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Geophysical data processing

2.1Introduction

Geophysical surveys measure the variation of some physical quantity,with respect either to position or to time.The quantity may,for example,be the strength of the E arth’s magnetic ?eld along a pro?le across an igneous intrusion.It may be the motion of the ground surface as a function of time associated with the passage of seismic waves.In either case,the simplest way to pre-sent the data is to plot a graph (Fig.2.1) showing the vari-ation of the measured quantity with respect to distance or time as appropriate.The graph will show some more or less complex waveform shape,which will re?ect physical variations in the underlying geology,superim-posed on unwanted variations from non-geological fea-tures (such as the effect of electrical power cables in the magnetic example,or vibration from passing traf?c for the seismic case),instrumental inaccuracy and data col-lection errors.The detailed shape of the waveform may be uncertain due to the dif?culty in interpolating the curve between widely spaced stations.The geophysicist’s task is to separate the ‘signal’from the ‘noise’and interpret the signal in terms of ground structure.

Analysis of waveforms such as these represents an es-sential aspect of geophysical data processing and inter-pretation.The fundamental physics and mathematics of such analysis is not novel,most having been discovered in the 19th or early 20th centuries.The use of these ideas is also widespread in other technological areas such as radio,television,sound and video recording,radio-astronomy,meteorology and medical imaging,as well as military applications such as radar,sonar and satellite imaging.Before the general availability of digital com-puting,the quantity of data and the complexity of the processing severely restricted the use of the known tech-niques.This no longer applies and nearly all the tech-niques described in this chapter may be implemented in standard computer spreadsheet programs.

The fundamental principles on which the various methods of data analysis are based are brought together

in this chapter.These are accompanied by a discussion of the techniques of digital data processing by computer that are routinely used by geophysicists.Throughout this chapter,waveforms are referred to as functions of time, but all the principles discussed are equally applicable to functions of distance.In the latter case,frequency (num-ber of waveform cycles per unit time) is replaced by spatial frequency or wavenumber(number of waveform cycles per unit distance).

2.2Digitization of geophysical data

W aveforms of geophysical interest are generally contin-uous (analogue) functions of time or distance.T o apply the power of digital computers to the task of analysis,the data need to be expressed in digital form,whatever the form in which they were originally recorded.

A continuous,smooth function of time or distance can be expressed digitally by sampling the function at a ?xed interval and recording the instantaneous value of the function at each sampling point.Thus,the analogue function of time f(t) shown in Fig.2.2(a) can be repre-sented as the digital function g(t) shown in Fig.2.2(b) in which the continuous function has been replaced by a series of discrete values at ?xed,equal,intervals of time. This process is inherent in many geophysical surveys, where readings are taken of the value of some parameter (e.g.magnetic ?eld strength) at points along survey lines. The extent to which the digital values faithfully repre-sent the original waveform will depend on the accuracy of the amplitude measurement and the intervals between measured samples.Stated more formally,these two para-meters of a digitizing system are the sampling precision (dynamic range) and the sampling frequency. Dynamic range is an expression of the ratio of the largest measurable amplitude A

max

to the smallest measurable

amplitude A

min

in a sampled function.The higher the

2Geophysical data processing

Geophysical Data Processing 9

dynamic range,the more faithfully the amplitude variations in the analogue waveform will be represented in the digitized version of the waveform.Dynamic range is normally expressed in the decibel (dB) scale used to de-

?ne electrical power ratios:the ratio of two power values P 1and P 2is given by 10log 10(P 1/P 2) dB.Since power is proportional to the square of signal amplitude A (2.1)

Thus,if a digital sampling scheme measures ampli-tudes over the range from 1 to 1024 units of amplitude,the dynamic range is given by

In digital computers,digital samples are expressed in binary form (i.e.they are composed of a sequence of dig-its that have the value of either 0 or 1).Each binary digit is known as a bit and the sequence of bits representing the sample value is known as a word .The number of bits in each word determines the dynamic range of a digitized waveform.For example,a dynamic range of 60dB requires 11-bit words since the appropriate amplitude ratio of 1024 (=210) is rendered as 10000000000 in binary form.A dynamic range of 84dB represents an amplitude ratio of 214and,hence,requires sampling with 15-bit words.Thus,increasing the number of bits in each word in digital sampling increases the dynamic range of the digital function.

Sampling frequency is the number of sampling points in unit time or unit distance.Intuitively,it may appear that the digital sampling of a continuous function inevitably leads to a loss of information in the resultant digital func-tion,since the latter is only speci?ed by discrete values at a series of points.Again intuitively,there will be no

20201024601010log log max min A A ()

=adB

101020101210122

1012log log log P P A A A A ()=()

=()

600500400300200

Distance (m)

(a)

151050–5–10

Time (milliseconds)

(b)G r o u n d v e l o c i t y (10-6 m /s )

M a g n e t i c f i e l d (n T )Fig. 2.1(a) A graph showing a typical magnetic ?eld strength variation which may be measured along a pro?le.(b) A graph of a typical seismogram,showing variation of particle velocities in the ground as a function of time during the

passage of a seismic wave.

1.0

–1.0

f (t )

g (t )1.0

–1.0

(a)(b)Fig. 2.2(a) Analogue representation of a sinusoidal function.(b) Digital representation of the same function.

10Chapter 2

signi?cant loss of information content as long as the fre-quency of sampling is much higher than the highest frequency component in the sampled function.Mathe-matically,it can be proved that,if the waveform is a sine curve,this can always be reconstructed provided that there are a minimum of two samples per period of the sine wave.

Thus,if a waveform is sampled every two milliseconds (sampling interval),the sampling frequency is 500 sam-ples per second (or 500Hz).Sampling at this rate will preserve all frequencies up to 250Hz in the sampled function.This frequency of half the sampling frequency is known as the Nyquist frequency (f N ) and the Nyquist interval is the frequency range from zero up to f N (2.2)

where D t =sampling interval.

If frequencies above the Nyquist frequency are pre-sent in the sampled function,a serious form of distortion results known as aliasing ,in which the higher frequency components are ‘folded back’into the Nyquist interval.Consider the example illustrated in Fig.2.3 in which sine waves at different frequencies are sampled.The lower frequency wave (Fig.2.3(a)) is accurately repro-duced,but the higher frequency wave (Fig.2.3(b),solid line) is rendered as a ?ctitious frequency,shown by the dashed line,within the Nyquist interval.The relation-ship between input and output frequencies in the case of a sampling frequency of 500Hz is shown in Fig.2.3(c).It is apparent that an input frequency of 125Hz,for exam-ple,is retained in the output but that an input frequency of 625Hz is folded back to be output at 125Hz also.T o overcome the problem of aliasing,the sampling frequency must be at least twice as high as the highest fre-quency component present in the sampled function.If the function does contain frequencies above the Nyquist frequency determined by the sampling,it must be passed through an antialias ?lter prior to digitization.The antialias ?lter is a low-pass frequency ?lter with a sharp cut-off that removes frequency components above the Nyquist frequency,or attenuates them to an insigni?cant amplitude level.

2.3Spectral analysis

An important mathematical distinction exists between periodic waveforms (Fig.2.4(a)),that repeat themselves at a ?xed time period T ,and transient waveforms (Fig.2.4(b)),

f N =()

12D t that are non-repetitive.By means of the mathematical technique of Fourier analysis any periodic waveform,however complex,may be decomposed into a series of sine (or cosine) waves whose frequencies are integer multiples of the basic repetition frequency 1/T ,known as the fundamental frequency .The higher frequency com-ponents,at frequencies of n/T (n =1,2,3,...),are known as harmonics.The complex waveform of Fig.2.5(a) is built up from the addition of the two individual sine wave components shown.T o express any waveform in terms of its constituent sine wave components,it is necessary to de?ne not only the frequency of each com-ponent but also its amplitude and phase.If in the above example the relative amplitude and phase relations of the individual sine waves are altered,

summation can

(a)

4f N

3f N 2f N f N 250125

1252505006257501000

Input frequency (Hz)

(c)O u t p u t f r e q u e n c y (H z )

0Fig. 2.3(a) Sine wave frequency less than Nyquist frequency.(b) Sine wave frequency greater than Nyquist frequency (solid line) showing the ?ctitious frequency that is generated by aliasing (dashed line).

Geophysical Data Processing 11

produce the quite different waveform illustrated in Fig.2.5(b).

From the above it follows that a periodic waveform can be expressed in two different ways:in the familiar time domain ,expressing wave amplitude as a function of time,or in the frequency domain ,expressing the amplitude and phase of its constituent sine waves as a function of frequency.The waveforms shown in Fig.2.5(a) and (b)are represented in Fig.2.6(a) and (b) in terms of their am-plitude and phase spectra.These spectra,known as line spectra,are composed of a series of discrete values of the amplitude and phase components of the waveform at set frequency values distributed between 0Hz and the Nyquist frequency.Transient waveforms do not repeat themselves;that is,they have an in?nitely long period.They may be re-garded,by analogy with a periodic waveform,as having an in?nitesimally small fundamental frequency (1/T ?0) and,consequently,harmonics that occur at in?nitesi-mally small frequency intervals to give continuous am-plitude and phase spectra rather than the line spectra of periodic waveforms.However,it is impossible to cope analytically with a spectrum containing an in?nite num-ber of sine wave components.Digitization of the wave-form in the time domain (Section 2.2) provides a means of dealing with the continuous spectra of transient wave-forms.A digitally sampled transient waveform has its

amplitude and phase spectra subdivided into a number of

Fig. 2.4(a) Periodic and (b) transient waveforms.

Fig. 2.5Complex waveforms resulting from the summation of two sine wave components of frequency f and 2f .(a) T he two sine wave components are of equal amplitude and in phase.(b) T he higher frequency component has twice the amplitude of the lower frequency component and is p /2 out of phase.

(After Anstey 1965.)

π–πf

Frequency

f 2f Frequency

A m p l i t u d e

P h a s e

A m p l i t u d e

(a)

(b)

Fig. 2.6Representation in the frequency domain of the waveforms illustrated in Fig.2.5,showing their amplitude and phase spectra.

12Chapter 2

thin frequency slices,with each slice having a frequency equal to the mean frequency of the slice and an ampli-tude and phase proportional to the area of the slice of the appropriate spectrum (Fig.2.7).This digital expression of a continuous spectrum in terms of a ?nite number of discrete frequency components provides an approximate representation in the frequency domain of a transient waveform in the time domain.Increasing the sampling frequency in the time domain not only improves the time-domain representation of the waveform,but also increases the number of frequency slices in the frequen-cy domain and improves the accuracy of the approxima-tion here too.

Fourier transformation may be used to convert a time function g (t ) into its equivalent amplitude and phase spectra A (f ) and f (f ),or into a complex function of frequency G (f ) known as the frequency spectrum ,where (2.3)

The time- and frequency-domain representations of a waveform,g (t ) and G (f ),are known as a Fourier pair ,represented by the notation

(2.4)

g t G f

()′()G f A f f ()=()()

e i

f Components of a Fourier pair are interchangeable,such that,if G (f ) is the Fourier transform of

g (t ),then g (t )is the Fourier transform of G (f ).Figure 2.8 illus-trates Fourier pairs for various waveforms of geophysical signi?cance.All the examples illustrated have zero phase spectra ;that is,the individual sine wave components of the waveforms are in phase at zero time.In this case f (f )=0 for all values of f .Figure 2.8(a) shows a spike func-tion (also known as a Dirac function ),whic

h is the shortest possible transient waveform.Fourier transformation shows that the spike function has a continuous frequency spectrum of constant amplitude from zero to in?nity;thus,a spike function contains all frequencies from zero to in?nity at equal amplitude.The ‘DC bias’waveform of Fig.2.8(b) has,as would be expected,a line spectrum comprising a single component at zero frequency.Note that Fig.2.8(a) and (b) demonstrate the principle of interchangeability of Fourier pairs stated above (equa-tion (2.4)).Figures 2.8(c) and (d) illustrate transient waveforms approximating the shape of seismic pulses,together with their amplitude spectra.Both have a band-limited amplitude spectrum,the spectrum of narrower bandwidth being associated with the longer transient waveform.In general,the shorter a time pulse the wider is its frequency bandwidth and in the limiting case a spike pulse has an in?nite bandwidth.

W aveforms with zero phase spectra such as those illus-trated in Fig.2.8 are symmetrical about the time axis and,for any given amplitude spectrum,produce the maximum peak amplitude in the resultant waveform.If phase varies linearly with frequency,the waveform re-mains unchanged in shape but is displaced in time;if the phase variation with frequency is non-linear the shape of the waveform is altered.A particularly important case in seismic data processing is the phase spectrum associated with minimum delay in which there is a maximum con-centration of energy at the front end of the waveform.Analysis of seismic pulses sometimes assumes that they exhibit minimum delay (see Chapter 4).

Fourier transformation of digitized waveforms is readily programmed for computers,using a ‘fast Fourier transform ’(FFT) algorithm as in the Cooley–T ukey method (Brigham 1974).FFT subroutines can thus be routinely built into data processing programs in order to carry out spectral analysis of geophysical waveforms.Fourier transformation is supplied as a function to standard spreadsheets such as Microsoft Excel.Fourier transformation can be extended into two dimensions (Rayner 1971),and can thus be applied to areal distribu-

tions of data such as gravity and magnetic contour maps.

A m p l i t u d e d e n s i t y

P h a s e Fig. 2.7Digital representation of the continuous amplitude and phase spectra associated with a transient waveform.

Geophysical Data Processing 13

In this case,the time variable is replaced by horizontal distance and the frequency variable by wavenumber (number of waveform cycles per unit distance).The application of two-dimensional Fourier techniques to the interpretation of potential ?eld data is discussed in Chapters 6 and 7.

2.4Waveform processing

The principles of convolution,deconvolution and cor-relation form the common basis for many methods of geophysical data processing,especially in the ?eld of seis-mic re?ection surveying.They are introduced here in general terms and are referred to extensively in later chapters.Their importance is that they quantitatively de-scribe how a waveform is affected by a ?lter.Filtering modi?es a waveform by discriminating between its con-stituent sine wave components to alter their relative am-plitudes or phase relations,or both.Most audio systems are provided with simple ?lters to cut down on high-

frequency ‘hiss’,or to emphasize the low-frequency ‘bass’.Filtering is an inherent characteristic of any system through which a signal is transmitted.2.4.1Convolution

Convolution (Kanasewich 1981) is a mathematical operation de?ning the change of shape of a waveform resulting from its passage through a ?lter.Thus,for ex-ample,a seismic pulse generated by an explosion is altered in shape by ?ltering effects,both in the ground and in the recording system,so that the seismogram (the ?ltered output) differs signi?cantly from the initial seismic pulse (the input).

As a simple example of ?ltering,consider a weight suspended from the end of a vertical spring.If the top of the spring is perturbed by a sharp up-and-down move-ment (the input),the motion of the weight (the ?ltered output) is a series of damped oscillations out of phase with the initial perturbation (Fig.2.9).

The effect of a ?lter may be categorized by its impulse

Time domain

Frequency domain

Fig. 2.8Fourier transform pairs for various waveforms.(a) A spike function.(b) A ‘DC bias’.(c) and (d) T ransient

waveforms approximating seismic pulses.

14Chapter 2

response which is de?ned as the output of the ?lter when the input is a spike function (Fig.2.10).The impulse re-sponse is a waveform in the time domain,but may be transformed into the frequency domain as for any other waveform.The Fourier transform of the impulse re-sponse is known as the transfer function and this speci?es the amplitude and phase response of the ?lter,thus de?ning its operation completely.The effect of a ?lter is described mathematically by a convolution operation such that,if the input signal g (t ) to the ?lter is convolved with the impulse response f (t ) of the ?lter,known as the con-volution operator,the ?ltered output y (t ) is obtained:

(2.5)

where the asterisk denotes the convolution operation.Figure 2.11(a) shows a spike function input to a ?lter whose impulse response is given in Fig.2.11(b).Clearly the latter is also the ?ltered output since,by de?nition,the impulse response represents the output for a spike input.Figure 2.11(c) shows an input comprising two separate spike functions and the ?ltered output (Fig.2.11(d)) is now the superposition of the two impulse re-sponse functions offset in time by the separation of the

y t g t f t ()=()()

*input spikes and scaled according to the individual spike amplitudes.Since any transient wave can be represented as a series of spike functions (Fig.2.11(e)),the general form of a ?ltered output (Fig.2.11(f )) can be regarded as the summation of a set of impulse responses related to a succession of spikes simulating the overall shape of the input wave.

The mathematical implementation of convolution involves time inversion (or folding) of one of the func-tions and its progressive sliding past the other function,the individual terms in the convolved output being de-rived by summation of the cross-multiplication products over the overlapping parts of the two functions.In gen-eral,if g i (i =1,2,...,m ) is an input function and f j (j =1,2,...,n ) is a convolution operator,then the convolu-tion output function y k is given by (2.6)

In Fig.2.12 the individual steps in the convolution process are shown for two digital functions,a double spike function given by g i =g 1,g 2,g 3=2,0,1 and an im-pulse response function given by f i =f 1,f 2,f 3,f 4=4,3,2,1,where the numbers refer to discrete amplitude values at the sampling points of the two functions.From Fig.2.11 it can be seen that the convolved output y i =y 1,y 2,y 3,y 4,y 5,y 6=8,6,8,5,2,1.Note that the convolved output is longer than the input waveforms;if the func-tions to be convolved have lengths of m and n ,the con-volved output has a length of (m +n -1).

The convolution of two functions in the time domain becomes increasingly laborious as the functions become longer.T ypical geophysical applications may have func-tions which are each from 250 to a few thousand samples long.The same mathematical result may be obtained by transforming the functions to the frequency domain,then multiplying together equivalent frequency terms of their amplitude spectra and adding terms of their phase spectra.The resulting output amplitude and phase spec-tra can then be transformed back to the time domain.Thus,digital ?ltering can be enacted in either the time

y g f k m n k i i m

k i ==+-()

=-?1121,,...,

Fig. 2.9The principle of ?ltering illustrated by the perturbation of a suspended weight system.

Fig. 2.10The impulse response of a ?lter.

Geophysical Data Processing

(a)

(c)

(e)Fig. 2.11Examples of ?ltering.(a) A spike input.(b) Filtered output equivalent to impulse response of ?lter.(c) An input comprising two spikes.(d) Filtered output given by summation of two impulse response functions offset in time.(e) A complex input represented by a series of contiguous spike functions.

(f) Filtered output given by the summation of a set of impulse responses.

4 × 2 =

4 × 0 + 3 × 2 =

4 × 1 + 3 × 0 + 2 × 2 =3 × 1 + 2 × 0 + 2 × 1 =

2 × 1 + 1 × 0 =

1 × 1 =

868521

Cross-products

Sum Fig. 2.12A method of calculating the convolution of two digital functions.

domain or the frequency domain.With large data sets,?ltering by computer is more ef?ciently carried out in the frequency domain since fewer mathematical opera-tions are involved.

Convolution,or its equivalent in the frequency domain,?nds very wide application in geophysical data processing,notably in the digital ?ltering of seismic and potential ?eld data and the construction of synthetic seismograms for comparison with ?eld seismograms (see Chapters 4 and 6).

16Chapter 2

2.4.2Deconvolution

Deconvolution or inverse ?ltering (Kanasewich 1981) is a process that counteracts a previous convolution (or ?ltering) action.Consider the convolution operation given in equation (2.5)

y (t ) is the ?ltered output derived by passing the input waveform g (t ) through a ?lter of impulse response f (t ).Knowing y (t ) and f (t ),the recovery of g (t ) represents a de-convolution operation.Suppose that f ￠(t ) is the function that must be convolved with y (t ) to recover g (t )

(2.7)

Substituting for y (t ) as given by equation (2.5)(2.8)

Now recall also that (2.9)where d (t ) is a spike function (a unit amplitude spike at zero time);that is,a time function g (t ) convolved with a spike function produces an unchanged convolution output function g (t ).From equations (2.8) and (2.9) it follows that (2.10)

Thus,provided the impulse response f (t ) is known,f ￠(t )can be derived for application in equation (2.7) to re-cover the input signal g (t ).The function f ￠(t ) represents the deconvolution operator.

Deconvolution is an essential aspect of seismic data processing,being used to improve seismic records by re-moving the adverse ?ltering effects encountered by seis-mic waves during their passage through the ground.In the seismic case,referring to equation (2.5),y (t ) is the seismic record resulting from the passage of a seismic wave g (t ) through a portion of the Earth,which acts as a ?lter with an impulse response f (t ).The particular prob-lem with deconvolving a seismic record is that the input waveform g (t ) and the impulse response f (t ) of the Earth ?lter are in general unknown.Thus the ‘deterministic’approach to deconvolution outlined above cannot be employed and the deconvolution operator has to be

f t f t t ()￠()=()

*d g t g t t ()=()()

*d g t g t f t f t ()=()()￠()** g t y t f t ()=()￠()

* y t g t f t ()=()()

*designed using statistical methods.This special approach to the deconvolution of seismic records,known as pre-dictive deconvolution,is discussed further in Chapter 4.2.4.3Correlation

Cross-correlation of two digital waveforms involves cross-multiplication of the individual waveform elements and summation of the cross-multiplication products over the common time interval of the waveforms.The cross-correlation function involves progressively sliding one waveform past the other and,for each time shift,or lag ,summing the cross-multiplication products to derive the cross-correlation as a function of lag value.The cross-correlation operation is similar to convolution but does not involve folding of one of the waveforms.Given two digital waveforms of ?nite length,x i and y i (i =1,2,...,n ),the cross-correlation function is given by

(2.11)

where t is the lag and m is known as the maximum lag value of the function.It can be shown that cross-correlation in the time domain is mathematically equivalent to multiplication of amplitude spectra and subtraction of phase spectra in the frequency domain.Clearly,if two identical non-periodic waveforms are cross-correlated (Fig.2.13) all the cross-multiplication products will sum at zero lag to give a maximum positive value.When the waveforms are displaced in time,how-ever,the cross-multiplication products will tend to cancel out to give small values.The cross-correlation function therefore peaks at zero lag and reduces to small values at large time shifts.T wo closely similar waveforms will likewise produce a cross-correlation function that is strongly peaked at zero lag.On the other hand,if two dissimilar waveforms are cross-correlated the sum of cross-multiplication products will always be near to zero due to the tendency for positive and negative products to cancel out at all values of lag.In fact,for two waveforms containing only random noise the cross-correlation function f xy (t ) is zero for all non-zero values of t .Thus,the cross-correlation function measures the degree of similarity of waveforms.

An important application of cross-correlation is in the detection of weak signals embedded in noise.If a wave-form contains a known signal concealed in noise at un-known time,cross-correlation of the waveform with the signal function will produce a cross-correlation function

f t t t

xy i i n i x y m m ()=

-<<+()

+=-?1

Geophysical Data Processing 17

centred on the time value at which the signal function and its concealed equivalent in the waveform are in phase (Fig.2.14).

A special case of correlation is that in which a wave-form is cross-correlated with itself,to give the autocorre-lation function f xx (t ).This function is symmetrical about a zero lag position,so that

(2.12)

The autocorrelation function of a periodic waveform is also periodic,with a frequency equal to the repetition frequency of the waveform.Thus,for example,the auto-correlation function of a cosine wave is also a cosine wave.For a transient waveform,the autocorrelation function decays to small values at large values of lag.These differing properties of the autocorrelation func-tion of periodic and transient waveforms determine one of its main uses in geophysical data processing,namely,the detection of hidden periodicities in any given wave-form.Side lobes in the autocorrelation function (Fig.2.15) are an indication of the existence of periodicities in the original waveform,and the spacing of the side lobes de?nes the repetition period.This property is particular-ly useful in the detection and suppression of multiple re?ections in seismic records (see Chapter 4).

f t f t xx xx ()=-()

The autocorrelation function contains all the am-plitude information of the original waveform but none of the phase information,the original phase relation-ships being replaced by a zero phase spectrum.In fact,the autocorrelation function and the square of the am-plitude spectrum A (f ) can be shown to form a Fourier pair

(2.13)

Since the square of the amplitude represents the power term (energy contained in the frequency component)the autocorrelation function can be used to compute the power spectrum of a waveform.

2.5Digital ?ltering

In waveforms of geophysical interest,it is standard prac-tice to consider the waveform as a combination of signal and noise .The signal is that part of the waveform that re-lates to the geological structures under investigation.The noise is all other components of the waveform.The noise can be further subdivided into two components,random and coherent noise.Random noise is just that,statistically

f t xx A f ()′()

lag

Fig. 2.13Cross-correlation of two identical waveforms.

18Chapter 2

random,and usually due to effects unconnected with the geophysical survey.Coherent noise is,on the other hand,components of the waveform which are generated by the geophysical experiment,but are of no direct interest for the geological interpretation.For example,in a seismic survey the signal might be the seismic pulse arriving at a detector after being re?ected by a geological boundary at depth.Random noise would be back-ground vibration due to wind,rain or distant traf?c.Coherent noise would be the surface waves generated by the seismic source,which also travel to the detector and may obscure the desired signal.

In favourable circumstances the signal-to-noise ratio (SNR) is high,so that the signal is readily identi?ed and extracted for subsequent analysis.Often the SNR is low and special processing is necessary to enhance the infor-mation content of the waveforms.Different approaches are needed to remove the effect of different types of noise.Random noise can often be suppressed by re-

peated measurement and averaging.Coherent noise may be ?ltered out by identifying the particular charac-teristics of that noise and designing a special ?lter to re-move it.The remaining signal itself may be distorted due to the effects of the recording system,and again,if the nature of the recording system is accurately known,suit-able ?ltering can be designed.Digital ?ltering is widely employed in geophysical data processing to improve SNR or otherwise improve the signal characteristics.A very wide range of digital ?lters is in routine use in geo-physical,and especially seismic,data processing (Robin-son & Treitel 2000).The two main types of digital ?lter are frequency ?lters and inverse (deconvolution) ?lters.2.5.1Frequency ?lters

Frequency ?lters discriminate against selected frequency components of an input waveform and may be low-pass (LP),high-pass (HP),

band-pass (BP) or band-reject

Waveform

Signal function

Cross-correlation function

Signal positions in waveform

Fig. 2.14Cross-correlation to detect occurrences of a known signal concealed in noise.

(After Sheriff 1973.)

(a)

(b)

Fig. 2.15Autocorrelation of the

waveform exhibiting periodicity shown in (a) produces the autocorrelation

function with side lobes shown in (b).The spacing of the side lobes de?nes the repetition period of the original waveform.

Geophysical Data Processing 19

(BR) in terms of their frequency response.Frequency ?lters are employed when the signal and noise compo-nents of a waveform have different frequency character-istics and can therefore be separated on this basis.

Analogue frequency ?ltering is still in widespread use and analogue antialias (LP) ?lters are an essential compo-nent of analogue-to-digital conversion systems (see Sec-tion 2.2).Nevertheless,digital frequency ?ltering by computer offers much greater ?exibility of ?lter design and facilitates ?ltering of much higher performance than can be obtained with analogue ?lters.T o illustrate the design of a digital frequency ?lter,consider the case of a LP ?lter whose cut-off frequency is f c .The desired out-put characteristics of the ideal LP ?lter are represented by the amplitude spectrum shown in Fig.2.16(a).The spec-trum has a constant unit amplitude between 0 and f c and zero amplitude outside this range:the ?lter would there-fore pass all frequencies between 0 and f c without atten-uation and would totally suppress frequencies above f c .This amplitude spectrum represents the transfer func-tion of the ideal LP ?lter.

Inverse Fourier transformation of the transfer func-tion into the time domain yields the impulse response of the ideal LP ?lter (see Fig.2.16(b)).However,this im-pulse response (a sinc function) is in?nitely long and must therefore be truncated for practical use as a convo-lution operator in a digital ?lter.Figure 2.16(c) repre-sents the frequency response of a practically realizable LP ?lter operator of ?nite length (Fig.2.16(d)).Convolu-

tion of the input waveform with the latter will result in LP ?ltering with a ramped cut-off (Fig.2.16(c)) rather than the instantaneous cut-off of the ideal LP ?lter.HP ,BP and BR time-domain ?lters can be designed in a similar way by specifying a particular transfer func-tion in the frequency domain and using this to design a ?nite-length impulse response function in the time do-main.As with analogue ?ltering,digital frequency ?lter-ing generally alters the phase spectrum of the waveform and this effect may be undesirable.However,zero phase ?lters can be designed that facilitate digital ?ltering with-out altering the phase spectrum of the ?ltered signal.2.5.2Inverse (deconvolution) ?lters

The main applications of inverse ?ltering to remove the adverse effects of a previous ?ltering operation lie in the ?eld of seismic data processing.A discussion of inverse ?ltering in the context of deconvolving seismic records is given in Chapter 4.

2.6Imaging and modelling

Once the geophysical waveforms have been processed to maximize the signal content,that content must be extracted for geological interpretation.Imaging and modelling are two different strategies for this work.As the name implies,

in imaging the measured wave-

(a)

(b)

(c)(d)

Filter operator

f c

Frequency domain

Time domain

Fig. 2.16Design of a digital low-pass ?lter.

20Chapter 2

forms themselves are presented in a form in which they simulate an image of the subsurface structure.The most obvious examples of this are in seismic re?ection (Chapter 4) and ground-penetrating radar (Chapter 9) sections,where the waveform of the variation of re?ect-ed energy with time is used to derive an image related to the occurrence of geological boundaries at depth.Often magnetic surveys for shallow engineering or archaeo-logical investigations are processed to produce shaded, coloured,or contoured maps where the shading or colour correlates with variations of magnetic ?eld which are expected to correlate with the structures being sought.Imaging is a very powerful tool,as it provides a way of summarizing huge volumes of data in a format which can be readily comprehended,that is,the visual image.A disadvantage of imaging is that often it can be dif?cult or impossible to extract quantitative informa-tion from the image.

In modelling,the geophysicist chooses a particular type of structural model of the subsurface,and uses this to predict the form of the actual waveforms recorded. The model is then adjusted to give the closest match be-tween the predicted (modelled) and observed wave-forms.The goodness of the match obtained depends on both the signal-to-noise ratio of the waveforms and the initial choice of the model used.The results of modelling are usually displayed as cross-sections through the struc-ture under investigation.Modelling is an essential part of most geophysical methods and is well exempli?ed in gravity and magnetic interpretation (see Chapters 6 and 7).

Problems

1.Over the distance between two seismic recording sites at different ranges from a seismic source, seismic waves have been attenuated by 5dB. What is the ratio of the wave amplitudes ob-served at the two sites?

2.In a geophysical survey, time-series data are sampled at 4ms intervals for digital recording.

(a) What is the Nyquist frequency? (b) In the absence of antialias ?ltering, at what frequency would noise at 200Hz be aliased back into the Nyquist interval?

3.If a digital recording of a geophysical time series is required to have a dynamic range of 120dB, what number of bits is required in each binary word?

4.If the digital signal (-1, 3, -2, -1) is convolved with the ?lter operator (2, 3, 1), what is the con-volved output?

5.Cross-correlate the signal function (-1, 3, -1) with the waveform (-2, -4, -4, -3, 3, 1, 2, 2) con-taining signal and noise, and indicate the likely position of the signal in the waveform on the basis of the cross-correlation function.

6.A waveform is composed of two in-phase components of equal amplitude at frequencies f and 3f. Draw graphs to represent the waveform in the time domain and the frequency domain.

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免费澳洲、英国、新西兰留学咨询与办理官网：https://www.wendangku.net/doc/2a5698963.html, 顾名思义研究国家和国际政治的专业。英国大学政治学主要开设专业分支有政治理论、国际关系和公共政策等。这一类专业申请比较多的是国际关系，因为国际关系相对于政治学，学习内容更加具体。例如国际关系会关注国际安全、人权与公平正义、比较政治经济学等。申请该专业的优势在于不需要专业背景，一般接受转专业申请的学生。分数要求也不高，例如去年有一个学生来自福建师范大学，本科是传播学，82分。申请到曼彻斯特大学和伯明翰大学。对于条件比较普通的学生，可以考虑这个专业。近期有一个学生来自于上海师范大学天华学院，本科国际商务贸易专业，分数是77分，学生比较想去好学校，在推荐学校和专业时，首先从文科出发，相对于教育学和传媒，学生申请国际关系更能申请到比较到的学校。传媒传媒属于我们申请的热门专业之一。传媒主要包括新闻，电影，媒体和创意产业等专业。新闻专业要求比较高的写作水平，所以新闻专业不太好申请，除非写作功底比较好的同学可以尝试。电影专业分支比较适合本科专业就是电影专业，因为课程涉及到一些动画设计等课程。媒体类和创意产业属于大家选择比较多的分支，因为专业背景比较宽泛，雅思要求比较适中，一般都是总分要求6.5（6.0）。典型学校有利兹大学、诺丁汉大学、华威大学、格拉斯哥大学、谢菲尔德大学。如果条件比较适中的学生可以选择纽卡斯尔大学、莱斯特大学、东英吉利亚大学等。去年有一个三本的学生，均分为 83，本科就读汉语言文学专业，拿到了上述3个大学的offer 。人类学和社会学

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声音的获取与处理

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第三讲声音的采集与处理

第三讲声音的采集与处理教学目标： 1．了解常见声音文件的格式。 2．掌握制作声音文件的一般流程。 3．会用Sound Forge等录音软件录制声音。 4．掌握用Sound Forge编辑声音的基本方法，能熟练地对声音文件进行剪辑与合成。 5．掌握熔炼五音，用Sound Forge对声音进行特殊效果处理的方法。重点：录音及对声音进行基本编辑的方法。难点：声音的剪辑、合成及特殊效果处理方法。一、常用声音文件格式常用的声音文件格式有：WAV格式、MIDI格式、MP3格式、CDA格式。 WAV格式：WAV格式是多媒体教学软件中常用的声音文件格式，它的兼容性非常好，但文件较大。WAV格式的声音属性，如采样频率、采样位数、声道数直接影响到WAV格式文件的大小。 MIDI格式：是电子乐器声音文件格式， MIDI文件本身只是一些数字信号，占用磁盘空间较小，常作为多媒体教学软件的背景音乐文件。 MP3格式：是一种经过压缩的文件格式，播放时需要专门的MP3播放器。占用磁盘空间较小。 CDA格式：CD唱片中的音乐文件常用CDA格式保存，一般为44kHz，16bits立体声音频质量。二、声音文件的制作流程我们在制作多媒体教学软件时，需要各种各样的声音文件，对声音的制作一般分为两个基本阶段：声音的获取阶段，声音的加工处理阶段。声音的获取有三种方法来源：剥离视频中的声音，录音，使用已有的声音文件。声音的处理流程是：首先打开声音文件，然后对声音进行基本剪辑，进一

第一节走进Sound Forge 三、走进Sound Forge 我们可以把Sound Forge视为熔炼声音的熔炉，它能够对音频文件（.wav 文件）、视频文件（.avi文件）中的声音进行各种处理，打造出我们需要的声音效果。在制作多媒体教学软件时，你想对获得的原始声音素材进行灵活的处理吗？那么走进Sound Forge，让我们来领略它神气强大的功能吧！好了，下面就让大家轻松亲身体验一下，为一多媒体教学软件制作声音。首选来欣赏：我为一年级小学语文课文《一次比一次有进步》教学软件制作的声音文件。下面就让我们用Sound Forge7.0一步步试着为课文录音、配音吧！要完成上面教学软件中的声音，要经过如下步骤：（一）录制声音 1．建立新的声音文件选择“File”菜单下的“New”命令，新建一声音文件。在弹出的对话框中，设置新建声音文件的格式，即采样位数，声道数（立体声/单声道），采样频率，然后单击“OK”。 2．开始录音 2．1启动录音功能：你可以用三种方法启动录音功能：按快捷键Ctrl+R；单击工具栏上的录音按钮——红色圆点键；选择菜单“Special”\“Transport”\下的“Record（录音）”命令； 2．2设置录音模式：当你按下录音键后，会弹出一个录音设置对话框。你可以设置：录音模式（Mode），录音起始（start）、停止（End）时间位置。录音时的采样率（samplerate）、采样位数（sample size）、立体声/单声道（stereo/mono）的选择。 2．3开始录音：设置完毕后，单击录音设置对话框中的红色录音按钮，即可用麦克风开始录音。 4．停止录音：按“End”停止按钮即可结束录音。 5．保存声音文件：选择菜单“File”下的“Save as”命令，保存文件。这样一个自己录制的声音文件已经录制好了。（听听我录制的声音吧）你想知道吗？（补充材料）（一）．声音文件的三个基本属性

武汉工程大学文科基金项目

所属学科及学科代码：项目编号：武汉工程大学文科基金项目申请书项目名称：项目负责人：联系电话：依托学院部门：申请日期：武汉工程大学科技处制 2007年9月

简表填写要求一、简表内容将输入计算机，必须认真填写，采用国家公布的标准简化汉字。简表中学科（专业）代码按GB/T13745-92“学科分类与代码”表填写。二、部分栏目填写要求：项目名称——应确切反映研究内容，最多不超过25个汉字（包括标点符号）。学科名称——申请项目所属的第二级或三级学科。申请金额——以万元为单位，用阿拉伯数字表示，注意小数点。起止年月——起始时间从申请的次年元月算起。项目组其他主要成员——指在项目组内对学术思想、技术路线的制定理论分析及对项目的完成起主要作用的人员。

一、项目信息简表

二、选题：本课题国内外研究现状述评；选题的意义。三、内容：本课题研究的基本思路和方法；主要观点。四、预期价值：本课题理论创新程度或实际应用价值。五、研究基础：课题负责人已有相关成果；主要参考文献。六、完成项目的条件和保证：包括申请者和项目组主要成员业务简历、项目申请人和主要成员承担过的科研课题以及发表的论文；科研成果的社会评价；完成本课题的研究能力和时间保证；资料设备；科研手段。（请分5部分逐项填写）。

七、经费预算

六、项目负责人承诺我确认本申请书及附件内容真实、准确。如果获得资助，我将严格按照学校有关项目管理办法的规定，认真履行项目负责人职责，积极组织开展研究工作，合理安排研究经费，按时报送有关材料并接受检查。若申请书失实或在项目执行过程中违反有关科研项目管理办法规定，本人将承担全部责任。负责人签字：年月日七、所在学院意见负责人签字：学院盖章：年月日八、科技处审核已经按照项目申报要求对项目申请人的资格及项目申请书内容进行了审核。项目如获资助，科技处将根据项目申请书内容，落实项目研究所需经费及其它条件；以保证项目按时顺利完成。科技处盖章年月日

第七课音频素材的简单加工

第七课音频素材的简单加工教学目标：知识与技能：会使用插入音频文件。过程与方法：可以通过键盘上的删除键删除不需要的音块。情感与价值：通过学习音频素材的加工让学生更懂得计算机的用途。难点和重点：是数字音频的录制、格式转换和基本编辑，难点是数字音频的特殊效果处理。教学过程：一、什么叫数字音频音频指的是对声音的数字化采样后形成的数据，由于声音以声波的形式传播，所以记录声波的文件又被称为波形文件。二、音频的相关概念 1、采样频率和采样位数数字音频是对模拟声音信号每秒上千次的采样，然后把每个样值按一定的比特数量化，最后得到标准的数字音频的码流。每秒采样的频率就是采样频率，而每次采样后保存的数值的比特数就是采样位数。对CD音质的信号来讲，每秒要44100次的采样，每个样值是16比特的量化，而立体声CD音质信号，它每秒的码流是44.1K×16×2≈1.4Mbit/S。这样高的码流和容量，对于数字音频的存储、处理和传输提出了很高的要求。 2、音频压缩对音频的压缩理论，是从研究人耳的听感系统开始的，首先第一个特点是人耳对各频率的灵敏度是不同的，在2K~4K频段，很低的电平就能被人耳听到，其他频段时，相对要高一点的电平才能听到，这就是说在听觉阈值以下的电平可以去掉，相当于压缩了数据。第二个特点就是频率之间的掩蔽效应，其

实就是指人耳接收信号时，不同频率之间的相互干扰。常见的音频压缩算法有MPEG、PCM、Dolby Digital 3、声道和音轨音轨（track）的概念非常广，可以认为，音轨即是存储着逻辑上并行播放的声音的轨道。比如说一首CD音乐就是一条音轨。 VCD、DVD播放的时候都有一条和视频一起播放的音轨。声道则是音轨上的声音流，一条音轨上可以有多条声道，只是这些声道是同时播放的。所谓的立体声，就是至少有两个声道的音轨。 4、音频编辑音频的采集可以通过声卡的捕捉得到，即通过话筒把声音录进电脑中，也可以到网上搜索得到。而声音的编辑，即通过裁剪、叠加、伴奏、加工、调速等等方法从而得到新的声音效果的过程。 5、常见的音频格式： 1）CD-DA 最常见的CD音轨格式。在CD机和电脑上都能正常播放。 2）wav 微软的标准声音格式。WAVE文件可以被存为立体声或单声道，8位或16位音响文件。同时它的采样频率也可调整。因为WAVE文件是以声音的波形来表示声音的，体积奇大，所以在大多数情况下应把它转换为其它格式。 3）mp3 MP3是MPEG-1 LAYER3的简写，在网上非常流行。 4）ra ReadAudio公司的拳头产品。如今已变为网上在线收听的标准，它将音频文件大大压缩，然后再以20kbps左右的速率实时播放。

声音信号的获取与处理

声音信号的获取与处理一、实验目的和要求本实验通过麦克风录制一段语音信号作为解说词并保存，通过线性输入录制一段音乐信号作为背景音乐并保存。为录制的解说词配背景音乐并作相应处理，制作出一段完整的带背景音乐的解说词。二、实验内容和步骤 1、软件与硬件的准备目前，多媒体计算机中的音频处理工作主要借助声卡，从对声音信息的采集、编辑加工，直到声音媒体文件的回放这一整个过程都离不开声卡。声卡在计算机系统中的主要作用是声音文件的处理、音调的控制、语音处理和提供MIDI接口功能等。进行录制音频信号所需的硬件除了声卡，还有麦克风、音箱以及外界的音源信号设备（如CD唱机、录音机等），把麦克风、音箱、外界音源信号设备与声卡正确连接完成硬件准备工作。在Windows的【控制面板】/【多媒体】中选择正确的录音和回放设备，并对其进行调试。 2、用Windows录音机录制解说词使用Windows录音机录制任意一段语音信号作为解说词，录制完毕后把文件存为Wav 格式，文件名为【示例1_1】。 3、使用Cool Edit录制背景音乐使用Cool Edit 2000录制任意一段语音信号作为背景音乐，要求录制的声音文件采样频率为44100Hz,立体声，量化位数为16位，保存文件的为Wav格式，文件名【示例1_2】。

4、使用WaveStuido编辑和处理背景音乐使用WaveStuido对【示例1_2】先进行回声处理，【幅度】值为100%，【回声延迟】为300毫秒。然后进行【淡入】和【淡出】处理，【幅度】值各为50%。 5、使用Cool Edit进行混音处理使用Cool Edit的【Mix paste】功能对【示例1_1】和【示例1_2】进行混音处理。把【示例1_2】加入【示例1_1】中去，编辑成为一个完整的带背景音乐的解说词，保存为【示例1_3】

202X美国留学文科专业申请建议.doc

202X美国留学文科专业申请建议现在，去美国留学，文科专业比较容易申请一些，但是，美国的文科专业众多，该如何申请呢，下面来说说美国留学文科专业申请建议。 1、文科专业非常庞杂，常见专业有：语言类，新闻和传播，政治学，社会学，人类学，历史学，经济学，法学，教育学，心理学，建筑学，城市规划和景观设计，艺术类等。 2、若是英语专业，可以申请教育学，比如教育心理，TESOL，早期教育等；文学类，比如比较文学等；传媒类，公关，广告等；或者政治学、社会学、历史学。具体申请什么专业根据申请人的背景经历进行确定。中文和日语专业的可以申请东亚研究，有东亚研究设置的专业都是TOP50的学校，所以申请难度也是很大的。 3、美国的顶尖大学综合排名前30的学校里设置传播学院的并不多。因为传播学是后兴起的专业才有七、八十年的历史，很多老牌的学校他们排斥这样的新兴学科，所以他们不设置这样的学院，例如哈佛，耶鲁，牛津，剑桥等根本没有传播学校。有些学校虽然有传播学院或者有相关的专业但是规模一般也很小，有些学生甚至只招生本校的本科生。例如：斯坦福大学，传播学一直没有权威的专业排名，比较出名的学校有：哥伦比亚大学，纽约大学，但是这两个牛校都只设置新闻学院，并没有传播学研究。而且这两个学校的新闻类专业是属于那种超级牛人才能申请到的，一般在国内每年招生只有1-2个，甚至没有。总的来说新闻类专业申请难度很大。并且对申请人的英语及GRE成绩要求非常高，并且申请人最好有在央视，新华社，这样的背景才会比较有利。 4、政治学，社会学，人类学和历史，一般硕士的申请很难拿到奖学金，博士奖学金设置会很多，但是同时对学生的研究兴趣有要求，并希望看到学生对未来的职业发展规划。

教案声音的获取与处理

【课题】声音的获取与处理【课时】2课时【教学目标】 1.了解声音文件的格式 2.初步掌握声音文件的获取方式 3.能够播放及转换声音，对声音进行删除、混合和淡入淡出处理【教学重点】 1.删除声音片段； 2.混合声音，对声音进行淡入淡出处理【教学难点】混合声音，对声音进行淡入淡出处理，声音文件的保存【教学过程】一、例比导入图片有不同的格式，有不同的种类，思考一下声音是不是也有不同的格式呢？二、探究新知（1）每个格式的特点是什么？任务一：试听不同格式茉莉花歌曲，并思考每个格式歌曲有什么不一样。（2）师：声音有哪些获取方式呢？生：从网上下载生：从视频截取生：自己录制生：从CD里获取师：同学们回答的很好，有人说可以从视频中截取，那么你们知道怎么截取吗？生：不知道师：有很多软件可以获取视频中的声音，老师今天给你们推荐个最简单的软件—千千静听（教师演示千千静听），千千静听还可以转换视频格式（演示）

（3）自己录制声音又用什么软件进行录制呢? windows 自带的录音软件自带的录音软件录制长度有限制，我们可以用另外一款软件录制声音，打开cool edit pro 介绍界面（4）请一位同学上台朗诵学生最近学的一首诗-《沁园春.雪》，教师演示录制过程，并保存。（5）教师给学生观看配乐诗朗诵，我们可以自己作一首配乐诗朗诵。教师演示cool edit pro 新建工程、添加音频、音频淡入淡出、删除片段等关键步骤，声音文件的保存。三、任务驱动，共同提高任务二：将环节一录制的音乐保存，添加背景音乐，删除无关片段，设置音乐淡入淡出效果，合成配乐诗朗诵。任务三：选择两首自己喜爱的歌曲，进行适当裁剪，合成音乐串烧。四、评价成果，归纳总结将学生做好的作品进行演示，自评以及共同评价作品的优缺点，教师和学生共同总结，并留几分钟时间给学生自行修改作品。五、教学反思 1.学生基本上能完成任务，在保存时不能很好区分保存工程文件和音乐文件； 2.有些学生没有搞清楚单轨道和多轨的特点，导致在合成音乐时将所有音乐放在一个轨道里；因此教师在讲课时要强调这一点。 3.由于软件自身的问题，有些音乐文件采样频率不兼容于软件工程频率，会导致文件无法导入。

高中理科申请书

篇一：高一文理分科申请表高一文理分科申请表明： 1、传媒含播音与主持艺术、广播电视编导、服饰艺术与表演、影视表演、空中乘务等专业；美术含绘画、书法艺术和书法教育、设计等专业；音乐含声乐、舞蹈、器乐、理论作曲、指挥等专业。 2、本申请表经学生、家长、班主任签名后，不能再更改，学校以此为依据重新分班。上梅中学教务处 2013年12月28日篇二：文理分科申请书请书敬的老师：我是，现在高（）班，本人喜欢科技制作、科普读物、科技发明和生命科学等相关知识与能力锻炼，对理科的兴趣大于对文科的兴趣。经过慎重考虑，并与家长商量，我决定申请就读（文科、理科、美术、体育、音乐、舞蹈、传媒）班，敬请批准。谢谢！（学生本人签名）年月日意上述申请。（家长签名）年月日篇三：转班申请书转班申请书尊敬的杨老师：您好！我想转到理科高二（16）班.因为经过假期的思考，我发现自己对文科没有很大的兴趣，而且

我也了解到自己在文科方面很难得到提高。对此，我恳请杨老师能让我转到理科高二（16）班，我真切希望自己能转理科高二（16）班，我总结了我转班的原因，有以下几点：一、我觉得自己对文科的兴趣没有了之前的那种热情，而且我觉得自己在理科方面还有待提高，我对它也很感兴趣。人们都说兴趣是学习最好的老师，有了兴趣就是成功的一半。而现在我对文科已经没有了那种热情，又怎么会对学习文科尽心尽力、认真努力的去做呢？所以我真心的想转到理科。二、我的文科成绩不是很好，而且从小我就贪玩把英语落下了很多。然而英语在文科当中可以占很大的优势，我的英语很差，读文科没有优势，所以我希望自己能转到理科。三、学习文科需要很好的记忆力，但我这个人很赖，不喜欢背诵，而文科又需要背诵和忆。四、根据社会的需求和我个人的发展空间，我想理科更适合我。亡羊补牢，为时不晚，希望杨老师能给我一次机会。此致敬礼申请人： 011年7月31日篇四：申请书尊敬的政府领导：我叫 xxx，19xx年xx月xx日生，系xxx居民，配偶19xx年生，也是xxx人，我们都无固定职业，现有家庭成员x人，家庭月收入xxx元。我们也曾经多次想买房子，但就我们这点收入想买完全属于我们自己的房子那简直是奢望。所以本人和家人至今也一直居无定所，但为了生活，又不得不在城区内四处奔波打工，并租房居住。由于本人家庭生活的实际困难和无住房的实际情况，现想申请政府廉租住房一套，望领导给予批准为盼！谢谢！请人：

【陈海兵】《声音的获取与加工》教学设计

声音的获取与加工南京市上元中学陈海兵 ■教材分析本节课的教学内容是江苏科学技术出社版、九年义务教育三年制初级中学教科书《初中（上册）信息技术》，包括基础知识、文档处理、数据分析、图片处理与多媒体技术等五个方面的内容，其中《第七章音视频获取与编辑》的第1节《声音的采集与加工》是多媒体技术的简单应用。一个好的多媒体作品，应该具备丰富的、高质量的声音素材，因此，声音素材的获取和处理是创作多媒体作品的关键。本节课主要是从声音的类型、获取与简单的编辑上让学生有一个初步的认识，没有刻意地学习完整的加工技术，而侧重让学生经历加工的过程，体验选择工具、加工声音信息的一般方法，感悟声音信息加工的目的和意义，通过本节课的学习可以为学生后面的视频相关操作奠定基础，同时让学生更深一层次地了解和认识多媒体技术，进而体验多媒体技术的魅力。 ■学情分析本课面对的是七年级的学生，他们对于音频的加工很感兴趣，很多学生曾在网上下载过音乐，或是用Windows程序中的“录音机”程序录制过声音，这些经验有利于学生继续探究学习，但是所学习的内容主要关注在采集的技术操作层面上，没有进行适当的加工和编辑，对于常用的声音文件的存储格式有哪些，这些常用声音文件的格式有哪些优缺点，如何获取到多媒体作品中所使用到的声音文件，对于这些问题学生还不是很了解。

在学习本章节之前，学生在第六章学习《图片的获取与加工》后，学生对多媒体中的图形图像的采集与加工有了一定地认识，为多媒体作品的制作打下了一定基础，为了让多媒体作品展现得更充分，对声音的采集与加工有较强的求知欲，利用学生对知识的渴望，我适时的导出了本堂课的学习内容。但由于声音信息加工软件较多，涉及技术不易掌握，深入学习存在困难。 ■教学目标 1．知识与技能 1．了解声音的类型、格式及其存储、呈现和传递的基本特征与基本方法； 2．能选择适当的工具，对声音信息进行采集； 3．能根据信息呈现需求，选择适当的工具和方法，对声音信息进行适当的编辑。 2．过程与方法 1．体验使用常见工具对声音素材进行采集与编辑的过程； 2．通过小组合作探索、互助学习来完成任务并能够对本组及其他组的状况进行合理的评价。 3．情感态度与价值观 1．在师生互动交流中培养学生的交流和理解能力； 2．在小组合作探究中，让学生逐渐养成自主探究意识和团体协作意识； 3．感受加工声音信息在表达、交流中的效果，培养学生以多种方式呈现信息的思维方式、音乐审美能力、学以致用的思想，让学生在自主解决问题的过程中培养成就感，建构规范合理的使用声音表达意图的思想。 4．行为与创新

实验一声音信号的获取与处理

多媒体技术与应用实验报告专业班级：计算机11-4班学号： 2011101593 学生姓名：齐国强指导教师：李海芳 2014年5月

实验一声音信号的获取与处理目的与要求 1、了解音频数据的获取和处理方法； 2、学会使用简单的声音编辑工具进行音频数据的录制、编辑和播放； 3、了解不同的音频文件在质量上和数据量上的差异。实验仪器与器件硬件：计算机、声卡、话筒、音箱或耳机软件：声音播放软件（如豪杰音频解霸、暴风影音等）、音频处理软件cool edit 实验内容 cool edit操作基础1）使用Cool Edit录制声音 ①运行用Cool Edit，打开主界面窗口 ②右击音轨1的空白处，插入伴奏音乐，如图2所示。

③按下音轨2的R键及左下方的红色录音键，跟随伴奏音乐开始演唱（或朗诵）和录制。图3 声音格式的设置 ④结束录音可按“停止”按钮。点左下方播音键进行试听，看有无严重的出错，无误后双击音轨 2 进入波形編辑界面，把录制的声音存储为无压缩的.wav 文件。“保存类型”可选择为“Windows PCM”。（也可以另存为其他格式） 2）用Cool Edit编辑音频文件 ①降噪处理：在波形编辑界面，找出一段适合用来作噪声采样波形，打开“效果--噪声

消除--降噪器”准备进行噪声采样，如图4和图5所示。在按默认参数值进行噪声采样后，关闭降噪器，回到波形編辑界面，全选录制的声音波形，进入降噪器并点击确定，完成降噪处理。图4 噪声选择示意图图5 噪声采样示意图 ②混响处理：打开【效果】|【常用效果器】|【混响】，调节混响长度、起始缓冲、高频吸收时间、干湿声比例等值，如图6所示，反复调节试听，达到最佳效果为止。图

《声音的获取与加工》教学设计及反思

大“话”西游 ——声音的获取与加工 ■教材分析本节课是江苏科学技术出版社出版的九年义务教育三年制初级中学教科书《初中信息技术（上册）》第7单元音视频获取与编辑第1节声音的获取与加工的教学内容。这部分内容安排在初一下学期最后来讲解，与第2节视频的获取与加工结合在一起，目的是为初二上学期将要学习的第9单元《制作多媒体作品》中获取声音、视频素材做铺垫，要求学生通过本节课的学习能了解声音文件的获取途径与方法，能正确选择适合的声音文件格式，并初步掌握声音文件的播放、转换和编辑，培养版权意识。 ■学情分析教授对象是初一学生，不少学生在小学阶段都已经学习过信息技术课程，有良好的操作基础，这部分内容对于学生来说是新奇的、有趣的、实用的，动手操作部分比较多，比较能够激发学生的主动性。 ■教学目标 1．知识与技能（1）了解音频文件的类型及获取方法。（2）学会使用GoldWave软件进行简单的音频编辑（删除空白多余、混合、特殊音效）。 2．过程与方法本节课通过给动画片《大闹天宫》片段分角色配音为主题，分析声音作品的一般制作过程与方法。同时通过任务分析—实践操作—知识小结的过程，强化和升华知识点，使学生主动构建起加工音频文件的一般思路和方法。 3．情感态度与价值观（1）调动学生学习积极性，培养学生根据实际需要主动运用多媒体处理工具加工和表达信息；（2）关注声音文件的版权问题，尊重知识产权。 4．行为与创新以学生通过给动画片片段配音为主线充分调动学生学习的积极和主动性，将学生提交的作品展示并投票，实现学生作品之间的互评而不是老师唱“独角戏”式的单一评价。

■课时安排安排1课时。 ■教学重点与难点 1．教学重点（1）音频文件加工的流程。（2）GoldWave的基本功能。 2．教学难点（1）根据任务利用软件做有效的剪辑。（2）掌握音频修饰的重点，使得所掌握的技巧为主题服务。 ■教学方法与手段教学方法：采用任务驱动、自主探究、合作学习为主，教师指导为辅的教学法教学手段：采用多媒体辅助教学 ■课前准备网络多功能教室、学习网站 ■教学过程

2声音的采集与处理

第二讲声音的采集与处理教学目标： 1．了解常见声音文件的格式。 2．掌握制作声音文件的一般流程。 3．会用Sound Forge等录音软件录制声音。 4．掌握用Sound Forge编辑声音的基本方法，能熟练地对声音文件进行剪辑与合成。 5．掌握熔炼五音，用Sound Forge对声音进行特殊效果处理的方法。重点：录音及对声音进行基本编辑的方法。难点：声音的剪辑、合成及特殊效果处理方法。一、常用声音文件格式常用的声音文件格式有：WAV格式、MIDI格式、MP3格式、CDA格式。 WAV格式：WAV格式是多媒体教学软件中常用的声音文件格式，它的兼容性非常好，但文件较大。WAV格式的声音属性，如采样频率、采样位数、声道数直接影响到WAV格式文件的大小。 MIDI格式：是电子乐器声音文件格式， MIDI文件本身只是一些数字信号，占用磁盘空间较小，常作为多媒体教学软件的背景音乐文件。 MP3格式：是一种经过压缩的文件格式，播放时需要专门的MP3播放器。占用磁盘空间较小。 CDA格式：CD唱片中的音乐文件常用CDA格式保存，一般为44kHz，16bits立体声音频质量。二、声音文件的制作流程我们在制作多媒体教学软件时，需要各种各样的声音文件，对声音的制作一般分为两个基本阶段：声音的获取阶段，声音的加工处理阶段。声音的获取有三种方法来源：剥离视频中的声音，录音，使用已有的声音文件。声音的处理流程是：首先打开声音文件，然后对声音进行基本剪辑，进一步美化声音，对声音进行特殊效果处理。成上面教学软件中的声音，要经过如下步骤：（一）录制声音 1．建立新的声音文件选择“File”菜单下的“New”命令，新建一声音文件。在弹出的对话框中，设置新建声

法国文科类专业留学指南如何申请适合自己的文科类专业.doc

法国文科类专业留学指南如何申请适合自己的文科类专业法国的商科和理工科在世界范围内都是非常出色的，文科虽稍微逊色，但也是很出色的，今天就和的我一起看看法国文科类专业留学指南如何申请适合自己的文科类专业？首先看看法国文科类专业留学有哪些优势？法国文学在欧洲乃至世界文坛上都很有影响力，今年再次获得诺贝尔文学奖就是明证，但是如果你打算去法国读文学专业，那么实在是不大正确的选择。任何一种文学需要本国本民族的人去学习才会比较顺手，而且一旦毕业之后也更容易获得专业认可。研究中国现当代文学的很少有西方学者，去法国读文学专业也同样如此。而去法国学习法国历史和社科类专业也同样如此。当然这类专业显得很高大上，但本身就是些“十年一坐冷板凳”的专业，更何况是外国人去研究法国的历史社会情况，我想大多数学生是不会去选择这类专业的。事实上也是很少有学生去法国留学报考这类专业。接着再来看看申请法国文科生专业要做好哪些准备？ 1.合理选择专业，明确职业规划法国公立学校原则上不允许学生自由转换专业，学生最好尽量申请跟自己国内专业相近或相关的专业，以避免专业跨度太大影响面签。而且法国学校非常重视学生的职业规划，建议学生申请时一定要明确自己未来计划学习的专业以及毕业后将要从事的领域，不能一味盲目追求热门专业，适合自己的才是最好的。 2.提前做好准备，语言学习充分法国留学整个申请流程比较长，通常建议最好提前至少一年开始着手申请并开始学习法语，而且艺术设计类学校一般为全法语授课(只有少部分学校有英语授课项目)，而且公立学校在法国读完语言后升专业对法语

要求相对较高，需要通过学校的考试。学好法语是保证顺利通过面签和顺利通过考试升学的必要条件。最后再来看看法国有哪些好的文科类专业？ 1.商学专业法国大多数的商学院是独立于公立大学之外的。以前法国不少集团公司鉴于自己很难找到自己想要的实习生，因此就索性根据自己的需求成立了不少商业学院。这样一来，对于学生来讲，理论和实践都能够结合起来。随着历史的发展，这类商学院毕业的学生能力普遍比较强，适合职场工作，因此很多法国公司甚至国际上的大型跨国公司都喜欢招聘这类商学院的毕业生。其中，欧洲工商管理学院(INSEAD)、巴黎高等商学院(HEC)、法国著名高等学府(ESSEC)都是在国际上颇有声誉的法国商学院。像INSEAD是世界最大和最有影响力的独立商学院之一，也是欧洲最受尊重、历年排名首位的商学院。可以看到在文科专业之中，去法国留学就读这一方向的专业还是很有前途的。当然也需要注意，这类商学院的学费也比较高昂，INSEAD一年就要3-4万欧元，因此申请者需要较好的经济实力。 2.法学专业学过法律的同学应该知道，现在我们国家的法系属于所谓的“大陆法系”。这里的“大陆”指的就是欧洲大陆。而欧洲大陆的法系的来源则是法国。因此到法国学习法学专业，可以对于法律有更加清晰的了解和认知，更能接触到世界法学的原始样本和先进理念，其法学体系知识是非常完备的。但是也要注意中国学生到法国去留学法学专业的话，难度会比较大。有个学生申请到法国里昂大学的法学院，可以说资质条件是非常好的，但是在那里学习的时候就曾反映，自己还是对于学习颇有些头疼。

文科主要科研项目申报信息汇总-推荐下载

主要文科科研项目申报信息所属部委或单位：全国哲学社会科学规划办公室所属部委或单位：国家自然科学基金委、管路敷设技术弯扁度固定盒位置保护层防腐跨接地线弯曲半径标高等，要求技术交底。管线敷设技术包含线槽、管架等多项方式，、电气课件中调试核与校对图纸，编写复杂设备与装置高中资料试卷调试方案，编写重要设备高中资料试卷试验方案以及系统启动方案；对、电气设备调试高中资料试卷技术常高中资料试卷工况进行自动处理，尤其要避免错误高中资料试卷保护装置动作，并且拒绝动作，来避免不必要高中资

所属部委或单位：国家科技部所属部委或单位：国家教育部、管路敷设技术通过管线不仅可以解决吊顶层配置不规范高中资料试卷问题，而且可保障各类管路习题到位。在管路敷设过程中，要加强看护关于管路高中资料试卷连接管口处理高中资料试卷弯扁度固定盒位置保护层防腐跨接地线弯曲半径标高等，要求技术交底。管线敷设技术包含线槽、管架等多项方式，为解决高中语文电气课件中管壁薄、接口不严等问题，合理利用管线敷设技术。线缆敷设原则：在分线盒处，当不同电压回路交叉时，应采用金属隔板进行隔开处理；同一线槽内，强电回路须同时切断习题电源，线缆敷设完毕，要进行检查和检测处理。、电气课件中调试装过程中以及安装结束后进行高中资料试卷调整试验；通电检查所有设备高中资料试卷相互作用与相互关系，根据生产工艺高中资料试卷要求，对电气设备进行空载与带负荷下高中资料试卷调控试验；对设备进行调整使其在正常工况下与过度工作下都可以正常工作；对于继电保护进行整核对定值，审核与校对图纸，编写复杂设备与装置高中资料试卷调试方案，编写重要设备高中资料试卷试验方案以及系统启动方案；对整套启动过程中高中资料试卷电气设备进行调试工作并且进行过关运行高中资料试卷技术指导。对于调试过程中高中资料试卷技术问题，作为调试人员，需要在事前掌握图纸资料、设备制造厂家出具高中资料试卷试验报告与相关技术资料，并且了解现场设备高中资料试卷布置情况与有关高中资料试卷电气系统接线等情况，然后根据规范与规程规定，制定设备调试高中、电气设备调试高中资料试卷技术电力保护装置调试技术，电力保护高中资料试卷配置技术是指机组在进行继电保护高中资料试卷总体配置时，需要在最大限度内来确保机组高中资料试卷安全，并且尽可能地缩小故障高中资料试卷破坏范围，或者对某些异常高中资料试卷工况进行自动处理，尤其要避免错误高中资料试卷保护装置动作，并且拒绝动作，来避免不必要高中资料试卷突然停机。因此，电力高中资料试卷保护装置调试技术，要求电力保护装置做到准确灵活。对于差动保护装置高中资料试卷调试技术是指发电机一变压器组在发生内部故障时，需要进行外部电源高中资料试卷切除从而采用高中资料试卷主要保护装置。

实验1 声音信号的获取与处理1

实验1 声音信号的获取与处理一、实验题目配乐诗朗诵（或MTV）二、实验目的 1、熟悉录音流程 2、会使用录音设备进行声音的录制 3、能利用波形文件的处理对录制声音进行优化 4、熟悉各种效果器的功能 5、掌握效果器的使用 6、利用Adobe Audition的多轨功能进行多路音频的控制与混合三、操作步骤本实验通过麦克风录制一段语音信号作为诗朗诵（歌曲）并保存，通过线性输入录制一段音乐信号作为背景音乐并保存。为录制的诗朗诵（歌曲）配背景音乐并作相应处理，制作出一段完整的带背景音乐的诗朗诵（歌曲）。 1.硬件与软件的准备目前，多媒体计算机中的音频处理工作主要借助声卡，从对声音信息的采集、编辑加工，直到声音媒体文件的回放这一整个过程都离不开声卡。声卡在计算机系统中的主要作用是声音文件的处理、音调的控制、语音处理和提供MIDI接口功能等。进行录制音频信号所需的硬件除了声卡，还有麦克风、音箱以及外界的音源信号设备（如CD唱机、录音机等），把麦克风、音箱、外界音源信号设备与声卡正确连接完成硬件准备工作。在Windows的【控制面板】/【声音和音频设备】中选择正确的录音和回放设备，并对其进行调试。要录取声音文件需要的硬件主要有:声卡、麦克风,为了回放所录取的声音还需要配备音箱，如图1.1所示。图1.1 麦克风、声卡、CD音源、音箱声卡后有几个接口,标有Midi/Game的梯形接口是接Midi键盘和游戏手柄的,

标有Audio Out的圆口是接音箱的,标有Mic的圆口是接麦克风的,标有Line In 的圆口是外接音频输入设备的。声卡、音箱和麦克风的连接，如图1.2所示。图1.2 电脑连线图在完成了硬件设备的连接后为了使声卡能正常工作还要进行软件的调试。进入Windows xp，选择【开始】/【设置】/【控制面板】，选【声音和音频设备】。在【声音和音频设备属性】对话框中选择的【音频】，在【回放】和【录音】的首选设备中选择声卡所对应的输入和输出选项，如图1.3所示。图1.3 【声音和音频设备属性】的对话框为确保麦克风和线性输入能正常使用，双击位于桌面右下任务栏的喇叭，

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