Method for analyzing and evaluating quality of three-dimensional seismic data of loess tableland

Abstract

The invention discloses a method for analyzing and evaluating the quality of three-dimensional seismic data of a loess tableland, and the method for evaluating the quality of the three-dimensional seismic data is determined according to main influence factors which influence the quality of the three-dimensional seismic data of the loess tableland in south of Hubei, and comprises the following specific steps: S100, analyzing the signal-to-noise ratio of the data; analyzing and evaluating by adopting two methods of a horizon time window and an isochronous time window; s200, analyzing the main frequency and the effective bandwidth, and evaluating the data resolution; s300, synthetic record correlation analysis: mainly analyzing the reflection structure and energy fidelity of the seismic data; s400, static correction processing effect analysis is mainly used for evaluating the authenticity of seismic data structural features; s500, analyzing an earth surface consistency processing effect, and evaluating the fidelity of seismic data energy; S600, evaluating and analyzing a multiple wave removal suppression effect, and evaluating the fidelity of the data from another aspect; and S700, evaluating and analyzing the imaging reliability.

Description

Technical Field

[0001] This invention relates to the field of seismic data quality evaluation technology, specifically a method for quality analysis and evaluation of three-dimensional seismic data from the Loess Plateau. Background Technology

[0002] The 3D seismic data of the Loess Plateau is affected by shallow surface topography, excitation and reception conditions, observation system, and data processing technology, mainly including static correction, high-fidelity noise reduction, surface consistency processing, and inaccurate migration imaging. To a certain extent, the seismic data cannot truly reflect the characteristics of underground structure and lithological changes. Therefore, some analytical evaluation methods for evaluating the quality of seismic data have emerged in the market.

[0003] For example, Chinese patent CN201910451883.0 discloses a method and storage medium for calculating a quantitative evaluation value of seismic data quality. The method includes: acquiring seismic data; selecting an analysis window for the seismic data, wherein the analysis window is selected within the effective wave distribution area of ​​the seismic data; picking wave group times and performing layer flattening processing on the seismic data within the analysis window; and superimposing the layer-flattened seismic data according to the trace records to obtain a quantitative evaluation value. This method and storage medium for calculating a quantitative evaluation value of seismic data quality eliminates the adverse effects of static correction and noise on seismic data quality, improves reliability and sensitivity, and the quantitative evaluation value calculated from the seismic data within the analysis window can be used to evaluate the quality of the seismic data.

[0004] The aforementioned patent provides a method and storage medium for calculating a quantitative evaluation value of seismic data quality; this method eliminates the adverse effects of static correction and noise on seismic data quality. However, in the southern Ordos Basin, due to the influence of the surface loess plateau and multiple coal seams of the Yan'an Formation, seismic data exhibits well-developed multiples, resulting in low data fidelity. Reflection structure and amplitude attributes are severely affected, leading to low signal-to-noise ratio and fidelity, strong interpretability ambiguity, and low reliability of reservoir prediction. The seismic geological conditions of the loess plateau determine the difficulty of data processing. Existing evaluation methods cannot effectively evaluate the quality of seismic data from the loess plateau. Therefore, it is essential to develop a set of evaluation methods and processes suitable for 3D seismic data from the loess plateau. Summary of the Invention

[0005] The purpose of this invention is to provide a method for quality analysis and evaluation of three-dimensional seismic data in the Loess Plateau, aiming to improve the existing evaluation methods that cannot effectively evaluate the quality of seismic data in the Loess Plateau, as the seismic geological conditions of the Loess Plateau determine the difficulty of data processing.

[0006] This invention is implemented as follows:

[0007] A method for quality analysis and evaluation of 3D seismic data in the Loess Plateau region is proposed. Based on the main influencing factors affecting the quality of 3D seismic data in the southern Hubei Loess Plateau region, a method for evaluating the quality of 3D seismic data is established. The specific steps of the quality evaluation method are as follows:

[0008] S100, Data signal-to-noise ratio analysis, using two methods: along-layer time window and isochronous time window;

[0009] S200, main frequency, and effective bandwidth analysis were conducted to evaluate the data resolution.

[0010] S300, correlation analysis of synthetic records, mainly analyzes the fidelity of seismic data reflection structure and energy;

[0011] S400 static correction processing effect analysis mainly evaluates the authenticity of the structural characteristics of seismic data;

[0012] S500, surface consistency processing effect analysis, evaluating the fidelity of seismic data energy:

[0013] S600, evaluation and analysis of the suppression effect of multiple wave removal, to evaluate the data fidelity from another perspective;

[0014] S700 Imaging Reliability Evaluation and Analysis.

[0015] Preferably, the specific steps for data signal-to-noise ratio analysis and evaluation in step S100 are as follows:

[0016] S110. Extraction along the layer, divided into shallow, medium and deep layers, with the target layer being the main evaluation layer;

[0017] S120. Extract along the time window and perform signal-to-noise ratio analysis in three time windows: shallow, medium and deep.

[0018] S130. Correlation analysis of signal-to-noise ratio (SNR) between the plane and the terrain is performed. Areas with an SNR greater than a certain value are considered qualified areas for data processing.

[0019] Preferably, the specific steps for analyzing the main frequency and effective bandwidth in step S200 are as follows:

[0020] S210. Perform frequency analysis on the shallow, medium and deep layers respectively, mainly analyzing the frequency of the target layer.

[0021] S220. Based on the fact that the maximum resolution of an earthquake is 1 / 4 of the wavelength, calculate the maximum stratigraphic thickness corresponding to the dominant frequency.

[0022] Preferably, in step S500, the specific steps for evaluating the fidelity of seismic data energy are as follows:

[0023] S510, Profile Analysis: Referring to the surface topographic map, cut different topographic profiles (with topographic lines) to analyze whether the amplitude intensity and the continuity of the in-phase axis are related to the topography under different topography.

[0024] S520. Extract the amplitude and frequency attributes of the phase axis of the shallow, medium and deep marker layers. Compare the plane maps of each attribute with the surface topography. Determine if there are significant differences in the amplitude and frequency ranges under different topography. Focus on analyzing the correlation between the amplitude and frequency attributes of the target layer and the surface topography.

[0025] Preferably, the specific steps of the imaging reliability evaluation and analysis in step S700 are as follows:

[0026] S710. The cross-sectional analysis should be conducted to determine whether the fault points are clear and whether the fault-crossing strata and the seismic phase axis orientation conform to the regional geological sedimentary patterns. If the fault points are not clear and the fault-crossing strata and the seismic phase axis orientation do not conform to the geological sedimentary patterns, then it is considered that there is a false fault problem.

[0027] S720. Combine the plane diagrams of the amplitude and frequency attributes of the phase axis of the shallow, medium and deep marker layers to analyze the consistency of longitudinal and transverse energy of the seismic wave group. If the consistency of longitudinal and transverse energy is poor, it is considered that there is a problem with the imaging.

[0028] Preferably, the quality evaluation method described herein divides the three-dimensional seismic data of the Loess Plateau into three types based on the shallow and deep seismic geological conditions: simple zone, single complex zone, and double complex zone.

[0029] Preferably, the simple zone has a thin loess layer, generally less than 30 meters. Regardless of whether the Yan'an Formation coal seam is developed or not, the data quality is good, the fidelity is high, and the reflection structure and amplitude properties can be used for reservoir prediction research, resulting in a high degree of reliability in reservoir prediction.

[0030] Preferred, the single complex area is characterized by a large loess layer thickness, greater than 100 meters, undeveloped coal seams, and relatively good seismic data quality, but with undeveloped multiple waves. The signal-to-noise ratio of data in such areas is somewhat affected, but the data has a certain degree of fidelity. The reflection structure and amplitude attributes can be applied to reservoir prediction to some extent, but there are multiple interpretations.

[0031] Preferred, the dual-complex region is characterized by a thick loess layer, well-developed coal seams, and a large number of coal seams; the development of multiple waves results in low data fidelity and a significant impact on data quality; such regions have low signal-to-noise ratios, and due to the influence of multiple waves, the reflection structure and amplitude properties are severely affected, resulting in low seismic data fidelity, strong interpretation ambiguity, and low reliability of reservoir prediction.

[0032] Preferably, the quality of the data evaluated by the aforementioned quality evaluation method is divided into three categories; as detailed below:

[0033] Class I: Wave group characteristics are clear, energy is strong, phase is stable, continuity is good, stratigraphic identification is reliable, geological phenomena are clear, and it can be reliably used for structural interpretation and reservoir prediction;

[0034] Class II: Wave group characteristics are basically clear, can be continuously compared and tracked, stratigraphic identification is basically reliable, geological phenomena can be identified and compared, and can be reliably used for structural interpretation and reservoir prediction.

[0035] Category III: Wave group characteristics are unclear, continuous comparison and tracking are not possible, data have problems such as multiple wave or static corrections, stratigraphic identification is questionable, geological phenomena are unclear, and structural interpretation and reservoir prediction are inferences.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. The method for evaluating 3D seismic data of the Loess Plateau provided by this invention effectively solves the problem of varying processing methods in complex regions. Specifically, it employs the following methods: signal-to-noise ratio analysis by comparing along layers, time windows, and topography; analysis of the maximum resolution of the data through dominant frequency analysis; assessment of the fidelity of seismic data through correlation analysis between synthetic records and seismic waveform traces; evaluation of the authenticity of structures through static correction analysis; evaluation of the energy fidelity of the data by extracting the amplitude and frequency attributes of the in-phase axis and comparing them with surface topographic lines; further evaluation of the signal-to-noise ratio and fidelity of the data through analysis of multiple waves; and assessment of the reliability of the data imaging through analysis of transect discontinuities. This series of data evaluation and analysis methods provides a complete set of evaluation and analysis tools for processed data from different seismic data processing units, and also provides a series of quality control measures for unprocessed data.

[0038] 2. This invention provides a method for quality analysis and evaluation of 3D seismic data from the Loess Plateau, enabling a systematic evaluation and understanding of seismic data, avoiding data pitfalls, and improving the reliability of data interpretation. This method can be effectively applied not only to 3D seismic data from the Loess Plateau but also to the quality evaluation of seismic data from other regions. Attached Figure Description

[0039] Figure 1 A flowchart illustrating a method for quality analysis and evaluation of 3D seismic data from the Loess Plateau;

[0040] Figure 2 A signal-to-noise ratio example analysis of a method for quality analysis and evaluation of 3D seismic data in the Loess Plateau;

[0041] Figure 3 This paper presents an example analysis of the dominant frequency and effective bandwidth of a method for quality analysis and evaluation of 3D seismic data in the Loess Plateau.

[0042] Figure 4An example analysis of the surface consistency processing effect of a method for quality analysis and evaluation of 3D seismic data in the Loess Plateau;

[0043] Figure 5 An example analysis of the imaging reliability evaluation effect of a method for quality analysis and evaluation of 3D seismic data in the Loess Plateau; Detailed Implementation

[0044] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0045] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details:

[0046] Example 1

[0047] like Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 This paper presents a method for quality analysis and evaluation of 3D seismic data in the Loess Plateau. Based on the shallow and deep seismic geological conditions, this method classifies the 3D seismic data of the Loess Plateau into three types: simple zone, single-complex zone, and double-complex zone. In the simple zone, the loess layer is thin, generally less than 30 meters. Regardless of whether the Yan'an Formation coal seams are developed or not, the data quality is good, with high fidelity. The reflection structure and amplitude attributes can be used for reservoir prediction studies, and the reliability of reservoir prediction is relatively high. In the single-complex zone, the loess layer is thick, greater than 100 meters, and coal seams are not developed. The seismic data quality is acceptable, but multiples are not well-developed. The signal-to-noise ratio of the data in this type of area is somewhat affected, but the data has a certain degree of fidelity. The reflection structure and amplitude attributes can be applied to reservoir prediction to some extent, but there are multiple interpretations. The complex loess zone is characterized by its thick loess layer, well-developed coal seams, and numerous coal seam layers. It also features well-developed multiple waves, resulting in low data fidelity and a significant impact on data quality. In such areas, the signal-to-noise ratio is low, and the reflection structure and amplitude properties are severely affected by multiple waves, leading to low seismic data fidelity, strong interpretation ambiguity, and low reliability of reservoir prediction.

[0048] The quality assessment method evaluates data quality in three categories, as detailed below:

[0049] Class I: Wave group characteristics are clear, energy is strong, phase is stable, continuity is good, stratigraphic identification is reliable, geological phenomena are clear, and it can be reliably used for structural interpretation and reservoir prediction;

[0050] Class II: Wave group characteristics are basically clear, can be continuously compared and traced, stratigraphic identification is basically reliable, geological phenomena can be identified and compared, and can be reliably used for structural interpretation and reservoir prediction;

[0051] Category III: Wave group characteristics are unclear, continuous comparison and tracking are not possible, data have problems such as multiple wave or static corrections, stratigraphic labeling is questionable, geological phenomena are unclear, and structural interpretation and reservoir prediction are inferences.

[0052] Based on the main factors affecting the quality of 3D seismic data in the Loess Plateau region of southern Hubei, a quality evaluation method for 3D seismic data was established. The specific steps of this quality evaluation method are as follows:

[0053] S100. Data signal-to-noise ratio (SNR) analysis is conducted using two methods: along-layer time windows and isochronous time windows. The specific steps for data SNR analysis and evaluation are as follows:

[0054] S110. Extraction along the layer, divided into shallow, medium and deep layers, with the target layer being the main evaluation layer;

[0055] S120. Extract along the time window and perform signal-to-noise ratio analysis in three time windows: shallow, medium and deep.

[0056] S130. Correlation analysis of signal-to-noise ratio (SNR) between the plane and the terrain is performed. Areas with an SNR greater than a certain value are considered qualified areas for data processing.

[0057] S200, main frequency, and effective bandwidth analysis are used to evaluate data resolution. The specific steps for main frequency and effective bandwidth analysis are as follows:

[0058] S210. Perform frequency analysis on the shallow, medium and deep layers respectively, mainly analyzing the frequency of the target layer.

[0059] S220. Based on the fact that the maximum resolution of an earthquake is 1 / 4 of the wavelength, calculate the maximum stratigraphic thickness corresponding to the dominant frequency.

[0060] S300, Synthetic Record Correlation Analysis, mainly analyzes the fidelity of seismic data reflection structure and energy. Based on the characteristics of seismic geological conditions in the Loess Plateau region, the correlation of synthetic records is evaluated from two aspects: the correlation between the waveform of the synthetic records and the seismic traces, and the consistency of energy matching. The correlation of the entire well section and the target section are calculated separately. When both are high, it indicates that the seismic data fidelity at that location is qualified.

[0061] S400, static correction effect analysis, mainly evaluates the authenticity of the structural characteristics of seismic data; assuming that there are no anomalies in the horizontal direction and the seismic propagation velocity changes little in the horizontal direction, calculate the time domain variation of the seismic double-layer travel time and the actual drilling depth domain variation, and see if the two trends are consistent. If there is a significant inconsistency or the trend is opposite, it can be basically determined that there is a static correction problem.

[0062] S500, Analysis of Surface Consistency Processing Effect, Evaluation of Seismic Data Energy Fidelity: The specific steps for evaluating the energy fidelity of seismic data are as follows:

[0063] S510, Profile Analysis: Referring to the surface topographic map, cut different topographic profiles (with topographic lines) to analyze whether the amplitude intensity and the continuity of the in-phase axis are related to the topography under different topography.

[0064] S520. Extract the amplitude and frequency attributes of the phase axis of the shallow, medium and deep marker layers. Compare the plane maps of each attribute with the surface topography. Determine if there are significant differences in the amplitude and frequency ranges under different topography. Focus on analyzing the correlation between the amplitude and frequency attributes of the target layer and the surface topography.

[0065] The S600 multiple wave removal suppression effect evaluation and analysis evaluates the data fidelity from another perspective; it analyzes the causes and characteristics of multiple wave formation and identifies them, analyzes their impact on reservoir prediction, and analyzes the amplitude, frequency and reflection structure of multiple waves on the target layer to identify the reservoir prediction influence zone and reduce exploration and development risks.

[0066] S700 Imaging Reliability Evaluation and Analysis; the specific steps of the imaging reliability evaluation and analysis are as follows:

[0067] S710. The cross-sectional analysis should be conducted to determine whether the fault points are clear and whether the fault-crossing strata and the seismic phase axis orientation conform to the regional geological sedimentary patterns. If the fault points are not clear and the fault-crossing strata and the seismic phase axis orientation do not conform to the geological sedimentary patterns, then it is considered that there is a false fault problem.

[0068] S720. Combine the plane diagrams of the amplitude and frequency attributes of the phase axis of the shallow, medium and deep marker layers to analyze the consistency of longitudinal and transverse energy of the seismic wave group. If the consistency of longitudinal and transverse energy is poor, it is considered that there is a problem with the imaging.

[0069] Example 2

[0070] like Figure 1 The following describes a method for quality analysis and evaluation of 3D seismic data in the Loess Plateau region. Based on the main influencing factors affecting the quality of 3D seismic data in the southern Hubei Loess Plateau region, a quality evaluation method for 3D seismic data is established. The specific steps of this quality evaluation method are as follows:

[0071] (1) Data signal-to-noise ratio analysis was conducted using two methods: time window along the layer and time window at equal intervals.

[0072] The extraction method used is along the layer time window, such as Figure 2 Signal-to-noise ratio (SNR) analysis was performed on seismic data from the same block processed by two different institutions, across three time windows: 200-500ms, 600-900ms, and 1200-1500ms. The analysis determined that areas with an SNR greater than 5 were considered acceptable areas for data processing.

[0073] (2) Analyze the main frequency and effective bandwidth to evaluate the data resolution;

[0074] like Figure 3 This involves performing dominant frequency and bandwidth analyses on seismic data from the same block processed by two different institutions, covering shallow, intermediate, and deep layers. Generally, a dominant frequency of around 26-28 Hz can resolve a stratum thickness of 35 meters. A dominant frequency of 29-31 Hz can resolve a stratum thickness of 38 meters. The analysis shows that although the dominant frequency of the data processed by institution A is lower, the overall bandwidth is wider. A wider bandwidth in seismic data indicates richer seismic information, and with increasing depth, high-frequency components attenuate while low-frequency components increase.

[0075] (3) Synthetic record correlation analysis, mainly analyzing the fidelity of seismic data reflection structure and energy;

[0076] Based on the characteristics of seismic geological conditions in the Loess Plateau region, the correlation evaluation of synthetic records was mainly carried out from two aspects: the correlation between synthetic records and seismic trace waveforms and the consistency of energy matching.

[0077] A. Coal seams and shale are widely developed in the work area, and their low impedance will cause strong reflections. Wells should be classified according to the thickness of coal seams and shale.

[0078] B. Based on well classification, perform statistical analysis on the correlation of synthetic records for the entire well section and the target layer;

[0079] C. The correlation of the entire well section is greater than 60%, and the correlation of the target layer is greater than 85%, indicating that the seismic data at this location is of acceptable fidelity.

[0080] (4) Analysis of the effect of static correction processing, mainly to evaluate the authenticity of the structural characteristics of seismic data;

[0081] A. Compare the overall trend of the contour map of the nearest marker layer t0 of the target layer with the structural map of the marker layer of the actual drilled well, and analyze whether there is a static correction problem based on the consistency of the two plane trends.

[0082] B. Comparative analysis of static correction issues in the area using vertical and horizontal wells: By comparing the actual drilling elevation and depth trends of all vertical well marker layers in the area with the t0 depth trends marked on the seismic profile, and analyzing the well-seismic time-depth relationship, if there are inconsistencies between the profile phase structure and the actual drilling depth, the time domain and depth domain structure show opposite relationships, i.e., greater burial depth corresponds to a smaller time value, and vice versa, indicating a serious static correction problem near these well points; By comparing the actual drilling trajectory of horizontal wells (basically representing the sand body attitude) with the attitude of the seismic phase axis, if the actual drilling trajectory of the horizontal section is inconsistent with the attitude of the seismic phase axis, it is considered that there is a static correction problem in the area near the horizontal well, which will lead to inaccurate local structural morphology and the illusion of inaccurate low-amplitude structural determination.

[0083] (5) Analysis of the surface consistency processing effect to evaluate the fidelity of seismic data energy:

[0084] like Figure 4 The image shows the amplitude attribute analysis of the marker layer bottom of seismic data from the same block processed by two different institutions, compared with the surface topography. The results indicate that there is still a significant correlation between topography and seismic attributes in some areas.

[0085] (6) Evaluation and analysis of the suppression effect of multiple wave removal, to evaluate the data fidelity from another perspective;

[0086] Preliminary analysis indicates that the multiple waves in southern Hubei are mainly caused by the Yan'an Formation coal seams. Statistics were compiled on the number and thickness of coal seams in all wells within the area. Specifically, the reflection patterns of the Yan'an Formation and the underlying strata show strong consistency; the underlying strata exhibit poor energy consistency; the coherence properties of thicker coal seams are clustered; and the strong reflection amplitudes of coal seams and shale show significant complementarity.

[0087] Multiple waves have a significant impact on the amplitude, frequency, and reflection structure of the target layer, and their influence should be minimized.

[0088] (7) Imaging reliability evaluation and analysis

[0089] like Figure 5 The left side shows the seismic profile processed by Unit A. Figure 5 The right side shows the maximum peak amplitude planar plot of the marker layer. As can be seen from the figure, the data processed by Unit A exhibits strong energy continuity, relatively clear and reliable discontinuities, and good imaging results.

[0090] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau, characterized in that, Based on the main influencing factors affecting the quality of 3D seismic data in the Loess Plateau region of southern Hubei, a 3D seismic data quality evaluation method was established. The specific steps of this quality evaluation method are as follows: S100, Data signal-to-noise ratio analysis, using two methods: along-layer time window and isochronous time window; S200, main frequency, and effective bandwidth analysis were conducted to evaluate the data resolution. S300, correlation analysis of synthetic records, mainly analyzes the fidelity of seismic data reflection structure and energy; S400 static correction processing effect analysis mainly evaluates the authenticity of the structural characteristics of seismic data; S500, surface consistency processing effect analysis, evaluating the fidelity of seismic data energy: S600, evaluation and analysis of the suppression effect of multiple wave removal, to evaluate the data fidelity from another perspective; S700 Imaging Reliability Evaluation and Analysis.

2. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 1, characterized in that, In step S100, the specific steps for data signal-to-noise ratio analysis and evaluation are as follows: S110. Extraction along the layer, divided into shallow, medium and deep layers, with the target layer being the main evaluation layer; S120. Extract along the time window and perform signal-to-noise ratio analysis in three time windows: shallow, medium and deep. S130. Correlation analysis of signal-to-noise ratio (SNR) between the plane and the terrain is performed. Areas with an SNR greater than a certain value are considered qualified areas for data processing.

3. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 1, characterized in that, In step S200, the specific steps for analyzing the main frequency and effective bandwidth are as follows: S210. Perform frequency analysis on the shallow, medium and deep layers respectively, mainly analyzing the frequency of the target layer. S220. Based on the fact that the maximum resolution of an earthquake is 1 / 4 of the wavelength, calculate the maximum stratigraphic thickness corresponding to the dominant frequency.

4. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 1, characterized in that, In step S500, the specific steps for evaluating the fidelity of seismic data energy are as follows: S510, Profile Analysis: Referring to the surface topographic map, cut different topographic profiles (with topographic lines) to analyze whether the amplitude intensity and the continuity of the in-phase axis are related to the topography under different topography. S520. Extract the amplitude and frequency attributes of the phase axis of the shallow, medium and deep marker layers. Compare the plane maps of each attribute with the surface topography. Determine if there are significant differences in the amplitude and frequency ranges under different topography. Focus on analyzing the correlation between the amplitude and frequency attributes of the target layer and the surface topography.

5. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 1, characterized in that, In step S700, the specific steps for imaging reliability evaluation and analysis are as follows: S710. The cross-sectional analysis should be conducted to determine whether the fault points are clear and whether the fault-crossing strata and the seismic phase axis orientation conform to the regional geological sedimentary patterns. If the fault points are not clear and the fault-crossing strata and the seismic phase axis orientation do not conform to the geological sedimentary patterns, then it is considered that there is a false fault problem. S720. Combine the plane diagrams of the amplitude and frequency attributes of the phase axis of the shallow, medium and deep marker layers to analyze the consistency of longitudinal and transverse energy of the seismic wave group. If the consistency of longitudinal and transverse energy is poor, it is considered that there is a problem with the imaging.

6. A method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to any one of claims 1-5, characterized in that, The quality evaluation method described herein divides the three-dimensional seismic data of the Loess Plateau into three types based on the shallow and deep seismic geological conditions: simple zone, single complex zone, and double complex zone.

7. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 6, characterized in that, The simple zone is characterized by a thin loess layer, generally less than 30 meters thick. Regardless of whether the Yan'an Formation coal seam is developed or not, the data quality is good, the fidelity is high, and the reflection structure and amplitude properties can be used for reservoir prediction research, resulting in a high degree of reliability in reservoir prediction.

8. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 6, characterized in that, The single complex area is characterized by a thick loess layer, exceeding 100 meters, with undeveloped coal seams, and relatively good seismic data quality, but lacking in multiple wave characteristics. The signal-to-noise ratio of data in such areas is somewhat affected, but the data retains a certain degree of fidelity. The reflection structure and amplitude attributes can be applied to reservoir prediction to some extent, but there are multiple interpretations.

9. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 6, characterized in that, The aforementioned complex region is characterized by a thick loess layer, well-developed coal seams, and numerous coal seam layers; it also features well-developed multiples, resulting in low data fidelity and a significant impact on data quality; such regions have low signal-to-noise ratios, and due to the influence of multiples, the reflection structure and amplitude properties are severely affected, leading to low seismic data fidelity, strong interpretability issues, and low reliability in reservoir prediction.

10. The method for quality analysis and evaluation of three-dimensional seismic data of the Loess Plateau according to claim 1, characterized in that, The quality assessment method described herein evaluates data quality in three categories, as detailed below: Class I: Wave group characteristics are clear, energy is strong, phase is stable, continuity is good, stratigraphic identification is reliable, geological phenomena are clear, and it can be reliably used for structural interpretation and reservoir prediction; Class II: Wave group characteristics are basically clear, can be continuously compared and traced, stratigraphic identification is basically reliable, geological phenomena can be identified and compared, and can be reliably used for structural interpretation and reservoir prediction; Category III: Wave group characteristics are unclear, continuous comparison and tracking are not possible, data have problems such as multiple wave or static corrections, stratigraphic labeling is questionable, geological phenomena are unclear, and structural interpretation and reservoir prediction are inferences.

Images