CN113466947A - Dead pixel automatic removing method applied to superconducting transient electromagnetism - Google Patents
Dead pixel automatic removing method applied to superconducting transient electromagnetism Download PDFInfo
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Abstract
The invention discloses a dead pixel removing method applied to superconducting transient electromagnetism, which comprises the following steps: s1, synchronously dividing SQUID magnetometer output signals obtained by superconducting transient electromagnetism into a plurality of periods according to the period parameters of emission current, carrying out second-order difference on each period signal, extracting a maximum value and a minimum value from the signals after the second-order difference, and taking the absolute value of the difference value of the maximum value and the minimum value as the dead pixel characteristic value of the period signal to form a dead pixel characteristic value set; s2, setting parameters to construct a Kmeans clustering model for clustering according to the dead pixel characteristic value set, and automatically finding out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel; and S3, carrying out proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and finishing the automatic pair removal of the dead pixels. The invention can realize the automatic positioning of the dead pixel and the periodic paired detection and deletion of the existing dead pixel.
Description
Technical Field
The invention relates to the technical field of transient electromagnetic application in geophysical, in particular to a method for automatically removing dead pixels of superconducting transient electromagnetic.
Background
With the continuous and high-speed development of economy in China, the consumption requirements on resources such as mineral products and the like are further expanded. Among them, the resources of iron ore, copper and bauxite, etc., which play an important role in economic development, are far lower than the average level in the world, and also have the problems of unbalanced distribution, small ore deposit scale and more lean and refractory ores. To get rid of this dilemma, people have to develop advanced measuring instruments to move to more complex exploration of deep mineral resources. The superconducting transient electromagnetism well makes up the defect, transmits bipolar pulse signals, receives underground response through a superconducting quantum interference device (SQUID), further analyzes SQUID output signals, and quickly searches for low-resistance ore body targets, and has the advantages of high precision and high depth.
Because the SQUID is the most sensitive magnetic sensor known at present, the sensitivity can reach the fT level, and the SQUID is the most ideal signal receiving end for transient electromagnetism. For the received signal, suppression of plus and minus period superimposed noise is generally performed. However, in actual field experiments, the SQUID is easily interfered by the sky electricity in the environment, the system itself and other reasons, a certain number of dead spots appear in individual periods, and the positive and negative superposition effects are seriously influenced, so that the working performance of the superconducting transient electromagnetic is restricted, and therefore dead spot removal becomes an important step for transient electromagnetic signal processing.
The current method for removing the dead pixel is mainly manual or algorithm removal based on a fixed threshold, has the defects of low efficiency, large error and the like, and cannot automatically and accurately eliminate the dead pixel with different amplitudes, so that the method has large limitation in actual transient electromagnetic signal processing.
Disclosure of Invention
The invention aims to provide an automatic dead pixel removing method applied to superconducting transient electromagnetism, which mainly extracts dead pixel characteristics through original signals, then realizes the positioning of a dead pixel cycle by using a kmeans clustering algorithm, and finally realizes the automatic elimination of positive and negative cycles with dead pixels by combining a proximity detection algorithm, thereby realizing the automatic elimination of the dead pixels of the transient electromagnetic signals and improving the accuracy of measurement.
The invention is realized by the following technical scheme:
a method for automatically removing dead spots applied to superconducting transient electromagnetism comprises the following steps:
s1, synchronously dividing SQUID magnetometer output signals obtained by superconducting transient electromagnetism into a plurality of periods according to the period parameters of emission current, carrying out second-order difference on each period signal, extracting a maximum value and a minimum value from the signals after the second-order difference, and taking the absolute value of the difference value of the maximum value and the minimum value as the dead pixel characteristic value of the period signal to form a dead pixel characteristic value set;
s2, setting parameters to construct a Kmeans clustering model for clustering according to the dead pixel characteristic value set, and automatically finding out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel;
and S3, carrying out proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and finishing the automatic pair removal of the dead pixels.
Optionally, in step S1, the periodic magnetic field signal B is first electromagnetically acquired based on the superconducting transientZTo B, pairZThe synchronization is divided into 2N periods, each period being denoted bzi(i ═ 0,1,. 2N-1); then for each period bziSeparately calculate the second order difference to obtain ddbziFrom each ddbziFinding the maximum value and the minimum value in the periodic signal, and calculating the absolute value r of the difference between the maximum value and the minimum valueiAs a dead pixel feature of each cycle, 2N dead pixel feature value sets R ═ { R ═ are constructedi，i∈0,1，...，2N-1}。
Alternatively, bziThe method for solving the second order difference comprises the steps of firstly solving the first order difference dbziFinding a second order difference ddb based on the first order difference resultziThe concrete formula is as follows:
dbzi＝bzi(i+1)-bzi(i)
ddbzi＝dbzi(i+1)-dbzi(i)i＝0,1，...，2N-1。
optionally, the method for extracting the dead pixel feature set from the second-order difference includes first obtaining a dead pixel feature value r of each periodiThen, a one-dimensional characteristic set sequence R is constructed,
ri＝|max(ddbzi)-min(ddbzi)|
wherein，max(ddbzi) And min (ddb)zi) Respectively the maximum value and the minimum value, r, in the second order difference of each periodiIs the dead pixel characteristic value of the period.
Optionally, in step S2, the cluster type K is set to 2, the dead pixel feature sequence R is normalized and then input as a cluster model, and any one point R in the dead pixel features is selectediAs an initial cluster center, calculating Euclidean distance d between other dead pixel characteristic values and the cluster centeriClassifying the dead pixel feature sequences according to the solved Euclidean distance, and increasing the clustering times by 1;
then judging whether the clustering times meet the setting requirement for 500 times;
if not, re-determining a new clustering center, clustering, and updating the clustering center and clustering times;
and if the clustering times are met, outputting the abnormal point serial number.
Optionally, the method for normalizing the dead pixel feature value sequence includes:
wherein the content of the first and second substances,
is a normalized value of the ith dead pixel feature value, mean (R) is the mean of the dead pixel feature sequence, and σ is the variance of the dead pixel feature sequence.
Optionally, the specific euclidean distance algorithm of the ith dead pixel feature value from the clustering center c is as follows:
wherein the content of the first and second substances,
is a normalized value, r, of the ith dead point feature valuecAnd i and c are respectively a dead pixel characteristic and a cluster center sequence number.
Optionally, in step S3, a null period sequence set D is first set, and sequence numbers B obtained in step S2 are sequentially traversediIf B isiIf the number is even, adding a serial number i and a serial number i +1 into the set D; if B isiIf the number is odd, adding a serial number i and a serial number i-1 into the set D; and then removing the repeated sequence number from the set D to obtain a periodic sequence set D ' to be deleted, subtracting the period corresponding to the sequence number in the set D from the whole periodic sequence number set R to obtain a sequence G ' without dead spots, and finally splicing the G ' into one-dimensional data according to the period time to finish the proximity detection and the paired deletion of the dead spot period.
As shown above, the method for removing the dead pixel applied to the superconducting transient electromagnetic has the following beneficial effects:
according to the method, the cycle of the dead pixel is locked by extracting the dead pixel characteristic value, constructing the Kmeans clustering model and finally completing cycle proximity detection and paired deletion of the dead pixel, so that the automatic positioning of the dead pixel and the cycle paired detection and deletion of the dead pixel are realized, the automatic elimination of the dead pixel of the transient electromagnetic signal is realized, the measurement accuracy is improved, the automatic accurate elimination can be realized aiming at the dead pixel with different amplitudes, and the actual transient electromagnetic signal is processed more comprehensively.
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In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of an automatic dead pixel removing method applied to superconducting transient electromagnetism according to an embodiment of the present invention.
Fig. 2 is a schematic view of a dead pixel feature extraction process provided in the embodiment of the present invention.
Fig. 3 is a schematic flow chart of solving a bad abnormal point sequence number based on a kmeans clustering model according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a superconducting transient electromagnetic output signal with a dead-spot period according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of the bad pixel cycle detection provided in the embodiment of the present invention.
Fig. 6 is a comparison diagram illustrating before and after the dead pixel removal according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
Referring to fig. 1, the present embodiment provides a dead pixel removing method applied to superconducting transient electromagnetism, including the following steps:
s1, synchronously dividing SQUID magnetometer output signals obtained by superconducting transient electromagnetism into a plurality of periods according to the period parameters of emission current, carrying out second-order difference on each period signal, extracting a maximum value and a minimum value from the signals after the second-order difference, and taking the absolute value of the difference value of the maximum value and the minimum value as the dead pixel characteristic value of the period signal to form a dead pixel characteristic value set;
s2, setting parameters to construct a Kmeans clustering model for clustering according to the dead pixel characteristic value set, and automatically finding out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel;
and S3, carrying out proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and finishing the automatic pair removal of the dead pixels.
In the present embodiment, particularly in step S1, referring to fig. 2, the dead pixel feature extraction step is: firstly, a periodic magnetic field signal B is obtained based on superconducting transient electromagnetismZTo B, pairZThe synchronization is divided into 2N periods, each period being denoted bzi(i ═ 0,1,. 2N-1); then for each period bziSeparately calculate the second order difference to obtain ddbziFrom each ddbziFinding the maximum value and the minimum value in the periodic signal, and calculating the absolute value r of the difference between the maximum value and the minimum valueiAs a dead pixel feature of each cycle, 2N dead pixel feature value sets R ═ { R ═ are constructedi，i∈0,1，...，2N-1}。
In the above-mentioned step of extracting the dead pixel feature, bzThe method for solving the second order difference comprises the steps of firstly solving the first order difference dbziFinding a second order difference ddb based on the first order difference resultziThe concrete formula is as follows:
dbzi＝bzi(i+1)-bzi(i)
ddbzi＝dbzi(i+1)-dbzi(i)i＝0,1，...，2N-1。
in the dead pixel feature extraction step, the dead pixel feature set is extracted from the second order difference by first obtaining a dead pixel feature value r for each periodiThen, a one-dimensional characteristic set sequence R is constructed,
ri＝|max(ddbzi)-min(ddbzi)|
wherein, max (ddb)zi) And min (ddb)zi) Respectively the maximum value and the minimum value, r, in the second order difference of each periodiIs the dead pixel characteristic value of the period.
In this embodiment, especially in step S2, referring to fig. 3, the cycle steps for locking the dead pixel are: firstly setting a clustering type K to be 2, standardizing a dead pixel feature sequence R and then inputting the dead pixel feature sequence R as a clustering model, and selecting any one point R in dead pixel featuresiAs an initial cluster center, calculating Euclidean distance d between other dead pixel characteristic values and the cluster centeriClassifying the dead pixel feature sequences according to the solved Euclidean distance, and increasing the clustering times by 1;
then judging whether the clustering times meet the setting requirement for 500 times;
if not, re-determining a new clustering center, clustering, and updating the clustering center and clustering times; and if the clustering times are met, outputting the abnormal point serial number.
In the above step of locking the period where the dead pixel is located, the method for normalizing the dead pixel characteristic value sequence includes:
wherein the content of the first and second substances,
is a normalized value of the ith dead pixel feature value, mean (R) is the mean of the dead pixel feature sequence, and σ is the variance of the dead pixel feature sequence.
In the above step of the cycle of locking the dead pixel, the specific euclidean distance algorithm of the ith dead pixel characteristic value from the clustering center c is as follows:
wherein the content of the first and second substances,
is a normalized value, r, of the ith dead point feature valuecAnd i and c are respectively a dead pixel characteristic and a cluster center sequence number.
In step S3, a null period sequence set D is first set, and the sequence numbers B obtained in step S2 are sequentially traversediIf B isiIf the number is even, add the serial number i and the serial number i +1 to the set D, if BiIf the number is odd, adding a serial number i and a serial number i-1 into the set D; and then removing the repeated sequence number from the set D to obtain a periodic sequence set G ' to be deleted, subtracting the period corresponding to the sequence number in the set D from the whole periodic sequence number set R to obtain a sequence G ' without dead spots, and finally splicing the G ' into one-dimensional data according to the period time to finish the proximity detection and the paired deletion of the dead spot period.
In this embodiment, for step 3, the sequence number obtained in the traversal step S2 is a dead pixel feature sequence number found by means of Kmeans; in addition, i +1, i-1 belong to the characteristic sequence numbers to be deleted, because the dead pixel sequence numbers found in step S2 cannot be deleted individually, adjacent sequence numbers need to be deleted in pairs, and the number of deleted dead pixel sequences is an even number, for example: step S1 extracts the serial number of the dead pixel feature {0,1,2,3,4,5,6,7,8,9}, step S2Kmeans finds out that the sequence of the dead pixel is {1,5,8}, step S3 deletes the sequence of the dead pixel as {0,1,4,5,8,9}, naturally, the actual data step S1 extracts thousands of serial numbers of the dead pixel feature, only a small part of the serial numbers to be deleted, and the serial numbers are left to be spliced into a signal without dead pixel.
Referring to fig. 4, fig. 4 is a schematic diagram of a superconducting transient electromagnetic output signal with a dead-spot period, in particular, a dashed line marked part in fig. 4 is a dead-spot part, and meanwhile, the dead-spot part in the schematic diagram of the transient electromagnetic output signal includes, but is not limited to, a dashed line marked part, and it should be known to those skilled in the art that the dashed line marked part is only used for indicating a dead spot, and the dead-spot removing method applied to the superconducting transient electromagnetic proposed in this embodiment is mainly used for performing automatic detection and paired deletion on the dead-spot part.
Referring to fig. 5, fig. 5 is a schematic view illustrating a dead pixel cycle detection provided in the present embodiment, and for convenience of distinguishing, fig. 5 is a distribution of normal cycles and abnormal cycles when the relative distances between cycle numbers and Kmeans are coordinates for showing different cycle numbers; in fig. 5, most of the normal period signals are located at a relative distance of Kmeans between 0 and 2, and the abnormal period signals gradually start to be reflected after the relative distance of Kmeans exceeds 2, and the abnormal period signals gradually become dispersed from dense to dense as the relative distance of Kmeans continuously increases.
Referring to fig. 6, fig. 6 is a schematic diagram for comparing before and after the dead pixel is removed according to this embodiment, where the schematic diagram before modification in fig. 6 is a schematic diagram before the dead pixel is removed (the schematic diagram is the same as the schematic diagram given in fig. 4), and the schematic diagram after modification in fig. 6 is a schematic diagram after the dead pixel is automatically eliminated by using the technical scheme of this embodiment, and it can be seen from the schematic diagram after modification in fig. 6 that the dead pixel can be effectively removed by using the technical scheme of this embodiment, so that the accuracy of measurement is improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention and the equivalent alternatives or modifications according to the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (8)
1. A method for automatically removing dead pixels applied to superconducting transient electromagnetism is characterized by comprising the following steps:
s1, synchronously dividing SQUID magnetometer output signals obtained by superconducting transient electromagnetism into a plurality of periods according to the period parameters of emission current, carrying out second-order difference on each period signal, extracting a maximum value and a minimum value from the signals after the second-order difference, and taking the absolute value of the difference value of the maximum value and the minimum value as the dead pixel characteristic value of the period signal to form a dead pixel characteristic value set;
s2, setting parameters to construct a Kmeans clustering model for clustering according to the dead pixel characteristic value set, and automatically finding out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel;
and S3, carrying out proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and finishing the automatic pair removal of the dead pixels.
2. The method for automatically removing the dead pixel of the superconducting transient electromagnet as claimed in claim 1, wherein in step S1, the periodic magnetic field signal B is first obtained based on the superconducting transient electromagnetZTo B, pairZThe synchronization is divided into 2N periods, each period being denoted bzi(i ═ 0,1,. 2N-1); then for each period bziSeparately calculate the second order difference to obtain ddbziFrom each ddbziFinding the maximum value and the minimum value in the periodic signal, and calculating the absolute value r of the difference between the maximum value and the minimum valueiAs a dead pixel feature of each cycle, 2N dead pixel feature value sets R ═ { R ═ are constructedi，i∈0,1，...，2N-1}。
3. The method for automatically removing the dead pixel of the superconducting transient electromagnetic device according to claim 2, wherein b isziThe method for solving the second order difference comprises the steps of firstly solving the first order difference dbziFinding a second order difference ddb based on the first order difference resultziThe concrete formula is as follows:
dbzi＝bzi(i+1)-bzi(i)
ddbzi＝dbzi(i+1)-dbzi(i)i＝0,1，...，2N-1。
4. the method as claimed in claim 3, wherein the dead pixel feature set is extracted from the second order difference by first determining the dead pixel feature value r for each periodiThen, a one-dimensional characteristic set sequence R is constructed,
ri＝|max(ddbzi)-min(ddbzi)|
wherein, max (ddb)zi) And min (ddb)zi) Respectively of second order per cycleMaximum and minimum values in the difference, riIs the dead pixel characteristic value of the period.
5. The method as claimed in claim 4, wherein in step S2, the cluster type K is set to 2, the dead pixel feature sequence R is input as a cluster model after being normalized, and any one point R in the dead pixel features is selectediAs an initial cluster center, calculating Euclidean distance d between other dead pixel characteristic values and the cluster centeriClassifying the dead pixel feature sequences according to the solved Euclidean distance, and increasing the clustering times by 1;
then judging whether the clustering times meet the setting requirement for 500 times;
if not, re-determining a new clustering center, clustering, and updating the clustering center and clustering times;
and if the clustering times are met, outputting the abnormal point serial number.
6. The method for automatically removing the dead pixel applied to the superconducting transient electromagnetism as claimed in claim 5, wherein the method for normalizing the dead pixel characteristic value sequence comprises the following steps:
wherein the content of the first and second substances,
is a normalized value of the ith dead pixel feature value, mean (R) is the mean of the dead pixel feature sequence, and σ is the variance of the dead pixel feature sequence.
7. The method for automatically removing the dead pixel applied to the superconducting transient electromagnetism according to claim 6, wherein a Euclidean distance algorithm of a specific ith dead pixel characteristic value from a clustering center c is as follows:
wherein the content of the first and second substances,
is a normalized value, r, of the ith dead point feature valuecAnd i and c are respectively a dead pixel characteristic and a cluster center sequence number.
8. The method of claim 7, wherein in step S3, a null period sequence set D is first set, and the sequence number B obtained in step S2 is sequentially traversediIf B isiIf the number is even, adding a serial number i and a serial number i +1 into the set D; if B isiIf the number is odd, adding a serial number i and a serial number i-1 into the set D; and then removing the repeated sequence number from the set D to obtain a periodic sequence set D ' to be deleted, subtracting the period corresponding to the sequence number in the set D from the whole periodic sequence number set R to obtain a sequence G ' without dead spots, and finally splicing the G ' into one-dimensional data according to the period time to finish the proximity detection and the paired deletion of the dead spot period.
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