143 results about "P-wave" patented technology

The invention provides a catheter tip accurate positioning method and system based on electrocardiogram guidance. The method is applied to a control module of a catheter tip accurate positioning device. The method comprises the following steps: acquiring a standard electrocardiogram corresponding to a to-be-positioned patient, and constructing P-wave baseline features of the patient according to the standard electrocardiogram; when the catheter is inserted into a preset area of a patient, electrocardiogram detection information sent by an electrocardiogram sensing module of the device is obtained and analyzed, and waveform information of P waves is obtained; obtaining first change information and second change information according to the P-wave baseline characteristics and the waveform information so as to obtain first position information corresponding to the catheter; if it is confirmed that the P wave is obviously increased when the catheter is inserted according to the P wave variability information and it is confirmed that the distance between the catheter and the right atrium is smaller than the preset distance according to the first position information, pushing of the catheter is stopped, and accurate positioning of the catheter tip is completed. The accuracy of catheterization is improved through electrocardiogram guidance, and the safety and accuracy of overall operation are improved.

The invention relates to the technical field of biomedical signal processing and intelligent health monitoring, and particularly discloses an intelligent heart rate monitoring method and system based on heart sound and electrocardiosignals, heart sound signals and electrocardiosignals of a user are collected in real time, quality evaluation is conducted on the collected signals through signal quality indexes, and accurate signals are extracted; extracting time domain and frequency domain features of the first heart sound and the second heart sound of the heart sound signal, fusing multiple features by adopting wavelet transform and a main frequency analysis algorithm, and calculating a heart sound abnormal coefficient to evaluate the abnormal degree of the heart sound signal; the vibration amplitude and frequency characteristics of P waves in the electrocardiosignals are extracted, and an electrocardiograph abnormal coefficient is calculated through a vibration amplitude abnormal coefficient and a vibration frequency fluctuation coefficient and used for evaluating the stability of the electrocardiosignals; based on the heart sound abnormal coefficient and the electrocardiogram abnormal coefficient, a decision tree model is constructed, a heart rate abnormal index is dynamically calculated, dynamic monitoring of the heart rate is achieved, a dynamic early warning mechanism is conducted, and heart rate abnormal information is output.

A vibration P wave arrival time pickup method of a lightweight transfer learning model belongs to the technical field of micro-seismic monitoring, and comprises the following steps: 1, importing earthquake and micro-seismic event data; 2, preprocessing the data in the step 1; and 3, making a training set and a test set by using the data in the step 2. And 4, constructing a seismic data arrival time pickup model. And 5, inputting data of the seismic data training set in the step 3 to the pickup model in the step 4 for training. And 6, inputting data of the seismic data test set in the step 3 to the pickup model in the step 4 for P-wave arrival time pickup, and evaluating the pickup effect of the model. And 7, inputting the micro-seismic data to the qualified pickup model in the step 6 for transfer learning, and finely adjusting the model to realize the arrival time pickup of the micro-seismic data. According to the method, the P-wave arrival time picking method with high accuracy and reliability is provided.

The invention discloses a micro-seismic multichannel data processing method and system based on a DBSCAN algorithm, and belongs to the technical field of micro-seismic monitoring. According to the invention, through feature vector construction and optimization distance calculation, the clustering reliability is enhanced; a multi-dimensional feature vector Xi containing P-wave refinement arrival time ti, an amplitude Ai and space coordinates (xi, yi, zi) is constructed, distance calculation is performed by combining a normalized cosine distance with wave velocity constraint, a micro-seismic wave propagation rule is fully utilized, the discrimination capability of DBSCAN clustering on physical consistency is enhanced, and the robustness of a screening result is improved, so that noise interference can be effectively processed in a complex environment, and the accuracy and the robustness of the screening result are improved. And the stability and the reliability of a micro-seismic positioning result under different geological conditions are ensured.

The invention discloses an S-wave seismic phase first arrival identification method based on actually measured blasting-induced particle vibration signals, and relates to the field of negative effect monitoring and safety evaluation of blasting vibration induced by rock blasting excavation, and the method comprises the steps: determining a blasting scheme based on a scanning result; arranging a blasting vibration sensor; acquiring actual measurement waveforms of blasting vibration at different blasting center distances; constructing an S-wave seismic phase first arrival recognition function; and obtaining the first arrival moments of the S-wave seismic phases of the actually measured vibration waveforms at different explosion center distances. Compared with the prior art that the recognition precision of the S wave of the blasting vibration signal under the engineering scale is low and the like, by comprehensively utilizing the differences of the S wave, a noise signal, a P wave and a P wave tail wave in the aspects of energy, frequency, polarization characteristics and the like, recognition parameters such as the ratio of transverse energy to total energy, the polarization degree and the deflection angle are introduced, and the transverse energy serves as a weight coefficient; an S-wave recognition function suitable for actual measurement of blasting vibration is provided, and the technology has the advantages of small recognition error, high efficiency and the like.

The invention discloses a mine microseismic event self-positioning method and a mine microseismic event self-positioning system without a prior velocity structure, which realize integration of underground velocity structure inversion and seismic source positioning by jointly utilizing microseismic signals and surface wave interference noise generated in a mining process. Comprising the following steps: arranging an earth surface detector array to collect micro-motion and micro-seismic mixed signals; extracting a surface wave dispersion curve to invert a stratum S-wave velocity model, and further constructing a P-wave velocity model when the stratum Poisson's ratio in the geological data is known; carrying out seismic phase pickup after carrying out noise reduction on the microseismic signal, and obtaining first arrival time of a P wave and an S wave; and constructing a target function taking the first arrival time, the seismic source position, the seismic moment and the velocity model as parameters, and optimizing the target function to realize accurate positioning. According to the method, early-stage field acoustic logging construction of a mine is avoided, surface wave interference noise is fully utilized, the utilization rate of monitoring data is improved, and mine microseism event positioning without a prior speed structure is achieved.

The invention discloses a multi-wave chromatography and reflection combined detection method and device for a thin-covering-layer urban active fault and a medium. The method comprises the steps that longitudinal wave seismic data and transverse wave seismic data are collected; recording vertical component data based on the longitudinal wave seismic data for first arrival travel time pickup and tomography inversion to obtain a longitudinal wave velocity structure model Vp; extracting Rayleigh surface wave frequency dispersion through the vertical component data and carrying out multichannel surface wave analysis inversion to obtain a transverse wave velocity structure model Vs; calculating a longitudinal and transverse wave velocity ratio parameter structure model Vp / Vs, and calculating to obtain a Poisson's ratio parameter structure model v; obtaining a longitudinal wave (P wave) reflection profile and a transverse wave (S wave) reflection profile; identifying and summarizing into an interpretation fracture set Fc, and recording key elements; and by taking the interpretation fracture set Fc as a structural skeleton, completing unified registration and superposition display on Vp, Vs, Vp / Vs and v, a longitudinal wave reflection profile and a transverse wave reflection profile, and outputting a shallow underground structure comprehensive interpretation map of the thin covering layer region.

The invention discloses a tunnel micro-seismic enhanced positioning method and system based on a DAS and a blasting active source signal. The method comprises the steps that firstly, optical fibers are arranged on the surface of a tunnel wall and in a drill hole; blasting the tunnel face to generate a blasting active source signal with known time and space; then, a blasting active source signal is collected through a DAS system, and P wave arrival time of each monitoring point on the optical fiber is recorded; establishing and optimizing a wave velocity field partition model according to the spatio-temporal information of the blasting active source signal and P wave arrival time of each monitoring point on the optical fiber; and when a micro-seismic event occurs in the surrounding rock, the optimized wave velocity field partition model and P wave arrival time measured at each monitoring point on the optical fiber are utilized, a geometric algorithm is combined to invert and position the micro-seismic event, and a positioning result is output. The system comprises a signal acquisition system, an optical fiber network and a DAS system. The method has the advantages that the wave velocity field model is optimized and dynamically updated by using the tunnel face blasting signal, and the distributed sensing is performed by using the DAS system, so that the capability of monitoring and evaluating the micro-seismic event of the tunnel is remarkably enhanced.

The invention discloses an ultra-deep reflection imaging method based on passive source seismic data and a related device, and relates to the technical field of earth depth structure imaging, and the method comprises the following steps: firstly, for original continuous seismic waveform data, intercepting seismic waveform data with a first arrival of a P wave and a non-arrival of an S wave, and carrying out the preprocessing of the data, so as to obtain a far earthquake event data segment; the method comprises the following steps: firstly, analyzing and identifying a far earthquake event with a high signal-to-noise ratio by using a time-frequency-wavenumber domain, removing data which are greatly influenced by noise, and then, reconstructing a reflection wave field of a high-quality far earthquake data fragment by using a multi-dimensional deconvolution technology, and carrying out convolution on a seismic source wavelet to obtain ultra-deep reflection wave field data. And finally, performing reflection imaging processing in combination with velocity analysis and superposition imaging technologies, optimizing the analysis capability of a deep reflection interface, and finally obtaining an earth deep reflection imaging profile, so that a continuous structure interface extending from the shallow earth crust to the deep mantle can be clearly revealed, and coupling characteristics between shallow and deep earth structures can be effectively captured.

The disclosure provides a method and device for velocity tomography imaging of seabed shallow media, and electronic equipment. The method includes performing multichannel analyses of Guided-P and Scholte waves on seabed multi-component seismic gathers to determine measured multi-order dispersion curves of the two waves; based on a first seabed shallow media model and a theoretical dispersion equation of Guided-P wave, performing joint inversion of multi-order dispersion curves of Guided-P wave to iteratively update the P-wave velocity of seabed media; based on a second seabed shallow media model and a theoretical dispersion equation of Scholte wave, performing joint inversion of multi-order dispersion curves of Scholte wave to iteratively update the S-wave velocity of seabed media under the constraint of the P-wave velocity determined by the Guided-P wave dispersion inversion; and performing tomography imaging of seabed velocity structures with the inverted P-wave and S-wave velocities along a survey line.

The invention relates to a seismic phase pickup method based on time-frequency analysis and deep learning, and belongs to the field of seismology, a deep neural network comprising an empirical mode decomposition module, a residual shrinkage module, an LSTM layer, a transformer layer, an up-sampling layer and a convolutional layer is established, a linear rectification function is adopted as an activation function, an Adam adaptive optimization function is adopted for back propagation, and a seismic phase is obtained. And taking a binary cross entropy loss function as a loss function, and taking a mean absolute error function, an error standard deviation, an accuracy rate, a recall rate and an F1 score as evaluation indexes to obtain a deep neural network model. When the frequencies of the noise, the P wave and the S wave are different, empirical mode decomposition can effectively separate the noise, the P wave and the S wave, so that the network is helped to better identify the P wave and the S wave. Even for broadband noise, the noise can be decomposed into different intrinsic mode functions, so that the noise content in the intrinsic mode functions where P waves and S waves are located is reduced. The time-frequency analysis method can effectively improve the anti-noise capability of the network.

Techniques are disclosed for segmenting electrocardiogram (ECG) signals. In one example, a method to segment an electrocardiogram (ECG) signal may include detecting consecutive heartbeats in an ECG signal. The method also includes segmenting the ECG signal into multiple ECG segments surrounding the detected consecutive heartbeats and generating an ECG data set by joining consecutive ECG segments. The generated the ECG data set represents the detected heartbeats. In some such examples, each ECG segment is of a duration to include a QRS complex, a P wave, and a T wave.

The invention relates to the technical field of earthquake prediction, and particularly provides a lightweight in-situ seismic oscillation parameter prediction model construction method and an earthquake early warning system. The lightweight in-situ seismic oscillation parameter prediction model construction method comprises the steps of training an initial prediction model according to acceleration waveform data when a seismic P wave arrives, and obtaining a pre-trained prediction model; taking the pre-trained prediction model as a teacher model, and migrating knowledge of the teacher model to a student model through distillation; and constructing a lightweight in-situ seismic oscillation parameter prediction model based on the student model. According to the method, knowledge in the pre-training prediction model is migrated to the student model through distillation, so that the complexity and the calculation amount of the student model can be reduced; and the lightweight in-situ seismic oscillation parameter prediction model is constructed based on the student model, so that the in-situ seismic oscillation parameter prediction efficiency can be ensured, the demand of the model for hardware resources can be reduced, and the deployable scene of the model can be expanded.

The invention discloses a mine earthquake P wave pickup method, system and device based on a self-adaptive characteristic function, and relates to the technical field of micro-earthquake signal processing.The method comprises the steps that on the basis of original mine earthquake records, an ICEEMDAN algorithm is adopted, and a mode function component without noise and a mode function component with noise are determined; processing the noise-containing modal function component by adopting a wavelet threshold to obtain a denoised modal function component; obtaining a denoised mine earthquake record based on the noise-free mode function component and the denoised mode function component; obtaining a self-adaptive characteristic function sequence based on the denoised mine earthquake record, and calculating the movement standard deviation of the sequence to obtain a movement standard deviation sequence; and determining an AIC window length based on the mobile standard deviation sequence and a preset pickup threshold value, calculating an AIC value of the adaptive feature function sequence corresponding to the AIC window length, and taking a moment corresponding to the minimum AIC value as the P wave arrival time of the original mine earthquake record. According to the invention, the accuracy and reliability of micro-seismic monitoring are improved.

According to the atrial fibrillation recognition method based on multi-feature fusion, multiple feature extraction task branches are constructed in the same deep learning model, the RR interval features, the P-wave features and the electrocardio waveform morphological features are extracted synchronously and parallelly, and the problems of feature loss and time sequence mismatching caused by multi-step processing are avoided; the model captures RR interval irregularity in a time domain, captures P wave missing and baseline f wave features in a morphological domain, forms a rhythm-morphological-frequency spectrum triple feature system, realizes more stable identification of atrial fibrillation, can effectively distinguish similar expressions of atrial fibrillation and ventricular premature, atrial premature, sinus arrhythmia and the like based on a coordination mechanism of appetite and morphological domain, and improves the accuracy of atrial fibrillation identification. And the identification rate of the atrial fibrillation signal in the electrocardiogram data can be effectively improved.

The invention discloses a multi-stage grouting reinforcement method for water-rich broken surrounding rock, which comprises the following steps: grading broken rock mass according to the breaking degree and the water content condition of disturbed rock mass and the result of a rock mass water content correction P wave test tested by a resistivity method, flexibly adjusting the type of grouting materials according to grading, and matching different grouting processes. While the grouting material is effectively utilized and the engineering budget is reduced, the reinforcing and plugging quality of multiple areas of the fractured rock mass can be guaranteed, the safe operation of the underground engineering is guaranteed, the reinforcing and plugging quality of the fractured rock mass is guaranteed, and the safe operation of the underground engineering is guaranteed. Rock masses with different crushing degrees can obtain the best reinforcing effect through the graded grouting strategy, the overall reinforcing strength is improved, and the anti-permeability performance and the reinforcing strength of a reinforcing body are greatly improved through application of the high polymer grouting material.

A method for performing an elastic full wave inversion (FWI) includes generating an initial P wave velocity model. The method also includes producing a subsurface seismic image or an image gather based upon the initial P wave velocity model. The method also includes estimating elastic properties based upon the subsurface seismic image or the image gather. The method also includes performing elastic full wave inversion (FWI) on the initial P wave velocity model and the elastic properties to produce updated elastic properties.

The invention provides an in-situ single-station earthquake early warning method, electronic equipment and a computer program product, and relates to the technical field of earthquake early warning. The method comprises the steps of obtaining first acceleration waveform data based on three-component acceleration of ground vibration, and judging whether an earthquake event occurs based on the first acceleration waveform data; if it is determined that the earthquake event occurs, obtaining P wave arrival time based on the first acceleration waveform data; obtaining second acceleration waveform data under the corresponding time window after the arrival of the P wave based on the obtained arrival time of the P wave; and on the basis of the second acceleration waveform data, related parameters predicted under the corresponding time window of the P-wave arrival time are obtained, and the related parameters comprise the epicentral distance, the azimuth angle, the epicentral position, the magnitude and the ground peak acceleration. The method is used for in-situ earthquake early warning earthquake event identification, P wave arrival time pickup, earthquake positioning, earthquake magnitude estimation and ground peak acceleration prediction so as to improve the reliability of in-situ earthquake early warning information.

The invention discloses a method and a system for detecting fracture impact elastic waves of a prestressed tendon of a prestressed composite steel cylinder concrete pipe. The method comprises the following steps: S1, obtaining the surface wave velocity of pipe core concrete; s2, obtaining the predominant frequency, the P wave arrival time and the P wave velocity of the signal; s3, calculating the component thickness of the measuring point; s4, executing S1-S3 on all the measuring points in the measuring area to obtain P-wave velocity, predominant frequency, P-wave arrival time and component thickness of all the measuring points to form a data set; and S5, reconstructing the P-wave velocity, the predominant frequency, the P-wave time, the component thickness and whether the label is broken into a time sequence, designing a double-layer LSTM network, and training an LSTM model. According to the invention, the machine learning technology is utilized to comprehensively consider the influence of the P-wave velocity change of the pipe core concrete caused by the breakage of the prestressed tendon and the stripping of an external mortar protective layer on the thickness of the component, and the breakage position of the prestressed tendon of the prestressed composite steel cylinder concrete pipe can be detected.

The invention provides a micro-seismic P-wave first arrival pickup method based on deep learning and an adaptive time window, and the method comprises the steps: (1) collecting and preprocessing a micro-seismic signal, making a data set, and dividing the data set into a training set and a test set; (2) building an MSC-NET deep learning neural network, taking a data set as input for training, and adjusting model parameters to achieve an optimal model effect; (3) performing a micro-seismic signal classification task by applying the trained deep learning model, and dividing a large number of micro-seismic signals into an effective available class and an ineffective unavailable class; and (4) finally, carrying out P-wave first arrival time pickup on the effective micro-seismic signals based on an energy ratio algorithm of an adaptive time window to obtain a relatively reliable pickup result. According to the method, the deep learning technology and the improved traditional signal processing technology are combined, the reliability and accuracy of the automatic pickup result of the micro-seismic signal P wave first arrival time are greatly improved, and meanwhile the method has the advantages of being high in robustness and applicability and the like.

The embodiment of the invention discloses an atrial fibrillation signal identification method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring an electrocardiogram signal through electrocardiogram acquisition equipment; performing noise reduction processing on the electrocardiogram signal to generate a denoised electrocardiogram signal; positioning an RR interval in the de-noised electrocardiogram signal to generate a to-be-marked RR interval sequence; identifying body position change interference and regular arrhythmia RR intervals of the RR interval sequence to be marked through XGboost and a random deep forest model, and generating an atrial fibrillation event group; performing morphological analysis on the de-noised electrocardiogram signals to generate electrocardiogram P-wave features; dynamically monitoring the electrocardiogram P wave features, and adjusting the atrial fibrillation starting time point and the atrial fibrillation ending time point of each atrial fibrillation event in the atrial fibrillation event group according to the dynamic monitoring result to obtain an optimized atrial fibrillation signal group. According to the embodiment, the accuracy of identifying the atrial fibrillation signal can be improved.

The invention discloses a reservoir water fluctuation propagation direct numerical simulation method and device in gravity dam anti-seismic analysis, a medium and equipment. According to the method, fluctuation propagation in reservoir water under combined incidence of P waves and SV waves is considered, a reservoir water-foundation site free field is analyzed and calculated on the basis of the fluctuation theory and the potential function theory, then the site free field is converted into node counterforce on fluid and solid boundaries on the basis of a fluid and solid power artificial boundary substructure model, and the reservoir water-foundation site free field is obtained. Applying node counterforce on the reservoir water-foundation artificial boundary, and directly performing numerical simulation on reservoir water-foundation site free field response; based on a static-dynamic artificial boundary conversion method, considering the influence of the static effect of the gravity dam-reservoir water-foundation system on the dynamic effect of the system, and constructing a gravity dam seismic oscillation input model for directly simulating reservoir water fluctuation propagation; according to the method, the anti-seismic analysis precision of the gravity dam is improved, and the limitations of single-wave incidence during reservoir water fluctuation propagation simulation, failure to consider the static effect and the like are overcome.

The invention discloses a magnetocardiogram P-wave detection method based on wavelet analysis and a local noise self-adaptive threshold, which is characterized in that accurate and robust detection of a P wave is realized by searching on a characteristic scale which can highlight the P wave, a self-adaptive threshold confirmation mechanism based on local noise estimation is introduced, and a self-adaptive threshold confirmation mechanism is established for a time domain search window of each cardiac cycle. According to the method, the internal noise level is dynamically evaluated, and an estimated value capable of accurately reflecting the local noise condition of the current heart beat can be obtained by carrying out statistical analysis on wavelet coefficients in a characteristic scale in an area where P waves are most likely to appear.

The invention provides a micro-seismic monitoring P-wave energy compensation method and system, and belongs to the field of micro-seismic signal processing. The method comprises the steps of firstly constructing a P-wave first arrival pickup function, performing P-wave energy pickup, then constructing a P-wave energy linear compensation function by taking a transmission angle of a detector as an independent variable, and finally performing micro-seismic P-wave energy compensation by using the P-wave energy linear compensation function. In the prior art, a compensation method for energy attenuation caused by stratum absorption in a P wave propagation process is mainly used, but in the invention, the stratum influence is not considered, and an energy consistency correction technology is developed only aiming at large difference of P wave energy received by detectors at different monitoring distances of a same-level seismic source of a horizontal well. The P-wave energy of microseism P-wave signals excited by the same level of seismic sources at different monitoring distances at the detector terminal is equivalent or as close as possible.

The invention relates to a seismic processing method and device based on seismic wave field numerical simulation, and belongs to the technical field of seismic exploration, and the method comprises the steps: carrying out the polynomial approximate expansion of an anisotropic pure-mode P wave and SV wave frequency dispersion relational expression, and obtaining an expanded polynomial; solving a target function corresponding to the expanded polynomial according to a preset optimization algorithm, and obtaining a polynomial optimization coefficient of the expanded polynomial; converting the optimized frequency dispersion relational expression of the P wave and the SV wave into a time-space domain, and obtaining an anisotropic pure mode wave equation; and solving the anisotropic pure-mode wave equation to realize numerical simulation of a seismic wave field, and performing prediction processing on a real-time earthquake based on a numerical simulation result. By adopting the seismic wave field numerical simulation method, high-precision numerical simulation of the seismic wave field can be realized, an effective wave field extrapolation tool is provided for subsequent anisotropic imaging and inversion, and earthquake prediction processing is realized.

The invention relates to the technical field of civil engineering, in particular to a method for calculating the seismic response of a pile group under the action of seismic P waves, a pile-soil coupling model is established based on a continuous elastic dynamics theory, and pile-pile dynamic interaction factors are calculated by decoupling displacement of a free field and a scattered field of a soil body and combining coordinate transformation, so that the seismic response of the pile group is calculated. The displacement response of a pile group system is solved according to the superposition principle, the influence of pile spacing and soil mass parameters on the anti-seismic performance of the pile group is quantitatively evaluated by adopting a method of combining frequency domain amplitude-frequency characteristics and time domain PGA attenuation analysis, and theoretical support is provided for pile foundation optimization design.

The present application relates to oil and gas geophysical exploration technology field, more particularly, it relates to a kind of P-S wave data matching method based on wavelet compression, it includes the following steps: S1: PS wave seismic data is converted to compress to PP time domain;Extract seismic PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum;S2: introduce frequency variable scale conversion factor, utilize Fourier transform method to carry out wavelet compression to PS wavelet;S3: obtain filter according to PS wavelet and new PS wavelet;S4: utilize filter to carry out filter processing to PS wave seismic data after time domain conversion;S5: carry out waveform matching.The present application is based on the pre-matching in time domain, introduce frequency variable scale conversion factor, utilize the Fourier scale conversion method of frequency variable to carry out compression to PS wavelet.Then filter is designed using regularization method to carry out filter processing to original PS wave seismic record, can improve the similarity of PP, PS wave both seismic record waveform, improve the precision of final matching.

A device for processing cardiac signals, comprises: - a data storage (114) arranged to receive input data sets each comprising a plurality of P wave segments each associated with an electrocardiogram track and with an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window and having one or more activation sequence(s), - a random convolutional kernel-based extractor arranged, for a given input data set, to determine an electrocardiogram feature vector, and - a machine-learning based locator using decision trees trained on data comprising sets of electrocardiogram feature vectors labelled with a value indicating a cardiac region identifier, and arranged to receive an electrocardiogram feature vector from the random convolutional kernel-based extractor as input, and to return a cardiac region identifier as output.

The invention discloses an inversion method based on three-dimensional reflection P wave monoclinic medium anisotropy parameters. The invention provides the three-dimensional reflection seismic wave travel time inversion method which is higher in stability, less in dependence on prior information and more sufficient in excavation of underground medium information. In the inversion process, firstly, dependence on prior information such as stratum thickness and vertical propagation speed is reduced, and meanwhile, more anisotropic parameters and relatively accurate stratum thickness can be obtained through inversion. Therefore, three-dimensional reflection seismic wave data can be explored more fully, richer underground medium information is provided, and a foundation is laid for a subsequent oil-gas exploration process, especially an unconventional oil-gas exploration process. A geophysical data processor can process the multi-azimuth single-layer horizontal plane reflection P wave travel time according to the method, anisotropy information of a bottom layer is obtained, or a multi-layer reflection P wave travel time and converted wave travel time inversion method is further developed according to the method provided by the invention.

To provide a new technology for highly reliably determining the existence of a P wave from time series data on ECG signals using signal processing.SOLUTION: A biological signal processing device separates a plurality of heart rate waveforms extracted from time series data on ECG signals into a plurality of to a plurality of heart rate waveform groups that at least include a first heart rate waveform group and a second heart rate waveform group; from each of the first heart rate waveform group and the second heart rate waveform group, acquires a first representative P wave section waveform and a second representative P wave section waveform, which are waveforms of representative P wave sections; and determines that a P wave is included in the time series data on the ECG signals when similarity between the first representative P wave section waveform and the second representative P wave section waveform is higher than a threshold.SELECTED DRAWING: Figure 12