CN103064111B - A kind of micro-seismic event recognition methods based on shape filtering - Google Patents

A kind of micro-seismic event recognition methods based on shape filtering Download PDF

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CN103064111B
CN103064111B CN201210536918.9A CN201210536918A CN103064111B CN 103064111 B CN103064111 B CN 103064111B CN 201210536918 A CN201210536918 A CN 201210536918A CN 103064111 B CN103064111 B CN 103064111B
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microearthquake
wave detector
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CN103064111A (en
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王润秋
宋炜
崔晓杰
牟培杰
徐刚
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China University of Petroleum Beijing CUPB
China National Petroleum Corp
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China University of Petroleum Beijing CUPB
China National Petroleum Corp
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Abstract

The invention provides a kind of micro-seismic event recognition methods based on shape filtering, the method comprises: carry out spectrum analysis to microearthquake data; The wave detector being used for receiving microearthquake signal is corrected; Selecting structure element, combines Glycerine enema with closure operation and forms morphology noise filter, carry out shape filtering to the described geological data through spectrum analysis; Calculate the energy Ratios of length time window according to the data of carrying out after shape filtering, described energy Ratios and activation threshold value are compared identify whether described wave detector exists micro-seismic event; Microearthquake pickup is carried out according to described micro-seismic event.The present invention can improve the signal to noise ratio (S/N ratio) of data by pre-service and further filtering process, utilize energy ratio function can detect micro-seismic event rapidly and accurately, identified the fluctuation types of micro-seismic event by polarization analysis, utilize adaptive threshold and energy ratio between the long window and the short window method can first break picking accurately.

Description

A kind of micro-seismic event recognition methods based on shape filtering
Technical field
The invention relates to geophysical prospecting for oil technology, particularly about a kind of micro-seismic event recognition methods based on shape filtering.
Background technology
By microearthquake method, real-time dynamic monitoring carries out to field produces significant to oil-field development.Because waterfrac treatment needs to obtain pressure break result more fast, thus evaluate fracturing effect, and then adjust in time recovery scheme, this just needs to carry out real-time process to waterfrac treatment micro-seismic monitoring data and explains.Currently used micro-seismic monitoring data processing method is mainly based on artificial cognition, first arrival inverting.Because micro-seismic monitoring Data Data writing time is long, data volume is large, adopt artificial cognition and carry out first break pickup to microearthquake validity event and can expend very large manpower and time, a process waterfrac treatment Monitoring Data obtains fracture distribution image on the one hand needs the long time can not meet the demand of Real-Time Monitoring; This method cannot be applicable to oil and gas development monitoring on the other hand.In addition, microearthquake signal is more weak, and signal to noise ratio (S/N ratio) is low, the identification difficulty of micro-seismic event.
Summary of the invention
The invention provides a kind of micro-seismic event recognition methods based on shape filtering, with by microearthquake automatically accurate first break picking.
To achieve these goals, the invention provides a kind of micro-seismic event recognition methods based on shape filtering, the method comprises: step 1: carry out spectrum analysis to microearthquake data; Step 2: the wave detector being used for receiving microearthquake signal is corrected; Step 3: selecting structure element, combines Glycerine enema with closure operation and forms morphology noise filter, carry out shape filtering to the described geological data through spectrum analysis; Step 4: the energy Ratios calculating length time window according to the data of carrying out after shape filtering, compares described energy Ratios and activation threshold value to identify whether described wave detector exists micro-seismic event; Step 5: carry out microearthquake pickup according to described micro-seismic event.
Further, the wave detector being used for receiving microearthquake signal is corrected, comprising: by the signal correction of the x component of wave detector on component direct P wave polarization direction, by y component correction on the direction perpendicular to component direct P wave polarization direction.
Further, by the signal correction of the x component of wave detector on component direct P wave polarization direction, by y component correction on the direction perpendicular to component direct P wave polarization direction, comprise: obtain according to the energy of ripple first arrival that each wave detector receives and to go directly the polarization direction of compressional wave and the angle theta of wave detector x component, then the microearthquake signal that 2 of each wave detector horizontal components receive is rotated θ corresponding to each wave detector.
Further, described selecting structure element comprises: the width of selecting structure element, height and shape.
Further, for multi-component and multilevel geophone, described step 4 also comprises: a fenestella opened by the wave detector of the rule occurred between neighboring track according to the effective geological data of micro-seismic event identified outside described wave detector and detects.
Further, described step 5 comprises: utilize erosion algorithm to remove random noise; Choose sizeable semicircular structure element, after microearthquake data described after shape filtering are taken absolute value, carry out corrosion treatment; Choose the mean value of the energy value of the signal after corrosion treatment as first arrival activation threshold value; Get forward at activation threshold value point place one sizeable time window; Choose sizeable long short time-window, when described in window with a sampled point for step-length, carry out energy ratio between the long window and the short window computing; Determine that the maximum point of energy ratio is Onset point.
The beneficial effect of the embodiment of the present invention is, the present invention can improve the signal to noise ratio (S/N ratio) of data by pre-service and further filtering process, utilize energy ratio function can detect micro-seismic event rapidly and accurately, and the fluctuation types of micro-seismic event is identified by polarization analysis, utilize the adaptive threshold of design and energy ratio between the long window and the short window method can accurate first break picking automatically.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.In the accompanying drawings:
Fig. 1 is that the embodiment of the present invention flies micro-seismic event recognition methods process flow diagram;
Fig. 2 is the spectrum diagram of the microearthquake signal that the wave detector x, y, z component of the embodiment of the present invention receives;
Fig. 3 is one microearthquake schematic diagram before and after the bandpass filtering of the embodiment of the present invention;
Fig. 4 is the three-component microearthquake signal record schematic diagram that the embodiment of the present invention extracts;
The change schematic diagram in three-component seismometer orientation when Fig. 5 is embodiment of the present invention microearthquake observation;
Fig. 6 a is one section of horizontal component schematic diagram of perforation data;
Fig. 6 b is the hodograph schematic diagram drawn according to Fig. 6 a;
Fig. 6 c is the curve synoptic diagram drawn according to energy criteria;
Fig. 7 a is the horizontal component schematic diagram after rotating θ angle;
Fig. 7 b is the hodograph schematic diagram made according to horizontal component after rotating;
Fig. 8 is the hodograph schematic diagram that horizontal component is located microearthquake the last period data and made;
Horizontal component is transformed into the hodograph schematic diagram to make after the coordinate system that is reference of component direct P wave polarization direction by Fig. 9;
Figure 10 a is the corrosion signal schematic representation utilizing semicircular structure element;
Figure 10 b is the expanding signal schematic diagram utilizing semicircular structure element;
Figure 11 is gray scale opening and closing schematic diagram;
Figure 12 a is opening and closing operation result schematic diagram;
Figure 12 b is make and break operation result schematic diagram;
Figure 12 c is the average result schematic diagram of opening and closing operation and make and break computing;
Figure 12 d is the comparing result schematic diagram of opening and closing operation and make and break computing;
Figure 13 is one embodiment of the invention seismic wavelet model schematic;
Figure 14 is the signal schematic representation after one embodiment of the invention different size structural element morphologic filtering;
Figure 15 is another embodiment of the present invention seismic wavelet model schematic;
Figure 16 is the different filtered signal schematic representation of semi-circular structure element morphology of radius;
Figure 17 is another embodiment of the present invention seismic wavelet model schematic;
Figure 18 is sinusoidal structural element filter effect schematic diagram;
Figure 19 is semicircular structure element filter effect schematic diagram;
Figure 20 is flat-structure element filter effect schematic diagram;
Figure 21 is triangular structure element filter effect schematic diagram;
Figure 22 is pretreated one microearthquake schematic diagram data;
Figure 23 is through opening closed, the closed schematic diagram data opened after process to it;
Figure 24 is the three-component energy Ratios curve synoptic diagram of certain road wave detector;
Figure 25 is the signal schematic representation after certain road original signal and corrosion treatment;
Figure 26 is signal energy value schematic diagram after corrosion;
Figure 27 is signal energy figure after the corrosion of partial enlargement;
Figure 28 is one section of untreated original microearthquake schematic diagram data;
Figure 29 is through the schematic diagram data after bandpass filtering treatment;
Figure 30 is the geological data schematic diagram after corresponding diagram 4 converts;
Figure 31 is one embodiment of the invention seismic wavelet model schematic;
Figure 32 to Figure 34 is the effect schematic diagram that three kinds of wave filters are suppressed random noise;
Figure 35 to Figure 38 is the pressing result figure of three kinds of wave filter paired pulses noises;
Figure 39 is pretreated one section of microearthquake schematic diagram data;
Figure 40 is through opening closed, the closed energy diagram after processing of opening;
Figure 41 is the energy Ratios curve synoptic diagram of the 12 road wave detectors that energy ratio function calculates;
Figure 42 is the polarization coefficient curve synoptic diagram of 12 road wave detectors;
Figure 43 is energy ratio figure schematic diagram;
Figure 44 is the theoretical model result schematic diagram of time difference method location;
Figure 45 is the ray tracing location algorithm theoretical model result schematic diagram based on polarization analysis.
Embodiment
For making the object of the embodiment of the present invention, technical scheme and advantage clearly understand, below in conjunction with accompanying drawing, the embodiment of the present invention is described in further details.At this, schematic description and description of the present invention is for explaining the present invention, but not as a limitation of the invention.
In order to adapt to the real-time process of microearthquake data, obtain pressure break result fast and accurately, and then recovery scheme is adjusted in time, the present embodiment provides a kind of micro-seismic event recognition methods based on shape filtering, thus provides basic data accurately for follow-up seismic source location.As shown in Figure 1, the method comprises:
Step S101: spectrum analysis is carried out to microearthquake data.
Step S102: the wave detector being used for receiving microearthquake signal is corrected.
Step S101 and step S102 belongs to the pre-treatment step of microearthquake data.The microearthquake data that field acquisition is arrived are the same with conventional earth's surface seismic section data, all will first through pre-service, i.e. road editor, Qu Fei road, gain recovery, then just can carry out follow-up data processing.But due to microearthquake signal self, its preconditioning technique is different from conventional disposal route, has the singularity of himself.
By step S101, spectrum analysis is carried out to microearthquake data, do bandpass filtering treatment, the noise beyond filter out signal frequency band.
Fig. 2 is the frequency spectrum of three the component microearthquake signals collected in certain exploration area, as can be seen from the figure, the present invention by microearthquake spectrum data signal to be processed probably at 10 ~ 600Hz; Microearthquake signal higher than 600Hz may be due to when ripple is propagated in the earth formation by formation absorption loss.After knowing the spectral range of signal, bandpass filtering can be utilized to process microearthquake data.Fig. 3 is one microearthquake data before and after bandpass filtering, contrast can find out bandpass filtering treatment after signal to noise ratio (S/N ratio) significantly improve.
The wave detector of step S102 corrects and comprises: comprising: by the signal correction of the x component of wave detector on component direct P wave polarization direction, by y component correction on the direction perpendicular to component direct P wave polarization direction.Detail is described below:
For receiving the three-component seismometer of microearthquake signal, in its process transferred in well, although Z-direction can be made vertically downward, but can not ensure that the orientation of two horizontal components of each wave detector is consistent, be actually random, which results in the microearthquake signal amplitude that each horizontal component on different wave detector receives to differ greatly, as shown in Figure 4, as can be seen from the figure, in original three-component microearthquake data, the different wave shape of horizontal component is very large, does not substantially see any lineups.Therefore, before subsequent treatment, rotation location must be carried out to wave detector, make the horizontal component orientation of each wave detector consistent.
Concretism is: source position and the speed of the microearthquake signal excited due to perforating and fracturing are known, and energy comparison is large, signal to noise ratio (S/N ratio) is high, thus first Seismic Direct Wave that perforating and fracturing excites can just be utilized, obtain according to the energy of ripple first arrival that each wave detector receives to go directly the polarization direction of compressional wave and the angle theta of wave detector x component, then the microearthquake signal that 2 of each wave detector horizontal components receive is rotated θ corresponding to each wave detector.
Carrying out directional correction to wave detector horizontal component can only utilize perforation data to ask for polarization angle.So-called perforation data refer to the seismic event utilizing perforating gun to penetrate sleeve-penetrating in down-hole to excite, and its energy comparison is strong, and first arrival is obvious.Utilizing perforation data to carry out horizontal component orientation is based upon in the prerequisite of such hypothesis: first the component direct P ripple passing to well seismometer from P ripple focus, its particle movement direction is consistent with direction of wave travel, all in the plane determined by focus and well, this component direct P wave polarization is linear, and its projection in surface level is also straight line.According to this hypothesis, just can use the projection of component direct P wave polarization direction in surface level as a reference, measure the relative orientation of horizontal component during three axle wave detector observation, and the signal of the horizontal component of observation is transformed into the consistent coordinate system that be projected as reference of component direct P wave polarization direction in surface level.Figure 5 shows that the orientation sketch of microearthquake observation three-component seismometer, the orientation of the horizontal component of wave detector is with degree of depth random variation, but the horizontal projection H of first P ripple proughly constant.The horizontal component (x, y) of observation is transformed into H pfor the coordinate projection system (x', y') of reference.Conversion formula is:
x'=xcosθ+ysinθ
y'=-xsinθ+ycosθ(1)
In formula, θ is the angle of x and Hp, is also called component direct P wave polarization angle.
Ask for first arrival when energy criteria method is and makes energy reach maximum to go directly a kind of method of the polarization direction of compressional wave and the angle of wave detector x component.Time window in sequentially get a pair sample value (x i, y i), wherein i for from time the window starting point then sequence number compiled of window terminal.If the expression formula of energy is:
E ( θ ) = Σ i ( x i cos θ + y i sin θ ) 2 - - - ( 2 )
When ENERGY E (θ) gets maximal value, corresponding angle is exactly the polarization angle of wave detector.
Fig. 6 a is one section of horizontal component of perforation data, and Fig. 6 b is the hodograph drawn according to Fig. 6 a, and Fig. 6 c is the curve drawn according to energy criteria, and what maximal value was corresponding is exactly angle is exactly required polarization angle.The angle that angle (about 74 degree) corresponding when energy is maximum is as we can see from the figure corresponding with the direction of extreme magnitude in hodograph is roughly consistent.
After obtaining polarization angle, according to formula (1) by seismologic record rotation of horizontal component θ angle, Fig. 7 a is the horizontal component after rotating θ angle, and Fig. 7 b is the hodograph made according to horizontal component after rotating, and the record of x' component just represents direct wave horizontal component Hp as seen from the figure.
Obtain the angle of 12 wave detectors according to energy criteria method after, horizontal component is transformed into in the coordinate system that is reference of component direct P wave polarization direction according to transformation for mula.Fig. 8 and Fig. 9 is the hodograph schematic diagram made according to one section of microearthquake data before conversion and after conversion respectively, can find out that the horizontal component of conversion post-detection device has been corrected to same direction.
Step S103: selecting structure element, combines Glycerine enema with closure operation and forms morphology noise filter, carry out shape filtering to the described geological data through spectrum analysis.
After the spectrum analysis of completing steps S102 and the wave detector of step S103 correct, just morphologic filter operation can be carried out.Introduction in detail below.
(1) mathematical morphology fundamental operation
The theoretical foundation of shape filtering is mathematical morphology.Mathematical morphology is that a kind of Nonlinear image processing based on set theory and logical operation is theoretical, its basic thought converts, measures and extract correspondingly-shaped wherein to target image with the structural element with certain form, simplified image data, keep the style characteristic that image is basic, get rid of incoherent graphic structure, thus reach the object of shape filtering.
The basic transformation of the mathematical morphology of bianry image has four kinds: i.e. burn into expansion, opening and closing, they can be combined into complicated image processing techniques.If A is the set of target image; B is the set of structural element, then:
Erosion operation is defined as: AΘB = ∪ b ∈ B A - b
Dilation operation is defined as: A ⊕ B = ∪ b ∈ B A + b
Glycerine enema is defined as:
Closure operation is defined as: A · B = ( A ⊕ B ) ΘB
Glycerine enema adopts first to corrode and expands afterwards, and play a part separation, filtering, the isolated portions less than structural element all will be filtered, to suppress signal peak (positive pulse) noise; Closure operation adopts the post-etching that first expands, play fill a vacancy, inner connection effect, to suppress signal bottom-valley (negative pulse) noise, Figure 10 a is the corrosion signal schematic representation utilizing semicircular structure element, and Figure 10 b is the expanding signal schematic diagram utilizing semicircular structure element.
Gray scale opening and closing also simply can use geometric interpretation, and we discuss by Figure 11.Open f with b, i.e. f zero b, the lower edge that b is against f can be regarded as and be rolled into the other end from one end.Figure 11 (b) provides the several positions of b in unlatching, and figure c provides the result of open operation.Can find out from Figure 11 (c), its height of all mountain peaks less than the diameter of b and sharpness are all reduced.In other words, when b againsts the lower to when rolling of f, the position do not contacted with b in f is all fallen and is contacted with b.In reality, conventional open operation eliminates the bright details that size is less compared with structural element, and keep figure overall gray value and large bright area substantially unaffected.The little bright details of the erosion removal of concrete 1st step also reduces brightness of image simultaneously, and the expansion of second step increases (substantially recovering) brightness of image but be not reintroduced back to the details removed above.Close f with b, i.e. f ● b, the upper edge that b is against f can be regarded as and be rolled into the other end from one end.Figure 11 (d) provides the several positions of b in closing, and Figure 11 (e) gives the result of closed procedure.Can find out that mountain peak does not change substantially from Figure 11 (e), and all mountain valleys less than the diameter of b obtain filling.In other words, when b againsts the upper along when rolling of f, the mountain valley do not contacted with b in f is all filled into and contacts with b.In reality, conventional closed procedure eliminates the dark details that size is less compared with structural element, and keep integral image gray scale and large dark areas substantially unaffected.Particularly, the expansion of the 1st step eliminates little dark details and enhances brightness of image simultaneously, and the corrosion of the 2nd step weakens (substantially recovering) brightness of image but is not reintroduced back to the details removed above.
(2) mathematical morphology filter principle
Different from digital filtering by shape filtering processing seismic data, the algorithm of shape filtering is not fixing algorithm, and determine along with the structural element selected, Glycerine enema can filter the bur less than structural element, cuts off elongated overlap joint and plays the effect of separation.Closure operation can be filled the breach less than structural element or hole, overlaps short interruption and plays connection effect.Due to noise kind quasi-complexity in geological data, often need the complex morphological filtering of Multi-structure elements.
1. array mode is on the impact of filter effect
Due to the anti-extendability (image doing closed operation is always positioned at the top of original image) of the non scalable (image doing opening operation is always positioned at the below of original image) of opening operation and closed operation, all there is statistical bias phenomenon in two kinds of wave filters, now open-close wave filter output amplitude is less than normal, and the output amplitude of closing-Kai wave filter is bigger than normal, in most instances, be used alone the filter effect that they can not obtain, preferably adopt the average combined form of two kinds of wave filters.If input signal is
x(k)=s(k)+n(k)(k=1,...,N)
In formula, s (k) is original signal; N (k) is noise.Then filtered output signals y (k) is
y(k)=[OC(x(k))+CO(x(k))]/2
In formula, OC represents and first carries out opening operation, then carries out closed operation, and CO represents that first carrying out closed operation carries out opening operation again.
If original signal expression formula: y (t)=e 50tsin2 π ft(is wherein: f=30, t=0 ~ 0.1, is spaced apart 0.0001), Figure 12 a, Figure 12 b, Figure 12 c and Figure 12 d sets forth opening and closing, make and break, the two result schematic diagram that is average and contrast.
2. the selection of structural element in seismic processing
Filter effect is relevant with structural element, obtain desirable filter effect, needs for problem choose reasonable structural element to be solved.The selection of structural element comprises the width (width of the field of definition of structural element) determining structural element, highly (amplitude of structural element) and shape.Below by provide we choice structure element research in result.
The width of structural element is on the impact of filter effect:
The wavelet expression of test: y (t)=e -50tsin2 π ft
In above formula: f=40, t=0 ~ 0.1, is spaced apart 0.0001.
Figure 13 is seismic wavelet model schematic, and the upper part of Figure 13 represents that the original signal of wavelet model, lower part represent that original signal adds random noise, s/n (useful signal energy/random noise energy)=57.403558.
In Figure 14, (a) and (b), (c) and (d) are 2,5,10,40 filtered results with flat-structure element width respectively, and filtered s/n is followed successively by 108.144122,196.832655,260.819392,142.8286.
As seen from Figure 14, after morphologic filtering, random noise is by filtering, and signal to noise ratio (S/N ratio) obviously increases, and the size of structural element is very large on result impact.C the filter effect of () is better than (b), b the effect of () is better than (a), this is because their size of structural element used increases gradually, c the structural element of () is of a size of 10 sampling points, b the structural element of () is of a size of 5 sampling points, the structural element of (a) is of a size of 2 sampling points.
But be not that the size of structural element is the bigger the better.The size of structural element is larger, and on the one hand, calculated amount can increase, and on the other hand, it may affect useful signal, sees (d).So, should according to the size (i.e. number of samples) of the suitable selecting structure element of the situation of noise and useful signal, size of structure element is too little, noise goes unclean, excessive, can useful signal be damaged again, so will according to the size of the signal characteristic selecting structure element that will keep after filtering.
For impulsive noise, if treat that filtering impulsive noise breadth extreme is T, the sampling period is T s, experimental result shows: the length M of structural element only need be a bit larger tham T/T s.
The height of structural element is on the impact of filter effect:
Consider the width of structural element to after the impact of filter effect, then see which type of the amplitude of structural element has affect on morphologic filtering.
The wavelet expression of test: y=e -50tsin2 π ft
In formula: f=40, t=0 ~ 0.1, is spaced apart 0.0001, structural element used is semi-circular structure element.
Figure 15 is seismic wavelet model schematic, and the upper part of Figure 15 represents that the original signal of wavelet model, lower part represent that original signal adds random noise, s/n (useful signal energy/random noise energy)=56.162128.
In Figure 16, (a), (b), (c), d () uses semi-circular structure element (0,0.0017,0.0020,0.0017 respectively, 0), (0,0.0173,0.0200,0.0173,0), (0,0.1732,0.2000,0.1732,0), (0,1.7321,2.0000,1.7321,0) filtered result, its radius is followed successively by 0.002, and 0.02,0.2,2, filtered s/n is followed successively by 267.392131,295.398691,216.562129,5.797541.As can be seen from Figure 16, the amplitude of structural element is not very large on result impact, but, if the radius of structural element is large, so after filtering, in beginning and ending place of signal, distortion is comparatively large, and the s/n after integrated filter and the waveform of filtered signal, the filter effect of (b) is quite a lot of.
Experimental result shows: the Amplitude Ration of structural element signal value to be filtered is to the when young order of magnitude.
The shape of structural element is on the impact of filter effect:
Here we discuss four kinds of structural elements: linear, triangle, semicircle, sinusoidal four kinds of structural elements are on the impact of result.
The wavelet expression of test: y=e -50tin sin2 π ft formula: f=30, t=0 ~ 0.1, is spaced apart 0.0001.
Figure 17 is seismic wavelet model schematic, and the upper part of Figure 17 represents that the original signal of wavelet model, lower part represent that original signal adds random noise, s/n (useful signal energy/random noise energy)=120.4241.
Figure 18 to Figure 21 suppresses random noise with sinusoidal, semicircle, linear and triangle four kinds of structural elements respectively.In Figure 18, (a) represents sinusoidal structural element, and (b) represents the signal after sinusoidal structural element shape filtering; In Figure 19, (a) represents semicircular structure element, and (b) represents the filtered signal of semicircular structure element morphology; In Figure 20, (a) represents flat-structure element, and (b) represents the signal after flat-structure element shape filtering.In Figure 21, (a) represents triangular structure element, and (b) represents the filtered signal of triangular structure element morphology.To the structural element of this several shape, we have used two kinds of different sizes to carry out filtering test, and what Selection effect was good carries out shape matching.Through the filtering of this several structural element, in signal, noise is obviously pressed, and signal to noise ratio (S/N ratio) significantly improves, filtered signal to noise ratio (S/N ratio) difference: 1.0755e+003,1.0488e+003,661.4504,846.2939.
As can be seen from experimental result, in general, the shape according to the signal that will keep after filtering is determined by the shape of structural element, the shape that the optional semicircle of the structural element for Seismic signal filtering process, sinusoidal etc. are similar with waveform.
Owing to there are differences on structural form between signal and noise, therefore by choosing suitable structural element, opening and closing can be combined construction form scratch filter and microseismograms is processed.Figure 22 is pretreated one microearthquake data, and Figure 23 is through opening closed, closed data of opening after process to it.Find out that shape filtering has filtered partial noise by figure, but do not change overall form, that is the waveform of useful signal is substantially constant, and the amplitude of useful signal, the feature of frequency are kept well.The method has higher actual application value.
Step S104: the energy Ratios calculating length time window according to the data of carrying out after shape filtering, compares described energy Ratios and activation threshold value to identify whether described wave detector exists micro-seismic event.
Energy spectrometer technology is most widely used a kind of automatic identification technology.Major part energy spectrometer realizes based on energy ratio between the long window and the short window method (STA/LTA), and he distinguishes effective seismic signal according to the difference of the energy Ratios of window when different component, different coordinates or difference.Because micro-seismic event and ground unrest are different on energy feature, this just for identifying that micro-seismic event provides possibility on three-component seismogram.When suitable component can being selected to calculate according to the actual conditions of data for when multi-components microearthquake data.For one three-component record (x (t), y (t), z (t)), z (t) is vertical component, and x (t), y (t) are horizontal component, can be used for asking the combination of energy to have:
E all ( t ) = z ( t ) 2 + x ( t ) 2 + y ( t ) 2
E Z(t)=z(t)|(3)
E H ( t ) = x ( t ) 2 + y ( t ) 2
These three kinds combinations are non-is not three-component gross energy and, vertical component amplitude and horizontal component energy.
Window, i.e. at a time t when selecting to roll in application process 0, window when choosing with certain length before and after it, the mean value of window self-energy when asking for, utilizes these the average energy value to calculate energy Ratios.
Energy ratio between the long window and the short window method (STA/LTA) fundamental function can be defined as:
R=STA(t)LTA(t)(4)
Wherein
LTA ( t ) = Σ t = t 1 t 0 E ( t ) / | t 0 - t 1 | - - - ( 5 )
STA ( t ) = Σ t = t 0 t 2 E ( t ) / | t 2 - t 0 | - - - ( 6 )
T 1for window initial time time long, t 0for window end time time long and short time-window initial time, t 2for short time-window end time.
In long short time-window, signal utilizes energy to portray the feature of micro-seismic event, then utilize fundamental function slide long short time-window calculate STA and LTA. long time window mean value (LTA) feature the variation tendency of signal background noise, short time-window mean value (STA) features amplitude (energy) variation tendency of microearthquake signal, when signal arrives, STA changes soon than LTA, corresponding STA/LTA value has an obvious kick, when short time-window mean value is with when the ratio of window mean value exceedes user-defined activation threshold value time long, be just judged to be micro-seismic event.A fixed value can be set as about threshold value, when estimating micro-seismic monitoring data SNR, usually can be set to 3/4 of signal to noise ratio (S/N ratio), under this thresholding, substantially can identify the microearthquake validity event that major part can utilize.
Figure 24 is the three-component energy Ratios curve synoptic diagram of certain road wave detector.Due to the method that this automatic identifying method is based on single-stage wave detector, and for the microseismograms of observing environment and low signal-to-noise ratio in the well of complexity, the signal that single utilization one-level wave detector records judges whether that there is microearthquake useful signal frequent appearance can pick up situation by mistake.Thus the aggregation of data on multi-component and multilevel geophone will be utilized to carry out the automatic identification of microearthquake effective time.If in certain time detecting of single-stage wave detector to microearthquake signal, so also should there will be signal on other wave detectors, therefore can to open on a fenestella at other wave detectors according to the occurrence law of effective microearthquake signal between neighboring track and detect.If all detect that on multi-component and multilevel geophone signal just thinks signal, very noisy on single-stage wave detector can be avoided like this to disturb the mistake caused to pick up.
When there being default threshold value to be triggered, be merely able to judge existing validity event, but cause the signal of triggering to be that P ripple or S ripple just cannot judge.For judging that the validity event identified is P ripple or S ripple, the polarization direction with regard to Water demand useful signal is distinguished these two kinds of ripples.
Microearthquake source location is unknown, but can according to the spatial dimension of certain prior imformation determination focus.If there is no medium anisotropy or produce the impact of shear wave splitting due to crack, polarizability between P ripple, SV ripple, this three of SH ripple is mutually perpendicular relation, and their polarization direction can be decomposed its maximal eigenvector of searching according to covariance matrix and try to achieve.
If a time window having N number of sampling point, each sampling point is defined by three-dimensional coordinate X, Y, Z three-component.Time window [T1, T2] in, the mean value of sampling point coordinate is
z ‾ = 1 N Σ i = n 1 n 2 z i y ‾ = 1 N Σ i = n 1 n 2 y i x ‾ = 1 N Σ i = n 1 n 2 x i - - - ( 6 )
(n in formula 2-n 1) Δ t=T 2-T 1, Δ t is sampling interval; N=n 2-n 1so+1 is sampling number, the covariance matrix of sampled point is
M = cov [ x , x ] cov [ y , x ] cov [ z , x ] cov [ x , y ] cov [ y , y ] cov [ z , y ] cov [ x , z ] cov [ y , z ] cov [ z , z ] - - - ( 7 )
Wherein cov represents covariance computing, asks for vector x (i) with the computing of the covariance of y (i) to be:
cov [ x , y ] = 1 N Σ i = n 1 n 2 ( x i - x ‾ ) · ( y i - y ‾ ) - - - ( 8 )
Three eigenvalue λ can be obtained by variance matrix 1, λ 2, λ 31> λ 2> λ 3) and three proper vector Λ 1, Λ 2, Λ 3.The maximal eigenvector that eigenvalue of maximum is corresponding can think the direction that ripple vibrates.
If there is no medium anisotropy or produce the impact of shear wave splitting due to crack, the polarizability between P ripple, SV ripple, this three of SH ripple is mutually perpendicular relation.P ripple direction of vibration is identical with the seismic ray direction of propagation, also just can think, in the P ripple three-component record that the wave detector in the stratum close with the hypocenter distributing degree of depth receives, vertical component is very little, and polarization direction and wave detector z component are close to vertical.Identical with the vibrations plane of P ripple, but the focus SV wave polarization direction of same position is partial to more to the vertical direction of wave detector.The P ripple on if fractured layer position and the degree of depth of wave detector are staggered and record, SV wave polarization can with same depth profile under contrary.For SH ripple, the plane of oscillation of its primary wave and place, direction of propagation plane and P wave polarization direction are orthogonal, and under stratified model, wave detector there will not be vertical component.Before carrying out validity event automatic Picking, according to the job design of hydraulic fracturing and the locus of monitor well fractured well, roughly estimate the scope of microearthquake focus, can estimate P ripple and SH wave polarization direction, in general both there will not be the scope overlapped or overlap very little.
Step S105: carry out microearthquake pickup according to described micro-seismic event.
Step 5 specifically comprises:
Step 501: utilize the erosion algorithm in mathematical morphology can realize removing random noise fast, thus further first break picking.First the amplitude of microearthquake signal is taken absolute value allow its all become on the occasion of, make it meet the requirement of erosion algorithm.
Step 502: choose sizeable semicircular structure element, carries out corrosion treatment after taking absolute value to microearthquake data described after shape filtering, the signal schematic representation after Figure 25 road original signal and corrosion treatment.
Step 503: get the mean value of the energy value of the signal after corrosion treatment as first arrival activation threshold value.Through a large number of experiments show that, average and more accurately can trigger primary wave as adaptive threshold, and can not interference noise be triggered.Figure 26 corrodes rear signal energy value schematic diagram, and the solid horizontal line of lowermost end is activation threshold value.
Step 504: get forward at activation threshold value point place one sizeable time window, Figure 27 be after the corrosion of partial enlargement signal energy figure, the t0 moment be trigger instants point, dotted line frame is got time window.
Step 505: choose sizeable long short time-window, when described in window with a sampled point for step-length, carry out energy ratio between the long window and the short window computing;
Step 506: determine that the maximum point of energy ratio is Onset point.Utilize the result of automatic Picking first arrival just can carry out microquake sources location, the main method using time difference method and ray casting co-located.
Below in conjunction with concrete example, micro-seismic event recognition methods of the present invention is described.
(1), bandpass filtering treatment
According to spectrum analysis, we are aware of the general frequency range of signal, and when carrying out bandpass filtering, frequency range is set as 10-600Hz by us.Figure 28 is one section of untreated original microearthquake data, and Figure 29 is through the data after bandpass filtering treatment.As seen from the figure, bandpass filtering eliminates part random noise, and the signal ratio of data is improved significantly.
(2), the location of horizontal component
Because the orientation of two horizontal components of each wave detector in the process that the three-component seismometer receiving microearthquake signal is transferred in well is random, before subsequent treatment, rotation location must be carried out to wave detector, make the horizontal component orientation of each wave detector consistent.Figure 30 is the geological data after corresponding diagram 4 converts, and because wave detector horizontal component has been corrected on a direction, energy distribution is relatively more even on wave detector at different levels, and the microearthquake signal obtained like this is just relatively more continuous, also makes signal be enhanced simultaneously.
(3) shape filtering process
In image procossing and signal analysis, the method for smothing filtering has a lot, as medium filtering and neighbour average filtering etc.Here we discuss, non-linear form filtering, non-linear median filtering, the effect that linear neighboring mean value is suppressed random noise and impulsive noise.
The wavelet expression of test: y=e -50tsin2 π ft
In formula: f=30, t=0 ~ 0.1, is spaced apart 0.0001.
Figure 31 is seismic wavelet model schematic, and the upper part of Figure 31 represents that the original signal of wavelet model, lower part represent that original signal adds random noise, s/n (useful signal energy/random noise energy)=107.6534.
Figure 32 to Figure 34 is the effect schematic diagram that three kinds of wave filters are suppressed random noise.Can find out from the signal to noise ratio (S/N ratio) after curve and process, three kinds of wave filters have some good effects, and random noise is pressed, and signal to noise ratio (S/N ratio) is improved, but on the whole, morphological filter effect is better than median filter and neighbour average filtering device.
Figure 35 to Figure 38 is the pressing result figure of three kinds of wave filter paired pulses noises.As can be seen from filtered waveform and signal to noise ratio (S/N ratio), when removing impulsive noise, morphologic filtering method is better than other two kinds.Above experimental result shows, morphologic filtering may be used for the denoising of geological data completely.
It is more than the denoising effect that shape filtering is discussed with model, we illustrate the effect of shape filtering by real data now, Figure 39 is pretreated one section of microearthquake schematic diagram data, can see that first wave detector has obvious strong vibration noise, this may be due to wave detector do not push against on the borehole wall or backup power inadequate, even collapsing appears in the borehole wall, makes wave detector and is coupled bad between stratum or sleeve pipe, thus produces stronger resonance.After utilizing shape filtering to carry out process, (Figure 40) can see, strong energetic resonance is obviously suppressed, thus illustrates that shape filtering can reach the object of denoising preferably, also can play the effect of strengthening weak signal.
(4) detection of micro-seismic event
After processing after filtering, utilize energy ratio function can identify microearthquake signal fast and accurately, thus for actual production.In order to energy ratio function advantage is described, we carry out the identification of micro-seismic event respectively by energy ratio function and polarization coefficient method.
Figure 41 is the energy Ratios curve of one section of microearthquake data, Figure 42 is corresponding polarization coefficient curve, energy ratio function has maximum value at signal place as seen from the figure, and P ripple, S wave energy are enough separated, although also there is maximum value on polarization coefficient curve, but on some seismic trace, but there is larger error (the 2nd, 5,12 roads), and if when two signal times differences are smaller, polarization coefficient can not separate identification completely.Arithmetic speed is convenient, energy ratio function is also faster than polarization coefficient many, the microearthquake data of same operation one section of 10s, be Intel (R) Core (TM) DuoCPUE45002.20GHz2.20GHz at processor, on the computer inside saving as 1.00GB, energy ratio function working time is 0.546000s, polarization coefficient working time is 1.822000s, and working time is 3.3 times of energy ratio function.Therefore energy ratio function can identify micro-seismic event fast and accurately.In addition, energy ratio function is utilized to need to utilize multiple tracks wave detector to carry out recognition detection.Can be seen by Figure 41 if microearthquake signal, it can show (red frame place) instead of occur peak value on certain one on multiple tracks wave detector, and we just can avoid some possibilities of by mistake picking up like this.And the identification of waveform can utilize polarization analyzing method, obtaining the direction of the polarization of microseism, can judge the classification of waveform more exactly, is P ripple or S ripple.
(5) microearthquake first arrival automatic Picking
In order to improve the precision of first break pickup, need to carry out pre-service to microearthquake data, shape filtering effectively can be removed random noise thus improve signal to noise ratio (S/N ratio), the method of tradition first break picking adopts fixed threshold, not there is the easy erroneous trigger of adaptability, therefore according to the feature of microearthquake data self, using the average of the Amplitude-squared value of pretreated data as adaptive threshold, correctly can trigger primary wave, improve pickup precision.And when local, trigger point place is got, window carries out energy ratio between the long window and the short window computing, improves arithmetic speed.
Figure 43 is the first break pickup result in actual microearthquake data road, and as can be seen from the figure energy ratio curve has an obvious peak value, and the time t1 that this peak value is corresponding is initial time.
(6) microearthquake seismic source location
Figure 44 is the theoretical model based on time difference method location, " * " represents detector position, and " o " represents actual source location, and "+" representative calculates gained source location, calculating focus and actual source location coincide, and illustrate that the method is located comparatively accurately stable when first break pickup precision is higher.Figure 45 is the ray tracing location algorithm theoretical model based on polarization analysis, and " * " is detector position, and " o " is the best convergence point of beam, is source location, also can be accurately located by the known the method for result in the obvious situation of P ripple first arrival.The larger situation of error is there will be separately with a kind of method location, if therefore two kinds of methods to be joined together location, constrained each other, can positioning error be effectively reduced.
The beneficial effect of the embodiment of the present invention is, the present invention can improve the signal to noise ratio (S/N ratio) of data by pre-service and further filtering process, utilize energy ratio function can detect micro-seismic event rapidly and accurately, and the fluctuation types of micro-seismic event is identified by polarization analysis, utilize the adaptive threshold of design and energy ratio between the long window and the short window method can accurate first break picking automatically.
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (3)

1. based on a micro-seismic event recognition methods for shape filtering, it is characterized in that, described method comprises:
Step 1: spectrum analysis is carried out to microearthquake data;
Step 2: the wave detector being used for receiving microearthquake signal is corrected;
Step 3: selecting structure element, combines Glycerine enema with closure operation and forms morphology noise filter, carry out shape filtering to the described geological data through spectrum analysis;
Step 4: the energy Ratios calculating length time window according to the data of carrying out after shape filtering, compares described energy Ratios and activation threshold value to identify whether described wave detector exists micro-seismic event;
Step 5: carry out microearthquake pickup according to described micro-seismic event;
Wherein, described selecting structure element comprises: the width of selecting structure element, height and shape;
The wave detector being used for receiving microearthquake signal is corrected, comprise: by the signal correction of the x component of wave detector on component direct P wave polarization direction, by y component correction on the direction perpendicular to component direct P wave polarization direction, comprise: obtain according to the energy of ripple first arrival that each wave detector receives and to go directly the polarization direction of compressional wave and the angle theta of wave detector x component, then the microearthquake signal that 2 of each wave detector horizontal components receive is rotated θ corresponding to each wave detector.
2. method according to claim 1, it is characterized in that, for multi-component and multilevel geophone, described step 4 also comprises: a fenestella opened by the wave detector of the rule occurred between neighboring track according to the effective geological data of micro-seismic event identified outside described wave detector and detects.
3. method according to claim 2, is characterized in that, described step 5 comprises:
Erosion algorithm is utilized to remove random noise;
Choose sizeable semicircular structure element, after microearthquake data described after shape filtering are taken absolute value, carry out corrosion treatment;
Choose the mean value of the energy value of the signal after corrosion treatment as first arrival activation threshold value;
Get forward at activation threshold value point place one sizeable time window;
Choose sizeable long short time-window, when described in window with a sampled point for step-length, carry out energy ratio between the long window and the short window computing;
Determine that the maximum point of energy ratio is Onset point.
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