CN115471615B - Ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging - Google Patents
Ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging Download PDFInfo
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Abstract
The invention discloses an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging, and relates to the technical field of ultra-deep hole detection. Aerial photographing is carried out through an unmanned aerial vehicle, and aerial photographing data are obtained; modeling based on aerial data to generate a three-dimensional texture model; acquiring formation data; and combining the three-dimensional texture model and the rock stratum data to generate a three-dimensional deep hole model. The drilling core sampling is responsible for determining rock properties, the unmanned aerial vehicle shooting is responsible for determining the geomorphology in the deep hole, and the accurate modeling of the geological environment of the ultra-deep hole is realized by combining the data acquired by the drilling core sampling and the unmanned aerial vehicle shooting.
Description
Technical Field
The invention relates to the technical field of ultra-deep hole detection, in particular to an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging.
Background
With the rapid development of national economy, deep development has become a popular direction. Traditional geological exploration often adopts a drilling coring method, and the method can intuitively see the properties and types of underground rock, but can not accurately depict karst cave, faults and the like existing in underground space, and traditional drilling core sampling can not obtain rock samples when a drill rod passes through the karst cave and the faults, so that misjudgment on geological phenomena of a detected region is caused.
When geological exploration is carried out, in order to more accurately and intuitively describe the geological characteristics of a certain ultra-deep hole, a method for combining unmanned aerial vehicle shooting with core drilling sampling is provided, the core drilling sampling is responsible for determining rock properties, the unmanned aerial vehicle shooting is responsible for determining the landform in the deep hole, and the geological environment of the ultra-deep hole is accurately modeled by combining data obtained by the unmanned aerial vehicle shooting and the core drilling sampling, so that the reduction of the specific environment inside the ultra-deep hole is a problem which needs to be solved by a person skilled in the art. Compared with the traditional method of installing a fixed camera on a detection rod, the unmanned aerial vehicle has the advantages of easy control, accurate positioning, high efficiency and clear imaging.
Disclosure of Invention
In view of the above, the invention provides an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging, which combines unmanned aerial vehicle shooting and core drilling sampling to achieve the purpose of accurately modeling an ultra-deep hole geological environment.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging specifically comprises the following steps:
aerial photographing is carried out through the unmanned aerial vehicle, and aerial photographing data are obtained;
modeling based on aerial data to generate a three-dimensional texture model;
acquiring formation data;
and combining the three-dimensional texture model and the rock stratum data to generate a three-dimensional deep hole model.
Optionally, the aerial photographing method through the unmanned aerial vehicle comprises the following steps:
the method comprises the steps of obtaining images in deep holes through an aerial unmanned aerial vehicle by adopting an oblique photographing method, wherein the aerial unmanned aerial vehicle has course overlapping degree.
Optionally, the aerial data comprises deep hole data and flight data; the aerial photographing data acquisition method comprises the following specific steps of:
controlling the unmanned aerial vehicle to enter the deep hole for shooting, and acquiring deep hole data;
and controlling the unmanned aerial vehicle to fly at a fixed speed, and recording flight data of the unmanned aerial vehicle.
Optionally, the aerial data further comprises tomographic data; the method for acquiring fault data comprises the following steps:
when the unmanned aerial vehicle detects a fault, the unmanned aerial vehicle is controlled to enter the detected fault according to requirements to acquire fault data.
Optionally, the aerial photographing data further comprises karst cave data; the method for acquiring karst cave data comprises the following steps:
when the unmanned aerial vehicle detects the karst cave, the unmanned aerial vehicle is controlled to enter the detected karst cave according to the requirements to acquire karst cave data.
Optionally, modeling based on the aerial data to generate the three-dimensional texture model includes the steps of:
establishing a model area in modeling software;
importing aerial photographing data into modeling software;
performing image screening in modeling software according to requirements to obtain a pre-screening image;
automatically performing aerial triangulation calculation by combining POS information contained in the image through a graphic processor in the computer;
resolving the aerial triangulation by using the aerial three encryption to generate point clouds and encrypting the point clouds into dense point clouds to form a triangular grid model;
and generating a three-dimensional texture model by combining pixel information in the aerial data.
Optionally, the method further comprises the step of checking a three-dimensional texture model:
the flight data comprise flight speed, flight time and flight trend;
processing the recorded unmanned aerial vehicle flight speed information and flight time information to obtain flight distance information;
obtaining flight trajectory modeling according to flight trend information and flight distance information of the unmanned aerial vehicle;
and comparing the flight track model with the three-dimensional texture model, and checking the size of the built three-dimensional texture model.
Optionally, the method further comprises the step of checking a three-dimensional texture model:
respectively establishing data measured by various types of unmanned aerial vehicles into three-dimensional texture prefabricated models; and comparing the built three-dimensional texture prefabricated models, and correcting the three-dimensional texture prefabricated models into three-dimensional texture models.
Optionally, the method for combining the three-dimensional texture model and the rock stratum data to generate the three-dimensional deep hole model comprises the following steps:
and marking rock types of strata at different heights in the three-dimensional texture model according to the rock stratum data to obtain a three-dimensional deep hole model.
Optionally, the step of acquiring formation data includes:
formation data is obtained by core sampling.
Compared with the prior art, the invention discloses an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging, so that the following beneficial effects can be obtained:
1. shooting and imaging by an unmanned aerial vehicle, and carrying out three-dimensional imaging treatment subsequently to restore the internal structure of the ultra-deep hole more accurately;
2. the unmanned aerial vehicle is used for aerial photography, so that the difficulty that a human being is difficult to reach the site due to the fact that the inner space of an ultra-deep cavity is complex is solved;
3. the drilling core sampling is responsible for determining rock properties, the unmanned aerial vehicle shooting is responsible for determining the geomorphology in the deep hole, and the geological environment of the ultra-deep hole is accurately modeled by combining data acquired by the drilling core sampling and the unmanned aerial vehicle shooting.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be 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 embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a structural flow chart of an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging;
FIG. 2 is a schematic diagram of an aerial data composition structure of the invention;
FIG. 3 is a schematic view of an application environment of the present invention;
in the figure: 1-ground, 2-karst cave, 3-fault and 4-deep hole.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
As shown in fig. 1, the embodiment of the invention discloses an ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging, which specifically comprises the following steps:
aerial photographing is carried out through the unmanned aerial vehicle, and aerial photographing data are obtained;
modeling based on aerial data to generate a three-dimensional texture model;
acquiring formation data;
and combining the three-dimensional texture model and the rock stratum data to generate a three-dimensional deep hole model.
Further, unmanned aerial vehicle has automatic function of making a video recording, and unmanned aerial vehicle is equipped with auxiliary lighting system, and unmanned aerial vehicle duration is about 30min. The deep hole data, the fault data and the karst cave data are video data, and the video data comprise image information and POS information.
Optionally, the aerial photographing method through the unmanned aerial vehicle comprises the following steps:
the images in the deep holes 4 are acquired by an aerial unmanned aerial vehicle through an oblique photographing method, wherein the aerial unmanned aerial vehicle has course overlapping degree.
Further, the course overlapping degree is 53% -60%. Unmanned aerial vehicle adopts oblique photography technique when gathering data. In order to improve the accuracy of measured data, the photographing equipment carried by the unmanned aerial vehicle is a multi-camera photographing system, and the images inside the deep hole 4 are simultaneously acquired from multiple angles such as vertical, inclined and the like in the flight process by carrying a multi-lens camera on the same unmanned aerial vehicle, so that more complete and accurate geomorphic information of the deep hole 4 is obtained. In the process of establishing the deep hole model and the surface texture, the vertical image contains deep hole horizontal size information, the inclined image can provide the view angle of the side face of the hole wall, and the two can meet the requirements of generating the deep hole 4 surface texture and the model. The oblique photography modeling software is three-dimensional modeling software developed based on the oblique photography principle.
As shown in fig. 2, optionally, the aerial data includes deep hole data and flight data; the aerial photographing data acquisition method comprises the following specific steps of:
controlling the unmanned aerial vehicle to enter the deep hole 4 for shooting, and acquiring deep hole data;
and controlling the unmanned aerial vehicle to fly at a fixed speed, and recording flight data of the unmanned aerial vehicle.
Optionally, the aerial data further comprises tomographic data; the method for acquiring fault data comprises the following steps:
when the unmanned aerial vehicle detects the fault 3, the unmanned aerial vehicle is controlled to enter the detected fault 3 according to the requirements to collect fault data.
Optionally, the aerial photographing data further comprises karst cave data; the method for acquiring karst cave data comprises the following steps:
when the unmanned aerial vehicle detects the karst cave 2, the unmanned aerial vehicle is controlled to enter the detected karst cave 2 according to requirements to acquire karst cave data.
As shown in fig. 3, further, in the unmanned aerial vehicle aerial photographing process, the advantages of rapidness, high efficiency and mobility of the unmanned aerial vehicle are fully exerted. In the vicinity of the ground 1 at the outlet of the deep hole 4, taking off and flying the aircraft into the deep hole 4, when the unmanned aerial vehicle flies to the karst cave 2, the karst cave 2 and other areas, in order to further determine the sizes of the karst cave 3 and the karst cave 2, the unmanned aerial vehicle can be controlled to enter the karst cave 3 and the inside of the karst cave 2 to shoot. In some key parts which need to be measured, ground penetrating radars can be installed on the unmanned aerial vehicle, so that more accurate and effective data can be obtained.
Optionally, modeling based on the aerial data to generate the three-dimensional texture model includes the steps of:
establishing a model area in modeling software;
importing aerial photographing data into modeling software;
performing image screening in modeling software according to requirements to obtain a pre-screening image;
further, filling in the modeling software with an option of setting a parameter threshold, and performing image screening by adjusting the percentage of the used image to the original image to obtain a pre-screened image, so as to reduce the information quantity required to be processed, thereby improving the speed of subsequent modeling;
automatically performing aerial triangulation calculation by combining POS information contained in the image through a graphic processor in the computer;
resolving the aerial triangulation by using the aerial three encryption to generate point clouds and encrypting the point clouds into dense point clouds to form a triangular grid model;
and generating a three-dimensional texture model by combining pixel information in the aerial data.
Furthermore, deep hole data acquired by unmanned aerial vehicle aerial photography and oblique photography with a certain overlapping degree are imported into modeling software, a graphic processing GPU in a computer automatically performs aerial triangulation calculation by combining POS information contained in a photo, point clouds are generated and encrypted into dense point clouds to form a triangular grid model, and finally a three-dimensional deep hole model rich in textures is generated by combining pixel information in an oblique image.
Optionally, the method further comprises the step of checking a three-dimensional texture model:
the flight data comprise flight speed, flight time and flight trend;
processing the recorded unmanned aerial vehicle flight speed information and flight time information to obtain flight distance information;
obtaining flight trajectory modeling according to flight trend information and flight distance information of the unmanned aerial vehicle;
and comparing the flight track model with the three-dimensional texture model, and checking the size of the built three-dimensional texture model.
Further, according to the formula h=v×t, the flight speed information and the flight time information are processed and converted into distance information. In order to ensure the video shooting quality, the vertical flight speed of the unmanned aerial vehicle is 0.5m/s.
Further, the transmitted video is processed, and the thickness of each geological layer and the heights of the karst cave 2 and the fault 3 are determined according to h=v×t. And the geological morphology of the ultra-deep hole after restoration is presented by a three-dimensional modeling technology. After the unmanned plane enters the deep hole 4, in order to ensure the transmission quality of signals, remote control equipment is positioned around the hole. The unmanned aerial vehicle can verify whether the measured data are accurate or not by transmitting the coordinates of the unmanned aerial vehicle to the control system and changing the altitude, and the numerical value after being converted into the length by the formula h=v×t.
Optionally, the method further comprises the step of checking a three-dimensional texture model:
respectively establishing data measured by various types of unmanned aerial vehicles into three-dimensional texture prefabricated models; and comparing the built three-dimensional texture prefabricated models, and correcting the three-dimensional texture prefabricated models into three-dimensional texture models.
Further, the unmanned aerial vehicle can be any unmanned aerial vehicle available in the market, and can be a fixed-wing unmanned aerial vehicle, a rotor unmanned aerial vehicle, an umbrella-wing unmanned aerial vehicle or a ornithopter unmanned aerial vehicle. However, in order to ensure the convenience and feasibility of detection, the unmanned aerial vehicle with smaller volume should be selected as much as possible on the premise of meeting the detection standard. When the unmanned aerial vehicle flies through the karst cave 2, the fault 3 and other positions, in order to better observe the details of the karst cave 2 and the fault 3, the flying speed is properly reduced, and hovering can be selected at certain key positions so as to obtain clearer videos and pictures. In order to ensure modeling accuracy, unmanned aerial vehicles of different models can be used for repeated shooting, further processing is carried out on the acquired images, modeling results shot by different unmanned aerial vehicles are taken for comparison, and therefore the purpose of reducing errors is achieved.
Optionally, the method for combining the three-dimensional texture model and the rock stratum data to generate the three-dimensional deep hole model comprises the following steps:
and marking rock types of strata at different heights in the three-dimensional texture model according to the rock stratum data to obtain a three-dimensional deep hole model.
Further, rock stratum data obtained through drilling in the early stage are combined with a model built by unmanned aerial vehicle aerial photography, and rock types of strata at different heights in the model are marked, so that complete stratum data of a measured region are obtained.
Optionally, the step of acquiring formation data includes:
formation data is obtained by core sampling.
Further, the formation data includes a formation type. When a drill rod passes through the karst cave 2 and the fault 3, a rock sample cannot be obtained at the moment, misjudgment on geological phenomena in a detected region is caused, and in order to more accurately and intuitively describe geological features of a certain ultra-deep hole, an unmanned aerial vehicle shooting and drill core sampling combined method is adopted. The drill core sampling is responsible for determining rock properties, the unmanned aerial vehicle shooting is responsible for determining the landform in the deep hole 4, and accurate modeling is performed by combining data obtained by the drill core sampling and the unmanned aerial vehicle shooting, so that the specific environment inside the ultra-deep hole 4 is restored.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging is characterized by comprising the following steps of:
aerial photographing is carried out through the unmanned aerial vehicle, and aerial photographing data are obtained;
modeling based on aerial data to generate a three-dimensional texture model;
acquiring formation data;
combining the three-dimensional texture model and the rock stratum data to generate a three-dimensional deep hole model;
aerial photographing data also comprise karst cave data; the method for acquiring karst cave data comprises the following steps:
when the unmanned aerial vehicle detects the karst cave (2), controlling the unmanned aerial vehicle to enter the detected karst cave (2) according to the requirements to acquire karst cave data;
and the verification of the three-dimensional texture model is also included:
the flight data comprise flight speed, flight time and flight trend;
processing the recorded unmanned aerial vehicle flight speed information and flight time information to obtain flight distance information;
obtaining flight trajectory modeling according to flight trend information and flight distance information of the unmanned aerial vehicle;
and comparing the flight track model with the three-dimensional texture model, and checking the size of the built three-dimensional texture model.
2. The ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging according to claim 1, wherein the method for aerial photography by unmanned aerial vehicle comprises the following steps:
the unmanned aerial vehicle acquires images in the deep hole (4) by adopting an oblique photographing method, wherein the unmanned aerial vehicle has course overlapping degree.
3. The ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging according to claim 1, wherein the aerial imaging data comprise deep hole data and flight data; the aerial photographing data acquisition method comprises the following specific steps of:
controlling the unmanned aerial vehicle to enter a deep hole (4) for shooting, and acquiring deep hole data;
and controlling the unmanned aerial vehicle to fly at a fixed speed, and recording flight data of the unmanned aerial vehicle.
4. A method of ultra-deep hole geological imaging based on unmanned aerial vehicle aerial imaging according to claim 3, wherein the aerial data further comprises tomographic data; the method for acquiring fault data comprises the following steps:
when the unmanned aerial vehicle detects the fault (3), the unmanned aerial vehicle is controlled to enter the detected fault (3) according to the requirements to collect fault data.
5. The ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial imaging according to claim 1, wherein modeling based on aerial imaging data to generate a three-dimensional texture model comprises the following steps:
establishing a model area in modeling software;
importing aerial photographing data into modeling software;
performing image screening in modeling software according to requirements to obtain a pre-screening image;
automatically performing aerial triangulation calculation by combining POS information contained in the pre-screening image through a graphic processor in a computer;
resolving the aerial triangulation by using the aerial three encryption to generate point clouds and encrypting the point clouds into dense point clouds to form a triangular grid model;
and generating a three-dimensional texture model by combining pixel information in the aerial data.
6. The ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging according to claim 1, further comprising verification of a three-dimensional texture model:
respectively establishing data measured by various types of unmanned aerial vehicles into three-dimensional texture prefabricated models; and comparing the built three-dimensional texture prefabricated models, and correcting the three-dimensional texture prefabricated models into three-dimensional texture models.
7. The ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging according to claim 1, wherein the method for combining the three-dimensional texture model and the rock stratum data to generate the three-dimensional deep hole model comprises the following steps:
and marking rock types of strata at different heights in the three-dimensional texture model according to the rock stratum data to obtain a three-dimensional deep hole model.
8. The ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging of claim 7, wherein the specific content of the rock stratum data is obtained:
formation data is obtained by core sampling.
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