CN102967884B - Reliability of wave impedance inversion data evaluation method and device - Google Patents

Abstract

The invention discloses a kind of reliability of wave impedance inversion data evaluation method and device, wherein reliability of wave impedance inversion data evaluation method comprises: the RMS amplitude variation characteristic spatially determining geological data; Determine the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component; Determine the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component; According to described similarity, the reliability of wave impedance inversion data is evaluated.The present invention is that wave impedance inversion provides a kind of brief and practical, objective effective Quantitative Evaluation System, improves the reliability utilizing wave impedance inversion data to carry out petroleum-gas prediction, can adapt to the inverting evaluation of complicated underground structure and deposition characteristics.

Description

Reliability of wave impedance inversion data evaluation method and device

Technical field

The present invention relates to the oil and gas reservoir electric powder prediction in seismic prospecting, particularly relate to reliability of wave impedance inversion data evaluation method and device.

Background technology

Seismic prospecting is the method for being explored underground structure by artificial excitation's seismic event, along with deepening continuously of petroleum exploration and development work, conventional seismic exploration data can not meet the Geologic Requirements of detection small scale underground structure, by means of well-log information and geological knowledge, utilize Optimum Impedance Inversion Method can strengthen the ability of seismic prospecting detection small scale geologic structure to a certain extent.

At the beginning of wave impedance inversion technique proposes, many experts and scholar just thoroughly discuss its multiresolution issue.The multiresolution issue of the loyal academician (1998) of Li Qing to band limit wavelet has carried out very classical discussion, points out that geological data itself cannot ensure the correctness of inversion result outside seismic data effective band.Horse strong wind (1999) have studied the impact of impedance initial value model on inversion result, draws the main conclusions of " although have well logging and the constraint of geologic information, still cannot avoid inverting multi-solution ".Yao Fengchang (2000) points out after analyzing the inversion method based on model, multi-solution is the inherent feature of inversion method, depend primarily on the matching degree of initial model and actual geological condition, do not overemphasize resolution, do not overemphasize the consistance of practical logging and the other inversion result of well.Chen Guangjun (2003) mistaken ideas to inverting are discussed, and emphasize " although wave impedance inversion is a kind of good method of reservoir description, geology man can not produce anaclisis to it, and decision maker more can not lay down hard-and-fast rule to it ".Zhang Mingzhen (2007), after adding up Jiyang Depression many mouthfuls of log datas and inversion result thereof, finds " only having the authenticity weakening high frequency confinement guarantee invert data body ".Foreign scholar Francis (1997), Juve (1997), Ghost (2000) and Cerney (2007) have also carried out analysisanddiscusion from different angles to the multi-solution of wave impedance inversion and consequent explanation " trap ".

Although the multi-solution of geophysicist to wave impedance inversion has more deep understanding and understanding, the current demand of exploratory development and the contradiction of seismic data resolution make wave impedance inversion technique still and become the indispensable technological means of thin reservoir prediction by continuing.With regard to some concrete inverting achievements, how evaluating and judge multi-solution and the reliability of its inverting achievement, is utilize seismic inversion data to carry out problem demanding prompt solution in prediction work to oil and gas reservoir.

Geological information and log data are the constraint condition of wave impedance inversion, synthetic seismic data and actual seismic data error minimum be the condition of convergence of wave impedance inversion.The existing method that inversion result reliability is evaluated or the constraint condition employing wave impedance inversion, or employ the condition of convergence of wave impedance inversion, obviously, the evaluation method of this " utilizing condition to bear results, recycling condition evaluation result " can not ensure the authenticity of inversion result.Owing to recognizing this type of evaluation method Problems existing, often according to geology expert and oil reservoir expertise, wave impedance inversion result is evaluated in real work, although this evaluation method with reference to the knowledge and experience of geology expert and oil reservoir expert, but evaluation result is lost objective, there is very large subjectivity and blindness.Li Guofa etc. (2011) are at the multi-solution produced inverting and explain on the basis that trap system is analyzed, give one and compare objective appraisal method, but this evaluation method is a kind of qualitative evaluation criterion, be difficult to the inverting evaluation adapting to complicated underground structure and deposition characteristics.

Summary of the invention

The embodiment of the present invention provides a kind of reliability of wave impedance inversion data evaluation method, and utilize wave impedance inversion data to carry out the reliability of petroleum-gas prediction in order to improve, the method comprises:

Determine the RMS amplitude variation characteristic spatially of geological data;

Determine the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component;

Determine the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component;

According to described similarity, the reliability of wave impedance inversion data is evaluated;

The described RMS amplitude variation characteristic spatially determining geological data, comprising:

Read geological data x _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

Calculate geological data x _i(t), i=1,2 ... RMS amplitude curve C (i) of n, i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\∫x_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization

$\stackrel{\‾}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}C\left(i\right)};$

The described RMS amplitude variation characteristic spatially determining reflection coefficient high fdrequency component, comprising:

Read Acoustic Impedance Data z _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n;

Computational reflect coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\∫r}_i^2\left(t\right)\mathrm{dt}};$

What calculate the reflection coefficient high fdrequency component after normalization divides equally root amplitude curve

$\stackrel{\‾}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}D\left(i\right)};$

The described similarity determining the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component, comprising:

Calculate the RMS amplitude curve of the geological data after normalization with the reflection coefficient high fdrequency component after normalization divide equally root amplitude curve similarity coefficient ρ:

$\mathrm{\ρ}=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\stackrel{\‾}{C}\left(i\right)\stackrel{\‾}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\stackrel{\‾}{C}}^2\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\stackrel{\‾}{D}}^2\left(i\right)}};$

Described according to described similarity, the reliability of wave impedance inversion data is evaluated, comprising:

When ρ >=0.9, determine that wave impedance inversion data are reliable;

When 0.8≤ρ <0.9, determine that wave impedance inversion data are reliable;

When ρ <0.8, determine that wave impedance inversion data are unreliable.

The embodiment of the present invention also provides a kind of reliability of wave impedance inversion data evaluating apparatus, and utilize wave impedance inversion data to carry out the reliability of petroleum-gas prediction in order to improve, this device comprises:

Fisrt feature determination module, for determining the RMS amplitude variation characteristic spatially of geological data;

Second feature determination module, for determining the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component;

Similarity determination module, for determining the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component;

Reliability evaluation module, for according to described similarity, evaluates the reliability of wave impedance inversion data;

Described fisrt feature determination module specifically for:

Read geological data x _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

Calculate geological data x _i(t), i=1,2 ... RMS amplitude curve C (i) of n, i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\&Integral;x_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization

$\stackrel{\&OverBar;}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}C\left(i\right)};$

Described second feature determination module specifically for:

Read Acoustic Impedance Data z _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n;

Computational reflect coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;r}_i^2\left(t\right)\mathrm{dt}};$

What calculate the reflection coefficient high fdrequency component after normalization divides equally root amplitude curve

$\stackrel{\&OverBar;}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}D\left(i\right)};$

Described similarity determination module specifically for:

Calculate the RMS amplitude curve of the geological data after normalization with the reflection coefficient high fdrequency component after normalization divide equally root amplitude curve similarity coefficient ρ:

$\mathrm{\&rho;}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\stackrel{\&OverBar;}{C}\left(i\right)\stackrel{\&OverBar;}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{C}}^2\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{D}}^2\left(i\right)}};$

Described reliability evaluation module specifically for:

When ρ >=0.9, determine that wave impedance inversion data are reliable;

When 0.8≤ρ <0.9, determine that wave impedance inversion data are reliable;

When ρ <0.8, determine that wave impedance inversion data are unreliable.

The reliability of wave impedance inversion data evaluation method of the embodiment of the present invention and device, for wave impedance inversion provides a kind of brief and practical, objective effective Quantitative Evaluation System, improve the reliability utilizing wave impedance inversion data to carry out petroleum-gas prediction, the inverting evaluation of complicated underground structure and deposition characteristics can be adapted to.

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 the processing flow chart of reliability of wave impedance inversion data evaluation method in the embodiment of the present invention;

Fig. 2 is the basic flow sheet of Study of The Impedence Inversion Restrained By Well Log in the embodiment of the present invention;

Fig. 3 is the model data schematic diagram in the embodiment of the present invention designed by the experiment embodiment of the present invention;

Fig. 4 be level and smooth in the embodiment of the present invention after mean amplitude spectrum schematic diagram;

Fig. 5 is the schematic diagram of wave impedance inversion data in the embodiment of the present invention;

Fig. 6 is the reflection coefficient sequence schematic diagram calculated by wave impedance in the embodiment of the present invention;

Fig. 7 is the reflection coefficient high fdrequency component schematic diagram in the embodiment of the present invention on the highest effective frequency of geological data;

Fig. 8 is the RMS amplitude curve synoptic diagram of the geological data in the embodiment of the present invention after normalization;

Fig. 9 is that the reflection coefficient high fdrequency component in the embodiment of the present invention after normalization divides equally root amplitude curve schematic diagram;

Figure 10 is the schematic diagram of A block original earthquake data in certain oil field in the embodiment of the present invention;

Figure 11 is the schematic diagram of certain oil field A block wave impedance inversion result in the embodiment of the present invention;

Figure 12 is certain oil field A block reflection coefficient diagrammatic cross-section in the embodiment of the present invention;

Figure 13 is the schematic diagram of the reflection coefficient high fdrequency component in the embodiment of the present invention on the highest effective frequency of certain oil field A block geological data;

Figure 14 is the RMS amplitude curve synoptic diagram of the actual seismic data in the embodiment of the present invention after the normalization of certain oil field A block;

Figure 15 is the RMS amplitude curve synoptic diagram of the reflection coefficient high fdrequency component in the embodiment of the present invention after the normalization of certain oil field A block;

Figure 16 is the schematic diagram of B block original earthquake data in certain oil field in the embodiment of the present invention;

Figure 17 is the schematic diagram of certain oil field B block wave impedance inversion result in the embodiment of the present invention;

Figure 18 is the schematic diagram of the reflection coefficient high fdrequency component in the embodiment of the present invention on the highest effective frequency of certain oil field B block geological data;

Figure 19 is the schematic diagram of the RMS amplitude curve of actual seismic data in the embodiment of the present invention after the normalization of certain oil field B block;

Figure 20 is the schematic diagram of the RMS amplitude curve of reflection coefficient high fdrequency component in the embodiment of the present invention after the normalization of certain oil field B block;

Figure 21 is the structural representation of reliability of wave impedance inversion data evaluating apparatus in the embodiment of the present invention.

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.

As previously mentioned, although the multi-solution of Study of The Impedence Inversion Restrained By Well Log is obtained for extensive approval in theory with in application, but with regard to a certain concrete inverting achievement, how to judge and to evaluate its multi-solution and reliability, in prior art, also there is no the scheme that operability is stronger.Based on above-mentioned present Research and current demand, the embodiment of the present invention provides a kind of reliability of wave impedance inversion data evaluation method.Concrete, inventor considers: the object of wave impedance inversion is the small scale change disclosing underground structure, and therefore, the frequency of wave impedance inversion is higher than the frequency of geological data.As can be seen from the algorithm of wave impedance inversion, the wave impedance component within geological data effective band is reliable, and the multi-solution of wave impedance inversion derives from the high fdrequency component outside geological data effective band.High fdrequency component amplitude variations feature is spatially limited by actual formation structure, similar Changing Pattern should be had with actual seismic data, both amplitude curves are the reflections of identical underground structure, very large similarity should be had between them, both similaritys can be utilized to carry out quantitative evaluation to the reliability of invert data.

Fig. 1 is the processing flow chart of the reliability of wave impedance inversion data evaluation method of the embodiment of the present invention.As shown in Figure 1, the reliability of wave impedance inversion data evaluation method of the embodiment of the present invention can comprise:

Step 101, determine the RMS amplitude variation characteristic spatially of geological data;

Step 102, determine the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component;

Step 103, determine the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component;

Step 104, according to described similarity, the reliability of wave impedance inversion data to be evaluated.

Flow process can be learnt as shown in Figure 1, in the reliability of wave impedance inversion data evaluation method of the embodiment of the present invention, determine RMS amplitude variation characteristic spatially and the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component of geological data respectively, similarity according to both carries out quantitative evaluation to the reliability of wave impedance inversion data, for wave impedance inversion provides objective effectively evaluating method and implementing procedure, improve the precision utilizing wave impedance inversion data to carry out reservoir prediction and layer description, the inverting evaluation of complicated underground structure and deposition characteristics can be adapted to.

Before specifically introducing flow process shown in Fig. 1, in order to content and the implementation step of the embodiment of the present invention are described better, first the ultimate principle of wave impedance inversion and background knowledge are briefly introduced.

The ultimate principle of wave impedance inversion:

The target of wave impedance inversion interface type geological data is converted into ground stratotype wave impedance record, while giving geological data formation information definitely and lithological information, improves the ability of geological data reflection laminate structure and deposition characteristics.Basic ideas and way find subsurface model by iterative manner, require the synthetic seismic data of this model and actual seismic data the most close, when there is error in synthetic seismic data and actual seismic data, broadcast mathematical method by error-duration model and constantly update model, until error between the two meets the condition of convergence of setting.Main target due to wave impedance inversion identifies and determines the unresolvable small scale tectonic structure of geological data, therefore, require that the resolution of wave impedance inversion is higher than the resolution of geological data itself, its high frequency components comes from well logging information, part comes from Sparse Pulse Inversion model, high fdrequency component has stronger multi-solution, is easy to " the explanation trap " that cause wave impedance inversion.

Fig. 2 gives the basic procedure of Study of The Impedence Inversion Restrained By Well Log.As shown in Figure 2, the basic procedure of Study of The Impedence Inversion Restrained By Well Log comprises:

First geological data, log data, geologic data is utilized to set up the initial model of wave impedance inversion along geologic horizon interpolation;

Geological data, log data, geologic data is utilized to extract seismic wavelet;

Convolution algorithm is adopted to produce synthetic seismic data by initial model and seismic wavelet;

Calculate the error of synthetic seismic data and actual seismic data;

Constantly amendment and Renewal model is broadcast by error-duration model;

When the error of synthetic seismic data and actual seismic data is less than given threshold value namely this error meets accuracy requirement, the final mask of output wave Impedance Inversion.

The basic procedure of Study of The Impedence Inversion Restrained By Well Log can be found out as shown in Figure 2, and Study of The Impedence Inversion Restrained By Well Log is to the continuous iterative modifications of surge impedance model and makes the process that its synthetic seismic data and actual seismic data are constantly approached.

Introduce the concrete enforcement of flow process shown in Fig. 1 below.

During concrete enforcement, determine the RMS amplitude variation characteristic spatially of geological data in step 101, can comprise:

Read geological data x _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

Calculate geological data x _i(t), i=1,2 ... RMS amplitude curve C (i) of n, i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\&Integral;x_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization

$\stackrel{\&OverBar;}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}C\left(i\right)};$

Determine the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component in step 102, can comprise:

Read Acoustic Impedance Data z _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n;

Computational reflect coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;r}_i^2\left(t\right)\mathrm{dt}};$

What calculate the reflection coefficient high fdrequency component after normalization divides equally root amplitude curve

$\stackrel{\&OverBar;}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}D\left(i\right)};$

Determine the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component in step 103, can comprise:

Calculate the RMS amplitude curve of the geological data after normalization with the reflection coefficient high fdrequency component after normalization divide equally root amplitude curve similarity coefficient ρ:

$\mathrm{\&rho;}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\stackrel{\&OverBar;}{C}\left(i\right)\stackrel{\&OverBar;}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{C}}^2\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{D}}^2\left(i\right)}};$

According to described similarity in step 104, the reliability of wave impedance inversion data is evaluated, can comprise:

When ρ >=0.9, determine that wave impedance inversion data are reliable;

When 0.8≤ρ <0.9, determine that wave impedance inversion data are reliable, namely the reliability of wave impedance inversion data can reach certain requirement, between reliable and unreliable;

When ρ <0.8, determine that wave impedance inversion data are unreliable.

During concrete enforcement, aforementioned by Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n, can comprise:

Computational reflect coefficient sequence ξ _i(t), i=1,2 ... n:

${\mathrm{\&xi;}}_i\left(t\right)=\frac{z_i(t+\mathrm{\&Delta;t})-z_i\left(t\right)}{z_i(t+\mathrm{\&Delta;t})+z_i\left(t\right)}$

Wherein, Δ t is time sampling interval, and unit is second or millisecond;

With geological data x _i(t), i=1,2 ... the highest effective frequency f of n _mfor low cut-off frequency, to reflection coefficient sequence ξ _i(t), i=1,2 ... n does high-pass filtering, obtains the high fdrequency component r of reflection coefficient sequence _i(t), i=1,2 ... n.

During concrete enforcement, to reflection coefficient sequence ξ _i(t), i=1,2 ... before n does high-pass filtering, geological data x can also be determined _i(t), i=1,2 ... the highest effective frequency f of n _m.

Concrete, geological data x can be determined as follows _i(t), i=1,2 ... the highest effective frequency f of n _m:

To geological data x _i(t), i=1,2 ... n does Fourier transform, obtains the geological data X of frequency field _i(f) and spectral amplitude A thereof _i(f):

X _i(f)＝∫x _i(t)e ^-i2πftdt，

A _i(f)＝|X _i(f)|；

Calculate mean amplitude spectrum

$\stackrel{\&OverBar;}{A}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{A}_i\left(f\right);$

To mean amplitude spectrum smoothing process, obtains the mean amplitude spectrum smoothly

$\stackrel{\&OverBar;}{B}\left(f\right)=\frac{1}{T}\underset{\mathrm{\&tau;}=-T}{\overset{T}{\mathrm{\&Sigma;}}}\cos \left(\frac{\mathrm{\&pi;\&tau;}}{2T}\right)\stackrel{\&OverBar;}{A}(f-\mathrm{\&tau;}),$

Wherein, T is smoothing operator length, T=5Hz;

Determine the highest effective frequency f of geological data _m:

f _m＝αf _p，

Wherein, f _pfor the mean amplitude spectrum after level and smooth crest frequency, α=2.8.

Lift an example below, be utilized as the model data implementation process to the embodiment of the present invention of experiment designed by the embodiment of the present invention and be described in detail.

Determine geological data x _i(t), i=1,2 ... the highest effective frequency f of n _m, concrete steps are as follows:

Read geological data x as shown in Figure 3 _i(t), i=1,2 ... n, n=101 in this example;

To geological data x _i(t), i=1,2 ... n does Fourier transform, obtains the geological data X of frequency field _i(f) and spectral amplitude A thereof _i(f):

X _i(f)＝∫x _i(t)e ^-i2πftdt，

A _i(f)＝|X _i(f)|；

Calculate mean amplitude spectrum

$\stackrel{\&OverBar;}{A}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{A}_i\left(f\right);$

To mean amplitude spectrum smoothing process, obtains the mean amplitude spectrum smoothly

$\stackrel{\&OverBar;}{B}\left(f\right)=\frac{1}{T}\underset{\mathrm{\&tau;}=-T}{\overset{T}{\mathrm{\&Sigma;}}}\cos \left(\frac{\mathrm{\&pi;\&tau;}}{2T}\right)\stackrel{\&OverBar;}{A}(f-\mathrm{\&tau;}),$

Wherein, T is smoothing operator length, T=5Hz, the mean amplitude spectrum after to obtain as shown in Figure 4 level and smooth

Determine the highest effective frequency f of geological data _m:

f _m＝αf _p，

Wherein, f _pfor level and smooth rear mean amplitude spectrum crest frequency, f in this example _p=28Hz, α=2.8, f _m=78Hz;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n, concrete steps are as follows:

Read Acoustic Impedance Data z as shown in Figure 5 _i(t), i=1,2 ... n, n=101 in this example;

Calculate reflection coefficient sequence ξ as shown in Figure 6 _i(t), i=1,2 ... n:

${\mathrm{\&xi;}}_i\left(t\right)=\frac{z_i(t+\mathrm{\&Delta;t})-z_i\left(t\right)}{z_i(t+\mathrm{\&Delta;t})+z_i\left(t\right)}$

Wherein, Δ t is time sampling interval, Δ t=1ms in this example;

With the highest effective frequency f of geological data _m=78Hz is low cut-off frequency, to reflection coefficient sequence ξ _i(t), i=1,2 ... n does high-pass filtering, obtains reflection coefficient sequence high fdrequency component r as shown in Figure 7 _i(t), i=1,2 ... n;

Calculate RMS amplitude curve C (i) of geological data, i=1,2 ... n and reflection coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\&Integral;x_i^2\left(t\right)\mathrm{dt}},$

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;r}_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization root amplitude curve is divided equally with the reflection coefficient high fdrequency component after normalization

$\stackrel{\&OverBar;}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}C\left(i\right)},$

$\stackrel{\&OverBar;}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}D\left(i\right)};$

Fig. 8 is the RMS amplitude curve of the geological data after the normalization of this example; Fig. 9 is that the reflection coefficient high fdrequency component after the normalization of this example divides equally root amplitude curve;

Calculate the similarity coefficient ρ of two amplitude curves:

$\mathrm{\&rho;}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\stackrel{\&OverBar;}{C}\left(i\right)\stackrel{\&OverBar;}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{C}}^2\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{D}}^2\left(i\right)}},$

ρ=0.83 in this example;

According to similarity coefficient, the reliability of wave impedance inversion data is evaluated.In this example, 0.8≤ρ <0.9, so invert data is reliable.

Lift the embody rule that an example illustrates the reliability of wave impedance inversion data evaluation method of the embodiment of the present invention below.Suppose the application example that this example is certain oil field A block, exploration targets layer is thin sand-mud interbed oil and gas reservoir, and single sand body thickness is below 10 meters, and Sand member thickness is at about 30 meters.

Figure 10 is the original earthquake data of this example, and Temporal sampling is Δ t=1ms, the highest effective frequency 50Hz.Figure 11 is the result of wave impedance inversion, only cannot carry out objective evaluation to its reliability according to wave impedance itself.Figure 12 is the reflection coefficient section calculated by wave impedance, it is carried out to the high-pass filtering of low section of 50Hz, obtains the high fdrequency component outside geological data effective band as shown in fig. 13 that.Figure 14 and 15 be respectively normalization after original earthquake data and the RMS amplitude curve of reflection coefficient high fdrequency component, both similarity coefficients are 0.94, and therefore, the wave impedance inversion result of this example is reliable.

Lift the embody rule that an example illustrates the reliability of wave impedance inversion data evaluation method of the embodiment of the present invention below again.Suppose the application example that this example is certain oil field B block, exploration targets layer is be also petroclastic rock oil and gas reservoir.Figure 16 is the original earthquake data of this example, and Temporal sampling is Δ t=2ms, geological data highest frequency 75Hz.Figure 17 is the result of Study of The Impedence Inversion Restrained By Well Log, is converted into reflection coefficient section, and carries out the high-pass filtering of low section of 75Hz, obtains the high fdrequency component of reflection coefficient as shown in figure 18.Figure 19 and 20 be respectively normalization after original earthquake data and the RMS amplitude curve of reflection coefficient high fdrequency component, both similarity coefficients are 0.67, judge that the inversion result of this example is unreliable accordingly.

Based on same inventive concept, additionally provide a kind of reliability of wave impedance inversion data evaluating apparatus in the embodiment of the present invention, as described in the following examples.The principle of dealing with problems due to reliability of wave impedance inversion data evaluating apparatus is similar to reliability of wave impedance inversion data evaluation method, therefore the enforcement of reliability of wave impedance inversion data evaluating apparatus see the enforcement of reliability of wave impedance inversion data evaluation method, can repeat part and repeats no more.

Figure 21 is the structural representation of reliability of wave impedance inversion data evaluating apparatus in the embodiment of the present invention.As shown in figure 21, the reliability of wave impedance inversion data evaluating apparatus of the embodiment of the present invention can comprise:

Fisrt feature determination module 2101, for determining the RMS amplitude variation characteristic spatially of geological data;

Second feature determination module 2102, for determining the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component;

Similarity determination module 2103, for determining the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component;

Reliability evaluation module 2104, for according to described similarity, evaluates the reliability of wave impedance inversion data.

In an embodiment, fisrt feature determination module 2101 specifically may be used for:

Read geological data x _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

Calculate geological data x _i(t), i=1,2 ... RMS amplitude curve C (i) of n, i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\&Integral;x_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization

$\stackrel{\&OverBar;}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}C\left(i\right)};$

Second feature determination module 2102 specifically may be used for:

Read Acoustic Impedance Data z _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n;

Computational reflect coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;r}_i^2\left(t\right)\mathrm{dt}};$

What calculate the reflection coefficient high fdrequency component after normalization divides equally root amplitude curve

$\stackrel{\&OverBar;}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}D\left(i\right)};$

Similarity determination module 2103 specifically may be used for:

Calculate the RMS amplitude curve of the geological data after normalization with the reflection coefficient high fdrequency component after normalization divide equally root amplitude curve similarity coefficient ρ:

$\mathrm{\&rho;}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\stackrel{\&OverBar;}{C}\left(i\right)\stackrel{\&OverBar;}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{C}}^2\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{D}}^2\left(i\right)}};$

Reliability evaluation module 2104 specifically may be used for:

When ρ >=0.9, determine that wave impedance inversion data are reliable;

When 0.8≤ρ <0.9, determine that wave impedance inversion data are reliable;

When ρ <0.8, determine that wave impedance inversion data are unreliable.

In an embodiment, second feature determination module 2012 specifically may be used for:

Computational reflect coefficient sequence ξ _i(t), i=1,2 ... n:

${\mathrm{\&xi;}}_i\left(t\right)=\frac{z_i(t+\mathrm{\&Delta;t})-z_i\left(t\right)}{z_i(t+\mathrm{\&Delta;t})+z_i\left(t\right)}$

Wherein, Δ t is time sampling interval, and unit is second or millisecond;

With geological data x _i(t), i=1,2 ... the highest effective frequency f of n _mfor low cut-off frequency, to reflection coefficient sequence ξ _i(t), i=1,2 ... n does high-pass filtering, obtains the high fdrequency component r of reflection coefficient sequence _i(t), i=1,2 ... n.

In an embodiment, above-mentioned reliability of wave impedance inversion data evaluating apparatus can also comprise:

Frequency determining module, for determining geological data x _i(t), i=1,2 ... the highest effective frequency f of n _m.

In an embodiment, frequency determining module specifically may be used for:

To geological data x _i(t), i=1,2 ... n does Fourier transform, obtains the geological data X of frequency field _i(f) and spectral amplitude A thereof _i(f):

X _i(f)＝∫x _i(t)e ^-i2πftdt，

A _i(f)＝|X _i(f)|；

Calculate mean amplitude spectrum

$\stackrel{\&OverBar;}{A}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{A}_i\left(f\right);$

To mean amplitude spectrum smoothing process, obtains the mean amplitude spectrum smoothly

$\stackrel{\&OverBar;}{B}\left(f\right)=\frac{1}{T}\underset{\mathrm{\&tau;}=-T}{\overset{T}{\mathrm{\&Sigma;}}}\cos \left(\frac{\mathrm{\&pi;\&tau;}}{2T}\right)\stackrel{\&OverBar;}{A}(f-\mathrm{\&tau;}),$

Wherein, T is smoothing operator length, T=5Hz;

Determine the highest effective frequency f of geological data _m:

f _m＝αf _p，

Wherein, f _pfor the mean amplitude spectrum after level and smooth crest frequency, α=2.8.

In sum, the reliability of wave impedance inversion data evaluation method of the embodiment of the present invention and device, determine RMS amplitude variation characteristic spatially and the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component of geological data respectively, similarity according to both carries out quantitative evaluation to the reliability of wave impedance inversion data, for wave impedance inversion provides a kind of brief and practical, objective effective Quantitative Evaluation System, improve the reliability utilizing wave impedance inversion data to carry out petroleum-gas prediction, the inverting evaluation of complicated underground structure and deposition characteristics can be adapted to.

Those skilled in the art should understand, embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the present invention can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to magnetic disk memory, CD-ROM, optical memory etc.) of computer usable program code.

The present invention describes with reference to according to the process flow diagram of the method for the embodiment of the present invention, equipment (system) and computer program and/or block scheme.Should understand can by the combination of the flow process in each flow process in computer program instructions realization flow figure and/or block scheme and/or square frame and process flow diagram and/or block scheme and/or square frame.These computer program instructions can being provided to the processor of multi-purpose computer, special purpose computer, Embedded Processor or other programmable data processing device to produce a machine, making the instruction performed by the processor of computing machine or other programmable data processing device produce device for realizing the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.

These computer program instructions also can be stored in can in the computer-readable memory that works in a specific way of vectoring computer or other programmable data processing device, the instruction making to be stored in this computer-readable memory produces the manufacture comprising command device, and this command device realizes the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.

These computer program instructions also can be loaded in computing machine or other programmable data processing device, make on computing machine or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computing machine or other programmable devices is provided for the step realizing the function of specifying in process flow diagram flow process or multiple flow process and/or block scheme square frame or multiple square frame.

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 (8)

1. a reliability of wave impedance inversion data evaluation method, is characterized in that, comprising:

Determine the RMS amplitude variation characteristic spatially of geological data;

Determine the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component;

Determine the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component;

According to described similarity, the reliability of wave impedance inversion data is evaluated;

The described RMS amplitude variation characteristic spatially determining geological data, comprising:

Read geological data x _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

Calculate geological data x _i(t), i=1,2 ... RMS amplitude curve C (i) of n, i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;x}_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization

$\stackrel{\&OverBar;}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}C\left(i\right)};$

The described RMS amplitude variation characteristic spatially determining reflection coefficient high fdrequency component, comprising:

Read Acoustic Impedance Data z _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n;

Computational reflect coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;r}_i^2\left(t\right)\mathrm{dt}};$

What calculate the reflection coefficient high fdrequency component after normalization divides equally root amplitude curve

$\stackrel{\&OverBar;}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}D\left(i\right)};$

The described similarity determining the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component, comprising:

Calculate the RMS amplitude curve of the geological data after normalization with the reflection coefficient high fdrequency component after normalization divide equally root amplitude curve similarity coefficient ρ:

$\mathrm{\&rho;}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\stackrel{\&OverBar;}{C}\left(i\right)\stackrel{\&OverBar;}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{C}}^2}\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{D}}^2\left(i\right)};$

Described according to described similarity, the reliability of wave impedance inversion data is evaluated, comprising:

When ρ >=0.9, determine that wave impedance inversion data are reliable;

When 0.8≤ρ <0.9, determine that wave impedance inversion data are reliable;

When ρ <0.8, determine that wave impedance inversion data are unreliable.

2. the method for claim 1, is characterized in that, described by Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n, comprising:

Computational reflect coefficient sequence ξ _i(t), i=1,2 ... n:

${\mathrm{\&xi;}}_i\left(t\right)=\frac{z_i(t+\mathrm{\&Delta;t})-z_i\left(t\right)}{z_i(t+\mathrm{\&Delta;t})+z_i\left(t\right)}$

Wherein, Δ t is time sampling interval, and unit is second or millisecond;

With geological data x _i(t), i=1,2 ... the highest effective frequency f of n _mfor low cut-off frequency, to reflection coefficient sequence ξ _i(t), i=1,2 ... n does high-pass filtering, obtains the high fdrequency component r of reflection coefficient sequence _i(t), i=1,2 ... n.

3. method as claimed in claim 2, is characterized in that, described to reflection coefficient sequence ξ _i(t), i=1,2 ... before n does high-pass filtering, also comprise:

Determine geological data x _i(t), i=1,2 ... the highest effective frequency f of n _m.

4. method as claimed in claim 3, is characterized in that, describedly determines geological data x _i(t), i=1,2 ... the highest effective frequency f of n _m, comprising:

To geological data x _i(t), i=1,2 ... n does Fourier transform, obtains the geological data X of frequency field _i(f) and spectral amplitude A thereof _i(f):

X _i(f)＝∫x _i(t)e ^-i2πftdt，

A _i(f)＝|X _i(f)|；

Calculate mean amplitude spectrum

$\stackrel{\&OverBar;}{A}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{A}_i\left(f\right);$

To mean amplitude spectrum smoothing process, obtains the mean amplitude spectrum smoothly

$\stackrel{\&OverBar;}{B}\left(f\right)=\frac{1}{T}\underset{\mathrm{\&tau;}=-T}{\overset{T}{\mathrm{\&Sigma;}}}\cos \left(\frac{\mathrm{\&pi;\&tau;}}{2T}\right)\stackrel{\&OverBar;}{A}(f-\mathrm{\&tau;}),$

Wherein, T is smoothing operator length, T=5Hz;

Determine the highest effective frequency f of geological data _m:

f _m＝αf _p，

Wherein, f _pfor the mean amplitude spectrum after level and smooth crest frequency, α=2.8.

5. a reliability of wave impedance inversion data evaluating apparatus, is characterized in that, comprising:

Fisrt feature determination module, for determining the RMS amplitude variation characteristic spatially of geological data;

Second feature determination module, for determining the RMS amplitude variation characteristic spatially of reflection coefficient high fdrequency component;

Similarity determination module, for determining the similarity of the RMS amplitude variation characteristic spatially of geological data RMS amplitude variation characteristic spatially and reflection coefficient high fdrequency component;

Reliability evaluation module, for according to described similarity, evaluates the reliability of wave impedance inversion data;

Described fisrt feature determination module specifically for:

Read geological data x _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

Calculate geological data x _i(t), i=1,2 ... RMS amplitude curve C (i) of n, i=1,2 ... n:

$C\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;x}_i^2\left(t\right)\mathrm{dt}};$

Calculate the RMS amplitude curve of the geological data after normalization

$\stackrel{\&OverBar;}{C}\left(i\right)=\frac{C\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}C\left(i\right)};$

Described second feature determination module specifically for:

Read Acoustic Impedance Data z _i(t), i=1,2 ... n, wherein, n is earthquake number of channels, and t is the time, and unit is second or millisecond;

By Acoustic Impedance Data z _i(t), i=1,2 ... the high fdrequency component r of n computational reflect coefficient sequence _i(t), i=1,2 ... n;

Computational reflect coefficient high fdrequency component divide equally root amplitude curve D (i), i=1,2 ... n:

$D\left(i\right)=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\&Integral;r}_i^2\left(t\right)\mathrm{dt}};$

What calculate the reflection coefficient high fdrequency component after normalization divides equally root amplitude curve

$\stackrel{\&OverBar;}{D}\left(i\right)=\frac{D\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}D\left(i\right)};$

Described similarity determination module specifically for:

Calculate the RMS amplitude curve of the geological data after normalization with the reflection coefficient high fdrequency component after normalization divide equally root amplitude curve similarity coefficient ρ:

$\mathrm{\&rho;}=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\stackrel{\&OverBar;}{C}\left(i\right)\stackrel{\&OverBar;}{D}\left(i\right)}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{C}}^2}\left(i\right)\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{D}}^2\left(i\right)};$

Described reliability evaluation module specifically for:

When ρ >=0.9, determine that wave impedance inversion data are reliable;

When 0.8≤ρ <0.9, determine that wave impedance inversion data are reliable;

When ρ <0.8, determine that wave impedance inversion data are unreliable.

6. device as claimed in claim 5, is characterized in that, described second feature determination module specifically for:

Computational reflect coefficient sequence ξ _i(t), i=1,2 ... n:

${\mathrm{\&xi;}}_i\left(t\right)=\frac{z_i(t+\mathrm{\&Delta;t})-z_i\left(t\right)}{z_i(t+\mathrm{\&Delta;t})+z_i\left(t\right)}$

Wherein, Δ t is time sampling interval, and unit is second or millisecond;

With geological data x _i(t), i=1,2 ... the highest effective frequency f of n _mfor low cut-off frequency, to reflection coefficient sequence ξ _i(t), i=1,2 ... n does high-pass filtering, obtains the high fdrequency component r of reflection coefficient sequence _i(t), i=1,2 ... n.

7. device as claimed in claim 6, is characterized in that, also comprise:

Frequency determining module, for determining geological data x _i(t), i=1,2 ... the highest effective frequency f of n _m.

8. device as claimed in claim 7, is characterized in that, described frequency determining module specifically for:

To geological data x _i(t), i=1,2 ... n does Fourier transform, obtains the geological data X of frequency field _i(f) and spectral amplitude A thereof _i(f):

X _i(f)＝∫x _i(t)e ^-i2πftdt，

A _i(f)＝|X _i(f)|；

Calculate mean amplitude spectrum

$\stackrel{\&OverBar;}{A}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{A}_i\left(f\right);$

To mean amplitude spectrum smoothing process, obtains the mean amplitude spectrum smoothly

$\stackrel{\&OverBar;}{B}\left(f\right)=\frac{1}{T}\underset{\mathrm{\&tau;}=-T}{\overset{T}{\mathrm{\&Sigma;}}}\cos \left(\frac{\mathrm{\&pi;\&tau;}}{2T}\right)\stackrel{\&OverBar;}{A}(f-\mathrm{\&tau;}),$

Wherein, T is smoothing operator length, T=5Hz;

Determine the highest effective frequency f of geological data _m:

f _m＝αf _p，

Wherein, f _pfor the mean amplitude spectrum after level and smooth crest frequency, α=2.8.