CN111399065A - Anti-interference earth electromagnetic measuring method and device - Google Patents
Anti-interference earth electromagnetic measuring method and device Download PDFInfo
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- CN111399065A CN111399065A CN202010188900.9A CN202010188900A CN111399065A CN 111399065 A CN111399065 A CN 111399065A CN 202010188900 A CN202010188900 A CN 202010188900A CN 111399065 A CN111399065 A CN 111399065A
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- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
- G01V3/081—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices the magnetic field is produced by the objects or geological structures
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
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Abstract
The invention relates to the technical field of magnetotelluric measurement, and discloses an anti-interference magnetotelluric measurement method and device, which comprise the following steps: the method comprises the following steps: obtaining geodetic data to be detected; step two: training a neural network; step three: performing a geological structure model by using a trained neural network algorithm; the device can collect electromagnetic signals, filter the electromagnetic signals, then denoise the frequency spectrum by adopting wavelet multi-resolution analysis and wavelet threshold algorithm, well remove the human noise in the signals, improve the signal-to-noise ratio of the noise, invert the geological structure by adopting neural network algorithm, and has simple operation and high inversion accuracy.
Description
Technical Field
The invention relates to the technical field of magnetotelluric measurement, in particular to an anti-interference magnetotelluric measurement method and an anti-interference magnetotelluric measurement device.
Background
The magnetotelluric measurement technology is one of the most important means for detecting the deep part of the earth, and plays an important role in understanding the geological structure of the deep part of the earth and detecting a deep heat source. Magnetotelluric techniques are the subject of transient natural electromagnetic fields on the earth's surface, which are generated by the varying earth's magnetic field and induced currents, and which have a broad spectral range and a very wide dynamic range. Because the natural electromagnetic field has the characteristics of wide frequency band and weak signals, when data acquisition is carried out in the field, the acquisition process is interfered by various noises. In the modern society, people have activities everywhere, and electromagnetic fields generated in people's lives can generate electromagnetic noise to natural electromagnetic fields, the noise belongs to human noise, and the human noise mainly comprises: the method comprises the steps of pulse noise, square wave noise, step noise, triangular wave noise and periodic noise, wherein the noises and electromagnetic signals are mixed in an aliasing mode, the analysis on the real geological condition is influenced, influences are brought to activities such as later geological exploration, the existing method cannot remove all the humanistic noise, and the accuracy of a reversed geological structure is low.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an anti-interference magnetotelluric measurement method and an anti-interference magnetotelluric measurement device.
In order to achieve the above purpose, the invention provides the following technical scheme:
the anti-interference earth electromagnetic measuring method and device includes the following steps: the method comprises the following steps: obtaining geodetic data to be detected; step two: training a neural network; step three: and (5) inversing the geological structure model by using the trained neural network algorithm.
In the present invention, it is preferable that, in the step one, the step one is performed in four steps: (1) collecting electromagnetic signals; (2) primarily intercepting a signal; (3) eliminating low-frequency noise in a frequency spectrum by adopting wavelet multi-resolution analysis, and eliminating high-frequency noise in the frequency spectrum by adopting a wavelet threshold algorithm; (4) the impedance is calculated.
In the present invention, preferably, in the step (3), the wavelet multi-resolution analysis is performed to layer the signal, the number of layers is determined according to the size of the signal to be removed, and the wavelet threshold algorithm is configured to remove the high frequency signal higher than the threshold by setting the threshold.
In the present invention, preferably, in the step (4), respective self-power spectrums and mutual-power spectrums are calculated according to the frequency spectrums after the noise elimination, so as to calculate the measured impedance, which can reflect the underground structure, and other parameters such as resistivity, impedance phase, and the like.
In the present invention, preferably, in step (1), the geology of the location to be detected is measured by using a geodetic electromagnetic measuring device, and electromagnetic signals are collected, wherein the electromagnetic signals include two electrical signals and three magnetic signals.
In the present invention, preferably, in the step (2), the effective frequency band in the electromagnetic signal is intercepted, and the signal with a break point or an excessive variation is removed.
In the present invention, preferably, in the second step, the neural network algorithm is trained by using other existing geological data, and the weight between layers in the neural network algorithm is determined.
In the present invention, preferably, in the third step, the calculated measured impedance and other parameters are used as input of a neural network algorithm, and through calculation and conversion of each layer in the neural network, the inverted geologic structure model is finally output by the neural network algorithm.
The detection device comprises an electrode and a magnetic sensor, the host comprises a box body, a control panel is fixed in the box body, an electrode placing bin, an electrode wiring port and a magnetic sensor wiring port are fixed on the control panel, the electrode wiring port is connected with the electrode through a cable, the magnetic sensor wiring port is connected with the magnetic sensor through a cable, an amplifier, a filter and a processor are connected below the control panel, one end of the amplifier is connected with the magnetic sensor wiring port, the other end of the amplifier is connected with the filter, the other end of the filter is connected with the processor, and a magnetic sensor placing area is further arranged below the control panel and used for containing the magnetic sensor.
In the invention, preferably, the box body is further provided with a box cover, and a clamping groove is formed in the inner side of the box cover and used for accommodating the cable.
Compared with the prior art, the invention has the beneficial effects that:
the device can collect electromagnetic signals, carry out filtering, then carry out noise elimination on the frequency spectrum by adopting wavelet multi-resolution analysis and wavelet threshold algorithm, can well remove the human noise in the signals, improve the signal-to-noise ratio of the noise, and simultaneously invert the geological structure by adopting the neural network algorithm, thereby having simple operation and high inversion accuracy.
Drawings
Fig. 1 is a flow chart of the anti-interference magnetotelluric measurement method according to the present invention.
Fig. 2 is a schematic structural diagram of a host of the anti-interference electromagnetic measuring apparatus of the present invention.
Fig. 3 is a block diagram of the structure of the anti-interference electromagnetic measuring device of the present invention.
In the drawings: 1-box body, 2-control panel, 3-electrode placing bin, 4-electrode wiring port, 5-magnetic sensor wiring port, 6-box cover and 7-clamping groove.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1 to fig. 3, the present invention provides an anti-interference magnetotelluric measurement method and apparatus, the basic principle of magnetotelluric measurement is as follows: the earth surface has transient natural electromagnetic field, in the electromagnetic field, electromagnetic waves with different frequencies are transmitted in the geology, alternating signals with different frequencies enter the geology due to different structures and compositions of layers in the geology, different penetration depths are generated, and therefore, an electromagnetic response sequence from high frequency to low frequency can be detected on the earth surface through the device, and then an electrical structure of the geology from shallow to deep can be obtained through related data processing and analysis.
In the present embodiment, the method includes the steps of: the method comprises the following steps: obtaining geodetic data to be detected; step two: training a neural network; step three: and (5) inversing the geological structure model by using the trained neural network algorithm.
In the present embodiment, in the first step, the following steps are performed: (1) collecting electromagnetic signals; (2) primarily intercepting a signal; (3) eliminating low-frequency noise in a frequency spectrum by adopting wavelet multi-resolution analysis, and eliminating high-frequency noise in the frequency spectrum by adopting a wavelet threshold algorithm; (4) the impedance is calculated.
In this embodiment, in step (1), a geodetic electromagnetic measuring device is used to measure the geology of the location to be detected, wherein the geodetic electromagnetic measuring device comprises three magnetic sensors and two motor pairs, the three magnetic sensors and the two motor pairs are connected with a host computer through cables, when the geodetic electromagnetic measuring device is placed, two electrodes of the electrode pairs are separately placed and form a straight line, the other electrode pair is vertically placed, the magnetic sensors are placed between the electrodes and are vertically placed in a space, and then electromagnetic signals are collected, wherein the electromagnetic signals comprise two electrical signals and three magnetic signals.
In the present embodiment, in step (2), a person observes the waveform of the electromagnetic signal to remove the signal with the break point or the excessive variation, and then cuts the effective frequency band in the electromagnetic signal according to the same time period.
In the embodiment, in the step (3), the signal is denoised by a wavelet algorithm, the wavelet algorithm is obtained by improving fourier transform, in the signal analysis process, the size of a frequency window and a time window is fixed, and time-frequency localization analysis is performed, wherein low-frequency noise in a frequency spectrum is removed by using wavelet multi-resolution analysis, the signal is layered by using the wavelet multi-resolution analysis, the number of layers is determined according to the size of the signal to be removed, in the embodiment, the total frequency is 0 to 80HZ, the signal is divided into six layers of 40 to 80HZ, 20 to 40HZ, 10 to 20HZ, 5 to 10HZ, 2.5 to 5HZ and 0 to 2.5HZ to remove the signal with the frequency of 0 to 2.5 HZ; wherein, the wavelet threshold algorithm removes the high-frequency noise in the frequency spectrum according to the formula
Calculating high-frequency coefficient and low-frequency coefficient of each layer, and then according to soft threshold function
The processing obtains high-frequency coefficients of one to six layers, and the coefficient exceeding the threshold is set to 0, thereby removing high-frequency noise. And finally, reconstructing the layered signals to obtain the signals subjected to noise reduction.
In this embodiment, in step (4), the self-power spectrum and the cross-power spectrum of each and each other are calculated according to the frequency spectrum after noise elimination, so as to calculate the measured impedance, and other parameters such as resistivity and impedance phase, wherein the measured impedance can reflect the underground structure.
In the embodiment, in the second step and the third step, the geologic structure model is inverted according to the calculated measured impedance and other parameters, wherein the inversion is mainly realized by using a neural network algorithm, wherein a BP neural network is mainly used and comprises three layers, an input layer, a hidden layer and an output layer, the number of neurons of the input layer is determined according to the number of the parameters, the number of neurons of the hidden layer is two thirds of that of the input layer, the number of neurons of the output layer is one, the weight between each layer is determined by training through known geologic data, then the measured impedance and other parameters are input into the neural network algorithm, and the geologic model structure can be obtained by inversion through the transmission and operation of each layer. The training rules are the existing ones, and the training rules are only carried out before the inversion of the geological structure, and the determination sequence is not specified.
In this embodiment, the main equipment that uses includes host computer and detection device, a serial communication port, detection device includes electrode and magnetic sensor, the host computer includes box 1, box 1 internal fixation has control panel 2, it places storehouse 3 to be fixed with the electrode on the control panel 2, electrode wiring mouth 4 and magnetic sensor wiring mouth 5, electrode wiring mouth 4 passes through the cable and is connected with the electrode, magnetic sensor wiring mouth 5 passes through the cable and is connected with magnetic sensor, be connected with the amplifier under the control panel 2, wave filter and treater, amplifier one end is connected with magnetic sensor wiring mouth 5, the other end is connected with the wave filter, the wave filter other end is connected with the treater, still be provided with magnetic sensor under the control panel 2 and place the district, be used for accomodating magnetic sensor.
In this embodiment, the processor includes a storage unit and a processing unit, the processing unit performs noise cancellation processing on the detection signal, and the storage unit stores the detection signal subjected to the noise cancellation processing.
In the present embodiment, the box body 1 is further provided with a box cover 6, a clamping groove 7 is arranged on the inner side of the box cover 6, and the clamping groove 7 is used for accommodating a cable.
The working principle is as follows:
when the earth electromagnetic measurement is carried out, after the measurement position is determined, the host is firstly put down, the detection device is taken out, the electrodes are connected with the electrode wiring port 4 by cables, the magnetic sensor is connected with the magnetic sensor wiring port 5, then the two electrodes of the electrode pair are separately arranged to form a straight line, the other pair of electrodes are vertically arranged, the magnetic sensor is arranged between the electrodes and is vertically arranged in the space; after the working is started, 5 time sequence data are collected by the electrode and the magnetic sensor, then are amplified by the amplifier, clutter is filtered by the filter, and then the data enter the processing unit, and after the processor in the processing unit carries out sorting and denoising processing on the data, the data are stored in the memory and are extracted at any time when needed; after the measurement is finished, the electrode and the magnetic sensor are taken out, the cable is pulled out, the electrode and the magnetic sensor are respectively placed in the electrode placing bin 3 and the magnetic sensor placing area in the box body 1, the cable is rolled up and clamped into the clamping groove 7, and the box cover 6 is closed.
In the processor, the following is completed: (1) personnel remove the signals with breakpoints or excessive variation by observing the waveforms of the electromagnetic signals, and then intercept the effective frequency bands in the electromagnetic signals according to the same time period; (2) adopting wavelet multi-resolution analysis to layer the signals and remove low-frequency noise, and then adopting a wavelet threshold algorithm to remove high-frequency noise; (3) according to the frequency spectrum after noise elimination, calculating respective self-power spectrum and mutual-power spectrum, thereby calculating the measured impedance; (4) and performing a geologic structure model by using a trained neural network algorithm according to the calculated measured impedance and other parameters.
The above description is intended to describe in detail the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the claims of the present invention, and all equivalent changes and modifications made within the technical spirit of the present invention should fall within the scope of the claims of the present invention.
Claims (10)
1. An anti-interference magnetotelluric measurement method, comprising the steps of: the method comprises the following steps: obtaining geodetic data to be detected; step two: training a neural network; step three: and (5) inversing the geological structure model by using the trained neural network algorithm.
2. A method of interference-free magnetotelluric measurement as claimed in claim 1, wherein in said first step, said step of: (1) collecting electromagnetic signals; (2) primarily intercepting a signal; (3) eliminating low-frequency noise in a frequency spectrum by adopting wavelet multi-resolution analysis, and eliminating high-frequency noise in the frequency spectrum by adopting a wavelet threshold algorithm; (4) the impedance is calculated.
3. The antijam magnetotelluric measurement method of claim 2, wherein in the step (3), wavelet multiresolution analysis is used to layer the signals, the number of layers is determined according to the size of the signals to be removed, and wavelet threshold algorithm is used to remove the high frequency signals above the threshold by setting the threshold.
4. The method and apparatus for interference-free magnetotelluric measurement as claimed in claim 3, wherein in said step (4), the respective self-power spectrum and cross-power spectrum are calculated according to the noise-eliminated frequency spectrum, so as to calculate the measured impedance, which can reflect the underground structure, and other parameters such as resistivity and impedance phase.
5. The antijam magnetotelluric measurement method of claim 2, wherein in the step (1), the geology of the position to be detected is measured by using a magnetotelluric measurement device, and electromagnetic signals are collected, wherein the electromagnetic signals comprise two electric signals and three magnetic signals.
6. The method according to claim 2, wherein in step (2), the effective frequency band of the electromagnetic signal is intercepted, and the signal with break point or excessive variation is removed.
7. The antijam magnetotelluric measurement method of claim 1, wherein in the second step, the neural network algorithm is trained by using other geological data, and the weight between layers in the neural network algorithm is determined.
8. The antijam magnetotelluric measurement method of claim 1, wherein in the third step, the calculated measured impedance and other parameters are used as input of a neural network algorithm, and after calculation and transformation of each layer in the neural network, the final neural network algorithm outputs an inverted geologic structure model.
9. The anti-interference earth electromagnetic measuring device is realized based on the device and is characterized by comprising a host and a detecting device, wherein the detecting device comprises an electrode and a magnetic sensor, the host comprises a box body (1), a control panel (2) is fixed in the box body (1), an electrode placing bin (3), an electrode wiring port (4) and a magnetic sensor wiring port (5) are fixed on the control panel (2), the electrode wiring port (4) is connected with the electrode through a cable, the magnetic sensor wiring port (5) is connected with the magnetic sensor through a cable, an amplifier, a filter and a processor are connected below the control panel (2), one end of the amplifier is connected with the magnetic sensor wiring port (5), the other end of the amplifier is connected with the filter, and the other end of the filter is connected with the processor, a magnetic sensor placing area is further arranged below the control panel (2) and used for containing the magnetic sensor.
10. Anti-interference magnetotelluric measurement device according to claim 9, characterized in that the housing (1) is further provided with a cover (6), the cover (6) being provided with a slot (7) on the inside, the slot (7) being adapted to receive the cable.
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Cited By (4)
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| CN112666617A (en) * | 2020-12-11 | 2021-04-16 | 广竣(徐州)机电有限公司 | Time-frequency domain full convolution neural network electromagnetic noise elimination method |
| CN113671580A (en) * | 2021-08-03 | 2021-11-19 | 展少辉 | High-density electrical measurement method for receiving magnetoelectric signals |
| CN114119982A (en) * | 2021-11-13 | 2022-03-01 | 中国地质科学院地球物理地球化学勘查研究所 | A magnetotelluric sounding method and system for geothermal exploration |
| CN115598714A (en) * | 2022-12-14 | 2023-01-13 | 西南交通大学（Cn） | Electromagnetic Wave Impedance Inversion Method for Ground Penetrating Radar Based on Space-Time Coupling Neural Network |
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| CN112666617A (en) * | 2020-12-11 | 2021-04-16 | 广竣(徐州)机电有限公司 | Time-frequency domain full convolution neural network electromagnetic noise elimination method |
| CN112666617B (en) * | 2020-12-11 | 2024-04-12 | 广竣(徐州)机电有限公司 | Electromagnetic noise elimination method for time-frequency domain full convolution neural network |
| CN113671580A (en) * | 2021-08-03 | 2021-11-19 | 展少辉 | High-density electrical measurement method for receiving magnetoelectric signals |
| CN114119982A (en) * | 2021-11-13 | 2022-03-01 | 中国地质科学院地球物理地球化学勘查研究所 | A magnetotelluric sounding method and system for geothermal exploration |
| CN115598714A (en) * | 2022-12-14 | 2023-01-13 | 西南交通大学（Cn） | Electromagnetic Wave Impedance Inversion Method for Ground Penetrating Radar Based on Space-Time Coupling Neural Network |
| CN115598714B (en) * | 2022-12-14 | 2023-04-07 | 西南交通大学 | Time-space coupling neural network-based ground penetrating radar electromagnetic wave impedance inversion method |
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Application publication date: 20200710 |