. The ULEMA data acquisition system seamlessly integrates IMU and GPS hardware to map and geolocate anomalies in measured EMI data.

[0111] The ULEMA system was constructed through a combination of customized and commercially available (COTS) hardware and firmware. The customized firmware was tailored specifically for UAS integration. The system comprises a custom-designed transmission (Tx) system responsible for generating a primary electromagnetic (EM) field in the time domain, employing a square wave with a 50% duty cycle. This field is instrumental in detecting subsurface UXO. During the on-time phase the current in the Tx system experiences an exponential rise and then maintains a constant current within the range of 10 to 12 Amperes. This current profile engenders a stable primary magnetic field encompassing high-conductivity metallic targets. This magnetic field effectively permeates these targets. After the on-time phase, the Tx current is swiftly interrupted, resulting in an abrupt cessation of the primary magnetic field within the targets. This swift change in the magnetic field induces eddy currents within conductive objects, leading to the generation of a slowly diminishing secondary magnetic field detected by receivers. The secondary magnetic fields give rise to electromotive forces (emf) within the receiving (Rx) coils, which are amplified, using a custom made two-stage instrumental amplifier, and subsequently measured. These secondary magnetic field measurements are harnessed for the purpose of detecting and categorizing subsurface metallic targets.

[0112] The classification of subsurface targets involves a series of steps. It begins with the deployment of dynamic electromagnetic induction (EMI) mapping systems to identify anomalies. Once anomalies are detected, the system switches to a static mode to gather data specifically over these anomalies. Advanced EMI models are then used to process this data and extract both the inherent and extrinsic properties of the targets. These anomalies are subsequently categorized as either UXO or non-UXO items based on their intrinsic features.

[0113] Over the past few decades, live UXO classification studies conducted in real UXO environments have pinpointed two primary challenges for on-site classification. The first challenge involves consistently selecting targets for in-depth analysis, while the second revolves around accurately positioning sensors over these anomalies in a cued mode to collect high-quality data sets for target classification. This is especially crucial in scenarios involving multiple targets or magnetic soil conditions.

[0114] After recording the responses from the targets, the raw data undergoes preprocessing and inversion using an innovative technique known as orthonormalized volume magnetic source (ONVMS). Throughout the inversion process, feature parameters for target classification are extracted from the sensor data. These extracted features serve as inputs for the chosen classifier. Simultaneously, the inversion process provides details such as object positions, orientations, and electromagnetic signatures like the principal axis of the inverted magnetic dipole polarizability tensor. These electromagnetic signatures are issued in distinguishing between detected objects, differentiating UXO from clutter. In cases where UXO is identified, the specific type of ordnance is determined. Furthermore, it is possible to differentiate between materials. For example, the different electromagnetic signatures of materials allows for distinguishing between copper and lead objects.

[0115] The classification phase draws upon extensive exploration and diverse methodologies, encompassing nonparametric statistics, neural networks, and statistical learning techniques. These methodologies include maximum likelihood approaches like finger printing, mixed models, support vector machines, as well as various classifiers such as linear, quadratic, or Mahalanobis distance classifiers.

[0116] To enhance the speed and accuracy of target detection and classification in real field applications, such as swiftly detecting subsurface targets after airfield bombings, we have developed a new strategy. This strategy involves integrating ULEMA unmanned systems, such as robots, drones underwater autonomous vehicles, advanced forward and inverse EMI models, and real-time data processing techniques.

[0117] Example 3

[0118] Systems and Methods for Detection and Identification of Improvised Explosive Devices, Non-metallic Landmines and Voids

[0119] Detecting and classifying buried Improvised Explosive Devices (lEDs) swiftly, effectively, and reliably in real-world settings stands as one of the most significant and urgent threats for the Department of Defense (DOD) and NATO allies. Buried lEDs persist as the primary and ongoing (as well as probable future) asymmetrical threat against US and coalition forces due to their ease of construction, accessibility, and destructive potential. These concealed lEDs could be situated anywhere: within vehicles, attached to animals, embedded in roads, or affixed to individuals, and can be deployed in diverse environments — from combat zones to bustling urban centers. Theadaptability of lEDs to virtually any scenario makes their detection, identification, and neutralization challenging through standard subsurface sensing technologies like Low-frequency electromagnetic induction (EMI, DC to 100kHz) and ground-penetrating radar (GPR, operating above 50MHz). Considerable research efforts in recent years have been dedicated to exploring, devising, and constructing novel systems for the detection of buried lEDs. One such technological innovation is the high-frequency EMI (HFEMI) sensor, developed as part of the ONR N00014- 16-l-2332project. Within the scope of the N00014-16-1-2332 project, studies on IED detection were carried out at the non-magnetic test site of Naval Support Activity (NSA) in Panama City utilizing the HFEMI system. These studies demonstrated the HFEMI system's effectiveness in successfully detecting various types of lEDs, including carbon rods, short wires, and explosive- filled containers.

[0120] An HFEMI system underwent design, construction, and testing, comprising several components: the Red-Pitaya 14-bit, 125 MHz FPGA board responsible for generating and receiving HFEMI signals, synchronizing Tx / Rx signals, and extracting real-time in-phase and quadrature parts of received signals. This system includes Rx and Tx amplifiers, DC-DC converters, and Tx / Rx coils integrated onto a PCB. Assessments were conducted concerning carbon rods, low-metal content lEDs, voids, as well as short and long wires. The findings indicate that the HFEMI sensing system can effectively detect and pinpoint carbon rods, wires, voids, and low-metal content targets. Essentially, the HFEMI system generates primary magnetic fields that reach targets, inducing eddy currents and magnetic dipoles within them and the surrounding soil. Subsequently, these induced currents and dipoles produce secondary magnetic fields sensed by the Rx coils. When target signals surpass noise levels, inverse HFEMI models can be employed to extract feature parameters for target classification. Studies demonstrated that highly conductive soils (while offsetting the sensor center from the target center) diminish the detection of deep intermediate and metallic IED targets but improve voids detection. Evaluation of the HFEMI system's detection and identification capabilities occurred at the non-magnetic test site of Naval Support Activity (NSA) in Panama City. HFEMI data were gathered in blind (varied environmental conditions like beach, wooded, and open grass areas) and semi-blind (well- controlled test-lanes) areas using a handheld HFEMI system. EMI signal processing approaches based on the method of auxiliary sources were employed to process the collected data. Results indicated successful detection of lEDs, including carbon rods, short wires, and explosive-filledcontainers, using the HFEMI system. This HFEMI system bridges the gap between standard low- frequency electromagnetic induction and electromagnetic wave sensing technologies. It enables the detection and localization of shallow subsurface IED targets across different soil types and conditions. This sensor represents a distinctive capability to detect and locate several previously undetectable components of lEDs. If integrated into upcoming sensors, this technology has the potential to save lives.

[0121] Example 4

[0122] System and Methods for Detecting and Mapping Permafrost

[0123] Since the middle of the previous century, the Earth's climate has undergone transformations primarily due to the industrial revolution, notably the combustion of fossil fuels. This activity has resulted in heightened levels of heat-trapping greenhouse gases in the Earth's atmosphere, leading to an increase in the average surface temperature by approximately 1.98°F (1.1°C) from 1901 to 2020. However, climate change encompasses more than mere temperature escalation; it includes occurrences like the swift thawing of permafrost in Arctic regions, elevating sea levels, alterations in weather patterns such as droughts and floods, and diverse other impacts. These shifts in climate significantly affect critical aspects of our lives and the environment, including water resources, energy, transportation, wildlife, agriculture, ecosystems, and human health.

[0124] The ongoing alterations will continue to exert substantial effects on ecosystems and living organisms, albeit with varying impacts across different regions. The Arctic, in particular, stands vulnerable to the consequences of climate change, experiencing warming at a rate at least double the global average. The dissolution of land ice sheets and glaciers in the Arctic significantly contributes to the overall rise in sea levels worldwide. One consequence of this climatic shift is the rapid thawing of permafrost in cold regions. The Arctic region, situated north of the Arctic Circle, is undergoing warming at twice the pace of the rest of the world, a phenomenon known as “Arctic amplification.” This accelerated thawing of permafrost leads to soil instability and subsidence, potentially causing the collapse of infrastructures like roads, bridges, and buildings. Permafrost, defined as ground continuously frozen for a minimum of two years, encompasses about 24% of the terrestrial surface in the Northern Hemisphere and holds approximately half of the Earth's organic carbon. Permafrost zones underlie approximately 65% of Russia’s land area and 80% of Alaska's, forming the foundation for a significant portion of the infrastructure in these regions.

[0125] Accurately mapping the subsurface composition of permafrost, especially on scales relevant to infrastructure planning and maintenance, remains a persistent challenge. Electromagnetic induction (EMI) emerges as a promising solution for studying underground soil layers, particularly permafrost, without causing physical disruption to the environment. EMI's adaptability stands out among other subsurface sensing technologies, as it can be deployed through various methods, including direct contact, ground-based, airborne, and even space-based techniques. Previous research has explored EMI's potential in determining permafrost depth, showing some success with airborne platforms. Moreover, higher-frequency approaches like ground-penetrating radar (GPR) have effectively examined surface-level ice and permafrost. However, there remains an unmet need for the development of a specialized system tailored for low-cost, small-scale airborne platforms designed specifically for permafrost detection.

[0126] In addition to the challenge of designing and constructing a sensor with the required resolution for permafrost detection, modeling layered soils with inclusions exhibiting conductivity values characteristic of permafrost and ice presents a complex endeavor. Conventional inversion software is inadequate for addressing this distinct problem since permafrost typically demonstrates considerably lower conductivity compared to most soils and water. Furthermore, current EMI sensors encounter difficulties in identifying intrusions like ice lenses, which form at depths ranging from inches to feet within soil or rock, and play a critical role in weathering and land subsidence in Arctic regions.

[0127] To overcome the aforementioned issues, a frequency domain system was developed and constructed. The system comprises primary and bucking transmitter coils, as well as vertical (z) and horizontal (r) magnetic dipole loops. Activation of the system is achieved through an FPGA board equipped with two output and two input SMA connectors, responsible for generating, receiving, and synchronizing signals. Notably, the Primary Transmitter (TX) and Receiver (RX) are positioned 1.68 meters apart. We successfully conducted preliminary data collection over various soil elevations. The experimental setup was effectively modeled and simulated using a forward EMI approach. The soil's conductivity was determined by solving an inverse scattering problem using MATLAB’s built-in fminmax minimization function. The extracted conductivity closely aligns with the conductivity measurements obtained using an Ohmmeter.

[0128] As used in this specification and the claims, the singular forms “a,” “an,” and “the” include plural forms unless the context clearly dictates otherwise. As used herein, “about”,“approximately,” “substantially,” and “significantly” will be understood by persons of ordinary skill in the art and will vary to some extent on the context in which they are used. If there are uses of the term which are not clear to persons of ordinary skill in the art given the context in which it is used, “about” and “approximately” will mean up to plus or minus 10% of the particular term and “substantially” and “significantly” will mean more than plus or minus 10% of the particular term.

[0129] As used herein, the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising.” The terms “comprise” and “comprising” should be interpreted as being “open” transitional terms that permit the inclusion of additional components further to those components recited in the claims. The terms “consist” and “consisting of’ should be interpreted as being “closed” transitional terms that do not permit the inclusion of additional components other than the components recited in the claims. The term “consisting essentially of’ should be interpreted to be partially closed and allowing the inclusion only of additional components that do not fundamentally alter the nature of the claimed subject matter.

[0130] The phrase “such as” should be interpreted as “for example, including.” Moreover, the use of any and all exemplary language, including but not limited to “such as”, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.

[0131] Furthermore, in those instances where a convention analogous to “at least one of A, B and C, etc.” is used, in general such a construction is intended in the sense of one having ordinary skill in the art would understand the convention (e. ., “a system having at least one of A, B and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together.). It will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description or figures, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

[0132] All language such as “up to,” “at least,” “greater than,” “less than,” and the like, include the number recited and refer to ranges which can subsequently be broken down into ranges and subranges. A range includes each individual member. Thus, for example, a group having 1-3members refers to groups having 1 , 2, or 3 members. Similarly, a group having 6 members refers to groups having 1, 2, 3, 4, or 6 members, and so forth.

[0133] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.

Claims

Claims1. A system for detecting and mapping a subsurface object, comprising: a transmitter configured to emit a primary electromagnetic field; a set of receivers spatially separated from the transmitter and configured to detect a secondary magnetic field generated by the subsurface object in response to the primary electromagnetic field; and a processor operably connected to the transmitter and the receivers and configured to: control the transmitters to generate the primary electromagnetic field; generate a data signal based on the secondary magnetic field detected by the set of receivers; and analyze the data signal to determine characteristics of the subsurface object based on the processed signal.

2. The system of claim 1, wherein the processor is further configured to: generate a data signal based on the secondary magnetic filed received by the set of receivers; remove background signals from the data signal and to generate a background subtracted data signal; process the background subtracted data signal using forward or inverse electromagnetic induction (EMI) models to determine physical or material properties of the subsurface object; and generate a report including the physical or material properties of the subsurface object.

3. The system of claim 1, wherein the processor is further configured to denoise the signal of the detected secondary magnetic field.

4. The system of claim 1, wherein the processor is further configured to co-register and interpolate the data signal with geolocation information.

5. The system of claim 4, further comprising a global positioning system (GPS) sensor or an inertial measurement unit (IMU) configured to communicate geolocation information to the processor.

6. The system of claim 1, wherein the processor is further configured to process the data signal using forward and inverse electromagnetic induction (EMI) models.

7. The system of claim 6, wherein processing the data signal comprises: generating initial values of a location and orientation of the subsurface object using the inverse EMI model; applying the initial values to the forward EMI model to generate model data; computing an objective function to determine an error between the initial values and the model data; and updating the initial values to reduce the error.

8. A method for detecting and mapping a subsurface object, the method including steps comprising: emitting a primary electromagnetic field from a transmitter; detecting, with a set of receivers spatially separated from the transmitter, a secondary magnetic field generated by the subsurface object in response to the primary electromagnetic field; processing a signal generated by detecting secondary magnetic field; and determining characteristics of the subsurface object from signal.

9. The method of claim 8, further comprising denoising the signal of the detected secondary magnetic field using at least one of a median filter, signal detrending, or wavelet denoising.

10. The method of claim 8, further comprising co-registering and interpolating the signal with geolocation information from the receiver.11 . The method of claim 8, wherein processing the signal comprises using forward and inverse electromagnetic induction (EMI) models.

12. The method of claim 11, wherein processing the signal further comprises: generating initial values of a location and orientation of the subsurface object using the inverse EMI model; applying the initial values to the forward EMI model to generate model data; computing an objective function to determine an error between the initial values and the model data; and updating the initial values to reduce the error.

13. The method of claim 12, further comprising iteratively repeating the applying, computing, and updating steps until the error is less than a predetermined threshold or a maximum number of iterations is reached.

14. A system of detecting and mapping a subsurface structure, comprising: a transmitter coil; two receiver coils; and a processor, configured to carry out steps including: emit a primary electric field from the transmitter coil in a range of the subsurface structure to induce a current in the subsurface structure to generate a secondary magnetic field; detect the secondary magnetic field using the two receiver coils as a first signal and a second signal; subtract the first signal from the second signal; and process the subtracted signal using forward and inverse electromagnetic induction (EMI) models to determine a length of the subsurface structure or crossings of a plurality of subsurface structures.

15. The system of claim 14, wherein the processor is further configured to perform at least a plurality steps that can include one or more of:(i) co-register and interpolate the subtracted signal with geolocation information of the two receiver coils to generate initial values of a location and orientation of the structure using the inverse EMI model;(ii) apply the initial value of the location and orientation of the structure to the forward EMI model to generate model data;(iii) compute an objective function configured to determine an error between the initial values of the location and orientation and the model data;(iv) update the initial values of a location and orientation of the structure to reduce the error; and(v) repeat steps (ii)-(iv) until the error between the initial value and model data is less than an expected error or reaches a maximum number of iterations.

16. The system of claim 15, wherein the processor is further configured to:(i) represent the subtracted signal as a set of orthogonal electric sources distributed along a line and scale a magnitude of each of the set of electrical sources with the primary electric field;(ii) calculate the secondary magnetic field form the structure at a location of the two receivers;(ii) compute a magnitude of the set of orthogonal electric sources by solving a liner system of equations;(iv) calculate the secondary magnetic field using the set of orthogonal sources; and(v) use the secondary magnetic field with the objective function to determine the error.

17. The system of claim 14, wherein the two receiver coils include single, or dual or triaxial receivers.

18. A system of detecting and mapping a subsurface object, comprising: two concentric transmitter loops; a concentric receiver coil aligned with and within the two concentric transmitter loops; a set of triaxial gradiometers; a processor, configured to:emit a primary electric field from the two transmitter coils in a range of the subsurface object to induce a current in the subsurface object to generate a secondary magnetic field; detect a signal of the secondary magnetic field using the receiver coil; process the signal using forward and inverse electromagnetic induction (EMI) models; and extract parameters of the signal and compare to a library of signals to identify the subsurface object.

19. The system of claim 18, wherein the library of signals includes plurality of previously recorded signatures of known objects.

20. The system of claim 18, wherein the processor is further configured to compare the parameters of the signal to the library to identify at least one material comprising the subsurface object.

21. A system of detecting and mapping a subsurface device, comprising: one or more transmitter coils; one or more receiver coils; a processor, configured to: emit a primary electric field from the one or more transmitter coils in a range of the subsurface device to induce a current in the subsurface device to generate a secondary magnetic field; detect a signal of the secondary magnetic field using the one or more receiver coils; process the signal using forward and inverse electromagnetic induction (EMI) models; and extract parameters of the signal and compare to a library of signals to identify the subsurface device.

22. The system of claim 21, wherein the device includes an improvised explosive device (IED).

23. The system of claim 21 , wherein the primary electric field is a high frequency signal.

24. The system of claim 21, wherein the library of signals includes plurality of previously recorded signatures of known devices.

25. A system of detecting and mapping a subsurface permafrost, comprising: a primary transmitter coil; a bucking transmitter coil; a receiver coil; a processor, configured to: emit a primary electric field from the transmitter coil in a range of the subsurface permafrost to induce a current in the subsurface permafrost to generate a secondary magnetic field; emit a bucking field to oppose the magnetic field of the of the primary transmitter coil at the receiver coil; detect a signal of the secondary magnetic field using the receiver coil; and process the signal using forward and inverse electromagnetic induction (EMI) models.

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