Shallow subsea horizon extraction method and device, electronic equipment and storage medium

Abstract

The invention discloses a shallow seabed horizon extraction method and device, electronic equipment and a storage medium. Comprising the steps of updating an initial seabed horizon time sequence corresponding to amplitude envelope data based on a median filtering algorithm and a linear interpolation algorithm, and generating a middle seabed horizon time sequence; for each trace in the amplitude envelope data, traversing each sampling point in the trace along a sampling time increasing direction under the seabed horizon time corresponding to the trace in the middle seabed horizon time sequence, and determining a first target sampling point of which the amplitude is greater than or equal to an amplitude threshold corresponding to the trace; updating the middle seabed horizon time sequence based on the sampling time corresponding to the target sampling point, and iteratively executing updating on the initial seabed horizon time sequence based on a median filtering algorithm and a linear interpolation algorithm to generate a target seabed horizon time sequence; and extracting the seabed horizon in the original shallow profile data based on the target seabed horizon time sequence. The method improves the efficiency and precision of seabed horizon extraction.

Description

Technical Field

[0001] The present invention relates to the technical field of marine geological surveys, and particularly to a method, device, electronic device, and storage medium for extracting shallow profile seafloor horizons. Background Art

[0002] Shallow sub-bottom profile detection systems are widely used in fields such as marine geological science exploration, marine engineering, and channel surveying, and are one of the commonly used technical means in marine geological surveys. The accurate extraction of the initial seafloor horizon is the key technical basis for shallow profile data processing and data interpretation. Its accuracy directly affects the quality of processing procedures such as swell static correction and high-precision imaging, and thus also determines the accuracy and reliability of the final shallow profile data interpretation.

[0003] In shallow sub-bottom profile data, the first arrival reflection signal generated by the sudden change in acoustic impedance at the seafloor interface has a significant energy advantage. Currently, commonly used automatic extraction algorithms such as the energy ratio method or the amplitude value method are both based on the reflection characteristics of strong seafloor energy. These algorithms can extract the seafloor reflection horizon well in data with high signal-to-noise ratio. However, when the signal-to-noise ratio of the data is low or there is strong direct wave signal interference in shallow water depth data, the extraction accuracy of these algorithms is poor, and it usually takes a lot of time and effort to correct the extracted horizon, seriously affecting the processing efficiency. And the automatic extraction algorithm based on neural network has good extraction accuracy in specific data, but this method requires a large amount of labeled data in the early stage and has poor generalization ability, so it is not commonly used in actual production processing. Summary of the Invention

[0004] The present invention provides a method, device, electronic device, and storage medium for extracting shallow profile seafloor horizons, which improves the efficiency and accuracy of extracting the seafloor horizon in shallow sub-bottom profile data.

[0005] According to one aspect of the present invention, there is provided a method for extracting shallow profile seafloor horizons, including:

[0006] In response to the triggering of the shallow profile seafloor horizon extraction event, obtain the original shallow profile data and convert the original shallow profile data into amplitude envelope data;

[0007] Determine the seafloor horizon time corresponding to each trace data in the amplitude envelope data respectively, and generate an initial seafloor horizon time series based on the seafloor horizon time corresponding to each trace data;

[0008] Update the initial seafloor horizon time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor horizon time series;

[0009] For each trace of the amplitude envelope data, below the seafloor layer position time corresponding to the trace data in the intermediate seafloor layer position time series, traverse each sampling point in the trace data along the direction of increasing sampling time, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data;

[0010] Update the intermediate seafloor layer position time series based on the sampling time corresponding to the target sampling point, use the updated intermediate seafloor layer position time series as the initial seafloor layer position time series, and return to execute the update of the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm until the preset condition is met to generate the target seafloor layer position time series;

[0011] Extract the seafloor layer position in the original shallow profile data based on the target seafloor layer position time series.

[0012] According to another aspect of the present invention, there is provided a shallow profile seafloor layer position extraction device, including:

[0013] An amplitude envelope data determination module, configured to, in response to the triggering of a shallow profile seafloor layer position extraction event, obtain the original shallow profile data and convert the original shallow profile data into amplitude envelope data;

[0014] An initial seafloor layer position time series generation module, configured to respectively determine the seafloor layer position time corresponding to each trace of the amplitude envelope data, and generate an initial seafloor layer position time series based on the seafloor layer position time corresponding to each trace data;

[0015] An intermediate seafloor layer position time series generation module, configured to update the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor layer position time series;

[0016] A target sampling point determination module, configured to, for each trace of the amplitude envelope data, below the seafloor layer position time corresponding to the trace data in the intermediate seafloor layer position time series, traverse each sampling point in the trace data along the direction of increasing sampling time, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data;

[0017] A target seafloor layer position time series generation module, configured to update the intermediate seafloor layer position time series based on the sampling time corresponding to the target sampling point, use the updated intermediate seafloor layer position time series as the initial seafloor layer position time series, and return to execute the update of the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm until the preset condition is met to generate the target seafloor layer position time series;

[0018] The seabed layer extraction module is used to extract the seabed layer in the original shallow profile data based on the target seabed layer time series.

[0019] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0020] At least one processor; and

[0021] A memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the shallow profile seabed layer extraction method according to any embodiment of the present invention.

[0023] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the shallow profile seabed layer extraction method according to any embodiment of the present invention when executed.

[0024] In the shallow profile seabed layer extraction solution of the embodiments of the present invention, in response to the triggering of the shallow profile seabed layer extraction event, the original shallow profile data is acquired and converted into amplitude envelope data; the seabed layer time corresponding to each trace data in the amplitude envelope data is determined respectively, and an initial seabed layer time series is generated based on the seabed layer time corresponding to each trace data; the initial seabed layer time series is updated based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seabed layer time series; for each trace data in the amplitude envelope data, below the seabed layer time corresponding to the trace data in the intermediate seabed layer time series, each sampling point in the trace data is traversed in the direction of increasing sampling time, and the first target sampling point with an amplitude greater than or equal to the amplitude threshold corresponding to the trace data is determined; the intermediate seabed layer time series is updated based on the sampling time corresponding to the target sampling point, and the updated intermediate seabed layer time series is used as the initial seabed layer time series, and the process returns to execute the update of the initial seabed layer time series based on the median filtering algorithm and the linear interpolation algorithm until a preset condition is met to generate a target seabed layer time series; the seabed layer in the original shallow profile data is extracted based on the target seabed layer time series. Through the technical solution provided by the embodiments of the present invention, the disadvantages of poor effect and low accuracy in automatically extracting the seabed layer in low signal-to-noise ratio data by traditional methods can be effectively overcome, the manual participation is reduced, and the efficiency and accuracy of seabed layer extraction in shallow stratigraphic profile data are improved.

[0025] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0027] Figure 1 Flowchart of a method for extracting shallow-sea bottom layers provided in Embodiment 1 of the present invention;

[0028] Figure 2a Waveform diagram corresponding to the original shallow-sea profile data provided in the embodiments of the present invention;

[0029] Figure 2b Spectrum diagram corresponding to the original shallow-sea profile data provided in the embodiments of the present invention;

[0030] Figure 2c Schematic diagram of the amplitude envelope data corresponding to the original shallow-sea profile data provided in the embodiments of the present invention;

[0031] Figure 3a Initial effect diagram of extracting shallow-sea bottom layers provided in the embodiments of the present invention;

[0032] Figure 3b Schematic diagram of the shallow-sea bottom layer extracted after six iterations shown in the amplitude envelope data;

[0033] Figure 3c Schematic diagram of the shallow-sea bottom layer extracted after six iterations shown in the original shallow-sea profile data;

[0034] Figure 4 Schematic diagram of the shallow-sea bottom layer extracted by iterative filtering 1, 3, 5, and 6 times respectively for the entire survey line provided in the embodiments of the present invention;

[0035] Figure 5 Schematic diagram of the structure of a device for extracting shallow-sea bottom layers provided in the embodiments of the present invention;

[0036] Figure 6 Schematic diagram of the structure of an electronic device for implementing the method for extracting shallow-sea bottom layers in the embodiments of the present invention. Detailed Embodiments

[0037] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used in appropriate cases can be interchanged so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0039] Figure 1 FIG. is a flowchart of a method for extracting shallow subsurface layer positions provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of extracting shallow subsurface layer positions from shallow subsurface profile data. This method can be executed by a shallow subsurface layer position extraction device, which can be implemented in the form of hardware and / or software, and the shallow subsurface layer position extraction device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0040] S110. In response to the triggering of the shallow subsurface layer position extraction event, obtain the original shallow profile data and convert the original shallow profile data into amplitude envelope data.

[0041] In the embodiment of the present invention, when a subsurface layer position extraction instruction input by the user is received, it can be determined that the shallow subsurface layer position extraction event is triggered. In response to the triggering of the shallow subsurface layer position extraction event, obtain the original shallow profile data (i.e., shallow subsurface profile data). Figure 2a FIG. is a waveform diagram corresponding to the original shallow profile data provided in the embodiment of the present invention, Figure 2b FIG. is a spectrogram corresponding to the original shallow profile data provided in the embodiment of the present invention. As Figure 2a and Figure 2bAs shown, the detection signal of the shallow stratum profile data has the characteristics of high frequency and narrow band, with a main frequency of about 6 kHz, an effective frequency band between 4 - 8 kHz. The low-frequency information is missing in the shallow stratum profile data, and the reflection signal shows a multi-period oscillation form. Due to the lack of a clear main peak marker, it is difficult to accurately locate the maximum amplitude value through conventional threshold methods or peak detection algorithms. Therefore, in the embodiments of the present invention, the original shallow profile data can be converted into amplitude envelope data through the Hilbert transform algorithm, thereby converting the multi-period oscillation waveform data into single-peak envelope data, which can eliminate the phase sensitivity of the oscillation waveform and make the energy extremum corresponding to the seabed reflection interface prominent in the time domain. Exemplarily, the original shallow profile data can be converted into amplitude envelope data according to the following formula:

[0042]

[0043] where s(t) represents the original shallow profile data, E(t) represents the amplitude envelope data, and H represents the Hilbert transform operator. Exemplarily, Figure 2c is a schematic diagram of the amplitude envelope data corresponding to the original shallow profile data provided by the embodiments of the present invention.

[0044] Optionally, before converting the original shallow profile data into amplitude envelope data, it further includes: determining the delay time recorded in the corresponding trace header of the original shallow profile data, and performing time difference correction on the original shallow profile data based on the delay time. Since the original shallow profile data usually adopts a delayed recording method, therefore, before converting the original shallow profile data into amplitude envelope data, the delay time recorded in the corresponding trace header of the original shallow profile data can be obtained, and the original shallow profile data can be corrected for time difference based on the delay time to ensure the continuity of the seafloor horizons. Optionally, after performing time difference correction on the original shallow profile data, band-pass filtering can also be performed on the time-difference-corrected original shallow profile data to avoid interference from out-of-band random noise.

[0045] S120. Respectively determine the seafloor horizon time corresponding to each trace data in the amplitude envelope data, and generate an initial seafloor horizon time series based on the seafloor horizon time corresponding to each trace data.

[0046] In the embodiments of the present invention, the seafloor horizon time corresponding to each trace data in the amplitude envelope data is respectively determined. For example, the seafloor horizon time corresponding to each trace data in the amplitude envelope data can be manually marked, where the seafloor horizon time can be understood as the initial horizon time of each trace data. Generate an initial seafloor horizon time series based on the seafloor horizon time corresponding to each trace data in ascending order of the trace number

[0047] Optionally, determining the seafloor horizon time corresponding to each trace of the amplitude envelope data respectively includes: determining the amplitude threshold corresponding to each trace of the amplitude envelope data respectively; for each trace of the amplitude envelope data, traversing each sampling point in the trace data along the direction of increasing sampling time, and taking the sampling time corresponding to the first sampling point with an amplitude greater than or equal to the amplitude threshold as the seafloor horizon time corresponding to the trace data. The advantage of such a setting is that the initial seafloor horizon time corresponding to each trace of the amplitude envelope data can be accurately determined.

[0048] Exemplarily, determining the amplitude threshold corresponding to each trace of the amplitude envelope data respectively, where the amplitude thresholds corresponding to each trace data may be the same or different. For example, the amplitude thresholds corresponding to each trace data can be set artificially according to the waveform transformation of each trace data. Optionally, determining the amplitude threshold corresponding to each trace of the amplitude envelope data respectively includes: for each trace of the amplitude envelope data, determining the amplitude mean and amplitude standard deviation of the trace data; determining the amplitude threshold corresponding to the trace data according to the amplitude mean and the amplitude standard deviation. Exemplarily, for each trace of the amplitude envelope data, determining the amplitude of each sampling point in the trace data, and calculating the amplitude mean μ E and amplitude standard deviation σ E of the entire trace data, and then determining the amplitude threshold corresponding to the trace data according to the amplitude mean and the amplitude standard deviation. Among them, the amplitude threshold corresponding to each trace data can be calculated according to the following formula:

[0049] A x =α·μ E +β·σ E ;

[0050] where, A x represents the amplitude threshold, and both α and β are empirical coefficients, usually taking 1.2≤α≤1.5, 2.0≤α≤3.0.

[0051] For each trace of the amplitude envelope data, traversing each sampling point in the trace data along the direction of increasing sampling time, determining the first sampling point with an amplitude greater than or equal to the amplitude threshold corresponding to the trace data, and taking the sampling time corresponding to the determined first sampling point as the seafloor horizon time corresponding to the trace data.

[0052] S130. Updating the initial seafloor horizon time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor horizon time series.

[0053] In an embodiment of the present invention, based on the spatial continuity of the seafloor horizons, a median filtering algorithm is used to filter the initial seafloor horizon time series, that is, the median filtering algorithm is used to filter out the abnormal seafloor horizon times in the initial seafloor horizon time series, and then through a linear interpolation algorithm, the filtered abnormal seafloor horizon times are filled according to the valid seafloor horizon times (that is, non-abnormal seafloor horizon times) within a preset range of the abnormal seafloor horizon times, so as to update the initial seafloor horizon time series, and the updated initial seafloor horizon time series is used as the intermediate seafloor horizon time series.

[0054] Optionally, updating the initial seafloor horizon time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor horizon time series includes: for each seafloor horizon time corresponding to the trace data in the initial seafloor horizon time series, determining M adjacent trace times within a preset adjacent range of the seafloor horizon time corresponding to the trace data from the initial seafloor horizon time series; where M is an even number; sorting the seafloor horizon time corresponding to the trace data and the M adjacent trace times in ascending or descending order to obtain an ordered time series; determining whether the seafloor horizon time corresponding to the trace data is abnormal based on the ordered time series, and if so, adding an abnormal flag to the seafloor horizon time corresponding to the trace data in the initial seafloor horizon time series; traversing each seafloor horizon time in the initial seafloor horizon time series, removing the seafloor horizon times with the abnormal flag added, and performing linear interpolation on the removal points based on the seafloor horizon times in the initial seafloor horizon time series without the abnormal flag added to generate an intermediate seafloor horizon time series.

[0055] In an embodiment of the present invention, since each seafloor horizon time in the initial seafloor horizon time series has a one-to-one correspondence with each trace data in the amplitude envelope data, therefore, for each seafloor horizon time in the initial seafloor horizon time series (that is, for each seafloor horizon time corresponding to the trace data in the initial seafloor horizon time series), M adjacent trace times within a preset adjacent range of the seafloor horizon time are determined from the initial seafloor horizon time series. For example, for the seafloor horizon time t 12 corresponding to the 12th trace data in the initial seafloor horizon time series, 12 determining 4 adjacent seafloor horizon times as adjacent trace times from the initial seafloor horizon time series. For example, the seafloor horizon time t 10 (the seafloor horizon time corresponding to the 10th trace data), t 11 (the seafloor horizon time corresponding to the 11th trace data), t 13 (the seafloor horizon time corresponding to the 13th trace data), and t 14(The seafloor horizon time corresponding to the 14th data) is used as the seafloor horizon time t 12 The 4 adjacent trace times of 12 . Ascending or descending order the M + 1 horizon times composed of the seafloor horizon time corresponding to this trace data and the M adjacent trace times to obtain an ordered time series, and determine whether the seafloor horizon time corresponding to this trace data is abnormal based on the ordered time series. If so, add an abnormal flag to the seafloor horizon time corresponding to the trace data in the initial seafloor horizon time series.

[0056] Optionally, determining whether the seafloor horizon time corresponding to the trace data is abnormal based on the ordered time series includes: determining the median in the ordered time series and using the median as the reference time; judging whether the seafloor horizon time corresponding to the trace data is abnormal according to the reference time, the mean value and the mean square deviation of the ordered time series. Exemplarily, determine the median in the ordered time series and use the median as the reference time corresponding to this trace data, and use the 3σ principle to judge whether the seafloor horizon time corresponding to this trace data in the initial seafloor horizon time series is abnormal. Exemplarily, if the seafloor horizon time corresponding to this trace data in the initial seafloor horizon time series satisfies the following condition (1), it is determined that the seafloor horizon time corresponding to this trace data in the initial seafloor horizon time series is abnormal:;

[0057] |t - t ref | > 3σ(1)

[0058] Wherein,

[0059]

[0060] Wherein, t represents the seafloor horizon time corresponding to this trace data in the initial seafloor horizon time series, and t ref represents the reference time corresponding to this trace data, σ represents the mean square deviation of the ordered time series corresponding to the trace data, and μ represents the mean value of the ordered time series corresponding to the trace data.

[0061] It can be understood that abnormal flags can be added to each abnormal seafloor horizon time in the initial seafloor horizon time series in the above manner. Traverse each of the seafloor horizon times in the initial seafloor horizon time series, remove the seafloor horizon times with abnormal flags added, and perform linear interpolation on the removed seafloor horizon times based on the seafloor horizon times without abnormal flags added in the initial seafloor horizon time series to fill in the removed seafloor horizon times, generating an intermediate seafloor horizon time series

[0062] S140. For each trace of the amplitude envelope data, below the seafloor layer position time corresponding to the trace data in the middle seafloor layer position time series, traverse each sampling point in the trace data along the direction of increasing sampling time, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data.

[0063] In an embodiment of the present invention, for each trace of the amplitude envelope data, below (including the seafloor layer position time) the seafloor layer position time corresponding to the trace data in the middle seafloor layer position time series, traverse each sampling point in the trace data along the direction of increasing sampling time, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data. It can be understood that for each seismic trace in the amplitude envelope data, starting from the initial sampling point, traverse each sampling point in the seismic trace along the direction of increasing sampling time, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the seismic trace, where the initial sampling point is the sampling point whose sampling time is equal to the seafloor layer position time corresponding to the trace data in the middle seafloor layer position time series, or the initial sampling point is the first sampling point whose sampling time is greater than the seafloor layer position time corresponding to the trace data in the middle seafloor layer position time series along the direction of increasing sampling time.

[0064] It can be understood that using the middle seafloor layer position time series as a constraint, re-search and extract in each trace of the amplitude envelope data the first target sampling point below (including the sampling point corresponding to the seafloor layer position time) the seafloor layer position time corresponding to the middle seafloor layer position time series that satisfies x E x (t)≥A

[0065] S150. Update the middle seafloor layer position time series based on the sampling time corresponding to the target sampling point, use the updated middle seafloor layer position time series as the initial seafloor layer position time series, and return to execute the update of the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm until a preset condition is met, and generate a target seafloor layer position time series.

[0066] In an embodiment of the present invention, update the seafloor layer position time corresponding to the trace data in the middle seafloor layer position time series based on the sampling time corresponding to the target sampling point. In this way, the update of the middle seafloor layer position time series can be completed. Use the updated middle seafloor layer position time series as the initial seafloor layer position time series again, and return to execute the update of the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm, that is, return to execute S130, until a preset condition is met, and generate a target seafloor layer position time series where the preset condition includes that the root mean square change amount between the seafloor layer position times of two adjacent iterations is less than a preset change amount threshold, that is Less than a preset change amount threshold ε (usually set to 0.1 ms), where N represents the number of trace data included in the amplitude envelope data, or the number of iterations reaches a preset number threshold.

[0067] S160. Extract the seafloor horizons in the original shallow profile data based on the target seafloor horizon time series.

[0068] In the embodiment of the present invention, the seafloor horizons corresponding to the traces in the original shallow profile data are extracted according to the respective seafloor horizon times included in the target seafloor horizon time series.

[0069] In the embodiment of the present invention, to further verify the effectiveness of the shallow profile seafloor horizon extraction method provided by the embodiment of the present invention, an automatic extraction test of the seafloor horizons is performed on the noisy shallow subsurface profile data in the actual shallow water area. Exemplarily, Figure 3a This is the initial seafloor horizon extraction effect diagram provided by the embodiment of the present invention. As Figure 3a shown, in the noise abnormal area, the result of extracting the seafloor horizons based on the initial seafloor horizon time series is very poor ( Figure 3a the red curve in), and after smoothing the initial seafloor horizon time series (median filtering and linear interpolation), some noise abnormalities will be filtered out ( Figure 3a the green curve in). Figure 3b This is a schematic diagram of the seafloor horizons extracted after six iterations shown in the amplitude envelope data. As Figure 3b shown, the seafloor horizons extracted by each iteration filtering are closer to the real seafloor horizons. From Figure 3b it can be seen that the sixth iteration result has completely filtered out the noise interference, and the effect of extracting the seafloor horizons is relatively ideal ( Figure 3b the red curve in). Project the extracted seafloor horizons into the waveform data, Figure 3c This is a schematic diagram of the seafloor horizons extracted after six iterations shown in the original shallow profile data. According to Figure 3c it can be seen that the seafloor horizons extracted using the envelope data are very accurate. Figure 4 This is a schematic diagram of the seafloor horizons extracted by 1, 3, 5, and 6 iterations of filtering for the entire survey line provided by the embodiment of the present invention. Among them, Figure 4 in Figure represents the seafloor horizons extracted by 1 iteration of filtering, Figure represents the seafloor horizons extracted by 3 iterations of filtering, Figure represents the seafloor horizons extracted by 5 iterations of filtering, Figure represents the seafloor horizons extracted by 6 iterations of filtering. According to Figure 4It can be seen that due to noise interference, the initially extracted seafloor horizons deviate significantly from the true values. After multiple iterations, the outliers are gradually filtered out, and the finally extracted seafloor horizons basically eliminate the abnormal noise values, with an ideal extraction effect.

[0070] In the seafloor horizon extraction solution of the embodiment of the present invention, in response to the triggering of the seafloor horizon extraction event, the original shallow profile data is acquired, and the original shallow profile data is converted into amplitude envelope data; the seafloor horizon time corresponding to each trace data in the amplitude envelope data is respectively determined, and an initial seafloor horizon time series is generated based on the seafloor horizon time corresponding to each trace data; the initial seafloor horizon time series is updated based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor horizon time series; for each trace data in the amplitude envelope data, below the seafloor horizon time corresponding to the trace data in the intermediate seafloor horizon time series, each sampling point in the trace data is traversed in the direction of increasing sampling time, and the first target sampling point with an amplitude greater than or equal to the amplitude threshold corresponding to the trace data is determined; the intermediate seafloor horizon time series is updated based on the sampling time corresponding to the target sampling point, and the updated intermediate seafloor horizon time series is used as the initial seafloor horizon time series, and the process returns to execute the update of the initial seafloor horizon time series based on the median filtering algorithm and the linear interpolation algorithm until a preset condition is met to generate a target seafloor horizon time series; the seafloor horizons in the original shallow profile data are extracted based on the target seafloor horizon time series. Through the technical solution provided by the embodiment of the present invention, the disadvantages of poor effect and low accuracy in automatically extracting seafloor horizons in traditional methods for low signal-to-noise ratio data can be effectively overcome, the manual participation is reduced, and the efficiency and accuracy of seafloor horizon extraction in shallow stratigraphic profile data are improved.

[0071] Figure 5 It is a schematic structural diagram of a seafloor horizon extraction device provided by an embodiment of the present invention. As Figure 5 shown, the device includes:

[0072] An amplitude envelope data determination module 510, configured to acquire the original shallow profile data and convert the original shallow profile data into amplitude envelope data in response to the triggering of the seafloor horizon extraction event;

[0073] An initial seafloor horizon time series generation module 520, configured to respectively determine the seafloor horizon time corresponding to each trace data in the amplitude envelope data and generate an initial seafloor horizon time series based on the seafloor horizon time corresponding to each trace data;

[0074] An intermediate seafloor horizon time series generation module 530, configured to update the initial seafloor horizon time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor horizon time series;

[0075] A target sampling point determining module 540, configured to, for each trace data in the amplitude envelope data, traverse each sampling point in the trace data in the direction of increasing sampling time under the seafloor layer position time corresponding to the trace data in the intermediate seafloor layer position time series, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data;

[0076] A target seafloor layer position time series generating module 550, configured to update the intermediate seafloor layer position time series based on the sampling time corresponding to the target sampling point, use the updated intermediate seafloor layer position time series as the initial seafloor layer position time series, and return to execute updating the initial seafloor layer position time series based on a median filtering algorithm and a linear interpolation algorithm until a preset condition is satisfied, and generate a target seafloor layer position time series;

[0077] A seafloor layer position extracting module 560, configured to extract the seafloor layer position in the original shallow profile data based on the target seafloor layer position time series.

[0078] Optionally, the initial seafloor layer position time series generating module includes:

[0079] An amplitude threshold determining unit, configured to respectively determine the amplitude threshold corresponding to each trace data in the amplitude envelope data;

[0080] A seafloor layer position time determining unit, configured to, for each trace data in the amplitude envelope data, traverse each sampling point in the trace data in the direction of increasing sampling time, and use the sampling time corresponding to the first sampling point whose amplitude is greater than or equal to the amplitude threshold as the seafloor layer position time corresponding to the trace data.

[0081] Optionally, the amplitude threshold determining unit is configured to:

[0082] For each trace data in the amplitude envelope data, determine the amplitude mean and the amplitude standard deviation of the trace data;

[0083] Determine the amplitude threshold corresponding to the trace data according to the amplitude mean and the amplitude standard deviation.

[0084] Optionally, the intermediate seafloor layer position time series generating module includes:

[0085] A neighboring trace time determining unit, configured to, for the seafloor layer position time corresponding to each trace data in the initial seafloor layer position time series, determine M neighboring trace times within a preset adjacent range of the seafloor layer position time corresponding to the trace data from the initial seafloor layer position time series; where M is an even number;

[0086] An ordered time series generation unit for ascending or descendingly arranging the sea bottom layer time corresponding to the trace data and the times of the M adjacent traces to obtain an ordered time series;

[0087] An abnormal flag adding unit for determining whether the sea bottom layer time corresponding to the trace data is abnormal based on the ordered time series, and if so, adding an abnormal flag to the sea bottom layer time corresponding to the trace data in the initial sea bottom layer time series;

[0088] An intermediate sea bottom layer time series generation unit for traversing each sea bottom layer time in the initial sea bottom layer time series, removing the sea bottom layer times with the abnormal flag added, and linearly interpolating the removal points based on the sea bottom layer times in the initial sea bottom layer time series without the abnormal flag added to generate an intermediate sea bottom layer time series.

[0089] The abnormal flag adding unit is used for:

[0090] Determining the median in the ordered time series and using the median as the reference time;

[0091] Judging whether the sea bottom layer time corresponding to the trace data is abnormal according to the reference time, the mean value and the mean square deviation of the ordered time series.

[0092] Optionally, the preset conditions include:

[0093] The root mean square change amount between the sea bottom layer times of two adjacent iterations is less than a preset change amount threshold, or the number of iterations reaches a preset number threshold.

[0094] Optionally, it further includes:

[0095] A time difference correction module for determining the delay time recorded in the trace header corresponding to the original shallow profile data before converting the original shallow profile data into amplitude envelope data, and performing time difference correction on the original shallow profile data based on the delay time.

[0096] The shallow profile sea bottom layer extraction device provided by the embodiments of the present invention can execute the shallow profile sea bottom layer extraction method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0097] Figure 6The structural schematic diagram of the electronic device 10 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described herein and / or claimed.

[0098] As Figure 6 shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0099] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the shallow subsurface layer bit extraction method.

[0101] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, the computer program including program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-described functions defined in the method of the embodiment of the present invention are performed.

[0102] In some embodiments, the method for extracting shallow subsurface horizons can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for extracting shallow subsurface horizons described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the method for extracting shallow subsurface horizons by any other suitable means (e.g., by means of firmware).

[0103] The various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0106] For providing interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0107] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0108] A computing system may include a client and a server. The client and the server are generally far away from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0109] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0110] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for extracting shallow subsurface horizons, characterized in that, Including: In response to the triggering of the shallow seismic bottom layer extraction event, obtaining the original shallow seismic data and converting the original shallow seismic data into amplitude envelope data; Respectively determining the bottom layer time corresponding to each trace data in the amplitude envelope data, and generating an initial bottom layer time series based on the bottom layer times corresponding to each trace data; Updating the initial bottom layer time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate bottom layer time series; For each trace data in the amplitude envelope data, traversing each sampling point in the trace data in the direction of increasing sampling time below the bottom layer time corresponding to the trace data in the intermediate bottom layer time series, and determining the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data; Updating the intermediate bottom layer time series based on the sampling time corresponding to the target sampling point, taking the updated intermediate bottom layer time series as the initial bottom layer time series, and returning to execute the update of the initial bottom layer time series based on the median filtering algorithm and the linear interpolation algorithm until a preset condition is met to generate a target bottom layer time series; Extracting the bottom layer in the original shallow seismic data based on the target bottom layer time series.

2. The method according to claim 1, wherein Respectively determining the bottom layer time corresponding to each trace data in the amplitude envelope data, including: Respectively determining the amplitude threshold corresponding to each trace data in the amplitude envelope data; For each trace data in the amplitude envelope data, traversing each sampling point in the trace data in the direction of increasing sampling time, and taking the sampling time corresponding to the first sampling point whose amplitude is greater than or equal to the amplitude threshold as the bottom layer time corresponding to the trace data.

3. The method according to claim 2, characterized in that, Respectively determining the amplitude threshold corresponding to each trace data in the amplitude envelope data, including: For each trace data in the amplitude envelope data, determining the amplitude mean and amplitude standard deviation of the trace data; Determining the amplitude threshold corresponding to the trace data according to the amplitude mean and the amplitude standard deviation.

4. The method according to claim 1, wherein Updating the initial bottom layer time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate bottom layer time series, including: For the bottom layer time corresponding to each trace data in the initial bottom layer time series, determining M adjacent channel times within a preset adjacent range of the bottom layer time corresponding to the trace data from the initial bottom layer time series; where M is an even number; Ascending or descendingly arranging the bottom layer time corresponding to the trace data and the M adjacent channel times to obtain an ordered time series; Judging whether the bottom layer time corresponding to the trace data is abnormal based on the ordered time series, and if so, adding an abnormality flag to the bottom layer time corresponding to the trace data in the initial bottom layer time series; Traverse each of the seafloor layer position times in the initial seafloor layer position time series, remove the seafloor layer position times with the abnormal identifier added, and perform linear interpolation on the removal points based on the seafloor layer position times in the initial seafloor layer position time series without the abnormal identifier added to generate an intermediate seafloor layer position time series.

5. The method according to claim 4, wherein Judging whether the seafloor layer position time corresponding to the trace data is abnormal based on the sequential time series includes: Determine the median in the sequential time series, and use the median as the reference time; Judge whether the seafloor layer position time corresponding to the trace data is abnormal according to the reference time, the mean value and the mean square deviation of the sequential time series.

6. The method according to claim 1, characterized in that, The preset conditions include: The root mean square change amount between the seafloor layer position times of two adjacent iterations is less than a preset change amount threshold, or the number of iterations reaches a preset number threshold.

7. The method according to claim 1, characterized in that, Before converting the original shallow profile data into amplitude envelope data, it further includes: Determine the delay time recorded in the trace header corresponding to the original shallow profile data, and perform time difference correction on the original shallow profile data based on the delay time.

8. An apparatus for extracting shallow subsurface formation horizons, characterized in that, It includes: An amplitude envelope data determination module, configured to, in response to the triggering of the shallow profile seafloor layer position extraction event, acquire the original shallow profile data, and convert the original shallow profile data into amplitude envelope data; An initial seafloor layer position time series generation module, configured to respectively determine the seafloor layer position time corresponding to each trace data in the amplitude envelope data, and generate an initial seafloor layer position time series based on the seafloor layer position times corresponding to each trace data; An intermediate seafloor layer position time series generation module, configured to update the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm to generate an intermediate seafloor layer position time series; A target sampling point determination module, configured to, for each trace data in the amplitude envelope data, traverse each sampling point in the trace data in the direction of increasing sampling time below the seafloor layer position time corresponding to the trace data in the intermediate seafloor layer position time series, and determine the first target sampling point whose amplitude is greater than or equal to the amplitude threshold corresponding to the trace data; A target seafloor layer position time series generation module, configured to update the intermediate seafloor layer position time series based on the sampling time corresponding to the target sampling point, use the updated intermediate seafloor layer position time series as the initial seafloor layer position time series, and return to execute the update of the initial seafloor layer position time series based on the median filtering algorithm and the linear interpolation algorithm until the preset conditions are met, and generate a target seafloor layer position time series; A seafloor layer position extraction module, configured to extract the seafloor layer position in the original shallow profile data based on the target seafloor layer position time series.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the shallow profile seafloor layer position extraction method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the shallow seafloor layer bit extraction method according to any one of claims 1-7 when executed by a processor.

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