Expression-based seismic trace data analysis method, apparatus, device, and medium

Abstract

This disclosure relates to the field of seismic data processing technology, and particularly to a method, apparatus, device, and medium for seismic trace data analysis based on expressions. The method includes: identifying rule fields based on rule-based condition requirements; generating rule selection expressions based on the rule fields; and identifying the rule features of the rule selection expressions; identifying random fields based on random condition requirements; generating random selection expressions based on the random rule fields; and identifying the random features of the random selection expressions; generating conditional variable features through the rule features and random features; generating conditional expressions based on the conditional variable features; filtering each seismic trace data in the seismic trace sample data one by one using the conditional expressions to obtain the seismic trace data range; and analyzing the data quality of the seismic trace sample data based on the seismic trace data range to obtain quality analysis indicators for the seismic trace sample data. This invention can improve the flexibility of seismic trace data analysis.

Description

Technical Field

[0001] This disclosure relates to the field of seismic data processing technology, and in particular to an expression-based method, apparatus, device, and medium for seismic trace data analysis. Background Technology

[0002] In the field of petroleum exploration, seismic exploration technology is an extremely important tool. Seismic data, as the basic data collected during the seismic exploration process, directly affects the accurate interpretation of underground geological structures and the accuracy of oil reservoir prediction. Seismic data processing often requires various calculations on seismic trace sample data, such as common addition and subtraction operations, multiplication and division operations, power functions, logarithmic functions, etc.

[0003] Traditional seismic trace data processing techniques select trace data for calculation based on certain conditions. These conditions are generally determined by logically judging the values ​​of certain fields in the trace header data. That is, the conditions are set through interface configuration or by combining scripting languages ​​to implement condition selection statements. However, both methods have some shortcomings in terms of functional completeness and flexibility, resulting in poor flexibility when performing seismic trace data analysis. Summary of the Invention

[0004] This disclosure provides an expression-based method, apparatus, device, and medium for seismic trace data analysis to address the technical problem of poor flexibility in performing seismic trace data analysis.

[0005] Firstly, this disclosure provides an expression-based method for analyzing seismic trace data, including:

[0006] Identify rule fields based on preset rule conditions, generate rule selection expressions based on the rule fields, and identify the rule features of the rule selection expressions;

[0007] Identify random fields based on preset random conditions, generate random selection expressions based on the random rule fields, and identify the random characteristics of the random selection expressions;

[0008] Conditional variable features are generated using the rule features and the random features, and conditional expressions are generated based on the conditional variable features.

[0009] The seismic data range is obtained by filtering each seismic trace in the pre-acquired seismic trace sample data one by one using the conditional expression.

[0010] The data quality of the seismic trace sample data is analyzed by measuring the range of the seismic trace data, and the quality analysis index of the seismic trace sample data is obtained.

[0011] In some embodiments, generating a rule selection expression based on the rule field includes:

[0012] Identify the field range and delimiter corresponding to the rule field;

[0013] Generate the rule field value corresponding to the rule field based on the field range and the pre-obtained rule conditions;

[0014] The rule selection expression is generated based on the rule field values ​​and the delimiter.

[0015] In some embodiments, generating a rule selection expression based on the rule field value and the delimiter includes:

[0016] Map the rule field values ​​to the rule fields to obtain the field mapping relationship;

[0017] Extract the first target field from the rule fields, and generate target field relationships according to the delimiter of the first target field;

[0018] Extract the second target field from the rule fields and generate the field quantity requirement corresponding to the second target field;

[0019] Determine the target field mapping corresponding to the second target field according to the required number of fields;

[0020] The target field mapping is used to update the field mapping relationship, and a rule selection expression is generated based on the updated field mapping relationship and the target field relationship according to the delimiter.

[0021] In some embodiments, the rule features for identifying the rule selection expression include:

[0022] The rule name characteristics are determined by selecting the field names of the expressions according to the rules.

[0023] The rule boundary features are determined by selecting the field data of the expression according to the rules.

[0024] The rule name feature and the rule boundary feature are combined into the rule feature of the rule selection expression.

[0025] In some embodiments, the step of filtering each seismic data point in the pre-acquired seismic trace sample data one by one using the conditional expression to obtain the seismic trace data range includes:

[0026] The first trace data in the seismic trace sample data is taken as the target trace data;

[0027] The logical value corresponding to the target channel data is calculated using the conditional expression;

[0028] When the logical value is a preset target value, extract the channel number corresponding to the target channel data;

[0029] When the logical value is not the preset target value, the next trace corresponding to the first trace is taken as the target trace according to the sorting in the seismic trace sample data, and the process returns to the step of calculating the logical value corresponding to the target trace using the conditional expression, until all seismic trace data in the seismic trace sample data has been calculated.

[0030] When all seismic trace data in the seismic trace sample data has been calculated, the trace number is collected as the seismic trace data range.

[0031] In some embodiments, calculating the logical value corresponding to the target channel data using the conditional expression includes:

[0032] Extract the rule data filtering range corresponding to the rule selection expression in the conditional expression, and calculate the rule logic value of the channel number corresponding to the target channel data based on the rule data filtering range;

[0033] Extract the random data filtering range corresponding to the random selection expression in the conditional expression, and calculate the random logical value of the channel number corresponding to the target channel data based on the random data filtering range;

[0034] The condition variable value between the rule logic value and the random logic value is calculated by a preset logical operator, and the condition variable value is used as the logic value corresponding to the target channel data.

[0035] In some embodiments, the step of analyzing the data quality of the seismic trace sample data based on the seismic trace data range to obtain quality analysis indicators for the seismic trace sample data includes:

[0036] Identify the key attributes corresponding to each seismic data trace within the range of the seismic trace data, count the missing values ​​of the key attributes, and determine the completeness index of each seismic data trace based on the missing values;

[0037] Extract the time series data corresponding to each seismic data within the range of the seismic trace data, calculate the fluctuation data of the time series data, and determine the stability index of each seismic data based on the fluctuation data;

[0038] Each seismic data point within the range of the seismic trace data is subjected to signal separation to obtain signal energy and noise energy. The purity index of each seismic data point is determined based on the signal energy and the noise energy.

[0039] The quality analysis indicators of the seismic trace sample data are determined based on the completeness index, the stability index, and the purity index.

[0040] Secondly, this disclosure provides an expression-based seismic trace data analysis apparatus, comprising:

[0041] The rule selection expression generation module is used to identify rule fields according to preset rule conditions, generate rule selection expressions according to the rule fields, and identify the rule features of the rule selection expressions.

[0042] The random selection expression generation module is used to identify random fields according to preset random conditions, generate random selection expressions according to the random rule fields, and identify the random characteristics of the random selection expressions;

[0043] The condition expression generation module is used to generate condition variable features through the rule features and the random features, and to generate condition expressions based on the condition variable features;

[0044] The seismic trace data range filtering module is used to filter each seismic data in the pre-acquired seismic trace sample data one by one using the conditional expression to obtain the seismic trace data range.

[0045] The seismic trace sample data quality analysis module is used to analyze the data quality of the seismic trace sample data based on the range of the seismic trace data, and obtain the quality analysis indicators of the seismic trace sample data.

[0046] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the expression-based seismic trace data analysis method described in the preceding aspects.

[0047] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the expression-based seismic trace data analysis method described above.

[0048] This disclosure provides an expression-based method, apparatus, device, and medium for seismic trace data analysis. By performing various logical combinations of rule-based selection expressions and random selection expressions, conditional expressions are obtained. These conditional expressions are then used to flexibly select the numerical range of different trace header words, ultimately enabling the selection of the data range to participate in trace calculation. This allows the method to be embedded in various stages of seismic data processing, playing a particularly important role in on-site preprocessing and data preprocessing, thereby enhancing the flexibility of seismic trace data analysis.

[0049] 1. The technical feature logic combination constructs conditional expressions, which solves the problem of insufficient flexibility in seismic data screening.

[0050] 2. The technical feature is that seismic trace data is filtered based on the numerical range of the trace header, which solves the problem of insufficient targeting of data filtering in each processing stage. Attached Figure Description

[0051] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0052] Figure 1 A flowchart illustrating an expression-based seismic trace data analysis method provided in this embodiment of the disclosure;

[0053] Figure 2 A schematic diagram illustrating the generation of conditional expressions provided in embodiments of this disclosure;

[0054] Figure 3 This is a schematic diagram of the conditional expression calculation process provided in the embodiments of this disclosure;

[0055] Figure 4 This is a functional block diagram of an expression-based seismic trace data analysis device provided in an embodiment of this disclosure.

[0056] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0058] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0060] Example 1

[0061] Figure 1 This is a flowchart illustrating an expression-based seismic trace data analysis method provided in an embodiment of this disclosure. Figure 1 As shown, an expression-based seismic trace data analysis method includes:

[0062] S1. Identify rule fields according to preset rule conditions, generate rule selection expressions based on the rule fields, and identify the rule features of the rule selection expressions.

[0063] In this embodiment of the invention, the rule condition requirement refers to specifying the criteria for selecting data from seismic trace data or other relevant datasets, which is equivalent to an overall screening principle and a custom target setting. The rule field refers to the field name included in the rule selection expression, including rule name, trace header name, start value, end value, and increment value.

[0064] In detail, the rule fields correspond to the rule condition requirements. For example, if the rule condition requirements include custom settings for the track header sub-name, start value, end value, and increment value, then the field names of the track header sub-name, start value, end value, and increment value can be identified.

[0065] Furthermore, specific rule selection expressions can be generated based on the rule fields, and these expression selects the range of seismic trace data to be used in the calculation using a relatively clear rule.

[0066] In this embodiment of the invention, generating a rule selection expression based on the rule field includes:

[0067] Identify the field range and delimiter corresponding to the rule field;

[0068] Generate the rule field value corresponding to the rule field based on the field range and the pre-obtained rule conditions;

[0069] The rule selection expression is generated based on the rule field values ​​and the delimiter.

[0070] In detail, the field range corresponding to a rule field refers to the range of values ​​allowed for that rule field under specific requirements or actual physical meaning. The field range of the track title name field is determined based on the start and end values ​​in the rule field. The separator symbol reflects the separation method between multiple rule fields or the same rule field under different constraints. That is, it is a relationship that connects different rule fields in some way, such as separating different values ​​with commas and separating different track titles with semicolons.

[0071] Specifically, the field values ​​corresponding to the rule fields can be determined according to the preset rule conditions, such as the field values ​​corresponding to the track header name, start value, end value, and increment value. Then, the field values ​​corresponding to the start value and end value are determined according to the field range. The field values ​​corresponding to the track header name and increment value are determined according to the requirement data in the rule conditions. Thus, a rule selection expression is generated according to the rule field values ​​and the delimiter.

[0072] For example, Reg1: rule name, FFID: track header name, 1: start value, representing the first value in the FFID selection range, 51: end value, representing the last value in the FFID selection range, 10: increment value, representing the increment value each time starting from the first FFID value. Based on the rule field and the corresponding field value, a rule selection expression is generated. Then the rule selection expression is Reg1 = FFID, 1, 51, 10, which means that the value of the track header FFID is selected between 1 and 51, and one is selected every 10, that is, the track headers with FFID values ​​of 1, 11, 21, 31, 41, and 51 are finally selected.

[0073] Furthermore, the requirements for data screening vary in different stages of earthquake data processing, such as on-site preprocessing, data preprocessing, and fine data analysis. By identifying the rule characteristics of the rule selection expression, it is possible to determine which stage it is more suitable for. For example, if the rule characteristics of a rule selection expression show that its screening conditions are relatively simple and broad, focusing on quickly excluding obvious abnormal data, then it may be more suitable for the on-site preprocessing stage.

[0074] In this embodiment of the invention, the rule features refer to the specific numerical constraints defined by the rule name and rule boundary features. By combining the two, the overall characteristics of the rule selection expression in filtering data can be fully presented. It is known which data attributes it operates on, and the specific value constraints of each attribute are clear, so as to fully grasp the constraints of the rule on the data.

[0075] In this embodiment of the invention, the rule feature for identifying the rule selection expression includes:

[0076] The rule name characteristics are determined by selecting the field names of the expressions according to the rules.

[0077] The rule boundary features are determined by selecting the field data of the expression according to the rules.

[0078] The rule name feature and the rule boundary feature are combined into the rule feature of the rule selection expression.

[0079] In detail, the rule name and the top-level name in the rule selection expression are determined as rule name features, and the start value, end value and increment value in the rule selection expression are determined as rule boundary features. Then, the rule name features and rule boundary features are combined into the rule features of the rule selection expression.

[0080] For example, if the rule selection expression is Reg1 = FFID, 1, 51, 10, then Reg1 corresponding to the rule name and FFID corresponding to the top name are used as rule name features, and the value 1 corresponding to the start value, the value 51 corresponding to the end value, and the value 10 corresponding to the increment value are used as rule boundary features, thus combining them into the rule features of the rule selection expression.

[0081] Furthermore, in addition to selecting the data range for calculation according to explicit rules, it is also necessary to determine the data range for trace head calculation according to random rules, so as to ensure the flexibility of seismic trace data calculation and processing.

[0082] S2. Identify random fields according to preset random conditions, generate random selection expressions according to the random rule fields, and identify the random characteristics of the random selection expressions.

[0083] In this embodiment of the invention, the random condition requirement refers to randomly selecting data that meets certain criteria from seismic trace data or other relevant datasets, which is equivalent to an overall screening principle and a custom target setting. The random field refers to the field name included in the random selection expression, including rule name, trace header name, and random value.

[0084] In detail, the random field corresponds to the random condition requirement. For example, if the random condition requirement includes custom settings for the track header name and random value, then the field names of the track header name and random value can be identified.

[0085] Furthermore, a specific random selection expression can be generated based on the random field, and the random selection expression determines the range of seismic trace data to be used in the calculation by specifying some random values.

[0086] In this embodiment of the invention, the step of generating a random selection expression based on the random rule field is the same as the step of generating a rule selection expression based on the rule field, and will not be described again here.

[0087] In detail, the random selection expression includes: rule name, track header name, and random value. The track header name and random value are separated by a comma, random values ​​are separated by "|", and different track headers are separated by a semicolon.

[0088] For example, Reg3: rule name, FFID: track header name, 1|11|21|31|41: FFID random value range, a total of 5 values, separated by "|". A random selection expression is generated based on the random field and the corresponding field value. The random selection expression is Reg3 = FFID, 1|11|21|31|41. The meaning of this random selection expression value is: select track headers with the track header FFID value of 1, 11, 21, 31, or 41.

[0089] In this embodiment of the invention, the random feature refers to the numerical constraints defined by the random name and random boundary features. By combining the two, the overall characteristics of the random selection expression in filtering data can be fully presented. It is known which data attributes it operates on, and the specific value constraints of each attribute are clear, so as to fully grasp the constraints of the rules on the data.

[0090] In detail, the steps for identifying the random features of the random selection expression are the same as those for identifying the rule features of the rule selection expression, and will not be repeated here.

[0091] Specifically, the rule name and the top character name in the random selection expression are determined as random name features, the random value in the random selection expression is determined as random boundary features, and then the random name features and random boundary features are combined into random features of the random selection expression.

[0092] For example, if the random selection expression is Reg3 = FFID, 1|11|21|31|41, then Reg3 corresponding to the random name and FFID corresponding to the top-level name are used as random name features, and the numerical value 1|11|21|31|41 corresponding to the random value is used as random boundary features, thus combining them into the random features of the random selection expression.

[0093] Furthermore, various logical combinations can be made based on the rule selection expression corresponding to the rule feature and the random selection expression corresponding to the random feature to generate conditional expressions, thereby flexibly selecting the numerical range of different key words.

[0094] S3. Generate condition variable features using the rule features and the random features, and generate condition expressions based on the condition variable features.

[0095] In this embodiment of the invention, the condition variable feature is a variable feature logically combined with regular features and random features.

[0096] In this embodiment of the invention, generating condition variable features through the rule features and the random features includes:

[0097] Determine the rule selection logic features based on the aforementioned rule features;

[0098] The random selection logic features are determined based on the aforementioned random features;

[0099] The condition variable features are determined by the rule-selected logical features and the random selection logical features.

[0100] In detail, the overall feature corresponding to the rule feature is used as the rule selection logic feature, that is, the rule selection expression is used as the rule selection logic feature, and the overall feature corresponding to the random feature is used as the random selection logic feature, that is, the random selection expression is used as the random selection logic feature, thereby combining the rule selection logic feature and the random selection logic feature into the condition variable feature.

[0101] For example, if Reg1 = FFID, 1, 51, 10 is used as the rule selection logic feature and Reg3 = FFID, 1|11|21|31|41 is used as the random selection logic feature, then Reg1 and Reg3 are used as condition variable features.

[0102] Furthermore, a conditional expression is generated based on the characteristics of the conditional variables. This involves flexibly combining the characteristics of the conditional variables with logical operators to obtain the conditional expression. Logical operators include: and, or, nand, nor, not, and xor. The value of the conditional variable is either true or false. The name of the conditional variable is either the name of a rule-selected expression or the name of a random selection expression. For example, the conditional expression: Reg1 and Reg2. Reg1 and Reg2 are the names of the conditional variables, and also the names of the rule-selected expression or the random selection expression. The value of this conditional expression is true only when both Reg1 and Reg2 are true. Figure 2 As shown in the diagram, the conditional expression is generated by combining the expression selected according to the rules (such as Reg1 = FFID, 1, 51, 10) and the randomly selected expression (such as Reg2 = ChNum, 50|100|150) into conditional variables (such as Reg1 and Reg2), and then combining them into conditional expressions (such as Reg1 and Reg2) according to logical operators (such as and).

[0103] Furthermore, the conditional expression flexibly selects the numerical range of different track headers by combining rule-based selection expressions and random selection expressions in various logical ways, ultimately achieving the selection of the data range to participate in track calculation.

[0104] S4. Using the conditional expression, filter each seismic data in the pre-acquired seismic trace sample data one by one to obtain the range of seismic trace data.

[0105] In this embodiment of the invention, the seismic trace data range refers to the set identifier of a portion of the seismic trace data that meets the screening conditions from the original seismic trace sample data through a specific conditional expression. It is not the actual seismic data itself, but a set containing trace numbers, which point to those seismic traces in the original sample data that meet the screening conditions.

[0106] In this embodiment of the invention, the step of filtering each seismic data point in the pre-acquired seismic trace sample data one by one using the conditional expression to obtain the seismic trace data range includes:

[0107] The first trace data in the seismic trace sample data is taken as the target trace data;

[0108] The logical value corresponding to the target channel data is calculated using the conditional expression;

[0109] When the logical value is a preset target value, extract the channel number corresponding to the target channel data;

[0110] When the logical value is not the preset target value, the next trace corresponding to the first trace is taken as the target trace according to the sorting in the seismic trace sample data, and the process returns to the step of calculating the logical value corresponding to the target trace using the conditional expression, until all seismic trace data in the seismic trace sample data has been calculated.

[0111] When all seismic trace data in the seismic trace sample data has been calculated, the trace number is collected as the seismic trace data range.

[0112] In detail, the first trace in the seismic trace sample data is selected as the target trace data. Assume there is a set of seismic trace sample data containing multiple seismic traces, each of which records various information about seismic waves (such as amplitude, frequency, first arrival time, etc.). The first seismic trace in this set of data is taken as the starting point for the analysis. The selected target trace data is calculated using conditional expressions to obtain the logical value corresponding to the target trace data, and the logical value is either true or false.

[0113] Specifically, the target value is a logical value of true. If the logical value obtained by calculating the target trace data through the conditional expression is equal to the preset target value, it means that the target trace data meets the set screening conditions. In this case, the trace number corresponding to the target trace data is extracted. The trace number can uniquely identify the trace data. That is, if the logical value of the first trace data is "true", its trace number is recorded. If the calculated logical value is not the preset target value (the logical value is false), it means that the target trace data does not meet the screening conditions. At this time, it is necessary to continue to check the next trace data. According to the sorting in the seismic trace sample data, the next trace data is taken as the new target trace data, and then the conditional expression is used again for calculation. The above logical value calculation steps are repeated. This process will continue until all seismic trace data has been evaluated by the conditional expression to ensure that no trace data is missed. After the screening and evaluation of all seismic trace data is completed, the previously extracted trace numbers that meet the conditions are gathered together to form a set, namely the seismic trace data range. It is actually an index set that contains the trace numbers of all seismic trace data filtered by the conditional expression.

[0114] For example, such as Figure 3 The diagram shows the calculation process of the conditional expression. First, seismic trace data is read one by one in the order of seismic traces. The value of the rule selection expression contained in the conditional expression is calculated one by one. The value of the random selection expression contained in the conditional expression is calculated one by one. The value of the conditional expression is calculated based on the value of the rule selection expression and the random selection expression. The truth value of the conditional expression determines whether to calculate the trace data. It is then checked whether there is any unprocessed seismic trace data. This process continues until all seismic trace data has been processed.

[0115] Furthermore, based on the seismic trace data range corresponding to the seismic trace data, it is necessary to analyze the overall seismic trace sample data. By conducting quality analysis on the data within this range, we can eliminate the interference caused by data that obviously does not meet the requirements, and more accurately focus on valuable data to evaluate its quality, thereby obtaining quality analysis indicators that can truly reflect the usability and reliability of the data.

[0116] S5. Analyze the data quality of the seismic trace sample data based on the range of the seismic trace data to obtain the quality analysis index of the seismic trace sample data.

[0117] In this embodiment of the invention, the quality analysis index is a series of numerical or graded representations used to quantify and comprehensively describe the quality status of seismic trace sample data. It measures data quality from different key dimensions (such as integrity, stability, purity, etc.) and aims to provide a clear and objective basis to understand the extent to which seismic trace sample data meets practical application needs and the quality of the data.

[0118] In this embodiment of the invention, the step of analyzing the data quality of the seismic trace sample data based on the seismic trace data range to obtain the quality analysis indicators of the seismic trace sample data includes:

[0119] Identify the key attributes corresponding to each seismic data trace within the range of the seismic trace data, count the missing values ​​of the key attributes, and determine the completeness index of each seismic data trace based on the missing values;

[0120] Extract the time series data corresponding to each seismic data within the range of the seismic trace data, calculate the fluctuation data of the time series data, and determine the stability index of each seismic data based on the fluctuation data;

[0121] Each seismic data point within the range of the seismic trace data is subjected to signal separation to obtain signal energy and noise energy. The purity index of each seismic data point is determined based on the signal energy and the noise energy.

[0122] The quality analysis indicators of the seismic trace sample data are determined based on the completeness index, the stability index, and the purity index.

[0123] In detail, seismic trace data contains several key attributes that play a crucial role in reflecting seismic wave characteristics and underground geological conditions. These include seismic wave amplitude, frequency, phase, and first arrival time. The process involves checking each seismic trace for missing values ​​of these key attributes—that is, whether corresponding records exist. For example, if a seismic trace should contain a specific amplitude value but displays a blank or invalid value, this indicates a missing key attribute. The missing data for each key attribute across all seismic traces is statistically analyzed, recording the frequency and proportion of missing values ​​for each attribute. Based on this statistical analysis of missing key attribute values, a completeness index is determined for each seismic trace. The proportion of missing values ​​to the total expected values ​​is calculated; higher completeness indicates more comprehensive and valid information contained in the data.

[0124] Specifically, for each seismic trace within the data range, the numerical sequence of its key attributes (such as amplitude, frequency, etc.) over time is extracted in chronological order; this is time series data. For example, seismic wave amplitude values ​​recorded at fixed time intervals (such as every 0.01 seconds) constitute a time series of seismic data for the amplitude attribute. The variance of the time series data is calculated; the larger the variance (or standard deviation), the more drastic the fluctuations in the time series data, reflecting the poorer stability of the seismic data in the time dimension. Based on the calculated fluctuation data (such as variance or standard deviation), the characteristics of each seismic trace are determined. In addition to stability indices for seismic trace data, each seismic trace within its data range is processed and decomposed into a signal component and a noise component. The signal component is the effective content that truly reflects the propagation characteristics of seismic waves and information about underground geological structures, while the noise component is the interference component introduced by various external disturbances (such as environmental noise, instrument noise, etc.). The proportion of noise energy to the total energy (the sum of signal energy and noise energy) is calculated, which is the purity index. The lower the purity index, the smaller the proportion of noise in the seismic trace data, the purer the data, and the easier it is to extract accurate seismic wave characteristic information from it.

[0125] Furthermore, the integrity, stability, and purity indices of each seismic trace data are comprehensively summarized to form a quality analysis index that can represent the overall quality of the seismic trace sample data. For example, the average value of each index for all trace data is calculated, or the indices are combined according to certain weights (e.g., weighted summation is performed after assigning corresponding weights based on the different emphases on integrity, stability, and purity in practical applications). This yields one or a set of comprehensive numerical or grade descriptions, which comprehensively and objectively assesses the overall quality of the seismic trace sample data. This quality analysis index is then used to determine whether the data meets the requirements for further analysis and application, and whether corresponding data processing measures are needed to improve data quality.

[0126] Example 2

[0127] Based on the above embodiments, the step of generating a rule selection expression according to the rule field value and the delimiter includes:

[0128] Map the rule field values ​​to the rule fields to obtain the field mapping relationship;

[0129] Extract the first target field from the rule fields, and generate target field relationships according to the delimiter of the first target field;

[0130] Extract the second target field from the rule fields and generate the field quantity requirement corresponding to the second target field;

[0131] Determine the target field mapping corresponding to the second target field according to the required number of fields;

[0132] The target field mapping is used to update the field mapping relationship, and a rule selection expression is generated based on the updated field mapping relationship and the target field relationship according to the delimiter.

[0133] In detail, mapping each rule field to its corresponding field value yields the field mapping relationship. The first target field, the track name field, is then extracted from the rule fields. Multiple track name fields can exist, and different track name fields are separated based on their different names and delimiters. The second target field is then extracted from the rule fields. This second target field consists of the track name, start value, end value, and increment value in the rule selection expression, or the track name and random value in the random selection expression. Specifically, up to three increment values ​​can be set simultaneously for three different track names, separated by commas and semicolons. Similarly, up to three sets of track names and random values ​​can be set simultaneously for three different track names. Track names and random values ​​are separated by commas, random values ​​by "|", and different track names by semicolons.

[0134] Specifically, when the top name, start value, end value, and incremental value can be set to a maximum of 3 groups simultaneously, or the top name and random value can be set to a maximum of 3 groups simultaneously, the target field mapping corresponding to each group can be determined, thereby updating all target field mappings to the rule selection expression or random selection expression, thus forming a rule selection expression or random selection expression containing multiple sets of selection rules.

[0135] For example, Reg2 = FFID, 1, 51, 10; Channel_Num, 50, 100, 1, where Reg2: rule name, FFID: channel header name, 1: start value, representing the first value in the FFID selection range, 51: end value, representing the last value in the FFID selection range, 10: increment value, representing the increment value starting from the first value of FFID, Channel_Num: channel header name, 50: start value, representing the first value in the Channel_Num selection range, 100: end value, representing the last value in the Channel_Num selection range. The last value in the channel_Num selection range, 1: increment value, represents the value incremented each time starting from the first value of Channel_Num. The meaning of this rule selection expression value is: select the channel header word FFID value between 1 and 51, and select one for every increment of 10, and at the same time select the channel_Num value between 50 and 100, and select one for every increment of 1. In fact, it is all values ​​between 50 and 100. Therefore, the final selection is the channel header with FFID values ​​of 1, 11, 21, 31, 41, and 51, and a channel_Num value between 50 and 100.

[0136] In addition, Reg4 = FFID, 1|11|21|31|41; Channel_Num, 50|100|150, where Reg3 is the rule name, FFID is the track header name, 1|11|21|31|41 is the random value range of FFID, a total of 5 values ​​separated by "|", Channel_Num is the track header name, and 50|100|150 is the random value range of Channel_Num, a total of 3 values ​​separated by "|". The meaning of this random selection expression value is: select track headers with FFID values ​​of 1, 11, 21, 31, 41 and Channel_Num values ​​of 50, 100, 150.

[0137] Example 3

[0138] Based on the above embodiments, the step of calculating the logical value corresponding to the target channel data using the conditional expression includes:

[0139] Extract the rule data filtering range corresponding to the rule selection expression in the conditional expression, and calculate the rule logic value of the channel number corresponding to the target channel data based on the rule data filtering range;

[0140] Extract the random data filtering range corresponding to the random selection expression in the conditional expression, and calculate the random logical value of the channel number corresponding to the target channel data based on the random data filtering range;

[0141] The condition variable value between the rule logic value and the random logic value is calculated by a preset logical operator, and the condition variable value is used as the logic value corresponding to the target channel data.

[0142] In detail, the rule data filtering range corresponding to the rule selection expression is a data filtering range determined for a specific rule range, and it checks whether the channel number corresponding to the target channel data exists within the rule data filtering range. If it exists within the rule data filtering range, the rule logic value is true; if it does not exist within the rule data filtering range, the rule logic value is false.

[0143] Specifically, the random selection expression corresponds to a random data filtering range, which is a data filtering range determined by a random rule range. It also checks whether the channel number corresponding to the target channel data exists within the random data filtering range. If it exists within the random data filtering range, the random logic value is true; otherwise, the random logic value is false.

[0144] Furthermore, a conditional expression is generated by combining the rule logical value and the random logical value using logical operators, such as (rule selection expression) ∧ (random selection expression). This means that the target channel data must simultaneously satisfy the conditions of both the rule selection expression and the random selection expression. That is, if both the rule logical value and the random logical value are true, the logical value corresponding to the target channel data is true; if both the rule logical value and the random logical value are false, the logical value corresponding to the target channel data is false; if either the rule logical value or the random logical value is false, the logical value corresponding to the target channel data is false.

[0145] Example 4

[0146] like Figure 4 As shown in the figure, this embodiment also provides a functional block diagram of an expression-based seismic trace data analysis device.

[0147] The expression-based seismic trace data analysis device 100 described in this embodiment can be installed in an electronic device. Depending on the functions implemented, the expression-based seismic trace data analysis device 100 may include a rule-selection expression generation module 101, a random selection expression generation module 102, a conditional expression generation module 103, a seismic trace data range filtering module 104, and a seismic trace sample data quality analysis module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0148] In this embodiment, the functions of each module / unit are as follows:

[0149] The rule selection expression generation module 101 is used to identify rule fields according to preset rule conditions, generate rule selection expressions according to the rule fields, and identify the rule features of the rule selection expressions.

[0150] The random selection expression generation module 102 is used to identify random fields according to preset random conditions, generate random selection expressions according to the random rule fields, and identify the random characteristics of the random selection expressions.

[0151] The condition expression generation module 103 is used to generate condition variable features through the rule features and the random features, and to generate condition expressions based on the condition variable features.

[0152] The seismic trace data range filtering module 104 is used to filter each seismic data in the pre-acquired seismic trace sample data one by one using the conditional expression to obtain the seismic trace data range.

[0153] The seismic trace sample data quality analysis module 105 is used to analyze the data quality of the seismic trace sample data based on the range of the seismic trace data, and obtain the quality analysis index of the seismic trace sample data.

[0154] In detail, each module in the expression-based seismic trace data analysis device 100 described in this embodiment of the invention employs the same technical means as the expression-based seismic trace data analysis method described in Embodiments 1 to 4, and can produce the same technical effects, which will not be repeated here.

[0155] Example 5

[0156] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0157] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0158] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0159] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0160] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0161] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0162] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0163] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0164] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0165] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0166] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0167] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A seismic trace data analysis method based on expressions, characterized in that, include: Identify rule fields based on preset rule conditions, generate rule selection expressions based on the rule fields, and identify the rule features of the rule selection expressions; Identify random fields based on preset random conditions, generate random selection expressions based on the random rule fields, and identify the random characteristics of the random selection expressions; Conditional variable features are generated using the rule features and the random features, and conditional expressions are generated based on the conditional variable features. The seismic data range is obtained by filtering each seismic trace in the pre-acquired seismic trace sample data one by one using the conditional expression. The data quality of the seismic trace sample data is analyzed by measuring the range of the seismic trace data, and the quality analysis index of the seismic trace sample data is obtained.

2. The expression-based seismic trace data analysis method according to claim 1, characterized in that, The step of generating a rule selection expression based on the rule field includes: Identify the field range and delimiter corresponding to the rule field; Generate the rule field value corresponding to the rule field based on the field range and the pre-obtained rule conditions; The rule selection expression is generated based on the rule field values ​​and the delimiter.

3. The expression-based seismic trace data analysis method according to claim 2, characterized in that, The step of generating a rule selection expression based on the rule field value and the delimiter includes: Map the rule field values ​​to the rule fields to obtain the field mapping relationship; Extract the first target field from the rule fields, and generate target field relationships according to the delimiter of the first target field; Extract the second target field from the rule fields and generate the field quantity requirement corresponding to the second target field; Determine the target field mapping corresponding to the second target field according to the required number of fields; The target field mapping is used to update the field mapping relationship, and a rule selection expression is generated based on the updated field mapping relationship and the target field relationship according to the delimiter.

4. The expression-based seismic trace data analysis method according to claim 1, characterized in that, The rule features for identifying the rule selection expression include: The rule name characteristics are determined by selecting the field names of the expressions according to the rules. The rule boundary features are determined by selecting the field data of the expression according to the rules. The rule name feature and the rule boundary feature are combined into the rule feature of the rule selection expression.

5. The expression-based seismic trace data analysis method according to claim 1, characterized in that, The process involves filtering each seismic trace in the pre-acquired seismic trace sample data one by one using the conditional expression to obtain the range of seismic trace data, including: The first trace data in the seismic trace sample data is taken as the target trace data; The logical value corresponding to the target channel data is calculated using the conditional expression; When the logical value is a preset target value, extract the channel number corresponding to the target channel data; When the logical value is not the preset target value, the next trace corresponding to the first trace is taken as the target trace according to the sorting in the seismic trace sample data, and the process returns to the step of calculating the logical value corresponding to the target trace using the conditional expression, until all seismic trace data in the seismic trace sample data has been calculated. When all seismic trace data in the seismic trace sample data has been calculated, the trace number is collected as the seismic trace data range.

6. The expression-based seismic trace data analysis method according to claim 5, characterized in that, The step of calculating the logical value corresponding to the target channel data using the conditional expression includes: Extract the rule data filtering range corresponding to the rule selection expression in the conditional expression, and calculate the rule logic value of the channel number corresponding to the target channel data based on the rule data filtering range; Extract the random data filtering range corresponding to the random selection expression in the conditional expression, and calculate the random logical value of the channel number corresponding to the target channel data based on the random data filtering range; The condition variable value between the rule logic value and the random logic value is calculated by a preset logical operator, and the condition variable value is used as the logic value corresponding to the target channel data.

7. The expression-based seismic trace data analysis method according to claim 1, characterized in that, The process of analyzing the data quality of the seismic trace sample data based on the seismic trace data range to obtain quality analysis indicators for the seismic trace sample data includes: Identify the key attributes corresponding to each seismic data trace within the range of the seismic trace data, count the missing values ​​of the key attributes, and determine the completeness index of each seismic data trace based on the missing values; Extract the time series data corresponding to each seismic data within the range of the seismic trace data, calculate the fluctuation data of the time series data, and determine the stability index of each seismic data based on the fluctuation data; Each seismic data point within the range of the seismic trace data is subjected to signal separation to obtain signal energy and noise energy. The purity index of each seismic data point is determined based on the signal energy and the noise energy. The quality analysis indicators of the seismic trace sample data are determined based on the completeness index, the stability index, and the purity index.

8. A seismic trace data analysis device based on expression, characterized in that, include: The rule selection expression generation module is used to identify rule fields according to preset rule conditions, generate rule selection expressions according to the rule fields, and identify the rule features of the rule selection expressions. The random selection expression generation module is used to identify random fields according to preset random conditions, generate random selection expressions according to the random rule fields, and identify the random characteristics of the random selection expressions; The condition expression generation module is used to generate condition variable features through the rule features and the random features, and to generate condition expressions based on the condition variable features; The seismic trace data range filtering module is used to filter each seismic data in the pre-acquired seismic trace sample data one by one using the conditional expression to obtain the seismic trace data range. The seismic trace sample data quality analysis module is used to analyze the data quality of the seismic trace sample data based on the range of the seismic trace data, and obtain the quality analysis indicators of the seismic trace sample data.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the expression-based seismic trace data analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the expression-based seismic trace data analysis method according to any one of claims 1 to 7.

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