CN113466947B - Automatic dead pixel removal method applied to superconducting transient electromagnetic - Google Patents
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Abstract
The invention discloses a dead point removing method applied to superconducting transient electromagnetic, which comprises the following steps: s1, synchronously dividing an output signal of a SQUID magnetometer obtained by superconducting transient electromagnetic into a plurality of periods according to a period parameter of a transmitting 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 a 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 a dead pixel characteristic value set, and clustering to automatically find out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel; and S3, performing proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and completing automatic paired 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 earth physics, in particular to an automatic dead point removing method applied to superconducting transient electromagnetic.
Background
With the continuous high-speed development of the economy in China, the consumption requirements on resources such as mineral products and the like are further expanded. In order to get rid of this dilemma, one has to develop advanced measuring instruments to shift to more complex exploration of deep mineral resources. The occurrence of superconducting transient electromagnetism well compensates the defect, and the superconducting transient electromagnetism further analyzes the SQUID output signal by transmitting bipolar pulse signals and receiving underground response through a superconducting quantum interference device (SQUID) so as to quickly find a low-resistance ore body target, thereby having the advantages of high precision and high depth.
Because the SQUID is the most sensitive magnetic sensor known at present, the sensitivity can reach fT level, and the SQUID is the most ideal signal receiving end of transient electromagnetism. For the received signal, suppression of plus-minus period superposition noise is generally performed. However, SQUID is easily interfered by the weather in the environment and the system itself in the actual field experiment, and a certain number of dead spots appear in a single period, so that the positive and negative superposition effect is seriously affected, and the working performance of superconducting transient electromagnetic is restricted, so that dead spot removal becomes an important step of transient electromagnetic signal processing.
The existing dead pixel removing method mainly comprises manual or fixed threshold algorithm removing, and has the defects of low efficiency, large error and the like, and can not realize automatic and accurate elimination of dead pixels with different amplitudes, so that the limitation of actual transient electromagnetic signal processing is larger.
Disclosure of Invention
The invention aims to provide an automatic dead pixel removing method applied to superconducting transient electromagnetic, which mainly extracts dead pixel characteristics through an original signal, then utilizes a kmeans clustering algorithm to realize the positioning of dead pixel periods, and finally combines a proximity detection algorithm to realize automatic elimination of positive and negative periods of dead pixels, thereby realizing automatic elimination of dead pixels of transient electromagnetic signals and improving measurement accuracy.
The invention is realized by the following technical scheme:
a dead pixel automatic removal method applied to superconducting transient electromagnetic comprises the following steps:
s1, synchronously dividing an output signal of a SQUID magnetometer obtained by superconducting transient electromagnetic into a plurality of periods according to a period parameter of a transmitting 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 a 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 a dead pixel characteristic value set, and clustering to automatically find out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel;
and S3, performing proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and completing automatic paired removal of the dead pixels.
Optionally, in step S1, a periodic magnetic field signal B is first acquired based on superconducting transient electromagnetic Z For B Z The synchronization is divided into 2N periods, each period being denoted b zi i=0, 1, … 2N-1; then for each period b zi Obtaining ddb by respectively obtaining second-order difference zi From each ddb zi Maximum value and maximum value are obtained from the periodic signalSmall value and absolute value r of difference between maximum value and minimum value i As the dead pixel characteristic of each period, 2N dead pixel characteristic value sets R= { R are constructed i ，i∈0,1，…，2N-1}。
Alternatively, b zi The method for solving the second-order difference comprises the steps of firstly solving a first-order difference db zi Second-order difference ddb is obtained based on the first-order difference result zi The specific formula is as follows:
db zi ＝b zi (i+1)-b zi (i)
ddb zi ＝db zi (i+1)-db zi (i)i＝0,1，…，2N-1。
optionally, the method for extracting the dead pixel feature set from the second-order difference comprises first solving the dead pixel feature value r of each period i Then a one-dimensional feature set sequence R is constructed,
r i ＝|max(ddb zi )-min(ddb zi )|
wherein max (ddb) zi ) And min (ddb) zi ) Respectively, r is the maximum value and the minimum value in the second-order difference of each period i The dead pixel characteristic value of the period.
Optionally, in step S2, a cluster type k=2 is set first, and the dead pixel feature sequence R is normalized and then is input as a cluster model, and any point R in the dead pixel feature is selected i As an initialized clustering center, calculating Euclidean distance d between other dead pixel characteristic values and the clustering center i Classifying the dead pixel characteristic sequences according to the solved Euclidean distance, and increasing the clustering times by 1;
then judging whether the clustering times meet the setting requirement 500 times or not;
if not, a new clustering center is determined again, clustering is carried out, and the clustering center and the clustering times are updated;
and if the clustering times are met, outputting the sequence number of the abnormal point.
Optionally, the method for normalizing the dead pixel characteristic value sequence comprises the following steps:
wherein,is the normalized value of the ith dead pixel characteristic value, mean (R) is the average value of the dead pixel characteristic sequence, and sigma is the variance of the dead pixel characteristic sequence.
Optionally, the Euclidean distance algorithm of the specific ith dead pixel characteristic value from the clustering center c is as follows:
wherein,is the normalized value of the characteristic value of the ith dead pixel, r c The clustering centers are the clustering centers under n times of iteration, and i and c are the dead pixel characteristics and the clustering center serial numbers respectively.
Optionally, in step S3, a set D of null period sequences is first set, and the sequence numbers B acquired in step S2 are sequentially traversed i If B i If the number is even, adding the sequence number i and the sequence number i+1 into the set D; if B i Adding the sequence number i and the sequence number i-1 into the set D if the sequence number i is odd; 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 pixels, and finally splicing the G ' into one-dimensional data according to the period time to finish the proximity detection and paired deletion of the dead pixel period.
As shown above, the dead point removing method applied to superconducting transient electromagnetic has the following beneficial effects:
according to the invention, the period of the dead pixel is locked by extracting the dead pixel characteristic value, constructing the Kmeans clustering model, and finally completing the period adjacent detection and paired deletion of the dead pixel, thereby realizing the automatic positioning of the dead pixel and the paired detection and deletion of the period of the dead pixel, further realizing the automatic elimination of the dead pixel of the transient electromagnetic signal, improving the measurement accuracy, realizing the automatic and accurate elimination of the dead pixel with different amplitudes, and enabling the actual transient electromagnetic signal to be more comprehensively processed.
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In order to more clearly illustrate the technical solutions and advantages of embodiments of the present application or of the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the prior art descriptions, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for automatically removing dead pixels applied to superconducting transient electromagnetic according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a dead pixel feature extraction flow provided in an embodiment of the present invention.
FIG. 3 is a schematic diagram of a process for solving bad outlier sequence numbers based on a kmeans cluster 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 point period according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of detecting a dead pixel period according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a comparison between before and after dead pixel removal according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described below by the specific embodiments with reference to the accompanying drawings.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to be limiting of the present patent; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated 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 embodiment provides a dead point removing method applied to superconducting transient electromagnetic, which includes the following steps:
s1, synchronously dividing an output signal of a SQUID magnetometer obtained by superconducting transient electromagnetic into a plurality of periods according to a period parameter of a transmitting 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 a 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 a dead pixel characteristic value set, and clustering to automatically find out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel;
and S3, performing proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and completing automatic paired removal of the dead pixels.
In this embodiment, particularly in step S1, referring to fig. 2, the dead point feature extraction steps are as follows: firstly, acquiring a periodic magnetic field signal B based on superconducting transient electromagnetic Z For B Z The synchronization is divided into 2N periods, each period being denoted b zi i=0, 1,..2N-1; then for each period b zi Obtaining ddb by respectively obtaining second-order difference zi From each ddb zi The maximum value and the minimum value are obtained from the periodic signal, and the absolute value r of the difference value between the maximum value and the minimum value is calculated i As the dead pixel characteristic of each period, 2N dead pixel characteristic value sets R= { R are constructed i ，i∈0,1，...，2N-1}。
In the above-described feature extraction step regarding dead pixels, b z The method for solving the second-order difference comprises the steps of firstly solving a first-order difference db zi Second-order difference ddb is obtained based on the first-order difference result zi The specific formula is as follows:
db zi ＝b zi (i+1)-b zi (i)
ddb zi ＝db zi (i+1)-db zi (i) i＝0,1，…，2N-1。
in the above-mentioned dead pixel feature extraction step, the dead pixel feature set is extracted from the second-order difference by first obtaining each cycleDead pixel characteristic value r of period i Then a one-dimensional feature set sequence R is constructed,
r i ＝|max(ddb zi )-min(ddb zi )|
wherein max (ddb) zi ) And min (ddb) zi ) Respectively, r is the maximum value and the minimum value in the second-order difference of each period i The dead pixel characteristic value of the period.
In this embodiment, particularly in step S2, referring to fig. 3, the steps of the period where the dead pixel is locked are as follows: firstly setting a cluster type K=2, normalizing a dead pixel characteristic sequence R, inputting the normalized dead pixel characteristic sequence R as a cluster model, and selecting any point R in the dead pixel characteristic i As an initialized clustering center, calculating Euclidean distance d between other dead pixel characteristic values and the clustering center i Classifying the dead pixel characteristic sequences according to the solved Euclidean distance, and increasing the clustering times by 1;
then judging whether the clustering times meet the setting requirement 500 times or not;
if not, a new clustering center is determined again, clustering is carried out, and the clustering center and the clustering times are updated; and if the clustering times are met, outputting the sequence number of the abnormal point.
In the above-mentioned periodic step of locking the dead pixel, the method for normalizing the dead pixel characteristic value sequence includes:
wherein,is the normalized value of the ith dead pixel characteristic value, mean (R) is the average value of the dead pixel characteristic sequence, and sigma is the variance of the dead pixel characteristic sequence.
In the step of locking the period where the dead pixel is located, the specific Euclidean distance algorithm of the characteristic value of the i dead pixel from the clustering center c is as follows:
wherein,is the normalized value of the characteristic value of the ith dead pixel, r c The clustering centers are the clustering centers under n times of iteration, and i and c are the dead pixel characteristics and the clustering center serial numbers respectively.
In step S3, a set of null period sequences D is first set, and the sequence numbers B obtained in step S2 are sequentially traversed i If B i For even number, add sequence number i and sequence number i+1 to set D, if B i Adding the sequence number i and the sequence number i-1 into the set D if the sequence number i is odd; 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 pixels, and finally splicing the G ' into one-dimensional data according to the period time to finish the proximity detection and paired deletion of the dead pixel period.
In this embodiment, for step 3, traversing the sequence number obtained in step S2 to obtain a dead pixel feature sequence number found by Kmeans; in addition, i, i+1, i-1 are feature sequence numbers to be deleted, because the dead pixel sequence numbers found in step S2 cannot be deleted individually, and adjacent sequence numbers need to be deleted in pairs, and the number of dead pixel sequences to be deleted is an even number, for example: step S1 extracts the dead pixel characteristic serial numbers {0,1,2,3,4,5,6,7,8,9}, step S2Kmeans finds out the dead pixel sequence {1,5,8}, step S3 deletes the dead pixel sequence {0,1,4,5,8,9}, of course, the actual data step S1 extracts thousands of dead pixel characteristic serial numbers, only a small part of the dead pixel characteristic serial numbers need to be deleted, and the remaining serial numbers are finally spliced into dead pixel-free signals.
Referring to fig. 4, fig. 4 is a schematic diagram of a superconducting transient electromagnetic output signal with a dead pixel period, especially a dashed marking portion in fig. 4, where the dashed marking portion is an extremely dead pixel portion, and meanwhile, the dead pixel portion in the schematic diagram of the transient electromagnetic output signal includes, but is not limited to, the dashed marking portion, and it should be understood by those skilled in the art that the dashed marking portion is merely used to indicate the dead pixel.
Referring to fig. 5, fig. 5 is a schematic diagram of detecting a dead pixel period according to the present embodiment, and in order to facilitate distinction, fig. 5 is a distribution of a normal period and an abnormal period occurring when coordinates are used to show different period numbers according to the relative distances between period numbers and Kmeans; in fig. 5, most of the normal periodic signals are located between 0 and 2Kmeans relative distances, and the abnormal periodic signals gradually start to be reflected after the Kmeans relative distances exceed 2, and meanwhile, the abnormal periodic signals gradually become dispersed from dense after the Kmeans relative distances become larger.
Referring to fig. 6, fig. 6 is a schematic diagram of the embodiment of the present invention, in which the schematic diagram before the modification in fig. 6 is a schematic diagram before the removal of the dead pixel (the same as the schematic diagram shown in fig. 4), and the schematic diagram after the modification in fig. 6 is a schematic diagram after the automatic removal of the dead pixel by adopting the technical scheme in the embodiment, it can be seen from the schematic diagram after the modification in fig. 6 that the technical scheme in the embodiment can effectively remove the dead pixel, and the measurement accuracy is improved.
The present invention is not limited to the above-mentioned embodiments, and any person skilled in the art, based on the technical solution of the present invention and the inventive concept thereof, can be replaced or changed within the scope of the present invention.
It should be noted that in this document, relational terms such as first and second, and the like are 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (8)
1. The dead pixel automatic removal method applied to superconducting transient electromagnetic is characterized by comprising the following steps of:
s1, synchronously dividing an output signal of a SQUID magnetometer obtained by superconducting transient electromagnetic into a plurality of periods according to a period parameter of a transmitting 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 a 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 a dead pixel characteristic value set, and clustering to automatically find out deviation points in the dead pixel characteristic value set so as to lock the cycle of the dead pixel;
and S3, performing proximity detection on the periods with the dead pixels so as to delete the periods with the dead pixels in pairs, and completing automatic paired removal of the dead pixels.
2. The method for automatically removing dead pixels applied to superconducting transient electromagnetic according to claim 1, wherein in step S1, a periodic magnetic field signal B is first obtained based on superconducting transient electromagnetic Z For B Z The synchronization is divided into 2N periods, each period being denoted b zi i=0, 1,..2N-1; then for each period b zi Obtaining ddb by respectively obtaining second-order difference zi From each ddb zi The maximum value and the minimum value are obtained from the periodic signal, and the absolute value r of the difference value between the maximum value and the minimum value is calculated i As the dead pixel characteristic of each period, 2N dead pixel characteristic value sets R= { R are constructed i ，i∈0,1，...，2N-1}。
3. The method for automatically removing dead pixels applied to superconducting transient electromagnetic according to claim 2, wherein b zi The method for solving the second-order difference comprises the steps of firstly solving a first-order difference db zi Second-order difference ddb is obtained based on the first-order difference result zi The specific formula is as follows:
db zi ＝b zi (i+1)-b zi (i)
ddb zi ＝db zi (i+1)-db zi (i)i＝0,1，...，2N-1。
4. the method for automatically removing dead pixels applied to superconducting transient electromagnetic according to claim 3, wherein the dead pixel feature set is extracted from the second-order difference by first obtaining the dead pixel feature value r of each period i Then a one-dimensional feature set sequence R is constructed,
r i ＝|max(ddb zi )-min(ddb zi )|
wherein max (ddb) zi ) And min (ddb) zi ) Respectively, r is the maximum value and the minimum value in the second-order difference of each period i The dead pixel characteristic value of the period.
5. The method for automatically removing dead pixels applied to superconducting transient electromagnetic according to claim 4, wherein in step S2, a cluster type k=2 is set first, a dead pixel feature sequence R is normalized and then is input as a cluster model, and any point R in dead pixel features is selected i As an initialized clustering center, calculating Euclidean distance d between other dead pixel characteristic values and the clustering center i Classifying the dead pixel characteristic sequences according to the solved Euclidean distance, and increasing the clustering times by 1;
then judging whether the clustering times meet the setting requirement 500 times or not;
if not, a new clustering center is determined again, clustering is carried out, and the clustering center and the clustering times are updated;
and if the clustering times are met, outputting the sequence number of the abnormal point.
6. The method for automatically removing dead pixel applied to superconducting transient electromagnetic according to claim 5, wherein the method for normalizing the dead pixel characteristic value sequence comprises the following steps:
wherein,is the normalized value of the ith dead pixel characteristic value, mean (R) is the average value of the dead pixel characteristic sequence, and sigma is the variance of the dead pixel characteristic sequence.
7. The method for automatically removing dead pixels applied to superconducting transient electromagnetic according to claim 6, wherein the specific Euclidean distance algorithm of the i dead pixel eigenvalue from the clustering center c is as follows:
wherein,is the normalized value of the ith dead pixel characteristic value, i and c are the dead pixel characteristic and the cluster center serial number respectively.
8. The method for automatically removing dead pixels applied to superconducting transient electromagnetic according to claim 7, wherein in step S3, a set of empty periodic sequences D is first set, and sequence numbers B obtained in step S2 are sequentially traversed i If B i If the number is even, adding the sequence number i and the sequence number i+1 into the set D; if B i Adding the sequence number i and the sequence number i-1 into the set D if the sequence number i is odd; then removing the repeated sequence number from the set D to obtain a set D' of periodic sequences to be deleted, and from the whole periodSubtracting the period corresponding to the sequence number in the set D from the sequence number set R to obtain a sequence G 'without dead pixels, and finally splicing the G' into one-dimensional data according to the period time to finish the proximity detection and paired deletion of the dead pixel period.
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