Electric Field Signal Research Articles

An assessment of controlled source EM for monitoring subsurface CO2 injection at the wyoming carbonSAFE geologic carbon storage site

We evaluate if electromagnetic (EM) geophysical methods for monitoring geologic carbon storage (GCS) efforts at the Wyoming CarbonSAFE project adjacent to the Dry Fork Station power plant near Gillette, Wyoming. This first involved acquiring both electric and magnetic fields at eleven different locations ranging in distance from immediately adjacent to 4 km from the plant. Passive EM measurements were made to provide spectral EM noise measurements generated by electricity production at the plant and to determine if useful magnetotelluric (MT) data can be successfully collected in the region. The processed data indicate that useful MT data can be collected as long as the site is located more than 2km away from the power plant as well as active roads and rail lines. Controlled source EM data were collected using three different source configurations, two of which connected to steel casings used to complete the injection wells. Comparing the EM noise measurements to the CSEM data show measurable electric and magnetic field signals at all sites. Next a series of three-dimensional (3D) numerical models were built that simulate resistivity changes caused by the proposed CO2 injection at depths ranging from 2.4 to 3.0km. These models were used to simulate various EM measurement configurations. The modeling shows that casing-source CSEM monitoring can provide sensitivity to the injected CO2 if source electrodes are connected to the bottom of one or both of the injection wells.

Analysis of Electromagnetic Field Characteristics of Wave Glider

A wave glider is an ocean observation platform that utilizes wave energy to drive and solar energy to power. Its metal structure will generate related electromagnetic fields due to corrosion and underwater motion. In the detection of weak electromagnetic field signals underwater, its own electromagnetic field characteristics will have an impact on signal detection. To study the applicability of electric field sensors and magnetic field sensors on wave glider platforms, the structural characteristics of the wave glider were analyzed, and the installation positions of electric field sensors and magnetic field sensors were designed based on the different motion states of the water surface mother body and underwater towing body. The measured electromagnetic field data of the wave glider platform were measured, and the measured data were analyzed. It was determined that the interference electric field energy under typical working conditions of the wave glider was mainly concentrated within 1 Hz, which decreased with increasing frequency, and the magnitude was mV/m. The magnitude of the interference magnetic field is several tens of nT, indicating that the electromagnetic field interference is significant during the working state of the wave glider. Installing an electric field sensor directly at the bottom of the wave glider will cause significant noise interference, while installing the magnetic field sensor directly at the bottom of the tractor will affect the servo and the shaking-induced magnetic field. Moreover, wave gliders should not use electric field signals below 1 Hz as signal sources, but they can utilize axial frequency electromagnetic fields to detect weak electromagnetic signals underwater.

Research on the prediction method of porcelain insulator pollution degree based on electric field monitoring

The online monitoring of insulator surface pollution states is crucial for predicting/preventing pollution flashover accidents. This paper conducts artificial pollution tests on porcelain insulators at five pollution levels to obtain the electric field signal during the flashover process. Subsequently, the time-frequency performance of the electric field signal waveform at various discharge stages is discussed in detail. Finally, a method for predicting pollution degrees by combining back propagation (BP) neural networks and non-contact electric field signal monitoring is proposed and validated. Research results indicate that during the pollution flashover process of the porcelain insulator, the peak value of the electric field signal shows an upward trend in sections. As the equivalent salt deposit density (ESDD) increases, the curves of electric field values rise. The development of dry-band arcing clearly distorts the electric field signal waveform at its crest and trough parts, increasing the total harmonic distortion (THD) rate. The THD can reach 100–130 % when closing to flashover, with the third harmonic acting as the main component. By taking kmax (the rising rate of the E-field signal amplitude), g (the ratio of Emax to Erms), K3 (the third harmonic coefficient), and THD as inputs to the BP neural network model, an ESDD prediction with a relative error lower than 10.63 % is realized. This research can offer novel technical support for predicting pollution degrees.

Analysis method of explosive electromagnetic radiation energy

AbstractThe energy of electromagnetic radiation from explosions is coupled to electronic equipment circuits, disrupting the initiation sequence or causing failure in an increasing number of cases, which seriously affects the stability of weapon systems. There is a significant difference between the characteristics of the explosive electromagnetic radiation signals and modulated electromagnetic signals. The electric field intensity and signal power cannot directly represent the magnitude of the explosive electromagnetic radiation energy, and traditional electromagnetic signal analysis methods are unsuitable for explosion electromagnetic signal analysis. To solve this problem, the mechanism of explosive electromagnetic radiation was first analyzed. Through verification experiments of the explosion electromagnetic intensity and temperature, it was concluded that there is a strong correlation between the explosion plasma temperature and electromagnetic intensity. The temperature of the explosive plasma is derived based on the measured surface temperature of the explosive fireball, a functional relationship is established between the energy of the explosive plasma and the temperature of the plasma, and plasma energy is introduced as a parameter for electric field intensity correction. The interference signal analysis method based on eye diagram is used to calibrate the electromagnetic radiation damage ability, achieving quantitative analysis of the interference degree of electromagnetic radiation energy on the signal, and providing a new approach for the analysis of explosive electromagnetic radiation energy.

Ship Shaft-Rate Electric Field Signal Denoising Method Based on VMD-MSS

The presence of complex electromagnetic noise in the ocean significantly impacts the accuracy of ship shaft-rate electric field signal detection, necessitating the development of an effective denoising method to enhance detection precision. Nevertheless, traditional denoising methods encounter issues like low frequency resolution, challenging threshold configuration, and mode mixing. This study introduces a method that integrates variational mode decomposition (VMD) with multi-window spectral subtraction (MSS). The intrinsic mode functions (IMFs) of noisy signals are extracted using VMD, and the noise components within different IMFs are identified. The spectral features of both signal and noise within different IMFs are leveraged to eliminate noise signals via MSS. Subsequently, the denoised components of IMFs are rearranged to derive the denoised ship shaft-rate electric field signals, achieving noise reduction across various frequency bands. Following validation using simulation signals and empirical data, the noise reduction efficacy of VMD-MSS surpasses that of alternative methods, demonstrating robust performance even at low signal-to-noise ratios. The marine electromagnetic noise is effectively suppressed in the empirical data, while preserving the characteristics of ship’s shaft-rate signals, thereby validating the method’s efficacy and demonstrating its practical engineering value.

A Low-Noise Amplifier for Submarine Electric Field Signal Based on Chopping Amplification Technology.

In the exploration of ocean resources, the submarine electric field signal plays a crucial role through marine electromagnetic methods. However, due to the field signal's low-frequency and weak characteristics, it often encounters interference from the instrument's own 1/f noise during its acquisition. To address this issue, we developed a low-noise amplifier for the submarine electric field signal based on chopping amplification technology. This amplifier utilizes low-temperature electronic components to adapt to the cold submarine environment and enhances its independence by incorporating a square wave generator. Additionally, we conducted simulations and experimental tests on the designed chopper amplifier circuit, evaluating the equivalent input voltage noise spectrum (EIVNS) and the frequency response within 1 mHz~100 Hz. The experimental results indicate that the amplifier designed in this study achieves sufficiently low noise 2 nV/√Hz@1 mHz, effectively amplifying the submarine electric field signal measured with the electric field sensor.

Time–frequency dual-domain electromagnetic detection technology for buried pipelines

ABSTRACT Monitoring and detecting buried pipelines under concrete pavement are often complex and difficult tasks. The time–frequency electromagnetic technology, with the fast information acquisition and strong anti-interference ability, is first applied in the buried pipeline inspection. The purpose is to verify if this method can monitor and detect the pipelines status change effectively, even if covered by concrete pavement. Three representative scenarios (normal, coating-damaged and leaking) and two types of pavement (plain concrete and reinforced concrete) are created and applied in the simulations with the finite element method. The simulated results are processed and analysed with the proposed method. In the frequency domain, the maximum change of the electric and magnetic field signals for the coating-damaged scenario from the normal one are 0.4% and 9.9% respectively, and 97.7% and 157.7% for the leaking scenario. In the time domain, the shifts of the electric and magnetic field signal peak are 6.2 μs and 6.5 μs respectively when the coating damage occurs, and 11.4 μs and 13.1 μs when leakage appears. The results indicate that the above technology is valid for buried pipelines. Moreover, it can be used to locate and identify the abnormalities and types of concrete pavement can be differentiated.

Bidirectional Jump Point Search Path-Planning Algorithm Based on Electricity-Guided Navigation Behavior of Electric Eels and Map Preprocessing.

The electric eel has an organ made up of hundreds of electrocytes, which is called the electric organ. This organ is used to sense and detect weak electric field signals. By sensing electric field signals, the electric eel can identify changes in their surroundings, detect potential prey or other electric eels, and use it for navigation and orientation. Path-finding algorithms are currently facing optimality challenges such as the shortest path, shortest time, and minimum memory overhead. In order to improve the search performance of a traditional A* algorithm, this paper proposes a bidirectional jump point search algorithm (BJPS+) based on the electricity-guided navigation behavior of electric eels and map preprocessing. Firstly, a heuristic strategy based on the electrically induced navigation behavior of electric eels is proposed to speed up the node search. Secondly, an improved jump point search strategy is proposed to reduce the complexity of jump point screening. Then, a new map preprocessing strategy is proposed to construct the relationship between map nodes. Finally, path planning is performed based on the processed map information. In addition, a rewiring strategy is proposed to reduce the number of path inflection points and path length. The simulation results show that the proposed BJPS+ algorithm can generate optimal paths quickly and with less search time when the map is known.

Comprehensive Simulation Research on Microprocess and Macroscopic Electromagnetic Signals of Surface Discharge in SF6

The surface discharge on the dielectric is a common type of partial discharge in GIS, but the microscopic development process of surface discharge in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> is unclear. To find out the micro-process of surface discharge and its correlation mechanism with the partial discharge detection signals of GIS, the fluid-chemical simulation model of the streamer is used to simulate the micro-process of surface discharge in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> . Then the current signal obtained by the micro-process simulation is used as the excitation to simulate the electromagnetic signals based on the FITD method. A close connection between the microscopic discharge and the macroscopic signals is established. The results show that the electron concentration at the streamer head and the streamer velocity decrease first and then increase during the discharge, with an average velocity of about 9.84×105m/s. The discharge current reached a peak value of about 0.22A when the surface streamer reaches the cathode. In terms of waveform correlation, the absolute value of the average correlation coefficient between the electric field signals simulated in this paper and the actual measured signals is above 0.7, which is much higher than the simulated signal excited by the Gaussian source. By changing the applied voltage and comparing with the experiment results, it is found that the simulation method in this paper can reflect the change of signals under the influence of external conditions. As the applied voltage rises, the amplitude of the electric field signals increases, and the main frequency component on the frequency spectrum grows, with the main frequency value at about 1.2 GHz. The research in this paper is an effective supplement to the existing study on the microscopic mechanism of partial discharge signals.

Identification of the seismoelectric field induced by a slow compressional wave at an ultrasonic frequency

Theoretical studies predict that a slow compressional wave propagating in a fluid-saturated porous medium can produce a coseismic electric field due to the electrokinetic effect, but the experimental proof is still lacking. Laboratory experiments are conducted to measure such a seismoelectric conversion inside a synthetic rock. Fluid pressure signals are recorded by using mini hydrophones 1.6 mm in diameter, and then electric field signals generated at the liquid-solid interface and inside the rock sample based on the seismoelectric effect are recorded by electrode arrays, respectively. The seismoelectric waves induced by fast and slow compressional waves can be clearly identified in the recorded electric signals and their attenuation properties are analyzed at an ultrasonic frequency, which confirms that the seismoelectric signals induced by fast/slow compressional waves are measurable in the experiments. To support our explanation of the experimental observation, theoretical simulations are conducted according to the experimental model and then compared with the recorded experimental data. The results find that the simulated wavefields are in excellent agreement with those signals measured in the measurements, which proves the theoretical prediction of the seismoelectric signal accompanying the slow compressional wave and suggests a feasible way for detecting the slow compressional wave property with seismoelectric conversions in field measurements.

Impedance spectroscopy and conduction mechanism analysis of bulk nanostructure Prussian blue pellets

The structure and morphology of the fabricated Prussian Blue (PB) pellets are examined using FTIR, XPS, XRD and FESEM techniques. The morphology of the crystalline Prussian blue (PB) revealed a nanostructure with an average crystallite size of 91 nm. The frequency (42 Hz–1 MHz) and temperature (303–393K) of the Prussian Blue (PB) in Bulk form are dependent. In the medium region (1 kHz–100 kHz), the electric dipoles relax and start to lag the applied ac electric field signal and the estimated activation energy of 0.247 eV. The investigated Nyquist plots (Z″ versus Z′) of the Prussian Blue (PB) samples at different temperatures (303–393 K) are interpreted by an equivalent parallel RgCg circuit. The bulk resistance of Prussian Blue samples was estimated at various temperatures, and its behaviour exhibited temperature dependence; generally, it follows the Nearest Neighbor Hopping (NNH) model. According to Meyer–Neldel rule, the calculated activation energy is 0.565 ± 0.001 eV. The evidence of localized states is investigated, and its density, N(EF) ∼ 5.47 × 1018 eV−1 cm−3. The ac conductivity, σac, merges into a plateau of two parts: the first is a frequency-independent dc conductivity, σdc, which is strongly temperature-dependent. The rest of the plateau at higher frequencies showed the temperature-insensitive ac conductivity obeying Jonscher's power law. The values of the exponent s were investigated to figure out the intimate conduction mechanism in Prussian Blue (PB) samples; it follows the correlated barrier hopping (CBH) theoretical model. The hopping distance and energy have values ∼ unity and ≥ kBT, respectively. The experimental results revealed a linear relationship between hopping distance and energy against temperature, with the first decreasing and the second increasing linearly.

Coherent dynamics of strongly interacting electronic spin defects in hexagonal boron nitride

Optically active spin defects in van der Waals materials are promising platforms for modern quantum technologies. Here we investigate the coherent dynamics of strongly interacting ensembles of negatively charged boron-vacancy ({{{{{{{{rm{V}}}}}}}}}_{{{{{{{{rm{B}}}}}}}}}^{-}) centers in hexagonal boron nitride (hBN) with varying defect density. By employing advanced dynamical decoupling sequences to selectively isolate different dephasing sources, we observe more than 5-fold improvement in the measured coherence times across all hBN samples. Crucially, we identify that the many-body interaction within the {{{{{{{{rm{V}}}}}}}}}_{{{{{{{{rm{B}}}}}}}}}^{-} ensemble plays a substantial role in the coherent dynamics, which is then used to directly estimate the concentration of {{{{{{{{rm{V}}}}}}}}}_{{{{{{{{rm{B}}}}}}}}}^{-}. We find that at high ion implantation dosage, only a small portion of the created boron vacancy defects are in the desired negatively charged state. Finally, we investigate the spin response of {{{{{{{{rm{V}}}}}}}}}_{{{{{{{{rm{B}}}}}}}}}^{-} to the local charged defects induced electric field signals, and estimate its ground state transverse electric field susceptibility. Our results provide new insights on the spin and charge properties of {{{{{{{{rm{V}}}}}}}}}_{{{{{{{{rm{B}}}}}}}}}^{-}, which are important for future use of defects in hBN as quantum sensors and simulators.

3DAEF-Based Thunderstorm Multipath Imaging System

This paper presents a multipath imaging system for thunderstorm developments, wherein data are three-dimensional atmospheric electric field signals (3DAEFSs) collected with a self-made 3DAEF apparatus. In this way, thunderstorms are presented in a staged and visual form. To start with, entropy-based intervals are constructed from historical AEF data, to classify denoised AEFS components, according to the entropy value of each component. Further, AEFS time sequences are reconstructed with a reference to whether components within the same entropy-based interval are sequential or not, providing time information for the subsequent clustering-based spatial denoising to thunderstorm point charge coordinates. Finally, predicted value intervals, which are used to divide and then reconstruct AEFS time periods, are acquired to realize the point charge multipath imaging corresponding to periods, based on the established stacked autoencoder and the extreme gradient boosting model. Empirical results demonstrate that the multipath better visualizes the whole process of thunderstorm activities. Comparisons with radar charts further confirm that the proposed system effectively images charge multipaths, and provides a valid reference for visual thunderstorm monitoring.

Coating Damage Detection of Vessels Using Machine Learning-Based Underwater Electric Field

There has been extensive research on corrosion electric fields because of their special characteristics. There are apparent distribution characteristics corresponding to different coating damage regions of a vessel. To accurately detect where the coating damage region is located on vessels using underwater electric field signature, in this study, a method called “refined composite multivariate multiscale fluctuation reverse dispersion entropy (RCMMFRDE)” was proposed. This method effectively extracted the multichannel signal in a complex environment of poor stability and high noise. By combining RCMMFRDE and hierarchical prototype (HP), a novel coating damage detection method was developed. First, the accumulated differential value was calculated to determine the side on which the vessel had the damaged coating. Second, RCMMFRDE was used to extract the features of electric field signals. Then, feature vector dimensionality reduction was carried out using pair-wise feature proximity (PWFP). The low-dimensional features were given as input to the HP classifier to detect the damaged region. Finally, a series of numerical and physical scale experiments were conducted to validate the feasibility of the proposed method. The damaged region was efficiently predicted, achieving satisfactory accuracy of 96.67% and 92.00% in numerical and measurement data respectively; thus, it is a suitable alternative to conventional methods, especially when there is a lack of previous environmental information.

Three types of bidirectional leader development in triggered lightning flashes

Eight cases of bidirectional leader (BL) development in artificially triggered lightning flashes are reported with synchronous high-speed camera images and electric field signals. Based on optical progressing characteristics, the eight cases can be divided into three types: a reflection type, a discontinuity type, and an inducement type. For the reflection type, the tail of a dart leader may begin to extend backward when the leader’s head reaches a branch point, or the top of the exploded triggering wire. For the discontinuity type, the initiation of a bidirectional leader below a decayed attempted leader may occur more than once preceding one return stroke. For the inducement type, the approach of another leader with the same polarity will turn a dart leader into a bidirectional leader. The reflection type and inducement type are first observed here. Two cases of the discontinuity type are observed, and both are multiple-bidirectional leaders observed for the first time. For the reflection type and inducement type, there are fluctuations in the electric field related to the BL development. The dissipation of the downward leader slows down the negative increase of the electric field. Once the BL development starts, the downward negative end of the BL moves towards the ground with the E-field negatively increasing. For the discontinuity type, the close electric field result shows no fluctuations. The BL development has a much longer duration than the other two BL types.

Space Electric Field Characteristics of Silicone Rubber Insulator Pollution Flashover and Its Application in Flashover Prewarning

Pollution flashover accidents are still happening in areas with severe environmental conditions, and the existing pollution flashover warning methods are still very simple. In this article, the space electric field characteristics of silicone rubber insulators during pollution flashover were revealed, and the application of the space electric field signal (SEFS) for pollution flashover prewarning was proposed for the first time. During the research, artificial pollution tests were conducted to obtain the SEFS waveform, and then its time-frequency characteristic parameters under different pollution flashover stages were extracted and analyzed. Research results indicate that, according to the SEFS amplitude variation, the development of pollution flashover can be divided into three zones: safe zone, dangerous zone, and critical zone. The four characteristic parameters, the rms increase factor, maximum value increase factor, peak factor, and third harmonic factor change obviously during different flashover stages. Combining the SEFS parameters and the pattern recognition algorithm, the recognition of pollution flashover zones is realized, which can be directly used to form the pollution flashover prewarning strategy.

Machine learning detection of dust impact signals observed by the Solar Orbiter

Abstract. This article presents the results of automatic detection of dust impact signals observed by the Solar Orbiter – Radio and Plasma Waves instrument. A sharp and characteristic electric field signal is observed by the Radio and Plasma Waves instrument when a dust particle impacts the spacecraft at high velocity. In this way, ∼ 5–20 dust impacts are daily detected as the Solar Orbiter travels through the interplanetary medium. The dust distribution in the inner solar system is largely uncharted and statistical studies of the detected dust impacts will enhance our understanding of the role of dust in the solar system. It is however challenging to automatically detect and separate dust signals from the plural of other signal shapes for two main reasons. Firstly, since the spacecraft charging causes variable shapes of the impact signals, and secondly because electromagnetic waves (such as solitary waves) may induce resembling electric field signals. In this article, we propose a novel machine learning-based framework for detection of dust impacts. We consider two different supervised machine learning approaches: the support vector machine classifier and the convolutional neural network classifier. Furthermore, we compare the performance of the machine learning classifiers to the currently used on-board classification algorithm and analyze 2 years of Radio and Plasma Waves instrument data. Overall, we conclude that detection of dust impact signals is a suitable task for supervised machine learning techniques. The convolutional neural network achieves the highest performance with 96 % ± 1 % overall classification accuracy and 94 % ± 2 % dust detection precision, a significant improvement to the currently used on-board classifier with 85 % overall classification accuracy and 75 % dust detection precision. In addition, both the support vector machine and the convolutional neural network classifiers detect more dust particles (on average) than the on-board classification algorithm, with 16 % ± 1 % and 18 % ± 8 % detection enhancement, respectively. The proposed convolutional neural network classifier (or similar tools) should therefore be considered for post-processing of the electric field signals observed by the Solar Orbiter.

Research on intrinsic expansion coefficients in Rydberg atomic heterodyne receiving link

Rydberg atom can respond to weak microwave electric field signal in real-time by using its electromagnetically induced transparency effect to realize down conversion of space microwave electric field signal, which can be used as a superheterodyne receiver. The Rydberg atom superheterodyne receiver is a new receiving system composed of Rydberg atoms, photodetectors, and electronic information processing modules. Presently, the physical response mechanism of Rydberg atomic superheterodyne receiving technology is studied in depth. However, no complete receiving link analysis model has been established, which is not conducive to optimizing its system performance. Based on the physical mechanism of the Rydberg atom responding to the microwave electric field, this paper introduces the concept of intrinsic expansion coefficient, establishes and experimentally verifies the receiving link model of the Rydberg atom superheterodyne receiver, and briefly discusses the influence of the intrinsic expansion coefficient on the system sensitivity and response characteristics, thereby providing the theoretical guidance for optimizing the performance of the Rydberg atom superheterodyne receiving system. In the end, the Rydberg atomic and the electronic receiving links' sensitivity performance is discussed and compared.

Online Monitoring of Magnetometer Signals for Estimating Induced Electric Fields in Power Systems

Powerful solar storms emit plasma that may travel towards the earth. Interactions between the plasma and the earth magnetic field cause geomagnetic disturbances (GMDs), which in turn induce quasi-dc voltage along long conductors in power systems. The induced electric field can be calculated by magnetic field data measured by magnetometers at several stations. In this paper, a set of online processing methods are proposed to improve signal to noise ratio (SNR) of the calculated electric field signals. In the first step, magnetic field signals are denoised inside a sliding window by a wavelet transform. The sliding window width is optimized and the introduced transients are compensated. Time derivative of magnetic field signals are estimated by a continuous wavelet transform in the next step, and the size of the sliding window is minimized for optimal computational speed. Ultimately, a method is proposed to further enhance the SNR by updating the previously processed data points. The magnetic and electric field signals obtained by the online and offline processing methods are compared with each other. It is demonstrated that the online processing methods can decrease the noises to a level comparable to that obtained by the offline methods. The results of this study can be implemented to protect key elements of power systems against severe solar storms.

Marine self-potential measurement tool for autonomous underwater vehicle.

Marine self-potential (SP) measurement is commonly conducted for seafloor sulfide detection and hydrothermal vent studies in deep water using instruments towed close to the seafloor. However, this method has the following shortcomings: (1) It relies on ships for deep towing, and the need for a dedicated ship time lowers its efficiency. (2) Owing to complex topography, most towed instruments are located far from the seafloor to ensure safety, resulting in large effective signal attenuation and low signal-to-noise ratio. (3) The measurement direction is generally a single axis, with only the electric field of the axial component observed, providing limited information. With the gradual maturity of autonomous underwater vehicle (AUV) technology, it has become possible to mount marine SP measurement tools on AUVs for detection. Compared with conventional methods, this method has significant advantages in terms of efficiency, signal-to-noise ratio, and multicomponent observation. The proposed tool is a lightweight underwater device having a compact design and low power consumption, making it suitable for AUVs. The overall volume of the tool is D50mm × L350mm, and the underwater weight is 0.6kg. Chopper amplification technology ensures the low-noise measurement of electric field signals. In addition, the reformed electrodes enhance stability, thereby reducing the mechanical vibration noise. Laboratory test results show that the noise of the data logger is 7.8 nV/rt (Hz)@1Hz. The marine test conducted in the southwest Indian Ocean verified the reliability of the proposed marine SP measurement tool. The maximum working depth was 4000m. The test lasted ∼25 h, and the effective electric field data were collected for ∼17 h. This survey found a maximum SP anomaly of 0.55 mV/m in the Yuhuang hydrothermal field, which provided effective data support for the discovery of new seafloor sulfide anomalies.