CN116341232B - Automatic generation method and system for sea breeze geological modeling horizon based on image recognition - Google Patents
Automatic generation method and system for sea breeze geological modeling horizon based on image recognitionInfo
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Abstract
The invention discloses a sea wind geological modeling horizon automatic generation method and system based on image recognition, which comprises the steps of firstly analyzing a seismic image obtained by engineering geophysical prospecting investigation, and obtaining seismic horizon data, determining the coring depth of the station according to the seismic horizon data, and performing drilling coring to obtain rock-soil data. And updating the seismic horizon data according to the rock-soil data, guiding drilling and coring according to the updated seismic horizon data, and repeating the iteration until the stratum comprehensive similarity of the rock-soil data and the seismic horizon data exceeds a set threshold. Through the interaction between the well drilling coring and the seismic horizon data, the iterative updating of the seismic horizon data is realized, and the accurate stratum horizon data is finally obtained.
Description
Technical Field
The invention relates to the technical field of marine wind farm geological survey data processing, in particular to a marine wind geological modeling horizon automatic generation method and system based on image recognition.
Background
With the development of renewable energy sources such as offshore wind power and the like, as one of important methods for geophysical prospecting data interpretation, the effect of seismic facies research on ocean engineering construction is also more and more obvious. In conventional geophysical prospecting investigation, formation descriptions are mainly used for determining sedimentary phases of the formation through earthquakes and combining geological, logging, rock and soil information, and the sedimentary phases are also called seismic phases in seismic data. The problems of geologic structure characteristics, lithology distribution, stratum characteristics and the like are judged by researching the changes of seismic phases including wave velocity, amplitude, phase, frequency, waveform and the like of seismic waves, so that accurate and comprehensive geologic information is provided for engineering design and construction.
At present, an image automatic tracking technology is mainly adopted for determining the horizon of the stratum, namely, a characteristic waveform is found, a seed point and a horizon azimuth angle are determined, and most similar pixel points at adjacent positions are tracked along the azimuth angle, so that horizon pickup is completed. However, in practical applications, offshore wind power is mostly built in shallow sea deposition environments with predominantly sand mud layers, blurred layers, ambiguous azimuth angles and discontinuities. Thus, the obtained formation horizon data is inaccurate.
Accordingly, the prior art is subject to improvement and advancement.
Disclosure of Invention
The invention mainly aims to provide an automatic generation method, an automatic generation system, an intelligent terminal and a storage medium for a sea wind geologic modeling horizon based on image recognition, and aims to solve the problem that stratum horizon data obtained in the prior art are inaccurate.
In order to achieve the above object, a first aspect of the present invention provides a method for automatically generating a sea-wind geologic modeling horizon based on image recognition, wherein the method comprises:
carrying out engineering geophysical prospecting investigation along the position of the fan to obtain a seismic image;
obtaining seismic horizon data based on the seismic image;
determining coring depth according to the seismic horizon data, and performing drilling coring to obtain rock-soil data;
Comparing the seismic horizon data with the stratum depth of each stratum in the rock-soil data to obtain the similarity of each stratum, and obtaining the comprehensive similarity according to the similarity of all the strata;
Updating the seismic horizon data according to the rock-soil data based on the consistency of the seismic homophase axis;
When the comprehensive similarity is smaller than a set threshold value, determining the coring depth of the next station according to the seismic horizon data, and performing well drilling coring and iterative updating on the seismic horizon data;
Outputting the seismic horizon data.
Optionally, the obtaining seismic horizon data based on the seismic image includes:
obtaining a plurality of stratum demarcation points according to the seismic image;
and obtaining seismic horizon data according to the stratum demarcation points based on seismic phase similarity and continuity.
Optionally, the obtaining seismic horizon data according to the stratum demarcation points based on the seismic phase similarity and the continuity includes:
Generating a plurality of automatic tracking line segments corresponding to the formation depth according to all formation demarcation points tracked by each formation depth;
the auto-tracked line segments of all formation depths constitute the seismic horizon data.
Optionally, the obtaining a plurality of formation demarcation points according to the seismic image includes:
Dividing the seismic image into a plurality of sub-images based on a preset sub-image size;
Screening all the sub-images of each depth to obtain a plurality of sub-images containing stratum demarcation points, and setting the stratum demarcation points as seed points;
Searching stratum demarcation points in a first preset number of sub-images in adjacent tracks by taking the sub-image in which the seed point is positioned as a center;
when a stratum demarcation point is found in the adjacent channel, setting the stratum demarcation point as a seed point and performing cyclic search;
And searching the next adjacent track until the second preset number of adjacent tracks are searched when the stratum demarcation point is not found in the adjacent tracks.
Optionally, the filtering all the sub-images of each depth to obtain a plurality of sub-images including the formation demarcation points includes:
And comparing the gray value of each pixel point in the sub-image with a preset pixel value, counting the number of the pixel points with gray values exceeding the preset pixel value, and setting the pixel point with the lowest gray value in the sub-image as the stratum demarcation point when the number of the pixel points exceeds a preset threshold value.
Optionally, the updating the seismic horizon data according to the geotechnical data based on the consistency of the seismic event comprises:
acquiring seismic homophase shafts near each stratum in the seismic horizon data and the rock-soil data;
and comparing the seismic in-phase axis of the rock-soil data of each stratum with the seismic in-phase axis of the seismic horizon data, and if the seismic in-phase axes are inconsistent, updating the seismic horizon data according to the rock-soil data.
Optionally, the number of strata of the geotechnical data is different from the number of strata of the seismic horizon data, and further includes:
And comparing the stratum of the geotechnical data and the stratum of the seismic horizon data to obtain a missing stratum in the seismic horizon data, and adding the missing stratum to the seismic horizon data.
The second aspect of the invention provides an automatic generation system of a sea wind geologic modeling horizon based on image recognition, wherein the system comprises:
The earthquake image module is used for carrying out engineering geophysical prospecting investigation along the position of the fan to obtain an earthquake image;
the seismic horizon module is used for obtaining seismic horizon data based on the seismic image;
The rock-soil data module is used for determining coring depth according to the seismic horizon data and performing drilling coring to obtain rock-soil data;
the comprehensive similarity module is used for comparing the seismic horizon data with the stratum depth of each stratum in the rock-soil data to obtain the similarity of each stratum and obtaining the comprehensive similarity according to the similarity of all the strata;
the updating module is used for updating the seismic horizon data according to the rock-soil data based on the consistency of the seismic event;
the comparison module is used for determining the coring depth of the next station according to the seismic horizon data and carrying out well drilling coring and iterative updating on the seismic horizon data when the comprehensive similarity is smaller than a set threshold value;
and the result module is used for outputting the seismic horizon data.
The third aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and an image-recognition-based sea-wind-geologic modeling horizon automatic generation program stored in the memory and executable on the processor, and the image-recognition-based sea-wind-geologic modeling horizon automatic generation program implements any one of the steps of the image-recognition-based sea-wind-geologic modeling horizon automatic generation method when executed by the processor.
A fourth aspect of the present invention provides a computer-readable storage medium, on which an image-recognition-based sea-wind-geologic modeling horizon automatic generation program is stored, which when executed by a processor, implements any one of the steps of the image-recognition-based sea-wind-geologic modeling horizon automatic generation method.
From the above, the method of the invention firstly analyzes the seismic image obtained by engineering geophysical prospecting to obtain the seismic horizon data, then determines the coring depth of the station according to the seismic horizon data, and performs drilling coring to obtain the rock-soil data. And updating the seismic horizon data according to the rock-soil data, guiding drilling and coring according to the updated seismic horizon data, and repeating the iteration until the stratum comprehensive similarity of the rock-soil data and the seismic horizon data exceeds a set threshold. Compared with the prior art, the method realizes the iterative updating of the seismic horizon data through the interaction between the drilling coring and the seismic horizon data, and finally obtains accurate stratum horizon data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an automatic generation method of a sea wind geologic modeling horizon based on image recognition, which is provided by the embodiment of the invention;
FIG. 2 is a schematic view of a seismic image of the embodiment of FIG. 1;
FIG. 3 is a schematic diagram of an auto-trace segment of the embodiment of FIG. 1;
FIG. 4 is a schematic view of four strata obtained from a seismic image in the embodiment of FIG. 1;
FIG. 5 is a schematic flowchart of step S200 in the embodiment of FIG. 1;
FIG. 6 is a schematic view of segmentation of an image in the embodiment of FIG. 1;
FIG. 7 is a schematic illustration of determining formation demarcation points in a sub-image in the embodiment of FIG. 1;
FIG. 8 is a schematic diagram of a search for adjacent track sub-images in the embodiment of FIG. 1;
FIG. 9 is a schematic diagram of comparison results of the F16 occupancy similarity in the embodiment of FIG. 1;
FIG. 10 is a diagram showing the comparison result of the F21 occupancy similarity in the embodiment of FIG. 1;
FIG. 11 is a diagram showing comparison results of F19 occupancy similarity in the embodiment of FIG. 1;
FIG. 12 is a schematic diagram of an automatic generation system of a sea-wind geologic modeling horizon based on image recognition provided by an embodiment of the invention;
FIG. 13 is a schematic diagram of an implementation of a sea-wind geologic modeling horizon automatic generation system based on image recognition;
fig. 14 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a condition or event described is determined" or "if a condition or event described is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a condition or event described" or "in response to detection of a condition or event described".
The following description of the embodiments of the present invention will be made more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown, it being evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Aiming at the problem that the stratum level data obtained by the current image automatic tracking technology for determining the stratum level is inaccurate, the invention provides a sea wind geological modeling horizon automatic generation method based on image recognition, which comprises the steps of firstly analyzing the seismic image obtained by engineering geophysical prospecting investigation to obtain the seismic horizon data, and guiding the drilling coring according to the seismic horizon data, adjusting and upgrading the seismic horizon data according to the rock-soil data obtained by the drilling coring, and realizing iterative updating of the seismic horizon data through interaction between the drilling coring and the seismic horizon data to finally obtain accurate stratum horizon data.
Exemplary method
As shown in fig. 1, an embodiment of the present invention provides a method for automatically generating a sea-wind geologic modeling horizon based on image recognition, which is deployed on various electronic terminals, such as a mobile terminal, a computer or a server, and specifically includes the following steps:
step 100, carrying out engineering geophysical prospecting investigation along the position of a fan to obtain a seismic image;
Specifically, in the offshore wind farm land exploration project, the exploration work in the feasibility research stage is the focus of the whole offshore wind power engineering exploration, and the stratum composition and distribution condition of a field, the physical mechanical parameters and the burial depth of a basic bearing layer of a fan unit, the main engineering geological problems and the like need to be ascertained. If the pre-investigation stage is completed, the preliminary layout of fans is combined before geotechnical drilling, and if the pre-investigation stage is completed, the seismic data obtained by the geophysical prospecting method is coarse and super or is far away from the layout position of fans, the seismic line or the encrypted line is re-run at the layout position of fans at the front edge of drilling, so that the seismic image shown in figure 2 is obtained, wherein the seismic line is a line arranged along the position of the design fans.
Step 200, obtaining seismic horizon data based on the seismic image;
In particular, seismic horizon data is made up of a series of seismic waveform sequences, adjacent which often appear laterally as seismic event axes. In the seismic facies interpretation, since the seismic in-phase axis on the seismic section is the seismic response of a certain stratum or the synthetic seismic response of a certain stratum, the seismic in-phase axis has a certain corresponding relation with the stratum, so that the continuous tracking of the seismic in-phase axis on the actual seismic section is a precondition of horizon interpretation.
The seismic horizon data may be obtained by analyzing the seismic image using a variety of image processing methods. In one example, the seismic image is smoothed using mean filtering, and the value of each byte of the original pixel is subtracted from the value of each byte of the smoothed pixel to obtain a new pixel (i.e., the value of this pixel is the difference between the two). After the gray level processing of the image is carried out, the pixel value smaller than the threshold value is assigned to 0, other values are assigned to 255, and the black whitening processing is completed, so that the basic seismic horizon data can be obtained after the black whitening.
In this embodiment, the earthquake image is first grayed, and then based on the principles of similarity and continuity of earthquake phases, a plurality of formation demarcation points are obtained according to the earthquake image, the formation demarcation points are tracking points on the earthquake section, and the connection lines of the formation demarcation points form automatic tracking line segments. Specifically, all stratum demarcation points tracked by each depth are connected, and a plurality of automatic tracking line segments corresponding to each depth are generated. All auto-tracking line segments constitute seismic horizon data. This method will be referred to as the full horizon tracking method hereinafter. After full-horizon tracking, several auto-tracking segments, also called seismic event, are obtained as shown in FIG. 3, which can be used to explain the formation in detail. In order to improve the accuracy of interpretation of the formation by the auto-trace line segments, it is preferable that the number of auto-trace line segments is 3 times or more the number of formation to be generated. Taking the preset fan F16 position as an example, as shown in fig. 3 and 4, four strata are generated, and a total of 14 automatic tracking line segments pass through the F16 fan position.
The method comprises the following steps of obtaining stratum demarcation points according to the seismic image, as shown in fig. 5;
Step S210, dividing the seismic image into a plurality of sub-images based on a preset sub-image size;
Specifically, a seismic section corresponds to a subsurface formation that is deployed from shallow to deep in depth from the surface along the survey line direction (also known as the gather direction). As shown in fig. 6, the image segmentation can be performed on the seismic data of a certain line which has been processed into a gray scale image by finding the upper left corner of the seismic image and setting the corner as the origin, expanding the seismic image by taking the gather direction as the horizontal axis and the time direction (also referred to as the depth direction) as the vertical axis, and sequentially and evenly segmenting the image from the upper left top point to the lower right bottom point along the gather direction and then along the time direction according to the preset sub-image size, such as 5x5 or 10x10 pixel points. For example, the seismic diagram of fig. 6 has 1580×990 pixels, 5×5 pixels are set as a sub-image, the left vertex fixed gather start point is set to 0, m=316 sub-images are set in the gather direction of the horizontal axis in a certain fixed time direction, and similarly, the left vertex time start point (T0) is set to 0, and n=198 sub-images are set in the time direction of the vertical axis in a certain fixed gather direction. Thus, the seismogram of fig. 6 is co-segmented into m×n= 62568 sub-images.
Step S220, screening all sub-images of each depth to obtain a plurality of sub-images containing stratum demarcation points, and setting the stratum demarcation points as seed points;
Specifically, each sub-image at each depth is searched from shallow to deep from a time starting point T0, and for example, the seismogram of fig. 6 is taken as an example, and whether each sub-image has a formation demarcation point is determined along the gather direction. The judging method comprises the steps of comparing the gray value of each pixel point in the sub-image with a preset pixel value, counting the number of the pixel points with gray values exceeding the preset pixel value, judging that the sub-image contains stratum demarcation points when the number of the pixel points exceeds a preset threshold value, and setting the pixel point with the lowest gray value in the sub-image as the stratum demarcation point (also called as an effective pixel point). Taking fig. 7 as an example, assume that the preset pixel value is 150, the preset threshold value is 9, the pixel values of all the pixels in the sub-image a in fig. 7 are higher than the preset pixel value, so that no formation demarcation point is included in the sub-image a, the pixel values of the 6 pixels in the sub-image B in fig. 7 are lower than the preset pixel value, but the number is smaller than the preset threshold value 9, so that no formation demarcation point is included in the sub-image B, and the pixel values of the 14 pixels in the sub-image C in fig. 7 are lower than the preset pixel value, and the number is larger than the preset threshold value 9, so that the formation demarcation point is included in the sub-image C. The pixel point corresponding to the lowest pixel value in the sub-image C is the stratum demarcation point. After the formation demarcation points of each depth are obtained, tracking and searching of adjacent channels are respectively carried out by taking the formation demarcation points as seed points in the subsequent steps.
Step S230, searching stratum demarcation points in a first preset number of sub-images in adjacent tracks by taking the sub-image in which the seed points are positioned as a center;
Specifically, the seed point is taken as a starting point, and the effective pixel points of the adjacent tracks are tracked at a certain angle. As shown in fig. 8, a first preset number of sub-images in adjacent tracks are obtained with the sub-image in which the seed point is located as the center. For example, assuming that the first preset number is five, a sub-image, which is the same as the ordinate of the sub-image where the seed point is located, in the adjacent track, and two sub-images above and two sub-images below are acquired, and five sub-images are obtained. And then, respectively adopting the same judging method in the step S220 for the five sub-images to judge whether the formation demarcation points exist in the sub-images. Although the present embodiment tracks the sub-image of the adjacent track with the sub-image of the seed point as the center, the angle is not particularly limited, and the sub-image of the adjacent track that is level with and below the sub-image of the seed point may be tracked.
Step S240, when a stratum demarcation point is found in the adjacent channel, setting the stratum demarcation point as a seed point and performing cyclic search;
specifically, when the stratum demarcation point is searched in the adjacent track, the searched stratum demarcation point is used as a seed point, and the sub-image of the adjacent track of the seed point is searched by adopting the method of step S230 to perform the cyclic search.
When more than one sub-image in the adjacent channels contains stratum demarcation points, comparing the pixel values of the stratum demarcation points, selecting the pixel point with the lowest pixel value as the final stratum demarcation point, and discarding the rest stratum demarcation points.
Step S250, searching the next adjacent track until the second preset number of adjacent tracks are searched when the stratum demarcation point is not found in the adjacent tracks.
Specifically, if no formation boundary point is found in the adjacent tracks, searching (also referred to as translation searching) is performed on the sub-image of the same vertical coordinate position of the next adjacent track of the current seed point, if no formation boundary point is found yet, searching is continued for the next adjacent track again until a second preset number of adjacent tracks are found yet, and if no formation boundary point is found yet, the searching process is ended. For example, assuming that the gather where the seed point is located is a, the second preset number is 3, and the adjacent tracks of a are arranged in the order B, C and D. If the stratum demarcation point is not searched in the adjacent channel B, searching the adjacent channel C, and still not searching, searching the adjacent channel D, and ending the searching process of the section when the stratum demarcation point is not searched yet.
And after the searching is finished, connecting the searched stratum demarcation points to obtain an automatic tracking line segment.
The full-layer position tracking method can also be applied to seismic images with fuzzy layers, ambiguous azimuth angles and discontinuous azimuth angles, and higher recognition accuracy is obtained.
In this embodiment, the full level bit-tracking is implemented by searching all sub-images globally in two directions, first from near to far in the gather direction and then from shallow to deep in the temporal direction. To avoid horizon crossing and repeated searches, the searched sub-images are marked. Taking fig. 6 as an example, firstly, at the time T0, traversing all (0, m) sub-images in the searching gather direction along the gather direction, and then, at the time, repeating the step at the time T0 to traverse all (n, m) sub-images of the gray map. And then evaluating the result in a man-machine interaction mode, and if the automatically picked result is coarser or transitionally picked, the seismic interpretation expert can carry out local correction and fine depiction by adjusting parameters such as the size of the sub-image, the preset pixel value, the preset threshold value, the first preset quantity, the second preset quantity and the like, so that the accuracy, the rationality and the reliability of the horizon detail and the overall trend are ensured.
Step 300, determining coring depth according to the seismic horizon data and performing drilling coring to obtain rock-soil data;
Specifically, marine geotechnical surveys typically integrate fixed-point geotechnical surveys with regional engineering geophysical surveys to build a regional engineering geological model, so that point-to-surface cognition of the regional deposition environment is achieved in a very fine manner. The earthquake phase analysis can accurately determine stratum layer position and target layer earthquake phase sign only by observing underground rock and soil samples and testing the samples after drilling and coring in geotechnical engineering investigation. Unlike marine oil and gas field exploration, offshore wind farms are characterized by large wind farm footprints and multiple wind turbine sites. Thus, the number and location of coring operations is particularly critical. Coring is typically done discontinuously, and under complex geological conditions insufficient coring numbers or position selection errors can cause significant difficulties in seismic phase investigation and horizon determination. In contrast, in areas where the geology of an area is relatively simple, coring each designed fan station will greatly increase the survey cost. The preferred solution is that the geotechnical engineer adjusts the coring scheme and the station position arrangement on site according to the coring result, however, the interpretation of the earthquake phase has multiple resolvability, and the geotechnical engineer is not an earthquake interpretation expert generally, and cannot accurately interpret the earthquake data on site in a short time. The embodiment determines coring depth according to the seismic horizon data to perform drilling coring to obtain geotechnical data.
Before the rock and soil data are not available, the stratum is obtained through full-layer bit tracking, as shown in fig. 4, and seismic interpretation is performed according to the seismic horizon data, so that four horizons (hor.10, hor.20, hor.30 and hor.40) are obtained. Also taking the preset fan F16 position as an example, before the actual measurement of the rock and soil data is not performed, the depth of the earthquake prediction first layer (hor.10) is 7.3 meters. When designing the coring depth of the first station, the seismic horizon data should be fully considered to enhance coring near the boundary surface of the formation inversion. It should also be appreciated that the seismic is interpreted as a function of the wave impedance of the formation interface, which does not fully reflect the formation medium.
Step 400, comparing the stratum depth of each stratum in the seismic horizon data and the rock-soil data to obtain the similarity of each stratum, and obtaining the comprehensive similarity according to the similarity of all the strata;
Specifically, after rock-soil data of the first station (F16) is obtained, the rock-soil data is subjected to time-to-sound conversion, and then the similarity of each stratum is compared with the seismic horizon data, and then the comprehensive similarity is calculated.
In this embodiment, the formation depth obtained by rock-soil analysis is directly divided from the formation depth of the seismic horizon data, and the similarity is calculated. As shown in fig. 9, the first layer (hor.10) of seismic horizon data (i.e., seismic predictions) is 7.3 meters below the mud level, the rock-soil data is measured (i.e., rock-soil measured) is 6.1 meters, the first layer similarity is 84%, and so on, the second layer similarity is 99%, the third layer similarity is 100%, and the fourth layer similarity is 97%. The similarity values of the four layers are averaged to obtain a comprehensive similarity (accuracy) of 94.0%.
Step S500, based on the consistency of the seismic event, updating seismic horizon data according to the rock-soil data;
Specifically, after the comparison is completed, the nearest seismic event near the measured depth of each stratum is automatically searched, and if the seismic event is different from the seismic horizon data, the system automatically modifies and upgrades the seismic horizon data. The step S200 is adopted to trace and search the actual measurement depth of each stratum, the seismic event obtained by searching is compared with the seismic event in the seismic horizon data, if the seismic event is different, the seismic event in the seismic horizon data is updated by the seismic event obtained by searching, namely, not only the stratum depth in the seismic horizon data is updated according to the rock-soil data, but also the tracking and searching are carried out according to the updated stratum depth, and stratum data is obtained to update the seismic event in the seismic horizon data.
Further, when the number of strata of the geotechnical data is different from the number of strata of the seismic horizon data, stratum omission is generally generated in the seismic horizon data, the stratum of the geotechnical data and the stratum of the seismic horizon data are compared, the stratum missing in the seismic horizon data is determined, and the stratum missing is added to the seismic horizon data.
Step 600, when the comprehensive similarity is smaller than a set threshold value, determining the coring depth of the next station according to the seismic horizon data, and performing well drilling coring and iterative updating of the seismic horizon data;
And S700, outputting the seismic horizon data.
Specifically, the integrated similarity can measure the correlation between the seismic horizon data and the earth and rock data obtained from the coring of the borehole. When the integrated similarity exceeds a set threshold, meaning that the seismic horizon data is close to the data obtained from the coring of the borehole, then the station sampling on the survey line may be ended. When the comprehensive similarity is smaller than the set threshold, the coring depth of the next station is required to be determined according to the seismic horizon data, drilling and coring are continued, and iteration updating is continuously performed on the seismic horizon data according to the rock and soil data obtained through drilling and coring, so that the accuracy of the seismic horizon data is improved.
In this embodiment, after the first station (F16) has been drilled, updated seismic horizon data (i.e., updated formation interpretation) after the previous drill coring should be fully considered when designing the coring depth for the second station (F21). Based on the coring result of the second station (F21), the seismic horizon data is updated again as in step S500. As shown in fig. 10, at the new site (F21), taking the first layer (hor.10) as an example, the earthquake prediction is 6.2 meters, the rock-soil actual measurement is 5.7 meters, the similarity is 91%, and the accuracy of the earthquake prediction value is obviously improved because the initial stratum interpretation is already corrected at the first site. The overall similarity of the five horizons is 96.8%. And the like, until the comprehensive similarity exceeds a preset threshold after the Nth station coring is completed, and the station sampling on the measuring line is finished.
As shown in fig. 11, at the third station (F19), the earthquake prediction of the first layer (hor.10) is 6.2 meters, the actual measurement of the rock and soil is 6.1 meters, the similarity is 98%, the comprehensive similarity (accuracy) is 97.3%, and if the integrated similarity exceeds the preset threshold value of 97.0%, the station sampling on the test line is finished. The obtained seismic horizon data is accurate stratum data.
The drilling and sampling station is in principle located on the fan station. For the situation of this embodiment, the conventional manner needs to drill and sample at six fan preset positions to perform the geotechnical investigation, but this embodiment can meet the investigation requirement only by sampling at three fan positions. Therefore, the field investigation cost can be effectively reduced.
By adopting full-layer phase tracking and updating the effective interpretation of the earthquake, the deviation generated by automatically interpreting the earthquake image is avoided to the greatest extent. The field on-site earthquake rock-soil data is compared with the real-time stratum interpretation, so that the accuracy of automatic tracking of the existing earthquake horizon is improved, and the practicability is also improved. Compared with the traditional method, the method improves the reliability and accuracy of the geological survey data, can be applied to more application scenes, can accurately explain the seismic facies, and has accurate, reasonable and reliable seismic facies horizon details and overall trends. In addition, the method is also beneficial to changing the existing geotechnical operation mode from the advanced design of the station position to the on-site determination of the station position, and the cost reduction and efficiency improvement of the sea-wind land investigation are realized by reducing the drilling station position.
Exemplary System
As shown in fig. 12, corresponding to the above-mentioned method for automatically generating a sea-wind geologic modeling horizon based on image recognition, the embodiment of the invention further provides a system for automatically generating a sea-wind geologic modeling horizon based on image recognition, where the system for automatically generating a sea-wind geologic modeling horizon based on image recognition includes:
The seismic image module 600 is used for carrying out engineering geophysical prospecting investigation along the position of the fan to obtain a seismic image;
A seismic horizon module 610 for obtaining seismic horizon data based on the seismic images;
a geotechnical data module 620, configured to determine a coring depth according to the seismic horizon data and perform drilling coring to obtain geotechnical data;
the comprehensive similarity module 630 is configured to compare the seismic horizon data and the formation depth of each formation in the geotechnical data, obtain the similarity of each formation, and obtain the comprehensive similarity according to the similarity of all formations;
An updating module 640, configured to update the seismic horizon data according to the geotechnical data based on consistency of the seismic event;
A comparison module 650 for determining a coring depth of a next station from the seismic horizon data and performing well coring and iteratively updating the seismic horizon data when the integrated similarity is less than a set threshold;
a results module 660 for outputting the seismic horizon data.
Specifically, the implementation architecture of the sea-wind geologic modeling horizon automatic generation system of the embodiment is shown in fig. 13, and mainly comprises a data layer, a functional layer and a display layer. The data layer is used for storing seismic data and rock-soil data, and comprises an original database, a processing database and a relational database. The method comprises the steps of storing shallow profile, single-channel and multi-channel high-resolution BOOMER, electric spark and seismic data (hereinafter collectively referred to as seismic data) of engineering geophysical prospecting in an original database, coring, field testing and CPT data (hereinafter collectively referred to as geotechnical data) of geotechnical engineering prospecting, storing processed seismic data (such as seismic horizon data updated each time) in a processing database, and storing correlation data (such as similarity and comprehensive similarity) established according to the seismic data and the geotechnical data in a relational database.
The functional layer mainly comprises functions of image processing, image recognition, phase tracking, stratum interpretation, seismic geotechnical data comparison and the like. The seismic image module 600 performs image processing functions, the seismic horizon module 610 performs functions such as image recognition, phase tracking, and stratum interpretation, the comprehensive similarity module 630 and the updating module 640 perform seismic geotechnical data comparison, and the geotechnical data module 620 performs processing on geotechnical engineering actual measurement data. The functional layer also includes data interaction functions for interacting with the data layer, the display layer, such as real-time data refresh, real-time data access, and relational data access.
The display layer is used for displaying the image processing process, the earthquake horizon, the rock-soil horizon, the combined display of earthquake and rock-soil and the stratum interpretation display.
It should be noted that formation interpretation is required before and during the geotechnical investigation operation. Horizon partitioning is based primarily on seismic characteristics, including tracking segment length, angle, continuity and repeatability, before there is no geotechnical data. The seismic and geotechnical data correlation analysis of the functional layer is embodied in geotechnical operation implementation. After the rock and soil data is acquired, the functional layer modifies and updates the previous earthquake prediction horizon in real time according to the rock and soil data and establishes iteration. After the field collection of each station is completed, the field geotechnical engineer inputs the actual measurement result into a data layer, establishes or modifies a relational database in a man-machine interaction mode, and sets a horizon to be modified. According to the principle of nearby, the automatic tracking line section closest to the rock-soil measured horizon is set as a new stratum horizon mark, the stratum horizon interpretation and prediction accuracy is updated in real time, and when the new correlation accuracy of the correlation coefficient database is greater than a preset threshold value, new rock-soil sampling is not performed in the line.
Specifically, in this embodiment, specific functions of each module of the above-mentioned image-recognition-based sea-wind-geologic modeling horizon automatic generation system may refer to corresponding descriptions in the above-mentioned image-recognition-based sea-wind-geologic modeling horizon automatic generation method, which are not described herein again.
Based on the above embodiment, the present invention also provides an intelligent terminal, and a functional block diagram thereof may be shown in fig. 14. The intelligent terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. The processor of the intelligent terminal is used for providing computing and control capabilities. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The nonvolatile storage medium stores an operating system and an automatic sea-wind geologic modeling horizon generation program based on image recognition. The internal memory provides an environment for the operation of an operating system in a nonvolatile storage medium and an automatic sea-wind geologic modeling horizon generating program based on image recognition. The network interface of the intelligent terminal is used for communicating with an external terminal through network connection. The method for automatically generating the sea-wind geologic modeling horizon based on the image recognition comprises the step of realizing any one of the automatic generation methods of the sea-wind geologic modeling horizon based on the image recognition when the automatic generation program of the sea-wind geologic modeling horizon based on the image recognition is executed by a processor. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be appreciated by those skilled in the art that the schematic block diagram shown in fig. 14 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the smart terminal to which the present inventive arrangements are applied, and that a particular smart terminal may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, there is provided a smart terminal including a memory, a processor, and an image-recognition-based sea-wind geologic modeling horizon auto-generation program stored on the memory and executable on the processor, the image-recognition-based sea-wind geologic modeling horizon auto-generation program executing by the processor:
carrying out engineering geophysical prospecting investigation along the position of the fan to obtain a seismic image;
obtaining seismic horizon data based on the seismic image;
determining coring depth according to the seismic horizon data, and performing drilling coring to obtain rock-soil data;
Comparing the seismic horizon data with the stratum depth of each stratum in the rock-soil data to obtain the similarity of each stratum, and obtaining the comprehensive similarity according to the similarity of all the strata;
Updating the seismic horizon data according to the rock-soil data based on the consistency of the seismic homophase axis;
When the comprehensive similarity is smaller than a set threshold value, determining the coring depth of the next station according to the seismic horizon data, and performing well drilling coring and iterative updating on the seismic horizon data;
Outputting the seismic horizon data.
Optionally, the obtaining seismic horizon data based on the seismic image includes:
obtaining a plurality of stratum demarcation points according to the seismic image;
and obtaining seismic horizon data according to the stratum demarcation points based on seismic phase similarity and continuity.
Optionally, the obtaining seismic horizon data according to the stratum demarcation points based on the seismic phase similarity and the continuity includes:
Generating a plurality of automatic tracking line segments corresponding to the formation depth according to all formation demarcation points tracked by each formation depth;
the auto-tracked line segments of all formation depths constitute the seismic horizon data.
Optionally, the obtaining a plurality of formation demarcation points according to the seismic image includes:
Dividing the seismic image into a plurality of sub-images based on a preset sub-image size;
Screening all the sub-images of each depth to obtain a plurality of sub-images containing stratum demarcation points, and setting the stratum demarcation points as seed points;
Searching stratum demarcation points in a first preset number of sub-images in adjacent tracks by taking the sub-image in which the seed point is positioned as a center;
when a stratum demarcation point is found in the adjacent channel, setting the stratum demarcation point as a seed point and performing cyclic search;
And searching the next adjacent track until the second preset number of adjacent tracks are searched when the stratum demarcation point is not found in the adjacent tracks.
Optionally, the filtering all the sub-images of each depth to obtain a plurality of sub-images including the formation demarcation points includes:
And comparing the gray value of each pixel point in the sub-image with a preset pixel value, counting the number of the pixel points with gray values exceeding the preset pixel value, and setting the pixel point with the lowest gray value in the sub-image as the stratum demarcation point when the number of the pixel points exceeds a preset threshold value.
Optionally, the updating the seismic horizon data according to the geotechnical data based on the consistency of the seismic event comprises:
acquiring seismic homophase shafts near each stratum in the seismic horizon data and the rock-soil data;
and comparing the seismic in-phase axis of the rock-soil data of each stratum with the seismic in-phase axis of the seismic horizon data, and if the seismic in-phase axes are inconsistent, updating the seismic horizon data according to the rock-soil data.
Optionally, the number of strata of the geotechnical data is different from the number of strata of the seismic horizon data, and further includes:
And comparing the stratum of the geotechnical data and the stratum of the seismic horizon data to obtain a missing stratum in the seismic horizon data, and adding the missing stratum to the seismic horizon data.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium is stored with an image-recognition-based sea-wind geologic modeling horizon automatic generation program, and when the image-recognition-based sea-wind geologic modeling horizon automatic generation program is executed by a processor, the steps of any one of the image-recognition-based sea-wind geologic modeling horizon automatic generation methods provided by the embodiment of the invention are realized.
It should be understood that the sequence number of each step in the above embodiment does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not be construed as limiting the implementation process of the embodiment of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units described above is merely a logical function division, and may be implemented in other manners, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of each method embodiment may be implemented. The computer program comprises computer program code, and the computer program code can be in a source code form, an object code form, an executable file or some intermediate form and the like. The computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. The content of the computer readable storage medium can be appropriately increased or decreased according to the requirements of the legislation and the patent practice in the jurisdiction.
The embodiments described above are only for illustrating the technical solution of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solution described in the foregoing embodiments may be modified or some of the technical features may be replaced equally, and that the modifications or replacements are not essential to the corresponding technical solution but are included in the scope of protection of the present invention.
Claims (8)
1. The sea wind geologic modeling horizon automatic generation method based on image recognition is characterized by comprising the following steps:
carrying out engineering geophysical prospecting investigation along the position of the fan to obtain a seismic image;
obtaining seismic horizon data based on the seismic image;
determining coring depth according to the seismic horizon data, and performing drilling coring to obtain rock-soil data;
Comparing the seismic horizon data with the stratum depth of each stratum in the rock-soil data to obtain the similarity of each stratum, and obtaining the comprehensive similarity according to the similarity of all the strata;
Updating the seismic horizon data according to the rock-soil data based on the consistency of the seismic homophase axis;
When the comprehensive similarity is smaller than a set threshold value, determining the coring depth of the next station according to the seismic horizon data, and performing well drilling coring and iterative updating on the seismic horizon data;
Outputting the seismic horizon data;
the obtaining seismic horizon data based on the seismic image includes:
obtaining a plurality of stratum demarcation points according to the seismic image;
based on the similarity and continuity of the seismic phases, obtaining seismic horizon data according to the stratum demarcation points;
the obtaining a plurality of stratum demarcation points according to the seismic image comprises the following steps:
Dividing the seismic image into a plurality of sub-images based on a preset sub-image size;
Screening all the sub-images of each depth to obtain a plurality of sub-images containing stratum demarcation points, and setting the stratum demarcation points as seed points;
Searching stratum demarcation points in a first preset number of sub-images in adjacent tracks by taking the sub-image in which the seed point is positioned as a center;
when a stratum demarcation point is found in the adjacent channel, setting the stratum demarcation point as a seed point and performing cyclic search;
And searching the next adjacent track until the second preset number of adjacent tracks are searched when the stratum demarcation point is not found in the adjacent tracks.
2. The method for automatically generating the sea-wind geologic modeling horizon based on image recognition according to claim 1, wherein the obtaining seismic horizon data from the formation demarcation points based on seismic phase similarity and continuity comprises:
Generating a plurality of automatic tracking line segments corresponding to the formation depth according to all formation demarcation points tracked by each formation depth;
the auto-tracked line segments of all formation depths constitute the seismic horizon data.
3. The method for automatically generating the sea-wind geologic modeling horizon based on image recognition according to claim 1, wherein the step of screening all sub-images of each depth to obtain a plurality of sub-images including formation demarcation points comprises the steps of:
And comparing the gray value of each pixel point in the sub-image with a preset pixel value, counting the number of the pixel points with gray values exceeding the preset pixel value, and setting the pixel point with the lowest gray value in the sub-image as the stratum demarcation point when the number of the pixel points exceeds a preset threshold value.
4. The method for automatically generating the sea-wind geologic modeling horizon based on image recognition according to claim 1, wherein updating the seismic horizon data based on the consistency of seismic event, according to the geotechnical data, comprises:
acquiring seismic homophase shafts near each stratum in the seismic horizon data and the rock-soil data;
and comparing the seismic in-phase axis of the rock-soil data of each stratum with the seismic in-phase axis of the seismic horizon data, and if the seismic in-phase axes are inconsistent, updating the seismic horizon data according to the rock-soil data.
5. The method for automatically generating a sea-wind geologic modeling horizon based on image recognition according to claim 4, wherein a number of strata of the geotechnical data is different from a number of strata of the seismic horizon data, further comprising:
And comparing the stratum of the geotechnical data and the stratum of the seismic horizon data to obtain a missing stratum in the seismic horizon data, and adding the missing stratum to the seismic horizon data.
6. A system for automatically generating a sea-wind geologic modeling horizon based on image recognition, wherein the system is configured to implement the method for automatically generating a sea-wind geologic modeling horizon based on image recognition of any one of claims 1-5, the system comprising:
The earthquake image module is used for carrying out engineering geophysical prospecting investigation along the position of the fan to obtain an earthquake image;
the seismic horizon module is used for obtaining seismic horizon data based on the seismic image;
The rock-soil data module is used for determining coring depth according to the seismic horizon data and performing drilling coring to obtain rock-soil data;
the comprehensive similarity module is used for comparing the seismic horizon data with the stratum depth of each stratum in the rock-soil data to obtain the similarity of each stratum and obtaining the comprehensive similarity according to the similarity of all the strata;
the updating module is used for updating the seismic horizon data according to the rock-soil data based on the consistency of the seismic event;
the comparison module is used for determining the coring depth of the next station according to the seismic horizon data and carrying out well drilling coring and iterative updating on the seismic horizon data when the comprehensive similarity is smaller than a set threshold value;
and the result module is used for outputting the seismic horizon data.
7. The intelligent terminal is characterized by comprising a memory, a processor and an image-recognition-based sea-wind geologic modeling horizon automatic generation program which is stored in the memory and can run on the processor, wherein the image-recognition-based sea-wind geologic modeling horizon automatic generation program realizes the steps of the image-recognition-based sea-wind geologic modeling horizon automatic generation method according to any one of claims 1 to 5 when being executed by the processor.
8. A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon an image-recognition-based sea-wind geologic modeling horizon automatic generation program, which when executed by a processor, implements the steps of the image-recognition-based sea-wind geologic modeling horizon automatic generation method according to any one of claims 1 to 5.
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