CN109001798B - Method and system for automatically identifying abnormal lane - Google Patents
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Abstract
The invention provides a method and a system for automatically identifying an abnormal track, wherein the method comprises the following steps: calculating the root mean square amplitude of each trace in the seismic record; sorting the root mean square amplitudes of the seismic traces from small to large; calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value; sequentially adding the root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the sorted root-mean-square amplitudes with the standard deviations calculated in the previous step to form a new number series, and calculating the standard deviation of the new number series; and comparing the standard deviation of the new sequence with the standard value to judge whether the track is an abnormal track. If it is an exception track, the calculation is terminated. The method can automatically identify the strong-amplitude abnormal channel in the seismic channel, has simple and practical operation process, and provides help for identifying the microseism event.
Description
Technical Field
The application relates to the field of ground micro-seismic data preprocessing and event identification, in particular to an abnormal track identification method and system.
Background
The microseism monitoring mainly monitors seismic waves emitted by rock fracture caused by hydraulic fracturing, and is an effective technical means for evaluating the fracturing modification effect of unconventional reservoirs. The ground micro-seismic monitoring is usually observed in an array mode, the complex ground surface environment and the coupling problem of a detector cause great influence on the acquired data, and further abnormal tracks can be generated. The signal-to-noise ratio of ground monitoring micro-seismic data is low, the micro-seismic event is difficult to identify, and the amplitude abnormal channel further increases the difficulty for identifying the micro-seismic event.
At present, the abnormal channel identification of seismic data is mainly carried out by identifying and removing by artificial naked eyes, and errors caused by human factors exist. Another characteristic of surface monitoring is multi-well staged fracturing, and the observation time is long, so the data volume collected is large. The huge collected data brings huge workload for manually identifying abnormal tracks, and the working mode has low efficiency. Obviously, the requirement of micro-seismic data processing cannot be met by manually identifying abnormal traces. How to quickly and effectively identify abnormal seismic traces in micro-seismic data has become an important problem which needs to be researched urgently.
In the high-density seismic exploration technology, two automatic abnormal trace identification methods are developed: artificial neural network methods and cluster analysis methods. The two methods have complex algorithm, large calculation amount and time consumption. In order to solve the problem of identification of amplitude abnormal channels in ground micro-seismic monitoring, a new automatic identification method of the abnormal channels is urgently needed in the field.
Disclosure of Invention
The signal-to-noise ratio of the ground monitoring microseism data is low, and the difficulty is still increased for identifying the microseism event even if the amplitude abnormal channel is overlapped by high-order numbers. The invention provides an abnormal trace identification method based on root mean square amplitude, aiming at the problem of micro-seismic amplitude abnormal trace identification.
According to one aspect of the present invention, there is provided a method of automatically identifying an abnormal track, the method comprising:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series (for example, adding one root-mean-square amplitude each time), and calculating the standard deviations of the new number series;
comparing the standard deviation of the new sequence with the standard value, and judging whether the track is an abnormal track;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
Further, if the standard deviation of the new array is greater than 4 times of the standard value, the trace is considered to be an abnormal trace, and the seismic traces with the root-mean-square amplitudes greater than the root-mean-square amplitudes of the abnormal trace are all abnormal traces.
Further, the predetermined number of rms amplitudes is the top 10% of the rms amplitudes after the ranking.
Further, calculating the root mean square amplitude of each track in the seismic record, wherein the calculation expression is as follows:
wherein N is the number of sampling points of seismic channel, aiThe amplitude of the ith sample point on the seismic trace.
Further, a standard deviation is calculated for a predetermined number of sorted root mean square amplitudes, and the standard deviation calculation formula is as follows:
in the formula xiRepresents the rms amplitude of the i-th trace.
According to another aspect of the present invention, there is provided a system for automatically identifying an abnormal track, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series (for example, adding one root-mean-square amplitude each time), and calculating the standard deviations of the new number series;
comparing the standard deviation of the new sequence with the standard value, and judging whether the track is an abnormal track;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
According to still another aspect of the present invention, there is provided a recording medium having stored therein computer-executable instructions; when the computer executable instructions are executed, the following steps are executed:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new sequence (for example, adding one root-mean-square amplitude each time), and calculating the standard deviation of the root-mean-square amplitudes of the new sequence;
comparing the standard deviation of the new sequence with the standard value, and judging whether the track is an abnormal track; if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
The method can automatically identify the strong-amplitude abnormal channel in the seismic channel, has simple and practical operation process, and provides help for identifying the microseism event.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
FIG. 1 shows a schematic diagram of a ground microseismic monitoring observation system.
FIG. 2 shows a first line monitored seismic record.
FIG. 3 shows the root mean square amplitude distribution of a first line monitor seismic record.
FIG. 4 illustrates a seismic recording after anomaly track identification.
FIG. 5 shows the RMS amplitude distribution after the abnormal trace is identified.
FIGS. 6(a) and 6(b) show 11 line 1848 traces of seismic and stack recordings, respectively.
FIGS. 7(a) and 7(b) show 11 line 1848 traces of seismic and stack traces, respectively, after anomaly trace identification.
FIG. 8 is a flow chart illustrating a method for automatically identifying an abnormal track according to the present invention.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The invention provides an abnormal trace identification method based on root mean square amplitude, aiming at the problem of micro-seismic amplitude abnormal trace identification.
Specifically, as shown in fig. 8, the present invention discloses a method for automatically identifying an abnormal track, which comprises:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series (for example, adding one root-mean-square amplitude each time), and calculating the standard deviations of the new number series;
comparing the standard deviation of the new sequence with the standard value, and judging whether the track is an abnormal track;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
First, the root mean square amplitude of each trace in the seismic record is calculated.
The root mean square amplitude is an amplitude attribute commonly used in seismic interpretation and is a valid parameter for evaluating seismic trace amplitude. Therefore, the invention measures the amplitude of the seismic traces by using the mean square component amplitude. The root mean square Amplitude (RMS Amplitude) is the mean value of the square of the Amplitude squared and is calculated as
N is the number of sampling points of the seismic trace, aiThe amplitude of the ith sample point on the seismic trace.
The root mean square amplitudes of the seismic traces are then sorted from small to large.
The standard deviation of the root mean square amplitude (excluding the empty lane) of a predetermined number (for example, the top 10%) after the sorting is calculated and taken as a standard value.
The standard deviation is a common parameter for statistics, is a parameter for measuring the deviation degree of the sample from the mean value, and is a common parameter for abnormal value rejection. Standard deviation is a measure of how well a set of values diverge from the mean. A large standard deviation, representing a large difference between most of the values and their averages; a smaller standard deviation indicates that these values are closer to the mean. The standard deviation calculation formula is as follows:
in the formula xiRepresents the rms amplitude of the i-th trace.
The remaining rms amplitudes are added sequentially to the previous predetermined number (e.g., the first 10%) rms amplitudes to form a new series, and the standard deviation of the new series is calculated.
And comparing the standard deviation of the new sequence with the standard value to judge whether the track is an abnormal track. Preferably, if the standard deviation is greater than 4 times of the standard value, the trace is considered as an abnormal trace, and the seismic traces with the root-mean-square amplitudes greater than the root-mean-square amplitudes of the abnormal traces are all abnormal traces.
According to another aspect of the present invention, there is provided a system for automatically identifying an abnormal track, the system comprising: a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series (for example, adding one root-mean-square amplitude each time), and calculating the standard deviations of the new number series;
comparing the standard deviation of the new sequence with the standard value, and judging whether the track is an abnormal track;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
According to still another aspect of the present invention, there is provided a recording medium having stored therein computer-executable instructions; when the computer executable instructions are executed, the following steps are executed:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the standard deviation calculation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series (for example, adding one root-mean-square amplitude each time), and calculating the standard deviations of the new number series;
comparing the standard deviation of the new sequence with the standard value, and judging whether the track is an abnormal track;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
The invention provides an automatic seismic abnormal channel identification method based on root mean square amplitude, which comprises the following steps: firstly, calculating the root mean square amplitude of each track in the seismic record; secondly, sequencing the root-mean-square amplitudes of the seismic channels from small to large; thirdly, calculating the variance of the root-mean-square amplitude (except for empty channels) of the first 10% after the sorting, and taking the variance as a standard value; and fourthly, sequentially adding the rest root-mean-square amplitudes into the previous 10% root-mean-square amplitudes to form a new array, calculating the standard deviation of the new array, and if the standard deviation is more than 4 times of the standard value, determining that the seismic channel is an abnormal channel, wherein the seismic channels with the root-mean-square amplitudes larger than the root-mean-square amplitudes of the abnormal channels are all abnormal channels.
In the embodiment, the ground monitoring microseismic data of a certain Fuling work area is selected for testing. FIG. 1 is a diagram of an observation system, wherein 11 receiving measuring lines are radially arranged, and 1848 detectors receive. In order to more clearly display the processing effect of the invention, the record in 3s observed by the first measuring line is selected for carrying out abnormal track identification processing. The original seismic record is shown in fig. 2, where the strong amplitude traces are white lines and have more near offsets. FIG. 3 shows the RMS amplitude distribution of the seismic recordings, and it can be seen that most traces have smaller RMS amplitudes and that there are strong amplitudes that deviate from normal. Comparing fig. 2 and 3, it is easy to find that large rms amplitudes often correspond to abnormal tracks, confirming that abnormal tracks can be distinguished from normal tracks using rms amplitudes. By the method, abnormal tracks are identified, the threshold value is selected to be 4 times of the normal standard deviation, and the seismic records after the abnormal tracks are identified are shown in figure 4. Comparing fig. 2 and fig. 4, it is found that the abnormal track can be better identified by the present invention. Fig. 5 shows the rms amplitude distribution of the cross-section after abnormal trace identification, and comparing fig. 3 and 5 shows that the rms amplitude distribution of the cross-section is more concentrated after the processing.
The method is adopted to identify the abnormal channel of 1848 seismic records observed by 11 survey lines, and the processing is as shown in figure 6(a) and figure 7 (a). In order to analyze the influence of the abnormal track rejection on the event recognition, overlapped tracks before and after the abnormal track recognition are respectively calculated, as shown in fig. 6(b) and 7 (b). The amplitude of the originally recorded abnormal trace is very strong, so that a microseism event is implicitly seen near 2200ms, and the time of the event on the superposed trace cannot be accurately identified. After abnormal trace identification processing, as shown in fig. 7, the amplitude distribution of the seismic traces of the whole section is uniform, and micro-seismic events can be easily identified to appear at 2200ms on the stack record.
In conclusion, the method can automatically identify the strong-amplitude abnormal trace in the seismic trace, has simple and practical operation process, and provides help for identifying the micro-seismic event.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A method for automatically identifying an abnormal track, the method comprising:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the calculation of the standard deviation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series, and calculating the standard deviation of the new number series;
comparing the standard deviation of the root-mean-square amplitude of the new array with the standard value, and judging whether the channel is an abnormal channel;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
2. The method of claim 1, wherein if the standard deviation of the new sequence is greater than 4 times the standard value, the trace is considered as an abnormal trace, and the seismic traces with the root-mean-square amplitudes greater than the root-mean-square amplitudes of the abnormal trace are all abnormal traces.
3. The method of automatically identifying an anomalous track in accordance with claim 1 wherein the predetermined number of rms amplitudes is the top 10% rms amplitude of the sequence.
4. The method of automatically identifying anomalous traces in claim 1 wherein the root mean square amplitude of each trace in the seismic record is calculated as:
wherein N is the number of sampling points of seismic channel, aiThe amplitude of the ith sample point on the seismic trace.
5. The method of automatically identifying an abnormal track according to claim 1, wherein a standard deviation is calculated for a predetermined number of sorted root mean square amplitudes, the standard deviation calculation formula is as follows:
in the formula xiRepresents the rms amplitude of the i-th trace.
6. A system for automatically identifying an abnormal track, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
calculating the root mean square amplitude of each trace in the seismic record;
sorting the root mean square amplitudes of the seismic traces from small to large;
calculating standard deviation according to the sorted predetermined number of root mean square amplitudes, and taking the standard deviation as a standard value;
sequentially adding the sorted root-mean-square amplitudes which do not participate in the calculation of the standard deviation in the previous step into the root-mean-square amplitudes which have the standard deviations calculated in the previous step to form a new number series, and calculating the standard deviation of the new number series;
comparing the standard deviation of the root-mean-square amplitude of the new array with the standard value, and judging whether the channel is an abnormal channel;
if the abnormal track is encountered, stopping the calculation, otherwise, continuously adding the root-mean-square amplitude which does not participate in the calculation of the standard deviation to form a new sequence, recalculating the standard deviation of the new sequence, judging whether the track is the abnormal track, and if so, iterating, and stopping iteration.
7. The system of claim 6, wherein if the standard deviation of the new sequence is greater than 4 times the standard value, the trace is considered as an abnormal trace, and the seismic traces with the root mean square amplitude greater than the root mean square amplitude of the abnormal trace are all abnormal traces.
8. The system of automatically identifying an abnormal track according to claim 6, wherein the predetermined number of RMS amplitudes are the top 10% of the ranked RMS amplitudes.
9. The system for automatically identifying anomalous traces in claim 6 wherein the root mean square amplitude of each trace in the seismic record is calculated by the expression:
wherein N is the number of sampling points of seismic channel, aiThe amplitude of the ith sample point on the seismic trace.
10. The system for automatically identifying an abnormal track according to claim 6, wherein a standard deviation is calculated for a predetermined number of sorted root mean square amplitudes, and the standard deviation calculation formula is as follows:
in the formula xiRepresents the rms amplitude of the i-th trace.
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