The electrical conductivity of the earth is used to help infer lithological and pore fluid properties. Various geophysical methods can provide estimates of the distribution of below ground electrical conductivity, with each method having certain limitations. This data release presents raw and processed results from land-based and water-based frequency domain electromagnetic induction imaging (EMI) data collected from March 31 to April 2, 2015. Data were primarily collected by walking throughout the wetland and riparian zones with the GEM-2 instrument (Geophex, Ltd.) at approximately 1 m off the ground in horizontal coplanar (ski flat) mode.
A survey along a section of the Colorado River in a kayak was also collected (with approximate 0.3 m of elevation above the water surface).

Surface electrical resistivity tomography (ERT), electromagnetic induction (EMI), and self-potential (SP) data were acquired March 9 - 20, 2018 by the U.S. Geological Survey, in collaboration with the U.S. Army Corps of Engineers, at the Jim Woodruff Lock and Dam near Chattahoochee, Florida. Frequency-domain electromagnetic induction data were acquired along approximately 9 line-kilometers with the Geophex GEM-2 system to map variations in structure up to about 10 m in depth. This data release includes the raw and processed frequency-dependent in-phase and quadrature data. They are provided as digital data, and data fields are defined in the data dictionary (https://www.sciencebase.gov/catalog/item/5e101094e4b0b207aa163768). Jim Woodruff Lock and Dam is located on the Apalachicola River just south of the confluence of the Flint and Chattahoochee Rivers along the Florida-Georgia border. Construction was completed in 1954 and impounds Lake Seminole. The dam has a long history of excessive seepage along the right abutment and below the fixed-crest spillway. Several karst features have been mapped over the years including sinkholes, both on land and along the lake bottom, and disappearing and reappearing streams. Such features were excavated and grouted during construction. Despite years of investigation of the dam foundation, there remains uncertainty on the flowpaths of water below the fixed-crest spillway and along the adjacent right abutment. REFERENCE Abraham, J.D., Deszcz-Pan, M., Fitterman, D.V., and Burton, B.L., 2006, Use of a handheld broadband EM induction system for deriving resistivity depth images, in 19th Annual Symposium on the Application of Geophysics to Engineering and Environmental Problems, Seattle, Washington, April 2–6, 2006, 18 p.

The electrical conductivity of the earth is used to help infer lithological and pore fluid properties. Various geophysical methods can provide estimates of the distribution of below ground electrical conductivity, with each method having certain limitations. This data release presents raw and processed results from hand-caried frequency domain electromagnetic induction imaging (EMI) data collected from June 27-28 along Blacktail Creek near Williston, North Dakota. Data were primarily collected by walking in the creek or along the riparian zones with the GEM-2 instrument (Geophex, Ltd.) at approximately 0.5 m off the ground in horizontal coplanar (ski flat) mode.

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Hand-carried frequency domain electromagnetic imaging (EMI) data were collected along the Sanuit River to indicate changes in streambed water quality and/or near surface sediments. These data are to be used in conjunction with fiber-optic distributed temperature sensing (FO-DTS) and ground penetrating radar (GPR) data. The combined dataset represents point in time mapping of preferential groundwater discharge points (FO-DTS), and the bed structure that controls where these points are located (GPR, EMI).

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The electrical conductivity of the earth is used to help infer lithological and pore fluid properties. Various geophysical methods can provide estimates of the distribution of below ground electrical conductivity, with each method having certain limitations. This data release presents raw and processed results from land-based and water-based frequency domain electromagnetic induction (EMI) data collected from August 23, 2017 to August 28, 2017. The raw data consist of .csv files from the Geophex GEM-2 unit. Data were primarily collected by walking with the instrument at approximately 1 m off the ground in horizontal coplanar (ski flat) mode. A survey along a section of the Little Wind River in a kayak (with about 0.3 m of elevation above the water surface) was also collected.

This dataset represents a collection of electromagnetic interference (EMI) signals and normal (un-affected) signals designed for classification tasks, with each signal consisting of 4000 -time steps. The amplitudes are represented in milli Volts (mV). It includes 8 distinct classes of EMI signals and normal signals, which represents electrical conditions and faults (Total 9 classes). Features are represented as raw signal values, capturing the temporal and amplitude characteristics of EMI patterns. Labels provide a categorical representation of the signal type, enabling supervised learning applications. This dataset is ideal for developing and evaluating signal processing and classification algorithms in fault detection, condition monitoring, and predictive maintenance systems.

Classes and Their Characteristics The dataset includes 9 classes, each representing a unique type of EMI-related signal or condition:

Arcing: Represents high-frequency bursts caused by electrical arcing. Signal characteristics: Periodic spikes with moderate noise.

Corona: Caused by ionization of air around high-voltage conductors. Signal characteristics: Continuous low-amplitude sinusoidal waveforms.

Partial Discharge (PD): Represents localized electrical discharges that do not fully bridge the insulation. Signal characteristics: Sudden impulses, irregular patterns.

Random Noise: Represents background noise unrelated to faults. Signal characteristics: Random fluctuations without specific patterns.

Process Noise: Noise generated during normal equipment operation. Signal characteristics: Low-frequency sinusoidal signals with added noise.

Exciter Pulses: High-energy signals associated with equipment excitation systems. Signal characteristics: High-amplitude periodic bursts.

Micro Sparking: Tiny discharges resulting in low-amplitude oscillations. Signal characteristics: Low-frequency modulated waves.

Data Modulation: Represents signals affected by external modulation. Signal characteristics: Combination of low- and high-frequency components.

Normal (Unaffected): Signals recorded from unaffected equipment. Signal characteristics: Flat or near-zero values with minimal noise.

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This paper presents a new on-line methodology for detecting intermittent disconnection failures. The detection principle operates on the fundamental Lorentz Law that states that sudden changes in flux create a large voltage, resulting in an arc. This arc propagates as a traveling wave through the circuit until all the energy associated with it is dissipated. It is possible to detect that traveling wave as an indication of an intermittent disconnection failure. A test bench was implemented to test and validate the theory. Preliminary results presented in this paper show the feasibility of detecting disconnection failures and the possibility of locating the failing connector based on distance.

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