CN103207413A - Electrical prospecting device and system - Google Patents

Abstract

The invention relates to an electrical prospecting method and system. A transmitter transmits signals, and a receiver processes received signals to obtain impulse response and frequency response. The method is characterized by including the following steps that the transmitter transmits a pseudorandom sequence and utilizes the pseudorandom sequence to conduct cross-correlation operation on a time sequence received by the receiver to further obtain the time domain impulse response which is converted to a frequency domain to obtain the frequency response, and a double cole- cole model is used for conducting fitting and inversion to obtain a parameter of a complex resistivity method.

Description

Electrical prospecting device and system

technical field

The present invention relates to the field of electrical prospecting, in particular to an electrical prospecting device and system.

Background technique

Electrical prospecting (especially IP method) is widely used in the search for metal minerals, but due to the existence of a large number of high-voltage transmission lines and various electrical facilities in and around the production mine, this will generate extremely strong electromagnetic interference signals; A large number of metal pipes, vehicle tracks and waste rocks in the underground mining tunnels have caused great difficulties in the power supply and electric field reception of the electric method. All of these have affected the application effect of the electrical prospecting method, so there is a serious noise interference problem when the electrical prospecting method solves the crisis mine problem.

Although the electromagnetic interference near production mines is very large, it is generally random interference, and correlation identification technology is a system identification method that can effectively remove random noise interference. It is widely used in machinery, automation, instrumentation, cognitive science, and biological information. It has been widely used in the field of geology and other fields, but it has not received enough attention and application in geophysical exploration.

At present, in electrical prospecting, noise is generally removed by methods such as stacking, filtering, and remote reference, but in mine electrical prospecting, these methods are powerless to remove noise, so the survey effect is poor, which directly brings about a huge waste of human and financial resources . Moreover, in order to improve the signal-to-noise ratio, a commonly used method is to increase the power of the transmitting power supply, which will inevitably cause the transmitter to be abnormally heavy, which will bring great difficulties to the exploration work in mountainous areas.

On the other hand, the IP method is the most effective method for prospecting in electrical prospecting, especially the complex resistivity method, which can provide more electrical parameters and has higher detection accuracy. The complex resistivity method refers to an electrical prospecting method that obtains the complex resistivity spectrum, that is, the frequency response, through measurement. However, it is necessary to measure the electric field response at multiple frequency points to obtain a frequency response curve, and it takes a long time at low frequencies. Long, so the detection efficiency is very low, which is the reason why the complex resistivity method can not be widely used although the effect is good.

If correlation identification technology is adopted in electrical prospecting, a frequency response curve can be obtained in one measurement, which can effectively solve the problem of low efficiency of the complex resistivity method, and has the advantage of high detection accuracy of the complex resistivity method, so this is a A new complex resistivity method for time-domain measurements.

Figure 1 (a) and (b) show the observation device and equivalent circuit of the complex resistivity method in the prior art.

The traditional complex resistivity method is to study the underground medium by observing the magnitude spectrum and phase spectrum or real component and imaginary component spectrum of the apparent complex resistivity in a fairly wide ultra-low frequency band. As shown in Figure 1(a) and (b), the sending current source AB sends a sine wave or square wave of a frequency each time, and the receiving terminal MN measures the corresponding voltage signal, and multiple measurements obtain multiple frequencies in a frequency band The amplitude and phase characteristics of the complex resistivity spectrum are obtained, and then the Cole-Cole model is used for fitting and inversion, and electrical parameters such as DC resistivity, charging rate, time constant and frequency correlation coefficient are obtained for geophysical interpretation.

This method can provide relatively rich IP information, but it needs to be observed at many frequencies to obtain a relatively complete spectrum, so the production efficiency is low.

Contents of the invention

The purpose of the present invention is to use correlation identification technology to remove random noise; a frequency response curve can be obtained once in the time domain to improve detection efficiency; in addition, by using the dual Cole-Cole model to explain, not only the electromagnetic coupling effect can be removed The electromagnetic parameters can also be obtained, and the detection accuracy can be improved.

According to one aspect of the present invention, an electrical prospecting method is provided, which utilizes a surveying device for surveying, and the surveying device includes a transmitter and a receiver, and is characterized in that the method includes the following steps: the transmitter sends pseudo A random sequence v(t), the receiver receives a time series signal u(t); using the pseudo-random sequence v(t) to perform cross-correlation operations on the time series u(t) received by the receiver, to obtain a time domain Impulse response _he (t); transform the time-domain impulse response _he (t) into the frequency domain, thereby obtaining the frequency response _He (ω) using the double Cole-Cole model for fitting and inversion , get the four IP parameters ρ ₀ , m ₁ , c ₁ , τ ₁ of the complex resistivity method and the three parameters m ₂ , c ₂ , τ ₂ of the electromagnetic coupling effect; where ρ ₀ represents the zero-frequency apparent resistivity : reflects the average resistivity in the exploration volume of the electrode arrangement; m ₁ is the apparent charging rate of the IP effect: the parameter of the intensity of the IP effect (%), positively correlated with the volume content of polarizable substances in the exploration volume of the electrode arrangement; τ ₁ is the apparent time constant of the IP effect: the characteristic parameter of the IP effect (seconds), which is related to the structural information such as the particle size of the polarizable material in the probe volume of the electrode arrangement; c ₁ is the video frequency correlation coefficient of the IP effect, which is Dimensionless base effect process parameter; related to the type of electrophoresis effect of polarizable substances in the probe volume of the electrode array and the uniformity of polarized substance mixing distribution; m ₂ is the apparent charging rate of the electromagnetic effect; τ ₂ is the electromagnetic effect The apparent time constant of ; c ₂ is the visual frequency correlation coefficient of the electromagnetic effect.

Preferably, the method further comprises the step of: performing a deconvolution operation to remove the influence of the measuring device.

Preferably, the true spectral parameters and geometric distribution of the target polarizer are obtained by means of multi-pole distance observation.

Preferably, the pseudo-random sequence is an m-sequence or an inverse repeated m-sequence.

Preferably, the influence of electrodes, wires, grounding impedance and measuring devices is attributed to a system, its impulse response is h _s (t), and the impulse response of the earth system is denoted as _he (t), then

u(t)=v(t)*h _e (t)*h _s (t)+n(t)

Both sides use the input pseudo-random sequence v(t) to perform cross-correlation operation to eliminate the influence of random noise, and get

R _vu (t)＝R _vv (t)*h _e (t)*h _s (t)

Therefore, the cross-correlation R _vu (t) of the input and output signals and the autocorrelation R _vv (t) of the input signal are calculated first, and the total impulse response _he (t) of the observation system and the earth system is obtained after deconvolution operation h _s (t), and then remove the influence of the observation system h _s (t) by deconvolution to obtain the impulse response _he (t) of the earth system;

Or implement the above operation in the frequency domain:

P _vu (ω)＝P _vv (ω)·H _e (ω)·H _s (ω)

Obtain the frequency response of the system to be identified: $h_e\left(\mathrm{\ω}\right)=\frac{P_{\mathrm{v\; u}}\left(\mathrm{\ω}\right)}{P_{\mathrm{vv}}\left(\mathrm{\ω}\right)\·h_{\mathrm{the\; s}}\left(\mathrm{\ω}\right)}$

The result of correlation identification near the transmitter is regarded as the impulse response h _s (t) of the observation system, which is H _s (ω) in the frequency domain;

Calculate the complex resistivity spectrum ρ(ω):

ρ(ω)=K H _e (ω)

where K is called the device coefficient,

$\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; AM</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; AN</span>}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; BN</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; BM</span>}}}$

In the formula, AM, AN, BN, and BM are the distances between electrodes, where A, B are a pair of transmitting electrodes, and M, N are a pair of receiving electrodes;

Using the dual Cole-Cole model

$\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j\&omega;</span>}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j\&omega;</span>}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \}</span>$

Four IP parameters ρ ₀ , m ₁ , c ₁ , τ ₁ and three electromagnetic coupling effect parameters m ₂ , c ₂ , τ ₂ are obtained by fitting.

Preferably, the selection method of the initial values of ρ ₀ , m ₁ , c ₁ , τ ₁ and m ₂ , c ₂ , τ ₂ during fitting is as follows: the value range of c ₁ is between 0.1-0.6, and the value of c ₂ is The value range is 0.9-1.0, the value range of m ₁ and m ₂ is between 0-0.98, and m ₁ <m ₂ , the initial value of ρ ₀ is set to the value of the amplitude spectrum or the low-frequency asymptote of the real component spectrum , according to the peak frequency of the imaginary component spectrum in the high frequency band, the initial value of τ ₂ is obtained, and the initial value of τ ₁ is set as 50 times the initial value of τ ₂ .

According to another aspect of the present invention, a transmitter for electrical prospecting is provided, characterized in that the transmitter includes a GPS (Global Positioning System) module (1), a pseudo-random signal generator (2) , drive and protection module (3), intelligent power module IPM (Intelligent Power Modules) (5), temperature control module (4) and power supply module (7), and sending electrode AB (6); among them, the GPS module (1) Transmit timing synchronization information to the pseudo-random signal generator (2) through the serial port, complete the storage of data information, and control the drive and protection module (3); the drive and protection module (3) drives the IPM (5) to work, and the protection circuit is used for Protect the safety of the entire system; the temperature control system (4) monitors the temperature inside the transmitter, and resets the machine or shuts down the machine when it exceeds a predetermined value; IPM (5) converts the output signal into a current signal for electrical exploration and sends it to The electrodes AB (6) deliver current; the power supply module (7) provides a low-voltage power supply for the board, and provides the required high voltage for the IPM (5).

Preferably, the pseudo-random signal generator (2) further outputs m-sequences of different orders and frequencies or inversely repeated m-sequences as required, and the LCD is configured to display the transmitter signal type, parameters and status.

According to another aspect of the present invention, a receiver for electrical prospecting is provided, characterized in that the receiver includes a GPS module (8), a pseudo-random signal generator (9), a receiving electrode MN (10), Pre-amplification module (11), filter and notch module (12), precision amplification module (13), autocorrelator (14), cross-correlator (15), first deconvolver (16), second Deconvolver (17), FFT converter (18), complex resistivity spectrum generator (19), fitter (20), invertor (21); wherein, GPS module (8) sends pseudo The random signal generator (9) transmits timing synchronization information to achieve precise synchronization and maintains accurate phase-in-phase with the pseudo-random sequence of the transmitter. The pseudo-random signal generator (9) outputs the m-sequence with the same order and frequency as the transmitter as required ; The receiving electrode MN (10) receives the potential difference transmitted through the underground medium, and then pre-amplifies the received electromagnetic signal through the pre-amplification module (11), and filters out the high Frequency noise and power frequency noise of the system, and then through the precision amplification module (13), the noise-filtered signal is amplified with low noise and precision; the autocorrelator (14) realizes the autocorrelation operation of the pseudo-random signal; the cross-correlator (15) Realize the cross-correlation operation between the pseudo-random signal and the received signal; the total impulse response is obtained through the first deconvolver (16), and the earth system impulse response is obtained after the second deconvolver (17) removes the influence of the observation system , the FFT converter (18) processes the ground impulse response to obtain the frequency response of the ground system, the frequency response of the ground system passes through the complex resistivity spectrum generator (19) to obtain the complex resistivity spectrum of the ground system, and the fitter (20 ) can obtain sight spectrum parameters according to double Cole-Cole model fitting, and the invertor (21) performs joint inversion to obtain true spectrum parameters, thereby obtaining useful geophysical parameter information, and performing real-time data graphic display and storage.

According to yet another aspect of the present invention, an electrical prospecting system is provided, which is characterized by comprising the above-mentioned transmitter and receiver.

Compared with the prior art, the technical solution according to the present invention can bring the following technical effects:

1. It can remove the random interference superimposed in the received signal. According to the Wiener-Hoff equation, the input pseudo-random signal is used to perform a cross-correlation operation on the output signal. Since the random noise superimposed in the input signal and the output signal is not correlated, the random noise is eliminated.

2. It can remove the influence of the observation system on the measurement results. For a practical system, electrodes, wires, ground impedance and measuring devices will all affect the measurement results and reduce the accuracy of identification. Using deconvolution operation, this effect can be easily removed.

3. The true spectral parameters and geometric distribution of the target polarizer can be obtained. The impulse response and frequency response obtained from the single-input-single-output identification reflect the overall effect of the subsurface medium in the survey volume between the sending and receiving electrodes. Using the high-density geometric sounding method with multiple pole distances, the true position of the target polarized body can be obtained. Spectral parameters and geometric distribution have the advantages of high horizontal and vertical resolution and large abnormal range, especially in geological exploration under complex terrain conditions.

4. It can remove the influence of electromagnetic coupling effect. How to remove the influence of electromagnetic coupling effect in IP method has always been an urgent problem to be solved. The complex resistivity method using the double Cole-Cole model can not only obtain the four parameters of the IP effect, but also obtain the three parameters of the electromagnetic coupling effect, so it can not only separate the influence of the electromagnetic effect on the IP parameters, but also use Electromagnetic parameters can also get more information, so the detection accuracy is greatly improved.

Description of drawings

Figure 1(a) shows the observation device of the complex resistivity method;

Figure 1(b) shows the equivalent circuit of the complex resistivity method;

Fig. 2 shows the identification model of the electrical prospecting method according to the present invention;

Figure 3 shows the impulse response of the system to be identified (taking a typical second-order system as an example);

Figure 4(a) shows a periodic m-sequence;

Fig. 4 (b) has shown the anti-repetitive m sequence of a cycle;

Figure 4(c) shows a schematic diagram of Gaussian white noise;

Figure 5 shows a schematic diagram of the comparison between the impulse response obtained by m-sequence identification and the real impulse response;

Figure 6 shows a schematic diagram of the comparison between the impulse response obtained by identifying the reverse repeated m-sequence and the real impulse response;

Fig. 7 shows the situation in which the influence of the observation system is considered in the identification model of the electrical prospecting method according to the present invention;

Figure 8 shows a schematic diagram of the comparison before and after the deconvolution of the impulse response of the system to be identified;

Figure 9 shows a schematic diagram of a multi-pole distance observation system;

Fig. 10 shows the comparative schematic diagram before and after the identification of the magnitude spectrum of the double Cole-Cole model;

Fig. 11 shows the comparative schematic diagram before and after phase spectrum identification of the dual Cole-Cole model;

Figure 12 shows a magnitude/phase schematic diagram of a dual Cole-Cole model fit;

Figure 13 shows a schematic diagram of the real/imaginary part of the dual Cole-Cole model fitting;

Fig. 14 shows a schematic diagram of a transmitter in an electrical surveying device according to the present invention;

Fig. 15 shows a schematic diagram of a receiver in an electrical surveying device according to the present invention.

Detailed ways

Fig. 2 shows the identification model of the electrical prospecting method according to the present invention. Its basic principle is the Wiener-Hoff equation. Its core is to use pseudo-random sequences (including m-sequences and reverse-repeated m-sequences) to correlate the received time series to obtain an impulse response, and transforming the impulse response into the frequency domain is the frequency response.

The system input is v(t), the output u(t) is the measured signal, h(t) is the impulse response of the system to be identified, r(t) is the response of the input v(t) after passing through the system under test , external random noise interference n(t), where the input v(t) and output u(t) are known, and the interference n(t) is unmeasurable and unknown.

The system input and output signals v(t), u(t) have the following relationship:

u(t)＝r(t)+n(t)＝v(t)*h(t)+n(t) (1)

Where h(t) is the time-domain impulse response of the system under test, and * is the convolution operation.

The input signal v(t) is used to perform cross-correlation calculation on both sides of formula (1):

R _vu (t)＝R _vv (t)*h(t)+R _vn (t) (2)

In the formula, R _vu (t) represents the cross-correlation between input and output, R _vv (t) represents the auto-correlation of input, and R _vn (t) represents the cross-correlation between interference n(t) and input signal.

Since the external random noise interference is not correlated with the input signal, R _vn (t) in (2) is 0. It can be seen that the result of the cross-correlation operation eliminates the interference of random noise. thus get

_Rvu (t)＝ _Rvv (t)*h(t) (3)

This is the famous Wiener-Hoff equation. Therefore, we only need to find out the autocorrelation function R _vv (t) of the input and the cross-correlation R _vu (t) of the input and output, and then the time-domain impulse response h(t) of the system under test can be obtained by deconvolution . Transforming to the frequency domain is the frequency response. In this way, a frequency response curve can be obtained by sending and receiving once, including amplitude-frequency response and phase-frequency response, which greatly improves the detection efficiency.

But if the input is a general signal, then solving equation (3) is more difficult. For this reason, we need to find some special signals as input signals to simplify the solution process. The autocorrelation function of the white noise signal has a special form, and its autocorrelation function is a delta function. When the input of the system to be identified is white noise, only the cross-correlation function between the input and output signals is required, and the impulse response of the system can be directly obtained by subtraction. But white noise is only an abstraction in mathematics, not easy to implement in engineering, let alone repeatable. Pseudo-random signals are often used as input signals in engineering. It has the property of approximating white noise, so it can guarantee good identification accuracy, and it is easy to realize in engineering.

Pseudo-random signals commonly used for identification are m-sequences or reverse-repeated m-sequences. The m-sequence refers to a maximum-length shift register sequence. Inversely repeated m-sequence is also called L-sequence, which is generated by doubling the m-sequence and then inverting every bit. Since the average value is 0 within one period, it has better characteristics than m-sequence in system correlation identification . Figure 3 is an impulse response h(t) of a system to be identified, taking a typical second-order system as an example. Accompanying drawing 4 (a) is a cycle, the length is 31 m-sequences, Fig. 4 (b) is a cycle anti-repetitive m-sequences, Fig. 4 (c) is superimposed noise n (t). Figure 5 is the result of correlation identification using m-sequence. It can be seen that the impulse response recovered after correlation identification basically coincides with the real impulse response, and the influence of noise becomes very small. Figure 6 is the result of correlation identification using reverse repeated m-sequence, and the identification effect is better than that of m-sequence.

In addition, in a preferred embodiment of the present invention, in order to remove the influence of electrodes, wires, grounding impedance and measuring devices, a deconvolution operation is carried out in the data processing process of the receiver, and its principle is as follows:

The influence of electrodes, wires, grounding impedance and measuring devices is attributed to a system, its impulse response is h _s (t), and the impulse response of the earth system is recorded as _he (t), as shown in Figure 7. Then (1) becomes

u(t)=v(t)*h _e (t)*h _s (t)+n(t) (4)

The influence of random noise can be eliminated by using the input pseudo-random sequence to perform cross-correlation operation on both sides of the formula (4), and we can get

R _vu (t)＝R _vv (t)*h _e (t)*h _s (t) (5)

Therefore, the cross-correlation R _vu (t) of the input and output signals and the auto-correlation R _vv (t) of the input signal are calculated first, and the total impulse response _he (t) of the observation system and the earth system is obtained after deconvolution operation h _s (t), and then remove the influence of the observation system h _s (t) by deconvolution operation to obtain the impulse response _he (t) of the earth system. It can be seen from Figure 8 that the impulse response after removing the influence of the observation system is closer to the impulse response of the system to be identified.

It is also possible to convert the convolution and deconvolution operations into multiplication or division operations in the frequency domain or complex frequency domain. So formula (5) becomes:

P _vu (ω)＝P _vv (ω) · _He (ω) · H _s (ω) (6)

In this way, the frequency response of the system to be identified is obtained:

${\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; v\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; vv</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

The result of correlation identification near the transmitter is regarded as the impulse response h _s (t) of the observation system, because the influence of the earth system can be ignored near the transmitter, and only the influence of the observation system is considered. Putting it into the frequency domain is H _s (ω).

What is used in geophysics is the complex resistivity spectrum ρ(ω). Its relationship to the frequency response is:

ρ(ω)=K H _e (ω) (8)

Here K is called the device coefficient. As shown in Figure 1(a), A and B are a pair of transmitting electrodes, and M and N are a pair of receiving electrodes, then the coefficient of the device depends on the relative positions of the four electrodes A, M, N, and B:

$<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; K</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; AM</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; AN</span>}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; BN</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; BM</span>}}}$

Where AM, AN, BN, BM are the distances between electrodes.

After obtaining the complex resistivity spectrum, the complex resistivity method is used for geophysical interpretation. Usually the complex resistivity method is fitted with a single Cole-Cole model:

$\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j\&omega;</span>}\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; c</span>}}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

In this way, four IP parameters ρ ₀ , m, τ, and c are obtained through fitting. Because the complex resistivity spectrum contains the influence of electromagnetic coupling effect, the IP parameters obtained in this way are not accurate enough.

However, using the double Cole-Cole model (shown in Equation (11)) for fitting can not only obtain four IP parameters ρ ₀ , m ₁ , c ₁ , τ ₁ , but also obtain three parameters about the electromagnetic coupling effect Parameters m ₂ , c ₂ , τ ₂ .

$\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j\&omega;</span>}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j\&omega;</span>}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

These seven parameters reflect the overall information in the exploration volume of the electrode arrangement, which are called spectral parameters, and their physical meanings are:

①ρ ₀ －Indicates the zero-frequency apparent resistivity: it reflects the average resistivity within the exploration volume of the electrode arrangement.

②m ₁ - Apparent charging rate of IP effect: parameter of IP effect intensity (%), which is positively related to the volume content of polarizable substances in the probe volume of electrode arrangement.

③τ ₁ －apparent time constant of the IP effect: characteristic parameter of the IP effect (seconds), which is related to structural information such as the particle size of the polarizable material in the electrode arrangement exploration volume.

④c ₁ - Video frequency correlation coefficient of IP effect: IP effect process parameter (dimensionless), which is related to the type of IP effect of polarizable substances in the probe volume of electrode arrangement and the uniformity of mixing and distribution of polarized substances.

⑤m ₂ －Apparent charging rate of electromagnetic effect.

⑥τ ₂ －The apparent time constant of the electromagnetic effect.

⑦c ₂ －Video frequency correlation coefficient of electromagnetic effect.

Therefore, this method can not only separate induced excitation spectrum and electromagnetic coupling spectrum, but also obtain residual electromagnetic effect (REM) parameters by using the obtained electromagnetic parameters. And electromagnetic apparent resistivity ρ _ω , etc., so the detection accuracy can be improved.

What we get at a measuring point is the view spectrum parameter, what we need is the true Cole-Cole parameter of the target polarized body (that is, the ore body or oil and gas reservoir to be proven) and its geometric distribution (including depth, Thickness, shape and other information), so the information of more than 2 measuring points is required. By adopting multi-pole distance observations, the amount of information is greatly enriched. Therefore, in a preferred embodiment of the present invention, the method of geometric bathymetry—multi-polar distance observation is adopted to obtain the information of abnormal bodies at different depths.

This working method is shown in Figure 9, AB is the sending electrode, M ₁ N ₁ is the first pair of receiving electrodes, N ₁ M ₃ (that is, M ₂ N ₂ ) is the second pair of receiving electrodes, M ₃ N ₃ is the The third pair of receiving electrodes. Multiple receivers arranged in the same line receive the electric field response of the sending electric dipole source at the same time, and the sending and receiving distance increases according to a certain interval. Since different transceiver distances have different detection depths, the greater the transceiver distance, the greater the depth reflected. Therefore, one power supply point can receive electric field responses of multiple frequencies at different depths. By moving the observation system multiple times along the section at a certain point distance, the radial electric field response of multiple frequencies with different transmitting and receiving distances on a section can be observed, and a wealth of electric field information can be obtained. The following two methods can be adopted to obtain the true spectral parameters and geometric distribution of the target polaroid from the observation results of multi-polar distances.

① The geometric distribution and true spectrum parameters of the target polarized body can be obtained by joint inversion of the measurement results of multi-pole distances, that is, the sight spectra of multiple measuring points. Firstly, the complex resistivity spectrum (including amplitude and phase) of each measuring point is inverted by the conventional complex resistivity method to separate the apparent spectrum of the polarized body and surrounding rock, and then the separated apparent phase The true spectral parameters and geometric distribution of the target polaroid are obtained through joint spectral inversion.

② Two electromagnetic parameters, the residual electromagnetic effect (REM) parameter and the electromagnetic apparent resistivity, can be calculated by using the separated electromagnetic effect parameters, which can more sensitively reflect the abnormality of underground conductivity than the conventional apparent resistivity. In this way, six parameters can be obtained by inverting the complex resistivity spectrum of each measuring point: four induced electric parameters and two electromagnetic parameters. The results of multi-pole distance observations are represented by pseudo-section diagrams with six parameters, which can reflect the changes of geoelectric structures along the section and with depth.

Those skilled in the art should understand that in FIGS. 1 and 9 , the transmitting end does not necessarily use an electric dipole source, and any active source for electrical exploration such as a magnetic source coil can also be used. The receiving end does not necessarily use a dipole to receive, and any receiver for electrical exploration such as a magnetic rod can be used. In addition, the sending and receiving does not necessarily use the multi-pole distance observation device as shown in Figure 9. It can be arranged in the same line, or it can be measured in the axial direction. , or overpayment and overpayment. In addition, besides using the complex resistivity method to explain the obtained frequency response, it can also be explained by the DC resistivity method, induced electricity method, and electromagnetic method. When the frequency is close to 0, the value of the frequency response curve at this point is the DC apparent resistivity of the measuring point. After obtaining the values of multiple measuring points, the DC resistivity method can be used for inversion. At high frequency (for example greater than 10 ⁴ Hz), some electromagnetic parameters can be obtained, and the data processing and inversion methods of electromagnetic method can be used. In the low frequency band, some IP parameters can be obtained, such as the video dispersion rate, the change rate of the video dispersion rate between channels, and the change rate of low-frequency complex resistivity between channels. Alternatively, a direct geophysical interpretation of the impulse response in the time domain is possible. When considering the complex resistivity method, it is not necessarily the double Cole-Cole model of the product form, but also the double Cole-Cole model of the sum form, Dias model, Brown model or other models .

Compared with the prior art, the difference of the technical solution according to the present invention is at least:

1. Use pseudo-random sequences (including m-sequences and reverse-repeated m-sequences) to perform cross-correlation operations on the time series received by the electrical receiver to obtain the system's impulse response and frequency response;

2. Use deconvolution to remove the influence of the observation system;

3. Obtain the true spectral parameters and geometric distribution of the target polarized body by means of multi-polar distance observation;

4. The complex resistivity method is used for inverse interpretation of the obtained frequency response. The electromagnetic parameters can be obtained by using the double Cole-Cole model, and the detection is more precise. In order to have a better understanding of the technical effect of the technical solution of the present invention, a simulation example is used below to illustrate.

Since the characteristics of a typical second-order system are well known to us, and the identification results are easy to compare, the identification results of a second-order system are taken as an example below. Suppose the impulse response of a second-order system to be identified is h(t)=ae ^bt .sin(ct), a=11.547, b=-5, c=8.66, as shown in Figure 3. The cut-off frequency of this second-order system is 2Hz, and its impulse response basically tends to zero after 1s, so the transition time of the system is about 1s. According to parameter selection formulas (12) and (13), the chip width of the pseudo-random sequence can be selected as 0.02s, and the period length of the sequence is 63.

$\mathrm{<span\; class="notranslate">\; 0.443</span>}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1.2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1.5</span>}<span\; class="notranslate">\; )</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}{\mathrm{<span\; class="notranslate">\; \&Delta;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

Here Δ is the chip width, f _max is the system cut-off frequency, T _s is the transition time of the system, and N is the cycle length of the sequence. In order to obtain a better identification effect, the chip width is taken as 0.01s, and the sequence length is taken as 100 points. In the actual identification process, a pseudo-random sequence of several cycles (for example, 3-5 cycles) can be sent to obtain a better identification effect. The receiver can also select the waveform with better effect for calculation.

In the process of identifying the actual underground medium system, in order to take into account the high-frequency and low-frequency resolution and the contradiction with the number of shift registers, it can be divided into several frequency bands. In addition, in order to improve the identification accuracy, the sampling frequency can be increased, and multiple points can be collected within one chip width.

Gaussian white noise is a common type of noise. Figure 4(c) is Gaussian white noise with a sampling frequency of 1kHz and 10,000 sampling points, and the standard deviation of the noise is 1. Figure 4(a) is the pseudo-random m-sequence current signal sent by the sending electrode, and Figure 4(b) is the pseudo-random reverse repetition m-sequence current signal sent by the sending electrode. The result of serial correlation identification. It can be seen that after correlation identification, the impact of random noise on the impulse response obtained by identification is very small, and the identification effect is better by using the reverse repeated m-sequence.

Consider removing the influence of the observation system. Figure 8 is a comparison of the actual impulse response of a typical second-order system to be identified and the impulse response before and after deconvolution. It can be seen that the result after using deconvolution to remove the influence of the observation system (white solid line) is better It is close to the impulse response of the actual system (red + word line), the two basically coincide, but the result without deconvolution (green diamond curve) is quite different from the actual system.

The impulse response obtained in this way is transformed into the frequency domain through Fourier transform, which is _He (ω). Use formula (8) to get the complex resistivity spectrum ρ(ω).

For a double Cole-Cole model system (taking the product form as an example), it is assumed that the set parameters are ρ ₀ =25, m ₁ =0.1, c ₁ =0.12, τ ₁ =1, m ₂ =0.67, c ₂ =0.95, τ ₂ =0.01, Gaussian white noise with a standard deviation of 1V is added, and the correlation identification result complex resistivity spectrum ρ(ω) can be expressed as amplitude spectrum (see Figure 10) and phase spectrum (see Figure 11) . The result after identification (white + word line) only takes the positive frequency part. It can be seen that the identification effect is good, and basically coincides with the actual system spectrum.

_{Using the} classical inversion fitting method damped least squares _method to fit the _obtained complex resistivity spectrum, _{we can} get _seven IP and electromagnetic parameters, see formula (11).

The selection of the initial value is very critical when the damped least squares method is used for fitting. Improper selection of the initial value may cause non-convergence, so the desired result cannot be obtained. There are 7 undetermined parameters in the fitting of the double Cole-Cole model, and it is not easy to obtain ideal results by fitting multiple parameters, so it is more critical to choose an appropriate initial value. Here is a detailed description of the selection method of the initial value.

First of all, these parameters have value ranges: the value range of c ₁ is between 0.1-0.6, the value range of c ₂ is 0.9-1.0, and the value range of m ₁ and m ₂ is 0-0.98 Between, and m ₁ <m ₂ , τ ₁ >>τ ₂ . And the value ρ ₀ of the zero-frequency resistivity is the value of the low-frequency asymptote of the amplitude or real part.

τ ₂ is also easy to obtain. When the electromagnetic coupling in the high frequency band is absolutely dominant, the imaginary component has a peak value, and this peak frequency corresponds to the time constant τ ₂ of the electromagnetic coupling effect. Then τ ₁ can be estimated according to τ ₁ >>τ ₂ .

m ₁ , c _{1 ,} and m ₂ , c ₂ do not have a large variation range, so it is enough to take an estimated value within their value range, such as m ₁ =0.25, c ₁ =0.25, m ₂ =0.75, c ₂ =1.

In addition, in order to prevent the final iterative result from being out of the valid range of the parameters or even having negative values due to the modification step being too large during the iterative process, ρ ₀ , m ₁ , c ₁ , τ ₁ , and m can be preset in the program _2. The value ranges of the seven parameters c ₂ and τ ₂ can not only speed up the speed of iterative fitting, but also ensure the accuracy of fitting.

Figures 12 and 13 are for a dual Cole-Cole model (parameters set to ρ ₀ =25, m ₁ =0.1, c ₁ =0.12, τ ₁ =0.1, m ₂ =0.67, c ₂ =0.95, τ ₂ =0.003), the fitting result reaches the set error threshold requirement after 8 iterations. Practice has proved that this method is effective.

The above-mentioned process is for a measuring point to obtain the sight spectrum parameters of the measuring point. In order to obtain the true spectral parameters and geometric distribution of the target polaroid, multi-point observations are required. The multi-pole distance observation method is a high-density geometric sounding method. This working method is shown in Figure 9. One power supply point can receive electric field responses at different depths and at different frequencies. AB is the sending electrode, M ₁ N ₁ is the first pair of receiving electrodes, N ₁ M ₃ (that is, M ₂ N ₂ ) is the second pair of receiving electrodes, and M ₃ N ₃ is the third pair of receiving electrodes. Using formula (8) to obtain the complex resistivity spectra at different measuring points, that is, under different polar distances, and then use the damped least squares method to invert and fit to obtain 7 spectral parameters, and then use the joint inversion method to obtain the true spectral parameters and Geometric distribution, or the quasi-sectional diagram of 6 IP and electromagnetic parameters obtained from these 7 spectral parameters, represents the change of geoelectric structure along the section and with depth.

Fig. 14 and Fig. 15 respectively show schematic diagrams of a transmitter and a receiver in an electrical surveying device according to the present invention. It should be noted that these two figures only show the main modules in the form of functional modules, and do not represent specific physical structural modules.

In Figure 14, the GPS module (1) transmits timing synchronization information to the pseudo-random signal generator (2) through the serial port, and the pseudo-random signal generator (2) can output m-sequences or inverse repetitions of different orders and frequencies as required m sequence, LCD is used to display the signal type, parameters and status of the transmitter, while the pseudo-random signal generator (2) completes the storage of data information, and controls the drive and protection module (3). The drive circuit in the drive and protection module (3) is used to drive the generated pseudo-random sequence to drive the IPM (5), and the protection circuit is used to protect the safety of the entire system. The temperature control system (4) monitors the temperature inside the transmitter at all times, and resets the machine or even shuts down the machine when it exceeds a predetermined value. The IPM (5) converts the output signal into a current signal for electrical exploration and sends current to the sending electrode AB (6). The power supply module (7) provides a low-voltage power supply for the board, and provides the required high voltage for the IPM (5).

In Figure 15, the GPS module (8) transmits timing synchronization information to the pseudo-random signal generator (9) through the serial port to achieve precise synchronization and maintain accurate phase with the pseudo-random sequence of the transmitter. The pseudo-random signal generator (9) can Output the m-sequence with the same order and frequency as the transmitter as required. The receiving electrode MN (10) receives the potential difference transmitted through the underground medium, and then pre-amplifies the received electromagnetic signal through the pre-amplification module (11), and filters out the high frequency through the filter and notch module (12) Noise and power frequency noise of the system, and then the noise-filtered signal is amplified with low noise and precision through the precision amplification module (13). The autocorrelator (14) realizes the autocorrelation operation of the pseudo-random signal. The cross-correlator (15) realizes the cross-correlation operation between the pseudo-random signal and the received signal. The autocorrelator (14) realizes the autocorrelation operation of the pseudo-random signal; the cross-correlator (15) realizes the cross-correlation operation of the pseudo-random signal and the received signal; the total impulse response is obtained through the first deconvolution device (16), The second deconvolver (17) removes the influence of the observation system to obtain the earth system impulse response, and the FFT converter (18) processes the earth impulse response to obtain the frequency response of the earth system. The frequency response of the earth system passes through the complex resistance The rate spectrum generator (19) obtains the complex resistivity spectrum of the earth system, the fitter (20) can obtain the view spectrum parameters according to the Cole-Cole model fitting, and the true Spectral parameters, so as to obtain useful geophysical parameter information, and real-time data graphics display and storage.

Claims (4)

1. sending and receiving machine that is used for resistivity prospecting, it is characterized in that described sending and receiving machine comprises global position system GPS (Global Positioning System) module (1), pseudo random signal midwifery device (2), driving and protection module (3), Intelligent Power Module IPM (Intelligent Power Modules) (5), temperature control modules (4) and supply module (7) and sending and receiving electrode A B(6);

Wherein, GPS module (1) is transmitted the time service synchronizing information by serial ports to pseudo random signal midwifery device (2), finishes the storage of data message, and control drives and protection module (3); Drive and protection module (3) driving IPM(5) work, holding circuit is for the protection of the safety of total system; Temperature control system (4) monitoring is received and is penetrated the machine temperature inside, and machine or closing machine reset after surpassing predetermined value; IPM(5) output signal is converted to for the current signal of resistivity prospecting and to sending and receiving electrode A B(6) the conveying electric current; Supply module (7) is for integrated circuit board provides low-tension supply, and is IPM(5) required high pressure is provided.

2. the sending and receiving machine for resistivity prospecting according to claim 1, it is characterized in that pseudo random signal midwifery device (2) is further exported the m sequence of different rank, different frequency as required or against Repeated m-Sequences, collocating LCD is used for showing machine signal type, parameter and the state penetrated of receiving.

3. a receiver that is used for resistivity prospecting is characterized in that described receiver comprises GPS module (8), pseudo random signal midwifery device (9), receiving electrode MN(10), pre-amplifying module (11), filtering and trap module (12), accurate amplification module (13), autocorrelator (14), cross-correlator (15), the first deconvolution device (16), the second deconvolution device (17), FFT transducer (18), complex resistivity spectrum maker (19), match device (20), inverter (21);

Wherein, GPS module (8) is transmitted the time service synchronizing information by serial ports to pseudo random signal midwifery device (9), realize precisely synchronously and and the pseudo-random sequence of sending and receiving machine keep accurate homophase, pseudo random signal midwifery device (9) is exported the pseudo-random sequence of identical exponent number with the sending and receiving machine, same frequency as required; Receiving electrode MN(10) receives the potential difference (PD) that transmits by underground medium, pass through pre-amplifying module (11) then the electromagnetic signal that receives is carried out preliminary amplification, and involve the industrial frequency noise of trap module (12) filter away high frequency noise and system after filtration, by accurate amplification module (13) signal of filtering noise is carried out again that low noise is accurate amplifies; Autocorrelator (14) is realized the auto-correlation computation of pseudo random signal; Cross-correlator (15) is realized pseudo random signal and is received the computing cross-correlation of signal; Obtain total impulse response through the first deconvolution device (16), the second deconvolution device (17) is removed the recording geometry influence and is handled back acquisition the earth system impulse response, FFT transducer (18) is handled the frequency response that obtains the earth system to the earth impulse response, the earth system frequency response obtains the complex resistivity spectrum of the earth system through complex resistivity spectrum maker (19), match device (20) obtains the sight-reading parameter according to two Coles-Cole's model match, inverter (21) carries out joint inversion and obtains true spectrum parameter, thereby obtain geophysical parameters information, and carry out the demonstration of real time data figure and storage.

4. a resistivity prospecting system is characterized in that comprising sending and receiving machine as claimed in claim 1 or 2 and receiver as claimed in claim 3.

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