Waveform Matching Research Articles

Assessment of arteriosclerosis based on lognormal fitting

Objective. Pulse pressure waves contain information about human physiology. There is a need for a simple, accurate way to know cardiovascular health in the clinic, so as to realize the implementation of convenient and effective early health monitoring for patients with arteriosclerosis.Approach. This study proposes an arteriosclerosis assessment method based on fitting a lognormal function, along with improving a conventional electronic sphygmomanometer. During the deflation phase of blood pressure measurement, the cuff pressure was kept constant (40 mmHg) and an additional 10 s of pulse signal was acquired. To derive the pulse pressure waveforms for a single cycle, the acquired pulse data of 101 cases were preprocessed in this study, including filtering for noise removal, onset point identification, removal of baseline drift, and normalization. In this study, an improved pulse resolution algorithm is proposed for the multimodal problem of the pulse wave, combining waveform matching and threshold setting, and finally obtaining the resolution parameters of the lognormal function with an average error less than 1.5%.Main results. According to the correlation analysis, the resolved parametersA1,W2,C2,W3, andC3were significantly correlated with brachial-ankle Pulse Wave Velocity, and the absolute correlation range in 0.17-0.53, which can be used as a reference index for arteriosclerosis. An arteriosclerosis assessment model was constructed based on the support vector mechanism, and the prediction accuracy was 91.1%.Significance. This study provides a new solution idea for the arteriosclerosis assessment method as well as the pulse resolution algorithm, which has a greater reference value.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

Firing properties of single axons with cardiac rhythmicity in the human cervical vagus nerve.

Microneurographic recordings of the human cervical vagus nerve have revealed the presence of multi-unit neural activity with measurable cardiac rhythmicity. This suggests that the physiology of vagal neurones with cardiovascular regulatory function can be studied using this method. Here, the activity of cardiac rhythmic single units was discriminated from human cervical vagus nerve recordings using template-based waveform matching. The activity of 44 cardiac rhythmic neurones (22 with myelinated axons and 22 with unmyelinated axons) was isolated. By consideration of each unit's firing pattern with respect to the cardiac and respiratory cycles, the functional identification of each unit was attempted. Of note is the observation of seven cardiac rhythmic neurones with myelinated axons whose activity was recruited or enhanced by slow, deep breathing, was maximal during the nadir of respiratory sinus arrhythmia, and showed an expiratory peak. This is characteristic of cardioinhibitory efferent neurones, which are responsible for respiratory sinus arrhythmia. The remaining 15 cardiac rhythmic neurones with myelinated axons were categorised as cardiopulmonary receptors or arterial baroreceptors based on the position of their peak in firing with respect to the R-wave of the cardiac cycle. This latter method is not viable for neurones with unmyelinated axons due to their slow and unknown conduction velocities. With the exception of three neurones whose expiratory modulation implicates them as cardiac-projecting efferent neurones, this population is likely dominated by arterial baroreceptors. In conclusion, the activity of single units with cardiovascular function has been discriminated within the human cervical vagus, enabling their systematic study. KEY POINTS: Recordings of the electrical activity of the vagus nerve have recently been made at the level of the neck in humans. Examination of the gross activity of this nerve reveals subpopulations of neurones whose activity fluctuates in time with the heart's beat, suggesting that the neurones that monitor or modify cardiac function can be studied using this method. Here, the activity of individual cardiac rhythmic neurones was isolated from human vagus nerve recordings using template-based spike sorting. The relationship between this activity and the cardiac and respiratory cycles was used as a means of classifying each neurone. Neuronal firing patterns that are consistent with that of neurones that modify cardiac function, including heart-slowing 'cardioinhibitory' neurones, as well as neurones that inform the brain of cardiovascular status were observed. This approach enables, for the first time, the systematic study of the function of these neurones in humans in both health and disease.

Attenuation Tomography Using Low-Frequency Ultrasound for Thorax Imaging: Feasibility Study.

Low-frequency ultrasound can permeate human thorax and can be applied in functional imaging of the respiratory system. In this study, we investigate the transmission of low-frequency ultrasound through the human thorax and propose a waveform matching method to track the changes in the transmission signal during subject's respiration. The method's effectiveness is validated through experiments involving ten human subjects. Furthermore, the experimental findings indicate that the traveltime of the first-arrival signal remains consistent throughout the respiratory cycle. Leveraging this observation, we introduce an algorithm for ultrasound thorax attenuation factor differential imaging. By computing the paths and energy variation of the first-arrival signal from the received waveform, the algorithm reconstructs the distribution of attenuation factor differences between two different thorax states, providing insights into the functional status of the respiratory system. Numerical experiments, using both normal thorax and defective thorax models, confirm the algorithm's feasibility and its robustness against noise, variations in transducer position and orientation. These results highlight the potential of low-frequency ultrasound for bedside, continuous monitoring of human respiratory system through functional imaging.

A novel application of waveform matching algorithm for improving monthly runoff forecasting using wavelet–ML models

ABSTRACT The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.

Multiple methods for acoustic emission source location in metal waveguides

Research on sound source location, which is widely used in engineering, is an important topic in acoustic emission (AE) technology. Traditional double-ended location methods have certain limitations, such as sound sources being located between two sensors, a single application scenario and possible time delays. In this study, three theoretically feasible location methods were proposed and experimentally verified based on AE propagation theory to test the accuracy of the AE source location and explore single/double-sensor line location in metal waveguides. The experiments measured the propagation speed of longitudinal and transverse waves in the waveguide and verified the feasibilities of the three acoustic source location methods based on waveform analysis, with waveform matching using the wave range and wave speed differences. The experimental results show a location error of less than 3% for the double-ended and double wave-speed methods and less than 6% for the double-waveguide and reflective-surface types.

Low-Level Radio-Frequency system integrated with feed-forward control and vector modulation

To provide a more accurate and stable Radio-Frequency (RF) signal in conditioning and processing test progress, it is necessary to design a Low-Level Radio-Frequency (LLRF) control system which can provide high precision RF driving signal based on meeting the amplitude and phase stabilization requirement. Through Feed-Forward operation, accurate phase adjustment and amplitude adjustment are realized inside the pulse, to realize the precision and automation of phase-inversion, amplitude stabilization, phase stabilization, and wave-form adaptation matching. An LLRF System integrated with feed-forward control and vector modulation output was designed and built, and the long-term working stability of the LLRF system was verified during a new 50MW S-band Klystron conditioning progress.

Design, Calibration, and Evaluation of Real-Time Waveform Matching on an FPGA-based Digitizer at 10 GS/s

Digitizing side-channel signals at high sampling rates produces huge amounts of data, while side-channel analysis techniques only need those specific trace segments containing Cryptographic Operations (COs). For detecting these segments, waveform-matching techniques have been established comparing the signal with a template of the CO’s characteristic pattern. Real-time waveform matching requires highly parallel implementations as achieved by hardware design but also reconfigurability as provided by FPGAs to adapt the matching hardware to a specific CO pattern. However, currently proposed designs process the samples from analog-to-digital converters sequentially and can only process low sampling rates due to the limited clock speed of FPGAs. In this paper, we present a parallel waveform-matching architecture capable of performing high-speed waveform matching on a high-end FPGA-based digitizer. We also present a workflow for calibrating the waveform-matching system to the specific pattern of the CO in the presence of hardware restrictions provided by the FPGA hardware. Our implementation enables waveform matching at 10 GS/s, offering a speedup of 50x compared to the fastest state-of-the-art implementation known to us. We demonstrate how to apply the technique for attacking the widespread XTS-AES algorithm using waveform matching to recover the encrypted tweak even in the presence of so-called systemic noise.

Seimic impedance inversion based on semi-supervised learning

In coalfield seismic exploration, seismic impedance inversion can infer subsurface physical properties through seismic data, allowing the identification of subsurface strata. Deep learning has shown great potential in the field of seismic impedance inversion. Typically, the quantity of labeled data during training significantly impacts model validity. To reduce model dependence on labels, we propose a semi-supervised learning workflow for impedance inversion. By using a small number of seismic and well logging labels, a closed-loop mapping is established from seismic data to impedance data, impedance data to generated seismic data. In addition, non near-well seismic data are introduced to participate in network training during each iteration update process of the network. The waveform matching of non near-well seismic data can ensure the prediction accuracy of non near-well impedance to a certain extent. We evaluated the algorithm using a dataset from the Xinjing coal mining area near the city of Yangquan in Shanxi Province, China. The results showed that the proposed semi-supervised inversion method performed better and demonstrated improved inversion results relative to the conventional single-supervised method.

Mag-ODO: Motion speed estimation for indoor robots based on dual magnetometers

In indoor environments, accurate speed estimation is crucial for providing continuous and reliable position of mobile robots. However, conventional odometry methods may suffer from performance degradation, particularly in scenarios involving wheel odometry slipping or visual odometry blurring. In this study, we propose Mag-ODO, a novel motion speed estimation method based on dual magnetometers aimed at enhancing the robustness of indoor robot positioning systems. The dual magnetometers are mounted at the front and back of the robot, and the speed is estimated by matching the magnetic-filed waveforms sampled from the magnetometers. We employ the dynamic time warping (DTW) algorithm to implement waveform matching and use both the magnetic field strengths and the changing trend of the magnetic field strengths as the matching cost function, effectively reducing the matching error. Mag-ODO has two key advantages: immunity to magnetometer bias and transient disappearance of magnetic field gradients within a matching time window does not affect speed estimation accuracy. Test results show that Mag-ODO performs similarly to Wheel-ODO in magnetic-rich environments (RMSE < 0.06 m/s) and comparable dead reckoning (DR) performance with inertial navigation systems (INS) in both straight and curved environments.

Thermal ages of the Huatung Basin determined from seismic waveform modeling: insights into Southeast Asia’s evolution

The Huatung Basin (HB), situated on the leading part of the Philippine Sea Plate, is directly involved in oblique subduction and mountain building in the Taiwan region. However, previous studies have reported a wide range of ages for the HB, from 30 to 130 Ma, making it difficult to properly constrain regional tectonics. We analyzed teleseismic waveforms recorded on Taiwan that traveled through the slab associated with the HB. By waveform matching, we have constrained the slab dimensions to approximately 400 km in length and 150 km in width, accompanied by an enhanced P-wave velocity of 6% within the slab core and an apparent dip angle of 55°. We used age-dependent subduction zone thermal models to estimate the thermal ages or the ages since the last thermal event of the HB. The best-fit thermal model indicates thermal ages ranging from 20 to 50 Ma, which is consistent with a suite of geophysical observations and the age inferred from geomagnetic anomaly data. However, our results differ considerably from the ages obtained through radiometric dating of rocks dredged from the seafloor. The discrepancy in age may be attributed to either thermal rejuvenation of the plate or dating of allochthonous samples dredged from the border of the basin.

Enhancing prestack seismic data by sparse radon transform and dynamic waveform matching

Abstract In the field of complex underground geological structures and irregular topography, prestack seismic data often have a low signal-to-noise ratio (SNR), in which weakly reflected signals are buried beneath strong incoherent, and scattered noise. Stacking, such as beamforming along the moveout surfaces of coherent local events, can significantly improve seismic data quality. Accurate and efficient estimation of the moveout for an irregular acquisition geometry and uneven illumination is important in a complex environment. In this paper, a new optimal stacking approach for enhancing weak prestack reflection signals is presented. The proposed method mainly includes regional division and moveout estimation. Optimal stacking should be implemented within local time and space domains. Based on beam-ray theory, we designed a reasonable regional division of the common-shot (CS), common-receiver (CR) and common-middle-point (CMP) domains. Then, we proposed using the sparse radon transform and dynamic waveform matching method to estimate the moveout surfaces of local reflection events. The sparse radon transform was applied to obtain the linear moveout to ensure the correctness of the reflection wave direction. The residual nonlinear disturbance was estimated using the dynamic waveform matching method. Tests on synthetic and field data demonstrated the effectiveness of the proposed method, which can effectively improve the SNR of prestack seismic data and attenuate incoherent noise.

Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization

AbstractWe model basin and Moho structure in the Permian Basin region of west Texas and southeastern New Mexico using a method for waveform matching via global optimization of P‐to‐S receiver functions, vertical autocorrelograms, and horizontal autocorrelograms. The algorithm is driven by Particle Swarm Optimization, whose search history can be used to assess the strength of data constraints on model parameters. A common drawback in receiver function modeling is the need to assume a value for Vp before Vs and layer thickness can be estimated. But constraints on Vp can be provided by vertical autocorrelograms of teleseismic arrivals, which detect reverberating P waves, because the phase delay times depend only on Vp and interface depth. P‐to‐S receiver functions and vertical and radial autocorrelograms are computed for M &gt; 5.5 events at 30°–100° epicentral distance, then edited and binned by ray parameter. Synthetic seismograms are computed for layered models using a 1D reflectivity method. The free parameters in the algorithm include basin depth, basin Vp, basin Vp/Vs, and thickness and Vp/Vs of the crystalline crust. Where vertical autocorrelograms prove insufficient for determining basin Vp, Vp is assumed from nearby stations that were modeled successfully. We find an average basin Vp of 4.57 km/s and basin depths to ∼8 km. Moho depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed to produce sufficient phase separation to allow an average Vp to be determined with confidence. Modeling autocorrelograms jointly with receiver functions therefore improves constraints on deep basin structure.

Research on high speed communication method of ELF-EM while drilling based on adaptive combined filtering algorithm

In terms of the electromagnetic wave measurement while drilling, the extremely low frequency electromagnetic(ELF-EM) signal below 20Hz is usually chosen as the downhole data carrier. To improve the transmission rate of ELF-EM signal and the signal to noise ratio (SNR) at the receiving end, the DQPSK modulation was proposed as the modulation method for the communication of electromagnetic wave system. Different from the traditional IQ orthogonal modulation and coherent demodulation methods, the proposed phase selection modulation and correlation algorithm demodulation are easier to implement and more practical. With regard to the communication synchronization, a fast algorithm, which based on the normalized cross-relation number, was used for waveform matching, and the maximum point of the correlation coefficient was used as the starting point of communication synchronization. The communication simulation results show that the proposed DQPSK modulation signal based on the adaptive combined filtering algorithm has better terminal error rate and transmission rate than the traditional modulation method. Under the same carrier frequency and code width, the transmission rate of DQPSK modulation is 4 to 5 times and 2 times that of PPM modulation and 2DPSK modulation respectively. The communication modulation and demodulation modes as well as the decoding algorithm with combined adaptive filter proposed in this paper can effectively solve practical engineering problems.

Matrix Quantization and LPC Vocoder Based Linear Predictive for Low-Resource Speech Recognition system

Over the last ten years, there has been significant progress in the use of low-rate speech coders in voice applications for computers, military communications, and civil communications. This advancement has been made possible by the development of new speech coders that can generate high-quality speech at low data rates. The majority of existing coders include spectral representation of speech, speech waveform matching, and ”optimization” of the coder’s performance for human hearing. The goal of this paper is to provide a thorough evaluation of voice coding methods for educational purposes, with a particular emphasis on the algorithms used in low-rate cellular communication standards. The algorithm we developed using a voice-excited LPC vocoder produces clear, low-distortion results. Ordinary LPCs, on the other hand, fall short of vocoders because they can handle signals other than speech, such as music. To improve quality, additional bandwidth is used to reduce the bit rate. To improve the quality, we tried two approaches. The first was to increase the number of bits required to quantize the DCT coefficients. This coefficient would outperform the inverse DCT in closer error rearrangements. The second possibility is to increase the total number of quantized coefficients. As a result, error array rearrangements would be more accurate. The goal is to identify the point at which a method improvement outperforms the previous, better result. Other coding methods become more complex, but this vocoder suffices.

Abstract WP159: Examining The Utility Of 2D DSA For Carotid Stenosis Hemodynamic Pressure Analysis

Introduction: Measurement of local hemodynamic behavior is useful for assessing the pathology of carotid stenosis. In this study, we explore a novel method using two-dimensional digital subtraction angiography (2D DSA) to provide time-dependent pressure data. We performed comparative validation with computational fluid dynamics (CFD) and flow guidewire measurements as reference methods. Methods: A silicone phantom model of a carotid stenosis was prepared, filled with physiological saline, and connected to a flow pump (Vascular Simulations, LLC Left Heart Replicator) applying pulsatile flow. The stenosis model narrowed from a diameter of 1.37 cm to 0.58 cm for a length of 0.64 cm (NASCET=40%). Iodinated contrast media was injected, and time dependent pressure profiles at four regions of interest: 2 cm proximal to the stenosis, proximal end of the stenosis, distal end of the stenosis and 2 cm distal to the stenosis were derived from contrast intensity (I) obtained from 2D DSA background-subtracted intensity plots using previously published PVEC software. A Volcano flow guidewire sensor was utilized at the same four locations to collect flow data. Pulsatile CFD flow analysis was performed to extract flow data. Results: DSA PVEC contrast analysis allowed observation of distinct flow pulsatility. At 30fps, strong agreement of DSA contrast waveform matching CFD and guidewire flow measurements was found at the distal end of the stenosis over a period of linear contrast increase (constant dI/dt). Bland-Altman analysis was used to compare derived pressure with the reference methods over 3.5 cardiac cycles, from 5.9 s to 8.7 s after the initial injection of contrast. 2D DSA-derived pressure showed a mean departure of -10.24% (std. dev 14.65%) pressure obtained from CFD analysis, and a mean departure of -15.32% (std. dev 14.24%) from guidewire measurements for the duration. Conclusion: A novel computational approach suggests 2D DSA can provide real-time results to approximate carotid flow from 2D DSA. We showed using this novel approach the flow data is comparable with guide wire and CFD methods. Future experiments may allow it to provide a supplementary, non-invasive means to examine local hemodynamic behavior that is relevant to the evaluation of carotid stenosis.

Horizontal Geolocation Error Evaluation and Correction on Full-Waveform LiDAR Footprints via Waveform Matching

The geolocation accuracy of spaceborne LiDAR (Light Detection And Ranging) data is important for quantitative forest inventory. Geolocation errors in Global Ecosystem Dynamics Investigation (GEDI) footprints are almost unavoidable because of the instability of orbital parameter estimation and GNSS (Global Navigation Satellite Systems) positioning accuracy. This study calculates the horizontal geolocation error of multiple temporal GEDI footprints using a waveform matching method, which compares original GEDI waveforms with the corresponding simulated waveforms from airborne LiDAR point clouds. The results show that the GEDI footprint geolocation error varies from 3.04 m to 65.03 m. In particular, the footprints from good orbit data perform better than those from weak orbit data, while the nighttime and daytime footprints perform similarly. After removing the system error, the average waveform similarity coefficient of multi-temporal footprints increases obviously in low-waveform-similarity footprints, especially in weak orbit footprints. When the waveform matching effect is measured using the threshold of the waveform similarity coefficient, the waveform matching method can significantly improve up to 32% of the temporal GEDI footprint datasets from a poor matching effect to a good matching effect. In the improvement of the ratio of individual footprint waveform similarity, the mean value of the training set and test set is about two thirds, but the variance in the test set is large. Our study first quantifies the geolocation error of the newest version of GEDI footprints (Version 2). Future research should focus on the improvement of the detail of the waveform matching method and the combination of the terrain matching method with GEDI waveform LiDAR.

A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter

The compressed sampling theory provides a good idea for sampling broadband linear frequency‐modulated (LFM) signal, but the signal reconstruction after sampling should be considered when using the compressed sampling system to sample the signal. For the problem of estimating the parameters of the LFM signal under the condition of nonreconstruction, a method combining random demodulation (RD) and waveform matching (WM) dictionary is proposed in this paper. Firstly, the RD system is used to collect the compressed sampling data of LFM signal. In order to ensure the validity of the compressed sampling data, the value of the compressed sampling rate R in the RD system is discussed. Then, the feasibility of using compressed sampling data to estimate LFM signal parameters is theoretically demonstrated. According to the construction principle of waveform matching dictionary, the corresponding relationship between sparse representation coefficients, dictionary atoms, and signal parameters can be determined. Using this theory, the LFM signal parameters can be estimated. Finally, simulation and real signal experiments verify that the proposed method can realize the estimation of initial frequency and chirp rate of LFM signal when some prior information is known.

3‐D S Wave Imaging via Robust Neural Network Interpolation of 2‐D Profiles From Wave‐Equation Dispersion Inversion of Seismic Ambient Noise

AbstractAmbient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two‐step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross‐correlation of ambient noise signals. However, the two‐step methods have a 1‐D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2‐D wave‐equation dispersion (WD) inversion method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave‐equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data, which makes WD method less prone to getting stuck to local minima. In our study, the observed Scholte waves are generated by cross‐correlating continuous ambient noise signals recorded by each ocean bottom node (OBN) in the 3‐D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2‐D S velocity structure beneath the first line. We then use a robust neural network (NN)‐based method to interpolate the inverted 2‐D velocity slices to a continuous 3‐D velocity model and also generate a corresponding uncertainty model. Moreover, we compared the predicted dispersion curves and waveforms to the observed data, and a robust waveform and dispersion match are observed across all of the Gorgon OBN lines.

Estimation of fluid salinity using coseismic electric signal generated by an earthquake

SUMMARY The seismoelectric effect is a coupling phenomenon between the seismic wavefield and electromagnetic field caused by the electric double layer in a fluid-saturated porous medium. As seismoelectric signals are sensitive to porous medium properties, such as the water saturation, salinity, porosity and permeability, they have good potential for imaging the structure and estimating underground parameters. In this study, we proposed an inversion method for estimating the salinity using coseismic electric fields generated by electrokinetic effects. The method was established by waveform matching between synthetic and observed coseismic electric signals based on a horizontally layered model. We used an L1 norm measure to construct the regularization term and achieve a high-resolution layer interface. Subsequently, we applied the first-order Taylor expansion to estimate the sensitivity and used logarithm transformation to constrain the range of parameters and reduce the solution space. Finally, we used an iteratively reweighted least-squares method to solve the final Gauss–Newton-type inversion function in each iteration to obtain the model update until the inversion converged. Numerical experiments were conducted to test the resolution, anti-noise ability and stability of the inversion algorithm. These results demonstrate that the proposed method can effectively recover the salinity structure, which broadens the application of seismoelectric effects. We further applied the method to the Mw 6.5 Jiuzhaigou earthquake in 2017 and used the observed coseismic electric field to estimate the salinity and conductivity beneath the station.