55 results about "Seismic noise" patented technology

The invention discloses a first-arrival travel time picking method and device for a seismic signal comprising noise, and the method comprises the following steps: carrying out the decomposition of an original seismic signal s(t) comprising noise through a noise adaption complete set EMD (Empirical Mode Decomposition) method, and obtaining a series of finite IMF components from high frequency to low frequency and residual components; screening out a plurality of high-frequency IMF components according to an EIF effective index function, and carrying out the wavelet threshold denoising; carrying out the accumulation and reconstruction of the high-frequency IMF components denoised through wavelet threshold denoising and the high-frequency IMF components which are not denoised, i.e., obtaining the denoised seismic signal x(t); initially determining the first-arrival travel time t0 through an improved sliding time window energy ration method; calculating a local extreme value in a time window comprising the moment t0 through an AIC information criterion method, wherein the time corresponding to the extreme value is the precise first-arrival travel time t. According to the invention, the method can effectively improve the seismic noise inhibition effect and improve the picking precision of the first-arrival travel time, and creates the conditions for the accurate processing and explanation of the subsequent seismic data.

A sensing unit comprising sensor means enclosed in a casing made of a ferromagnetic material, to achieve a ferromagnetic shield. The sensing unit may further include signal processing means, also enclosed in the ferromagnetic casing.

A distributed system for measuring, processing and displaying information from a plurality of sensors comprises a plurality of sensor units. A method for quick detection of offenders of oil, gas or other pipes, by monitoring the protective cathodic voltage and detecting quick changes in the voltage. A method for detecting leakage from a pipe using multiple channels/inputs, wherein a low frequency range input measures seismic noises, and a high frequency range input measures cavitation noises, and wherein a leakage indication is issued if both the low frequency and high frequency noises are simultaneously detected.

Application of nonlinear resonance interferometry is introduced as a new geophysical approach to improve predictability in characterization of subsurface porosity, rock and fluid properties. In contrast to reflection methods that remove random information noise, nonlinear resonance interferometry exploits the full seismic acquisition spectrum to assess how low frequency and high-frequency noise is differentially and directly modulated by varying levels of porosity and hydrocarbon content in the lithologies of interest. In some examples, systems and techniques implement novel computational interactions between acquired seismic wavefield attributes and a nonlinear system in software to amplify distortions in seismic noise and exploits injection of synthetic noise, in software format, to detect hydrocarbon traps and lithology changes at spatial scales below seismic resolution, thereby increasing the information value of low-resolution data. The techniques are broadly applicable to de-risking conventional clastic and carbonate reservoirs and non-conventional shale gas resource prospects.

The invention relates to a seismic signal random noise suppression processing method. The method includes the following steps that: original noisy seismic signal s(t) are decomposed by using an improved noise-adaptive complete set empirical modal decomposition method, so that a finite number of IMF components and residual components are obtained; an energy demarcation point l of effective signalsand noises in the IMF components of each order is determined according to a permutation entropy theory; and singular value decomposition and noise reduction processing is performed on the high-frequency IMF components of the (1-l)-th order with more noises; and the high-frequency IMF components which have been subjected to the secondary noise reduction processing, low-frequency IMF components which are not processed, and the residual components are accumulated and reconstructed, so that de-noised seismic signals can be obtained. According to the seismic signal random noise suppression processing method of the invention, a multi-step joint processing technology is adopted, and therefore, a seismic noise suppression processing effect can be effectively improved, the details of the lineups ofa seismic channel set can be improved, and conditions can be provided for subsequent seismic data processing, interpretation and forward and inverse calculation.

The invention discloses noise inhibition which is an important processing step in a micro-seismic signal processing process. Complete ensemble empirical mode decomposition CEEMD and wavelet transformation WT are widely applied to seismic noise elimination; however, the CEEMD is lack of theoretical foundations and the self-adaptability of the WT is relatively weak. Therefore, the noise eliminationeffect is poor. According to the invention, experience wavelet transformation (EWT) is combined with various threshold functions to carry out micro-seismic noise elimination for the first time. The EWT is used for establishing a self-adaptive wavelet filter group through spectrum segmentation to extract different frequency blocks of a detected signal. In the EWT, four types of spectrum segmentation methods are adopted; an experiment finds out that an adaptive algorithm can be used for separating an effective signal and noises of micro-seismic data very well; after the EWT is carried out, the signal can be divided into two components through analyzing a spectrum and energy of each module. A hard threshold function is applied to the component containing more effective signals and an improvedthreshold function is applied to the component containing less effective signals. An extraction method is compared with the CEEMD and the WT in an analogue signal and an actual signal to prove the effectiveness of a provided method.

The invention provides an adaptive hybrid norm dictionary learning seismic wave impedance inversion method, and belongs to the field of seismic inversion. Through the combination of the post-stack wave impedance inversion in exploration geophysics with dictionary learning and denoising, the adaptive denoising and dictionary learning based post-stack wave impedance inversion method can be provided.The method adopts an adaptive hybrid norm objective function to replace an original 2-norm objective function, so that an objective function can better overcome non-gaussian noise; well logging information is introduced through a dictionary learning method, so that influences of different seismic noise can be effectively overcome, and the vertical resolution of inversion can be enhanced by fullyutilizing the well logging information; and compared with traditional methods of least squares, robustness can be stronger, the noise immunity and inversion result precision of the seismic wave impedance inversion can be enhanced, multiple solutions can be reduced, and convenient data processing in geological exploration can be achieved.

The invention discloses a ground array type micro seismic data independent component separation denoising method, comprising the steps of performing packet processing for close demodulator channels according to the similarity principle of micro seismic data; performing DC component and whitening processing for packets of seismic data; using negative entropy as independence determination conditions of a micro seismic effective signal component and a micro seismic noise component, using the Newton iterative method for an optimization solution, and achieving the uncertainty separation of the components; and performing cross-correlation processing between the two components after uncertainty separation and original micro seismic data, and achieving the certainty separation of the micro seismic effective signal component from the micro seismic noise component through the magnitude and sign of a cross-correlation numerical value. The invention can be used for dramatically improving the signal to noise ratio of ground array type micro seismic data, achieving accurate identification of effective events in the micro seismic data, and improving the accuracy of micro seismic fracturing monitoring results.

Disclosed is a method for determining seismic event data from indications of seismic noise, the method including receiving seismic trace data from a plurality of locations, and providing a virtual trace value (E_Rvirtual) as seismic event data for a virtual trace location from the seismic trace data. A system and computer program product for determining seismic event data is also disclosed.

The invention discloses a reflection coefficient inversion reservoir prediction method based on a weighted stacking noise suppression strategy. The method includes six main steps: 1), through a noise disturbance analysis, determining a source of noise influencing the inversion effect, and distinguishing seismic noise and inversion noise; 2) performing seismic noise suppression preprocessing on seismic data through a post-stack wavelet frequency division method; 3) for de-noised seismic data, constructing reflection coefficient inversion key parameters (a wavelet inverse transform factor F, an inversion predominant frequency band Q, and an inversion initial model S(n)); 4) suppressing inversion noise through weighted stacking of inversion results of multiple regularization parameters to obtain reflection coefficient inversion results; 5) performing quality control (QC) on the inversion results through spectrum analysis and a calibrated sound wave curve to obtain final result data; and 6) performing sand body fine interpretation based on the reflection coefficient result data, and predicting sand body spatial distribution characteristics. Compared with other thin reservoir prediction methods, according to the reflection coefficient inversion reservoir prediction method, the inversion anti-noise performance is high, thin-layer interference in the original seismic data can be eliminated, the prediction precision of the thin layer is greatly improved, and oil and gas exploration risks are reduced.

A system and method for isolating signal in seismic data representative of a subsurface region of interest including receiving a seismic dataset representative of seismic signal and seismic noise and a seismic data guide; transforming the seismic dataset and the seismic data guide into a domain wherein the seismic data have a sparse or compressible representation to create a first set and a second set of representative coefficients; comparing the first set of representative coefficients to the second set of representative coefficients to identify and select desirable members of the first set of representative coefficients that are within a defined threshold of the second set of representative coefficients to create an improved first set of representative coefficients; and performing an inverse transform of the improved first set of representative coefficients to generate a modified seismic dataset.

The embodiment of the invention discloses a method, device and system for suppressing seismic noise, and the method comprises the steps: carrying out the curvelet transformation processing of originalseismic data, and obtaining a curvelet coefficient; solving a gradient for the curvelet coefficient to obtain a local wave vector; carrying out first threshold processing on the size of the local wave vector to obtain a curvelet coefficient after threshold processing; and performing inverse curvelet transformation on the curvelet coefficient subjected to threshold processing to obtain seismic data after noise suppression. According to the embodiment of the invention, the method can achieve the more effective suppression of the seismic noise, and improves the signal-to-noise ratio of the seismic data.

The invention discloses a high-temperature-resistant and shock-resistant sound-insulating plastic alloy composite material, which belongs to the technical field of plastic alloys. Prepared from the following components by weight: 20-35 parts of linear low-density polyethylene, 15-35 parts of polypropylene, 15-35 parts of methyltriethoxysilane, 0.8-35 parts of ethylene-tetrafluoroethylene copolymer 2.5 parts, 2‑6 parts of calcium stearate, 5‑8 parts of epoxy soybean oil, 2‑3 parts of composite flame retardant, 2‑3 parts of ABS resin, 2‑6 parts of sodium hydroxide, 5 parts of crosslinking agent 9 parts, 1-3 parts of coupling agent, 5-9 parts of light calcium carbonate, 1-2 parts of stabilizer. The plastic alloy produced by the invention has good antibacterial and antifungal properties, high strength and strong aging resistance, excellent flame retardancy, high temperature resistance, high product strength, good impact strength, low cost, simple operation and corrosion resistance.

A method for filtering noise from seismic data is disclosed. The method includes: receiving at least one seismic data set; computing a value of at least one subset of a plurality of subsets of the at least one seismic data set; computing quartiles and an upper moustache (M) for the plurality of subsets; comparing the upper moustache (M) to the value of the at least one subset; and one of discarding and retaining the at least one subset as filtered seismic data. A system and computer program product for filtering noise from seismic data is also disclosed.

The embodiment of the invention discloses a seismic noise imaging exploration method and apparatus, and a storage medium. The method that is applied to an exploration system including a plurality of three-component low-frequency detectors that are arranged on one straight line at a preset interval comprises: acquiring a plurality of low-frequency three-component data including first data and two horizontal components and second data of a vertical component by the plurality of three-component low-frequency detectors; acquiring a measurement dispersion curve characterizing a correspondence relationship between a surface wave phase velocity and a frequency by using the plurality of the second data; acquiring a plurality of velocity models characterizing a correspondence relationship between the depth and velocity of the stratum of each measuring point according to the measurement dispersion curve and the plurality of low-frequency three-component data; and on the basis of the plurality ofvelocity models, determining a stratum velocity profile of the testing area based on the plurality of velocity models.

The embodiment of the invention discloses a seismic noise imaging exploration method and apparatus, and a storage medium. The method is applied to an exploration system including a three-component detector and a plurality of single-component detectors; and the plurality of single-component detectors are arranged on the circumference using the location of the three-component detector as the center.The method comprises: acquiring first data, including two horizontal component data and one vertical component data, of by the three-component detector; acquiring a plurality of second data characterizing a vertical direction by the plurality of single-component detectors; determining the velocity of a surface layer based on the plurality of second data and the vertical component data; and determining a velocity model that characterizes a correspondence relationship between the depth and velocity of the stratum according to the surface layer velocity and the first data; and determining a velocity profile of the testing area based on the velocity model.

The invention discloses an algorithm for separating micro-seismic noise mixed signals by using an SVD-EMD algorithm. The algorithm comprises the following steps: (S1) preprocessing the micro-seismic noise mixed signals by using a singular value decomposition method SVD; (2) obtaining a signal f (x, t) subjected to SVD preprocessing, carrying out EMD decomposition on the signal, calculating currentstep signals f0 (x, t) of all extreme points by initializing a residual function res (x, t), and then removing an average envelope value from the original signal to enable the remaining part of signals f1 (x, t) to become signals of the next step; (S3) placing the current step signal f1 (x, t) and the previous step signal f0 (x, t) in a stop state, and repeating the screening process through an intrinsic mode function and a final remaining function; and (S4) utilizing Hilbert transform to obtain frequency characteristics of each intrinsic mode function decomposed through EMD, and calculatinga correlation coefficient Ri so as to complete signal reconstruction. By means of the scheme, the purposes of improving microseism seismic phase recognition and realizing the optimal P wave picking effect are achieved, and high practical value and promotional value are achieved.