Frequency Energy Distribution Research Articles

Enhancing speech emotion detection with Windowed Long-Term Average Spectrum and Logistic-Rectified Linear Unit

Speech emotion detection can help monitor people's mental health by analyzing their speech patterns and recognizing emotional indicators. The raw data is initially augmented using various techniques such as shifting, pitching, noise injection, and stretching. Some feature extraction methods may only produce a limited set of features, potentially missing out on important information present in the data. This can result in reduced accuracy and the inability to capture complex patterns and variations in speech signals. The planned work consists of two phases: attribute extraction and classification. In the first phase, the WLTAS (Windowed Long-Term Average Spectrum) feature extraction extracts spectral information from speech signals, providing a compact and representative feature representation. It captures the frequency content and energy distribution of speech over time, enabling the analysis of important spectral characteristics. In the second phase, the Logistic-Rectified Linear Unit (LoRLU) introduces sparsity. It enhances the discriminative power of the classifier by allowing only positive values to pass through while discarding negative values. The Gibbs Restricted Boltzmann Machine (GRBM) is a generative probabilistic model that studies a joint probability delivery of the input data. It is particularly effective in capturing dependencies and patterns in high-dimensional data. The performance analysis of WLTAS-L-GRBM (Windowed Long-Term Average Spectrum - Logistic-Rectified Linear Unit - Gibbs Restricted Boltzmann Machine) has been conducted using evaluation metrics such as accuracy (99%), precision (0.97), recall (1), F1 score (0.98), MSE (0.01), and AUC-ROC (0.97) and also evaluated against various speech emotion datasets.

Comparing different segments in shut-in pressure signals: new insights into frequency range and energy distribution

Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0 to 0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0 − 1 Hz, an exponential decay was observed in all three segments. In 1 − 100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100 − 500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.

Energy estimation of small-scale jets from the quiet-Sun region

Context. Solar jets play a role in coronal heating and the supply of solar wind. Aims. In this study, we calculate the energies of 23 small-scale jets emerging from a quiet-Sun region in order to investigate their contributions to coronal heating. Methods. We used data from the High-Resolution Imager (HRI) of the Extreme Ultraviolet Imager (EUI) on board the Solar Orbiter. Small-scale jets were observed by the HRIEUV 174 Å passband in the high cadence of 6 s. These events were identified by the time–distance stacks along the trajectories of jets. Using the simultaneous observation from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), we also performed a differential emission measure (DEM) analysis on these small-scale jets to obtain the physical parameters of plasma, which enabled us to estimate the kinetic and thermal energies of the jets. Results. We find that most of the jets exhibit common unidirectional or bidirectional motions, while some show more complex behaviors; namely, a mixture of unidirection and bidirection. A majority of jets also present repeated eruption blobs (plasmoids), which may be signatures of the quasi-periodic magnetic reconnection that has been observed in solar flares. The inverted Y-shaped structure can be recognized in several jets. These small-scale jets typically have a width of ∼0.3 Mm, a temperature of ∼1.7 MK, an electron number density of ≳109 cm−3, with speeds in a wide range from ∼20–170 km s−1. Most of these jets have an energy of 1023–1024 erg, which is marginally smaller than the energy of typical nanoflares. The thermal energy fluxes of 23 jets are estimated to be (0.74–2.96)×105 erg cm−2 s−1, which is almost on the same order of magnitude as the energy flow required to heat the quiet-Sun corona, although the kinetic energy fluxes vary over a wide range because of their strong dependence on velocity. Furthermore, the frequency distribution of thermal energy and kinetic energy both follow the power-law distribution N(E)∝E−α. Conclusions. Our observations suggest that although these jets cannot provide sufficient energy to heat the whole quiet-Sun coronal region, they are likely to account for a significant portion of the energy demand in the local regions where the jets occur.

Experimental Study on the Sloshing of a Rectangular Tank under Pitch Excitations

Fluid sloshing within containers subjected to external motion is a crucial yet intricate phenomenon with implications across various industries. This study investigates sloshing in a rectangular liquid tank through a series of experiments examining pitch excitations with diverse excitation frequencies and amplitudes across different liquid carrying rates. By analyzing pressure data and imagery of the free liquid surface, statistical trends in peak pressure at measurement points within the tank are identified, revealing the nonlinear behavior of the fluid. Spectral analysis generates power spectrum curves that delineate frequency components and energy distribution within the sloshing dynamics. Key findings include the identification of resonance-induced violent sloshing at a 20% liquid-carrying rate and a resonant frequency shift at a 70% liquid-carrying rate due to nonlinearity, displaying a “soft spring” characteristic in the frequency response. The free liquid surface exhibits four distinct waveforms depending on frequency. Notably, at a 70% liquid-carrying rate and resonant frequency excitation, three-dimensional vortex waves emerge, highlighting a complex three-dimensional effect within the tank. The power spectrum shows that the dominant response frequency aligns with the excitation frequency and its multiples. This investigation enhances our understanding of the intricate nature of sloshing in various liquid-carrying conditions, offering insights valuable for diverse industrial applications.

Low-Frequency Intensity Modulation of High-Frequency Rotor Noise

Acoustic spectra of rotor noise yield frequency distributions of energy within pressure time series. However, they are unable to reveal phase relations between different frequency components while these play a role in the fundamental understanding of low-frequency intensity modulation of higher-frequency rotor noise. A methodology to quantify interfrequency modulation is applied to a comprehensive acoustic dataset of a fixed-pitch benchmark rotor, operating at a low Reynolds number and at advance ratios ranging from J=0 to 0.61. Our findings strengthen earlier observations in case of a hovering rotor, in which the modulation of the high-frequency noise is strongest around an elevation angle of θ=−20° (below the rotor plane). For the nonzero advance ratios, modulation becomes dominant in the sector −45°≲θ≲0° and is most pronounced at the highest advance ratio tested (J=0.61). Intensity modulation of high-frequency noise is primarily the consequence of a far-field observer experiencing a cyclic sweep through the noise directivity pattern of relatively directive trailing-edge noise. This noise component becomes more intense with increasing J and is associated with broadband features of the partially separated flow over the rotor blades.

Defect detection of grouting sleeve connection with energy ratio change in frequency domain

Controlling the grouting density is the key to ensure the construction quality of precast structures. Since the grouting sleeve is embedded in concrete, it is difficult to detect the grouting compactness of the sleeve. In this paper, the frequency domain analysis technique is used for non-destructive testing of concrete components with grouting defects. The collected ultrasonic testing signals are calibrated-denoised-decomposed-reconstructed. The damage degree of the grouting signal is evaluated and analyzed by quantifying the energy value of different frequency signals with two norms. Considering the effect of the random distribution of aggregates on the propagation of ultrasonic stress waves, a three-dimensional multi-point detection model and a two-dimensional plane single-point model based on piezoelectric effect are set up respectively. Results show that frequency energy distribution of the grout-deficient signal is concentrated to low-frequency range than that of grout-compact signal. When grouting defect accumulates to a certain extent, the main frequency may move from high-frequency band to lower-frequency band. Although the aggregates in different concrete components are randomly distributed, the test results of the two-dimensional plane model show that the validity of the defect quantitative index is 72%. The experimental tests results show that the method of using frequency energy transfer to lower-frequency band to determine grouting defects is effective, with a detection validity of 66.7%. Therefore, the non-destructive testing method for grouting sleeve compactness proposed in this paper can provide reference for engineering testing application.

Time evolution of temporal, spatial and spectral distribution of high-energy electron radiation in intense laser pulses

To study the time evolution of high-energy electron radiation in circularly polarized intense laser pulses in detail, a model of the interaction between the high-energy single electron and intense laser pulses is constructed based on the Lagrangian equation and the electron energy equation. Through simulation, this article vividly displays the evolution process of radiation in the spatial, frequency and time domain. By modulating the interaction time between the laser and electron and referring to the spatial distribution image of energy, the value and direction of the maximum radiation energy per unit solid angle are calculated. In addition, in specific directions, this paper discusses the effects of interaction duration on the energy frequency distribution and the power variation pattern. The results prove that the maximum radiation energy per unit solid angle will appear when the interaction time comes to about 450 fs, which is also the boundary moment when the frequency and time spectrum no longer change obviously. Therefore, by modifying the duration of the electron–laser interaction, it is possible to produce the electron radiation with desired characteristics more precisely.

Deep auto-encoder network for mechanical fault diagnosis of high-voltage circuit breaker operating mechanism

Abstract To improve the accuracy of the mechanical fault diagnosis of the operating mechanism and fully exploit the characteristic information in the vibration signal of the high-voltage circuit breaker, a mechanical fault diagnosis method of the operating mechanism of the high-voltage circuit breaker based on the deep self-encoding network is proposed. First, the vibration signal of the switch operating mechanism is extracted, the wavelet packet conversion is performed, and the vibration signal of each frequency band is divided into equal times. The energy of the time–frequency subplane of the vibration signal is then calculated, and the time–frequency energy distribution is used as a switch. Finally, a breaker failure diagnostic model based on the deep self-coding network is established. Pretraining and tuning and a 126 kV high-voltage switch are used to simulate different types of faults and validate the method. Experimental results show that this method can acquire sample failure data and perform failure diagnosis, and the diagnosis accuracy rate reaches 97.5%. The deep self-coding network can fully pierce deep information on the switch vibration signal.

Modeling Exact Frequency-Energy Distribution for Quakes by a Probabilistic Cellular Automaton.

We develop the notion of Random Domino Automaton, a simple probabilistic cellular automaton model for earthquake statistics, in order to provide a mechanistic basis for the interrelation of Gutenberg-Richter law and Omori law with the waiting time distribution for earthquakes. In this work, we provide a general algebraic solution to the inverse problem for the model and apply the proposed procedure to seismic data recorded in the Legnica-Głogów Copper District in Poland, which demonstrate the adequacy of the method. The solution of the inverse problem enables adjustment of the model to localization-dependent seismic properties manifested by deviations from Gutenberg-Richter law.

Similar properties between gamma-ray emission of 3C 454.3 and solar GeV flares

ABSTRACTBased on the survey data observed by Fermi-Large Area Telescope (LAT), we elaborate the statistical characteristics of gamma-ray flares from 3C 454.3 and solar GeV flares among flare parameters, such as isotropic energy (Eγ), peak luminosity (LP), and duration time (TDuration). We find two significant correlations as: $T_{\rm Duration} \propto E_{\gamma }^{0.31\pm 0.03}$ and $L_{\rm P} \propto E_{\gamma }^{0.61\pm 0.03}$ for 3C 454.3. The exponents are in a better agreement those of the Sun from the first Fermi-LAT solar flare catalogue, namely $T_{\rm Duration,\odot } \propto E_{\gamma ,\odot }^{0.38\pm 0.08}$ and $L_{\rm P,\odot } \propto E_{\gamma ,\odot }^{0.81\pm 0.08}$. The relationship of TDuration–Eγ and LP–Eγ could be interpreted naturally as due to magnetic dissipation through reconnection. On top of that the frequency distributions of isotropic energy, peak luminosity, and duration time of gamma-ray emission of 3C 454.3 show power-law forms, and the waiting time distribution can be described by a non-stationary Poisson process. These distribution behaviours are comparable to those of the Sun, Sagittarius A*, and M87, and follow the prediction of a self-organized criticality model. All statistical properties suggest that similar to the physical process accounting for solar GeV flares and X-ray flares in supermassive black hole systems, magnetic reconnection cloud govern the energy-release, and particle acceleration process for gamma-ray flares of 3C 454.3.

Empirical mode decomposition and power spectrum filtering for detection of frequency channels related to multi-scale geochemical anomalies: Metal exploration targeting in Moalleman district, NE Iran

Geochemical anomaly patterns for metal exploration targeting usually reveal non-stationarity due mainly to the non-linear synthesis of ore-forming processes. The spectrum–area (S–A) multifractal filtering and the bidimensional empirical mode decomposition (BEMD) are robust methodologies for detecting geochemical anomalies with different scaling properties. The former portrays the generalized scale invariance characteristics of a geochemical image in the two-dimensional Fourier frequency domain. It relies on estimating scaling exponents of power-law functions in the spectral energy density plane to design filter functions that amplify a geochemical signal in specific frequency channels. The BEMD decomposes a geochemical image into a number of bidimensional intrinsic mode functions (BIMFs). It represents the spatial–frequency–energy distribution of a given image that suits non-stationary geochemical signals arising from non-linear Earth systems. This paper gauges the relevance of BEMD and S–A in signalizing frequency channels that correspond to regional- and local-scale geochemical anomalies through a comparative experiment in the Moalleman district, NE Iran. The BEMD filtered the multi-element anomaly map adaptively into a set of BIMFs (BIMF1, BIMF2, and BIMF3) and a residual signal, in order of decreasing frequency, yielding geochemical patterns with scaling characteristics from local to regional. The S–A model filtered the multi-element anomaly map into very high, high, and low frequency channels that characterize noise signatures and geochemical anomalies associated with local and regional ore-forming processes, respectively. The results indicate that the high frequency channel derived from the BEMD filtering (i.e., BIMF1) can portray more effectively local-scale metal-vectoring anomalies distributed at/around ore-controlling faults/fractures. However, the low frequency channel yielded by S–A filtering has enhanced effectively geochemical anomaly architectures that were developed through regional metallogenesis. Therefore, we considered the local-scale geochemical anomalies (i.e., BIMF1) to delimit metal-vectoring targets, whereby four exploration priorities were defined as new areas for further metal prospecting within the study area.

Pressure field characteristics of underwater detonation gas jet below and above free water surface

An underwater detonation tube (DT) experiment is carried out to investigate the pressure field characteristics of the detonation gas jet propagating under and above a free surface. In the experiment, a 0.78 L DT filled with 0.12 MPa methane-oxygen mixture is detonated. The evolution of jet shape and pressure field are recorded using high-speed photography, pressure sensors, hydrophones and microphones. There are mainly three bubble pulsation periods determined from the bubble evolution and field pressure evolution, and the bubble shape tends to be stable after the pulsation ends. The fast Fourier Transform and Wavelet Transform method are adopted to analyze the frequency characteristics and energy distribution of the pressure signals. The energy of the bubble pulsation and the energy of the shock wave have different contributions in different media. Specifically, the high-frequency energy formed by reverberation contributes more in water, while the low-frequency energy formed by bubble pulsation is the main part of the energy in the air. The shock wave formed by the decoupling of the detonation wave attenuates sharply during its propagation. The distribution characteristics of the detonation acoustic signal in the air are analyzed using the band-pass filter pair, and the transmission coefficient is defined to study the ability of the detonation acoustic signal to transmit through the water–air interface. The result indicates that the low-frequency signals, such as bubble pulsations, can transmit out of the water–air interface at a larger transmission angle more easily than the high-frequency signals. The signal of bubble pulsation has a stronger transmission ability through the water–air interface compared to the shock wave. Combining the bubble evolution and pressure evolution is helpful to analyze the different signal components in the water and air, which plays an important role in the analysis of the pressure field characteristics.

Flares and rotation of M dwarfs with habitable zones accessible to TESS planet detections

Context. More than 4000 exoplanets have been discovered to date, providing the search for a place capable of hosting life with a large number of targets. With the Transiting Exoplanet Survey Satellite (TESS) having completed its primary mission in July 2020, the number of planets confirmed by follow-up observations is growing further. Crucial for planetary habitability is not only a suitable distance of the planet to its host star, but also the star’s properties. Stellar magnetic activity, and especially flare events, expose planets to a high photon flux and potentially erode their atmospheres. Here especially the poorly constrained high-energy UV and X-ray domain is relevant. Aims. We characterize the magnetic activity of M dwarfs to provide the planet community with information on the energy input from the star; in particular, in addition to the frequency of optical flares directly observed with TESS, we aim at estimating the corresponding X-ray flare frequencies, making use of the small pool of known events observed simultaneously in both wavebands. Methods. We identified 112 M dwarfs with a TESS magnitude ≤11.5 for which TESS can probe the full habitable zone for transits. These 112 stars have 1276 two-minute cadence TESS LCs from the primary mission, which we searched for rotational modulation and flares. We study the link between rotation and flares and between flare properties, for example the flare amplitude-duration relation and cumulative flare energy frequency distributions (FFDs). Assuming that each optical flare is associated with a flare in the X-ray band, and making use of published simultaneous Kepler/K2 and XMM-Newton flare studies, we estimate the X-ray energy released by our detected TESS flare events. Our calibration also involves the relation between flare energies in the TESS and K2 bands. Results. We detected more than 2500 optical flare events on a fraction of about 32% of our targets and found reliable rotation periods only for 12 stars, which is a fraction of about 11%. For these 12 targets, we present cumulative FFDs and FFD power law fits. We construct FFDs in the X-ray band by calibrating optical flare energies to the X-rays. In the absence of directly observed X-ray FFDs for main-sequence stars, our predictions can serve for estimates of the high-energy input to the planet of a typical fast-rotating early- or mid-M dwarf.

A Novel MSFED Feature for the Intelligent Fault Diagnosis of Rotating Machines

The vibration energy distribution pattern usually changes with the rotating machine’s health state and is a good indicator for intelligent fault diagnosis (IFD). The existing initial features such as RMS are less effective in revealing the vibration energy distribution pattern, and the frequency spectrum cannot provide a rich and hierarchical description of the vibration energy distribution pattern. Addressing this issue, we proposed a multi-scale frequency energy distribution (MSFED) feature for the IFD of rotating machines. The MSFED feature can reveal the vibration energy distribution patterns in the frequency domain in a multi-scale manner, and its one-dimensional vector and two-dimensional map formats make it usable for most IFD models. Experimental validation on the gearbox and bearing datasets verified that the MSFED feature achieved the highest diagnostic accuracy among commonly used initial features, in typical fault diagnosis scenarios except for the variable-load scenario. Furthermore, the separability and transferability of the MSFED feature were evaluated by distance-based metrics, and the results were in agreement with the features’ diagnostic performance. This work provides an important reference for the IFD of rotating machines, not only proposing a novel MSFED feature but also opening a new avenue for model-independent methods of the initial quality evaluation.

Music Emotion Classification Method Using Improved Deep Belief Network

Aiming at the problems of difficult data feature selection and low classification accuracy in music emotion classification, this study proposes a music emotion classification algorithm based on deep belief network (DBN). The traditional DBN network is improved by adding fine-tuning nodes to enhance the adjustability of the model. Then, two music data features, pitch frequency and band energy distribution, are fused as the input of the model. Finally, the support vector machine (SVM) classification algorithm is used as a classifier to realize music emotion classification. The fusion algorithm is tested on real datasets. The results show that the fused feature data of pitch frequency and band energy distribution can effectively represent music emotion. The accuracy of the improved DBN network fused with the SVM classification algorithm for music emotion classification can reach 88.31%, which shows good classification accuracy compared with the existing classification methods.

Numerical analysis of stochastic processes in the aggregation erythrocyte molecules : Application to deoxy-hemoglobin S

Previously, by analytical method, some authors have shown that the harmonic noise is more indicated in the aggregation of deoxy-hemoglobin S. The problem that still arises is the relationship between the energy spectra and the microscopic properties of dielectric substances. The numerical approach of the correlation function’s Fourier transform of different random processes have permit to obtain the frequency distribution spectra of energy and the fluctuations of the amplitudes. The results have shown that (i) only a fine analysis of the curves can permit obtain sufficient precision on the noise indicated in the aggregation of deoxy-hemoglobin S, (ii) in the presence of harmonic noise, the frequency distribution of energy and fluctuations of the amplitudes are low compared to white and colored noise, (iii) method exploiting the frequency distribution of energy and the fluctuations of the amplitudes justify well that the harmonic noise is the best indicated in the aggregation of the deoxy-hemoglobin S. Â

Effects of flares on the habitable zones of M dwarfs accessible to TESS planet detections

AbstractPhotometric space missions such as Kepler and TESS continuously discover new exoplanets and advance the search for a second habitable world. The light curves (LCs) recorded by these telescopes also reveal signs of magnetic activity, such as star spot modulation and flares, which can influence habitability. Searching for these characteristics, we analyzed TESS LCs of 112 M dwarfs selected according to the criterion that TESS can spot planet transits over their entire habitable zone (HZ). We detected 2,532 flare events occurring on 35 stars; thus the flaring fraction is . For only of our stars, we found rotation periods. We calculated bolometric flare energies and luminosities, flare energy frequency distributions (FFDs), and the bolometric flux reaching the HZ at the peak of the flare. We estimated the effects of flaring on the atmosphere of an Earth‐like planet in the HZ from the viewpoint of both ozone depletion and the enabling of chemical reactions necessary to build ribonucleic acid (RNA). None of our targets exhibits highly energetic flares at a frequency large enough to trigger ozone depletion or RNA formation.

Information Entropy Analysis of Frictional Vibration under Different Wear States

To reveal the chaotic characteristics of friction vibration, pin–plate wear experiments were carried on a wear tester. Different wear states were designed by changing the amount of lubricating oil and distinguished with the friction coefficient. In order to improve the reliability of the collected data, the fictional vibration signals were denoised using an ensemble empirical mode decomposition (EEMD) method. The method of information entropy was proposed to measure the disorder of frequency energy distribution of friction vibration signals. The information entropy of friction vibration decreases in the running-in wear state, fluctuates steadily in the stable wear state, and increases rapidly in the severe wear state. The variation of information entropy of friction vibration signals is closely related to the surface topography of friction pairs. The results indicate that the information entropy of friction vibration can be applied to monitor and recognize the wear state of friction pairs.

Research on electromagnetic radiation (EMR) waveform characteristics of coal failure process using Hilbert-Huang transform (HHT)

Electromagnetic radiation (EMR) waveforms contain rich information on coal fracture law, which is of great value to the refined study of their characteristics. In this paper, the Hilbert-Huang transform (HHT) method is used to analyze and process EMR waveform of coal failure process. The results show that HHT method can adapt well to the nonstationary and nonlinear characteristics of EMR waveform. It adaptively decomposes EMR waveforms by empirical mode decomposition (EMD), extracts the intrinsic mode function (IMF) characteristics of EMR waveforms, and obtains the law of instantaneous energy changes of EMR waveforms. At the same time, EMR waveform energy is quantitatively expressed on the three-dimensional Hilbert energy spectrum (time–frequency-energy distribution), and the refined features of EMR waveform signal are effectively extracted. The instantaneous energy of EMR and the three-dimensional Hilbert energy spectrum processed by HHT method more easily describe the fracture law of coal body. Compared with conventional characteristic parameters, such as EMR amplitude and dominant frequency, the identification degree of the precursor characteristics of coal body instability is greatly improved. Among the short-term Fourier transform (STFT), wavelet transform (WT) and HHT nonstationary signal processing methods, HHT method has an improved adaptability and superiority and can reveal the characteristics of coal deformation and failure more carefully and accurately, which provides a new means for better using and improving EMR method to monitor coal instability and failure.

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave.

Accurately identifying the location of loss zone after lost circulation is the key to subsequent plugging operation. In view of the difficulty of identifying the location of lost circulation zone, a method of identifying the location of loss zone by transient pressure wave signal is proposed. When lost circulation occurs, transient back pressure is applied to the wellhead at the surface choke manifold to produce transient pressure wave. The transient pressure wave propagates downward from the wellhead. The propagation process of transient pressure wave in an annulus system is analyzed, and the position of loss zone is determined according to the change of pressure signal at the choke manifold. Based on the simulation of this method, relevant experiments are also carried out. Aiming at the problem of excessive noise of the pressure wave signal collected in the experiment, variational modal decomposition (VMD) is used to decompose the signal into multiple band-limited intrinsic mode function (BIMF) components. Combined with a Hilbert spectrum, the time–frequency characteristics and energy distribution of each BIMF component are analyzed in turn. The main frequency component is selected to reconstruct the signal to achieve the denoising effect. On this basis, a wavelet modulus maxima method is used to decompose the denoised signal, extract the characteristic points of the signal, identify the loss circulation information in the signal, and then identify the thief zone position by a time–domain method. Through experimental verification, the existence of loss zone will affect the change trend of pressure wave; a VMD–wavelet modulus maxima algorithm can effectively remove the noise of the pressure wave signal and locate the pressure change point. The experimental recognition error range of this method is 0.10–9.22%, which has certain guiding significance for field application.