Seismic source excitation method and device for air-drop heavy hammer of unmanned aerial vehicle and electronic equipment

Abstract

The invention relates to the technical field of seismic source excitation, in particular to a seismic source excitation method and device for an unmanned aerial vehicle air-drop heavy hammer and electronic equipment. The method comprises the following steps: acquiring the mass of each heavy hammer and the release height of each heavy hammer in a heavy hammer set corresponding to a seismic exploration area through an image acquired by a first unmanned aerial vehicle set for the seismic exploration area; drop hammer points corresponding to all unmanned aerial vehicles in a second unmanned aerial vehicle set are obtained through the images, all the unmanned aerial vehicles in the second unmanned aerial vehicle set are in one-to-one correspondence with all the heavy hammers, and all the unmanned aerial vehicles are used for releasing all the heavy hammers; and a release instruction is sent to each unmanned aerial vehicle, the release instruction is used for instructing each unmanned aerial vehicle to perform time synchronization and then releasing a heavy hammer corresponding to each unmanned aerial vehicle according to a drop hammer point corresponding to each unmanned aerial vehicle, the release height of each heavy hammer and a release time point corresponding to the heavy hammer set, and each heavy hammer is used for impacting a first seismic signal excited by the ground of the seismic exploration area. According to the invention, the exploration accuracy and convenience of the seismic exploration area can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of seismic source excitation technology, and in particular to a seismic source excitation method, device and electronic equipment for airdropping a heavy hammer by a drone. Background Technology

[0002] With the development of science and technology, seismic exploration in coal mines can provide corresponding support for subsequent mining. The seismic sources used in coal mine seismic exploration can include explosive sources, controlled sources, and manual hammer sources. However, among these sources, explosive sources have complex operating procedures and poor safety; controlled sources have large equipment size and high terrain requirements; and manual hammer sources have limited energy and the limitation of hammering force makes them less mobile. Therefore, improving the convenience of coal mine seismic exploration has become a key focus for users. Summary of the Invention

[0003] This disclosure provides a method, apparatus, and electronic equipment for source excitation of a drone-dropped heavy hammer, which can improve the accuracy and convenience of seismic exploration in seismic exploration areas. The technical solution of this disclosure is as follows:

[0004] According to a first aspect of the present disclosure, a method for source excitation of a drone-dropped heavy hammer is provided, comprising:

[0005] The mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained by using images collected by the first set of drones targeting the seismic exploration area.

[0006] The image is used to obtain the hammer landing point corresponding to each drone in the second drone set, wherein each drone in the second drone set corresponds one-to-one with each hammer, and each drone is used to release each hammer.

[0007] A release command is sent to each of the UAVs. The release command is used to instruct each UAV to synchronize its time and then release the hammer corresponding to each UAV according to the hammer drop point, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground of the seismic exploration area to generate the first seismic signal.

[0008] According to some embodiments, the step of obtaining the mass of each hammer in the hammer set corresponding to the seismic exploration area and the release height of each hammer from images collected by a first set of drones targeting the seismic exploration area includes:

[0009] The hammering energy of each hammer in the hammer set corresponding to the seismic exploration area is obtained by using images collected by the first set of drones targeting the seismic exploration area.

[0010] Based on the impact energy of each hammer, the relative velocity error of each hammer, and the energy conversion efficiency, the mass of each hammer in the set of hammers corresponding to the seismic exploration area and the release height of each hammer are obtained.

[0011] According to some embodiments, the step of obtaining the impact source energy of each hammer in the hammer set corresponding to the seismic exploration area by using images collected by a first set of drones targeting the seismic exploration area includes:

[0012] By using images collected by the first UAV targeting the seismic exploration area, the ground sampling distance corresponding to each pixel in the image is obtained;

[0013] The radius corresponding to the seismic exploration area is obtained based on the number of pixels between the two farthest pixels in the image and the ground sampling distance of each pixel;

[0014] Based on the radius corresponding to the seismic exploration area, the sensitivity threshold of the seismic detector, and the formation cutoff absorption coefficient, the impact source energy of each hammer in the set of hammers corresponding to the seismic exploration area is obtained.

[0015] According to some embodiments, obtaining the landing point corresponding to each drone in the second drone set through the image includes:

[0016] Obtain the two farthest pixels in the image;

[0017] Based on the line segment between the two pixels, the first landing point corresponding to the first drone in the second drone set is obtained, and based on the first landing point, the second landing point of the second drone in the second drone set is obtained, wherein the first drone is the first drone in the second drone set to determine the landing point, and the second drone is any drone in the second drone set other than the first drone.

[0018] The shortest path planning algorithm is used to obtain the path from the starting point of each drone to the landing point of each drone in the second drone set;

[0019] Control each drone to hover along the path to the height corresponding to the landing point of each drone, where the height of each drone is the release height of the hammer corresponding to each drone.

[0020] According to some embodiments, obtaining the first landing point corresponding to the first drone in the second drone set based on the line segment between the two pixels, and obtaining the second landing point of the second drone in the second drone set based on the first landing point, includes:

[0021] The latitude and longitude coordinates of the midpoint of the line segment between the two pixels are used as the first hammer landing point corresponding to the first drone in the second drone set;

[0022] The second landing point of each second drone in the second drone set is symmetrically set on both sides of the first landing point, and the excitation source array corresponding to the second drone set is evenly distributed on the line segment between the two pixels, wherein the distance between any two adjacent drones in the second drone set is the same.

[0023] According to some embodiments, the method further includes:

[0024] A sensor array is used to acquire the first seismic signal generated by each hammer in the set striking the ground in the seismic exploration area, and the first seismic signal is processed to acquire the second seismic signal.

[0025] The time-frequency characteristics of the second seismic signal were obtained by using wavelet packet energy entropy (WPE) and short-time fourier transform (STFT) algorithms.

[0026] The spectral characteristics of the second seismic signal were obtained by using Mel-Frequency Cepstral Coefficients (MFCC) and spectral centroid.

[0027] The dynamic response characteristics corresponding to the second seismic signal are obtained by using the strain gradient algorithm;

[0028] An adaptive attention weighting algorithm is used to fuse the time-frequency features, the spectral features, and the dynamic response features to obtain the processed features;

[0029] The processed features are inferred using geophysical inversion methods to obtain the wave impedance profile corresponding to the seismic exploration area.

[0030] According to some embodiments, the step of using a sensor array to acquire the first seismic signal excited by the impact of each hammer in the set of hammers on the ground of the seismic exploration area, and processing the first seismic signal to acquire the second seismic signal, includes:

[0031] The vibration signals excited by each hammer in the set of hammers striking the ground in the seismic exploration area are obtained by using a three-component ground geophone subarray in the sensor array.

[0032] Using a ring microphone subarray in a sensor array, acoustic signals excited by each hammer in the set of hammers striking the ground in the seismic exploration area are acquired. The acoustic signals include at least one of airborne acoustic signals and ground-transmitted acoustic signals.

[0033] The physical quantity signals excited by each hammer in the set of hammers striking the ground in the seismic exploration area are obtained by using the fiber optic grating sensor subarray in the sensor array.

[0034] The vibration signal, sound wave signal, and physical quantity signal are time-domain aligned to obtain a third seismic signal.

[0035] The third seismic signal was denoised using a combined wavelet threshold denoising and adaptive filtering algorithm to obtain the second seismic signal.

[0036] According to a second aspect of the present disclosure, a vibration source excitation device for airdropping a heavy hammer by a drone is provided, comprising:

[0037] The data acquisition unit is used to acquire the mass of each hammer in the hammer set corresponding to the seismic exploration area and the release height of each hammer by using images collected by the first set of drones targeting the seismic exploration area.

[0038] The hammer drop point acquisition unit is used to acquire the hammer drop point corresponding to each drone in the second drone set through the image, wherein each drone in the second drone set corresponds one-to-one with each hammer, and each drone is used to release each hammer.

[0039] The hammer release unit is used to send a release command to each UAV. The release command is used to instruct each UAV to synchronize its time and then release the hammer corresponding to each UAV according to the hammer drop point, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground of the seismic exploration area to generate the first seismic signal.

[0040] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0041] processor;

[0042] Memory used to store the processor's executable instructions;

[0043] The processor is configured to execute the instructions to implement the source excitation method for the drone-dropped heavy hammer as described in any of the preceding aspects.

[0044] According to a fourth aspect of the present disclosure, a storage medium is provided that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the source excitation method for airdropping a heavy hammer by a drone as described in any of the preceding aspects.

[0045] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described in any one of the preceding aspects.

[0046] The technical solutions provided by the embodiments of this disclosure bring at least the following beneficial effects:

[0047] In some or related embodiments, images collected by a first set of drones targeting a seismic exploration area are used to obtain the mass and release height of each hammer in a set of hammers corresponding to the seismic exploration area; the images are used to obtain the impact point of each drone in a second set of drones, wherein each drone in the second set of drones corresponds one-to-one with each hammer, and each drone is used to release each hammer; a release command is sent to each drone, the release command instructing the drones to synchronize their time and release the hammers corresponding to each drone according to the impact point, release height, and release time point of the set of hammers, and each hammer is used to impact the ground in the seismic exploration area to generate a first seismic signal. Therefore, by acquiring images of the seismic exploration area, information on the hammers and the drones releasing each hammer can be obtained. The drones can then release the hammers at the impact points, eliminating the need for complex approval procedures and poor safety associated with using explosive seismic sources. This improves the safety of seismic source activation, reduces the need for large-scale equipment requiring specific terrain conditions for controllable seismic sources, and expands the applicability of this technical solution. The weight and height of the hammers can be adjusted to generate different impact forces, reducing the limited energy and impact force of manual hammering that hinders mobility. This, in turn, improves the convenience and accuracy of seismic exploration, increases seismic source deployment efficiency, enhances the spatial distribution density of exploration data, and meets the needs of green, efficient, and intelligent seismic exploration.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0050] Figure 1This is a flowchart of the first method for generating a seismic source for airdropping a heavy hammer by a drone, provided in this embodiment of the disclosure;

[0051] Figure 2 This is a flowchart of the second method for generating a seismic source for airdropping a heavy hammer by a drone, provided in this embodiment of the disclosure;

[0052] Figure 3 This is a schematic diagram illustrating an example of a vibration source excitation system for an unmanned aerial vehicle (UAV) airdropping a heavy hammer, provided in an embodiment of this disclosure.

[0053] Figure 4 This is a schematic diagram illustrating an example of a method for generating a seismic source for airdropping a heavy hammer by a drone, as provided in an embodiment of this disclosure.

[0054] Figure 5 This is an example diagram illustrating the first method of determining the hammer drop point provided in this embodiment of the disclosure;

[0055] Figure 6 This is an example diagram illustrating the second method of determining the hammer drop point provided in this embodiment of the disclosure;

[0056] Figure 7 This is a block diagram illustrating a seismic source excitation device for airdropping a heavy hammer by a drone, according to an exemplary embodiment.

[0057] Figure 8 This is an example schematic diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0059] This disclosure provides a method, apparatus, electronic device, and storage medium for generating a seismic source for a drone-dropped hammer. In some embodiments, the terms "method for generating a seismic source for a drone-dropped hammer" can be interchanged with "information processing method" and "communication method," and the terms "device for generating a seismic source for a drone-dropped hammer" can be interchanged with "information processing device" and "communication device," and the terms "information processing system" and "communication system" can be interchanged.

[0060] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0061] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0062] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0063] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0064] In the embodiments disclosed herein, "multiple" refers to two or more.

[0065] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0066] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0067] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0068] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0069] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0070] According to some embodiments, commonly used seismic sources in coal mine seismic exploration include explosive sources, controlled sources (such as hydraulic sources, vibration sources, etc.), and artificial hammer-driven sources. The basic principle of an explosive source is that after the explosive is detonated, a violent oxidation-reduction reaction occurs, generating high-temperature (thousands of degrees Celsius) and high-pressure (tens of thousands of atmospheres) gas, which instantly expands and compresses the surrounding rock or soil medium, thereby generating a broadband pulse signal (10–200 Hz) covering shallow to deep exploration needs. Controlled sources (such as hydraulic sources, electric vibration sources, etc.) are the core technology to replace explosive sources in seismic exploration; their basic principle is to generate seismic waves of a specific frequency through controlled mechanical vibration. Artificial hammer-driven sources are a commonly used non-explosive artificial source in geophysical exploration (especially shallow seismic exploration); their basic principle is that a heavy hammer falling freely and impacting the ground generates mechanical impact, exciting seismic waves to propagate in the underground medium.

[0071] Among these, explosive seismic sources are highly dangerous, involve complex approval procedures, and are easily subject to policy restrictions; while controllable seismic sources are relatively safe, they are usually bulky, poorly adaptable to terrain, and difficult to deploy in complex environments such as mountains and subsidence areas; manual hammer-driven seismic sources have limited excitation energy and low efficiency, failing to meet the needs of deep structural exploration. Therefore, in some implementations, there is still no seismic source technology that balances mobility, excitation energy, safety, and environmental adaptability, severely restricting high-resolution seismic exploration in complex environments.

[0072] Figure 1 This is a flowchart of the first method for generating a seismic source for airdropping a heavy hammer by a drone, as provided in this disclosure embodiment. Figure 1 As shown, the source excitation method for the drone-dropped heavy hammer can be used in scenarios where seismic signals are generated in seismic exploration areas, and includes the following steps:

[0073] In step S11, the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained by using the images collected by the first UAV set for the seismic exploration area.

[0074] In some embodiments, the executing entity of this disclosure may be, for example, an electronic device. This electronic device does not specifically refer to a particular fixed electronic device. For example, when the device identifier changes, the electronic device may also change accordingly. For example, when the structure of the electronic device changes, the electronic device may also change accordingly. Furthermore, the executing entity of this disclosure may also be a server, which may be a single server or a server cluster; this disclosure does not limit this.

[0075] In some embodiments, the electronic device disclosed herein may be, for example, a device that communicates with a drone and a signal acquisition device. The communication method is not limited thereto.

[0076] According to some embodiments, a drone is, for example, an aircraft that can be controlled remotely or autonomously without a human pilot. This drone does not specifically refer to a particular fixed drone. For example, the drone may change as its components change. For example, the drone's identifier may change as its identification changes.

[0077] In some embodiments, a drone ensemble may be a collective of at least one drone. Specifically, a first drone ensemble may be a collective of at least one drone including a camera device. Each first drone in the first drone ensemble may be a drone that acquires images of a seismic exploration area. This first drone ensemble is not specifically a fixed set. For example, when a drone in the first drone ensemble changes, the first drone ensemble may also change accordingly. For example, when the number of drones in the first drone ensemble changes, the first drone ensemble may also change accordingly.

[0078] In some embodiments, the seismic exploration area may be, for example, the area to be explored. The seismic exploration area does not specifically refer to a fixed region. For example, when the area corresponding to the seismic exploration area changes, the seismic exploration area may also change accordingly. For example, when the geological parameters corresponding to the seismic exploration area change, the seismic exploration area may also change accordingly.

[0079] According to some embodiments, the image may be, for example, an image acquired for a seismic exploration area. The image may be, for example, an image set, meaning the image may be, for example, at least one image. The image does not specifically refer to a single, fixed image. For example, the image may change when the acquisition method corresponding to the image changes. For example, the image may change when the seismic exploration area changes.

[0080] In some embodiments, the hammer can be, for example, a hammer used to strike the ground to generate seismic signals. This hammer is not specifically a fixed hammer. For example, the hammer can change when the structure corresponding to it changes. Similarly, the hammer can change when the filling material corresponding to it changes.

[0081] According to some embodiments, the set of weights may be, for example, a collection of at least one weight. This set of weights is not specifically a fixed set. For example, when a particular weight in the set changes, the set of weights may also change accordingly. For example, when the number of weights in the set changes, the set of weights may also change accordingly.

[0082] In some embodiments, the mass can be, for example, the mass of each weight. Different weights can correspond to different masses. The mass of each weight is not specifically a fixed mass. The mass of each weight and the release height can be determined in combination, for example, one changing while the other changes accordingly.

[0083] According to some embodiments, the release height may refer to the height at which the drone releases the weight it carries. This release height is not specifically defined as a fixed height. For example, when the weight's markings change, the corresponding height of the weight may also change accordingly.

[0084] In some embodiments, the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained by using images collected by a first set of drones targeting the seismic exploration area.

[0085] In step S12, the landing point of each drone in the second drone set is obtained through the image. Each drone in the second drone set corresponds to each hammer, and each drone is used to release each hammer.

[0086] In some embodiments, each drone in the second drone ensemble may be, for example, a drone used to release a hammer. The first drone ensemble may also reuse drones from the second drone ensemble, or it may be a completely different drone ensemble. This disclosure does not limit this aspect.

[0087] According to some embodiments, the second drone ensemble may, for example, be a group comprised of at least one drone that releases a hammer. This second drone ensemble is not specifically defined as a fixed group. For example, the second drone ensemble may change accordingly when the number of hammers changes. Similarly, the second drone ensemble may change accordingly when a particular drone within it changes.

[0088] In some embodiments, the number of drones included in the second drone set may be the same as the number of hammers included in the hammer set, that is, each drone in the second drone set corresponds one-to-one with each hammer.

[0089] In some embodiments, the point of impact can be, for example, the point where the drone releases the hammer. This point of impact is not specifically a fixed location. For example, different hammers can correspond to different points of impact. For example, when the method for determining the point of impact changes, the point of impact can also change accordingly.

[0090] In some embodiments, the landing point of each drone in the second drone set is obtained through images, wherein each drone in the second drone set corresponds one-to-one with each hammer, and each drone is used to release each hammer.

[0091] In step S13, a release command is sent to each UAV. The release command is used to instruct each UAV to synchronize its time and then release the corresponding hammer of the UAV according to the hammer drop point of each UAV, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground in the seismic exploration area to generate the first seismic signal.

[0092] According to some embodiments, the release command is used to release the hammer, and this release command is not specifically a fixed command. For example, the release command can be a click-to-send release command or a timed release command. For instance, when the release time point corresponding to the release command changes, the release command can also change accordingly. For instance, when the second drone ensemble changes, the release command can also change accordingly.

[0093] In some embodiments, the first seismic signal may be, for example, a seismic signal generated by a hammer impacting the ground in a seismic exploration area. The "first" in this first seismic signal is used to distinguish it from other seismic signals and does not specifically refer to a particular fixed signal. For example, when the amplitude of the first seismic signal changes, the first seismic signal may also change accordingly.

[0094] In some embodiments, a release command can be sent to each UAV. The release command is used to instruct each UAV to synchronize its time and then release the corresponding hammer of the UAV according to the hammer's landing point, the release height of each hammer, and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground in the seismic exploration area to generate the first seismic signal.

[0095] According to some embodiments, embodiments of this disclosure can use a drone to lift a high-quality hammer to a predetermined altitude and release it at a target excitation point, allowing it to fall freely and impact the ground to generate seismic waves. This replaces explosives or controlled seismic sources for geological structure exploration, featuring controllable excitation energy, flexible excitation location, strong adaptability to terrain, and no explosion risk. It is suitable for geological exploration in coal mines and complex terrain areas. It can improve the efficiency of seismic source deployment, enhance the spatial distribution density of exploration data, and meet the needs of green, efficient, and intelligent seismic exploration.

[0096] In some or related embodiments, the mass and release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained from images collected by the first set of drones targeting the seismic exploration area; the impact point of each drone in the second set of drones is obtained from the images, wherein each drone in the second set of drones corresponds one-to-one with each hammer, and each drone is used to release each hammer; a release command is sent to each drone, which instructs each drone to synchronize its time and then release the corresponding hammer according to the impact point of each drone, the release height of each hammer, and the release time point corresponding to the hammer set, and each hammer is used to impact the ground in the seismic exploration area to generate a first seismic signal. Therefore, by acquiring images of the seismic exploration area, information on the hammers and the drones releasing each hammer can be obtained. By using drones to release the hammers at the impact point, the excitation point, height, and energy can be precisely controlled and repeated. This eliminates the need for complex approval procedures and poor safety associated with explosive seismic sources, improving the safety of seismic source excitation and meeting green mining requirements. It also reduces the need for large equipment sizes and terrain-required conditions for controllable seismic sources. This disclosed technical solution is independent of roads or terrain conditions, increasing its applicability. It is suitable for areas difficult to access with traditional seismic sources, such as mountains, subsidence areas, and densely vegetated areas. The weight and height of the hammers can be adjusted to generate different impact forces, reducing the limited energy and impact force limitations of manual hammering, which hinders mobility. This disclosed technical solution is low-cost, fast-deployment, and highly automated, making it applicable to high-resolution seismic exploration in complex environments. It improves the convenience and accuracy of seismic exploration, increases seismic source deployment efficiency, enhances the spatial distribution density of exploration data, and meets the needs of green, efficient, and intelligent seismic exploration.

[0097] Figure 2 This is a flowchart of the second method for generating a seismic source for airdropping a heavy hammer by a drone, as provided in this disclosure embodiment. Figure 2 As shown, the source excitation method for the drone-dropped heavy hammer can be used to generate seismic signals in seismic exploration areas. In scenarios involving the acquisition of seismic signals, the method includes the following steps:

[0098] In step S21, the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained by using the images collected by the first UAV set for the seismic exploration area.

[0099] The relevant descriptions can be as described above, and will not be repeated here.

[0100] According to some embodiments, by using images collected by a first set of unmanned aerial vehicles (UAVs) targeting a seismic exploration area, the mass and release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained, including:

[0101] The hammering energy of each hammer in the hammer set corresponding to the seismic exploration area is obtained by using images collected by the first set of drones targeting the seismic exploration area.

[0102] Based on the impact energy of each hammer, the relative velocity error of each hammer, and the energy conversion efficiency, the mass and release height of each hammer in the hammer set corresponding to the seismic exploration area can be obtained. Therefore, by obtaining the mass and release height of each hammer based on the impact energy, the accuracy of hammer release and the accuracy of seismic signal acquisition can be improved.

[0103] According to some embodiments, the hammer-driven source energy can, for example, be used to indicate the source energy required for exploration of a seismic exploration area. This hammer-driven source energy is not specifically a fixed energy. For example, the hammer-driven source energy can also change when the hammering area changes. For example, the hammer-driven source energy can also change when the ground information corresponding to the seismic exploration area changes.

[0104] According to some embodiments, the relative velocity error can be, for example, used to indicate the error between the theoretical landing velocity and the actual landing velocity. This relative velocity error is not specifically a fixed error. For example, the relative velocity error can also change when environmental information changes. For example, the relative velocity error can also change when the release altitude changes.

[0105] In some embodiments, energy conversion efficiency can be used, for example, to indicate the conversion efficiency between the calculated energy of a hammer-driven seismic source and the actual energy of a hammer-driven seismic source. This energy conversion efficiency is not specifically defined by a fixed efficiency. For example, the energy conversion efficiency can change accordingly when the surface information corresponding to the seismic exploration area changes.

[0106] In some embodiments, the set of counterweights can be set according to a setting command, or it can be determined according to exploration requirements. This disclosure does not limit this aspect.

[0107] According to some embodiments, by using images collected by a first set of unmanned aerial vehicles (UAVs) targeting a seismic exploration area, the impact energy of each hammer in the set of hammers corresponding to the seismic exploration area is obtained, including:

[0108] By using the first UAV to collect images of the seismic exploration area, the ground sampling distance corresponding to each pixel in the image is obtained;

[0109] The radius of the seismic exploration area is obtained based on the number of pixels between the two farthest pixels in the image and the ground sampling distance of each pixel.

[0110] Based on the radius of the seismic exploration area, the sensitivity threshold of the seismic detector, and the formation cutoff absorption coefficient, the impact source energy of each hammer in the hammer set corresponding to the seismic exploration area is obtained. Therefore, the impact source energy can be obtained based on multiple pieces of information, improving the accuracy of hammer release, the accuracy of seismic signal acquisition, and the accuracy of seismic exploration in the exploration area.

[0111] In some embodiments, Figure 3 This is a schematic diagram illustrating an example of a seismic source excitation system for airdropping a heavy hammer by a drone, provided in an embodiment of this disclosure. Figure 4 This is an example schematic diagram of a method for generating a seismic source by airdropping a heavy hammer using a UAV, provided in this embodiment. The UAV can, for example, transmit acquired images to an electronic device via an aerial survey camera sensor. The electronic device can establish a mapping relationship between ground points and pixels, where the ground sampling distance (GSD) corresponding to each pixel is determined by the following formula (1):

[0112]

[0113] Where H is the UAV flight altitude, a is the pixel size, and f is the lens focal length. For example, if the pixel size a is 2.41 μm and the camera focal length f is 8.8 mm, GSD≈2.73×10-4×H can be obtained according to formula (1). If the flight altitude H is 182 m, then GSD is approximately 0.05 m / pixel. For example, the total number of pixels between the two farthest pixels in the image corresponding to the seismic exploration area can be calculated by Geographic Information System Software (GIS). Then, the minimum radius R that can completely cover the seismic exploration area can be calculated by the following formula (2):

[0114]

[0115] According to some embodiments, after obtaining the radius R, the area S of the seismic exploration zone is obtained by formula (3):

[0116] S=πR 2 (3)

[0117] According to some embodiments, the required source energy can be calculated based on the radius R of the seismic exploration zone, assuming the sensitivity threshold of the seismic detector is I. min If the exploration radius is R, then the minimum required source energy E0 is calculated using formula (4):

[0118] E0=4πR 2 ×I min ·e αR (4)

[0119] Here, α is the absorption coefficient of the formation medium, with units of dB / m or Np / m, which can be determined experimentally, for example. The value of α varies at different distances from the seismic source. For example, in the calculation of the vibration attenuation law of natural earthquakes, a piecewise function can be used, while in active source microseismic monitoring, due to the limited range of source distances, a fixed value of α can be used within a certain range of source distances.

[0120] According to some embodiments, in free fall, the theoretical landing speed of a hammer falling from a height h can be, for example, as shown in formula (5):

[0121]

[0122] According to some embodiments, due to friction with the air during the hammer's fall, there is a certain energy loss, and the relative speed error δ is defined as:

[0123]

[0124] Where v′ is the actual landing velocity, which can be obtained as v′=(1-δ)v. For example, for a hammer with high density and small frontal area, δ can be less than 5%, and experiments have shown that δ≈2.1%. According to the kinetic energy theorem, E=mv′ 2 / 2=(1-δ) 2 mv 2 / 2, according to the law of conservation of mechanical energy mv 2 =2mgh, which yields the mechanical energy E = (1-δ) of the hammer at the moment of impact. 2 mgh. Since the energy of the actual seismic waves generated is only a portion of the theoretical energy, and is affected by the energy conversion efficiency η (energy transfer ratio), the η value for hard bedrock is between 70% and 90%, for loose sand / clay it is between 40% and 60%, and for saturated soil layers it is between 30% and 50%. Therefore, the effective source energy E0 generated by a hammer of mass m falling from a height h from rest is calculated using the following formula:

[0125] E0=η×(1-δ) 2 ×mgh(7)

[0126] Therefore, according to formulas (4) and (7), the product of the hammer mass m and the drop height h can be determined to satisfy:

[0127]

[0128] Therefore, given the mass m of the hammer, the height of the hammer from the point of impact is... Similarly, given a fixed height h from the point of impact, the mass of the hammer can be...

[0129] In step S22, the landing point of each drone in the second drone set is obtained through the image. Each drone in the second drone set corresponds to each hammer, and each drone is used to release each hammer.

[0130] The relevant descriptions can be as described above, and will not be repeated here.

[0131] According to some embodiments, the landing point of each drone in the second drone set is obtained through images, including:

[0132] Find the two farthest pixels in the image;

[0133] Based on the line segment between two pixels, obtain the first landing point corresponding to the first drone in the second drone set, and based on the first landing point, obtain the second landing point of the second drone in the second drone set. Here, the first drone is the first drone in the second drone set to determine the landing point, and the second drone is any drone in the second drone set other than the first drone.

[0134] The shortest path planning algorithm is used to obtain the path from the starting point of each drone to the landing point of each drone in the second drone set;

[0135] Each drone is controlled to hover along a path to its corresponding height directly above its designated landing point. This height corresponds to the release height of the hammer for that drone. Therefore, by obtaining the first landing point from the two furthest pixels and then determining the remaining landing points, the accuracy of landing point acquisition and hammer release can be improved.

[0136] According to some embodiments, based on the line segment between two pixels, the first landing point corresponding to the first drone in the second drone set is obtained, and based on the first landing point, the second landing point of the second drone in the second drone set is obtained, including:

[0137] The latitude and longitude coordinates of the midpoint of the line segment between two pixels are used as the first hammer landing point corresponding to the first drone in the second drone set.

[0138] The second landing point of each second drone in the second drone set is symmetrically set on both sides of the first landing point, and the excitation source array corresponding to the second drone set is evenly distributed on the line segment between two pixels. The distance between any two adjacent drones in the second drone set is the same.

[0139] According to some embodiments, for example, the second drone ensemble includes K drones, and the K drones can simultaneously drop the hammer, meaning there are K hammer drop points that generate K seismic sources. Here, K is an odd number. As mentioned before, for example, a Geographic Information System (GIS) can be used to find the two farthest pixels in the seismic exploration area image. The midpoint of the line segment between these two pixels, with latitude and longitude coordinates (x1, y1), is the first hammer drop point, i.e., the first hammer drop point corresponding to the first drone. For example, it could be as follows: Figure 5 As shown, the remaining K-1 hammer points are symmetrically distributed on both sides of the first hammer point. That is, at least one second hammer point of the second UAV is symmetrically distributed on both sides of the first hammer point. The excitation source array composed of all K hammer points is uniformly distributed on the line segment between these two pixels. For example, it can be as follows: Figure 6 As shown, the line segment length is 2R, the distance between adjacent drop points is 2R / (K-1), the coordinates of the second drop point are (x2, y2), and so on, the coordinates of the Kth drop point are (x2, y2). K ,y K ).

[0140] According to some embodiments, the shortest path planning algorithm is used to determine the latitude and longitude coordinates (x, y, y) of each UAV from the starting point to the target landing point. i ,y i The optimal path S directly above i For example, as shown in formula (9), the goal of path planning is to minimize flight time T. f and energy consumption E f :

[0141]

[0142] Where λ is the weighting parameter for energy consumption and time.

[0143] According to some embodiments, each drone in the second drone ensemble can be controlled to lock onto the target hovering point, i.e., the latitude and longitude coordinates (x, y, y), through an onboard real-time dynamic differential positioning (RTK) module and laser rangefinder. i ,y i Directly above, and the height is determined to be h, with the errors in the x, y, and z directions all being less than the preset error, which can be, for example, 1 cm.

[0144] In step S23, a release command is sent to each UAV. The release command is used to instruct each UAV to synchronize its time and then release the corresponding hammer of the UAV according to the hammer drop point of each UAV, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground of the seismic exploration area to generate the first seismic signal.

[0145] The relevant descriptions can be as described above, and will not be repeated here.

[0146] According to some embodiments, when instructing high-precision time synchronization among the UAVs, for example, each UAV can obtain precise Global Positioning System (GPS) or BeiDou Navigation Satellite System (BDS) time synchronization through its onboard Global Navigation Satellite System (GNSS) module. The times of the K UAVs are denoted as T1, T2, ..., T K The time difference between any two drones is Δt ≤ ±10 ns.

[0147] According to some embodiments, each UAV uses an onboard high-precision timer module to time itself. When the predetermined time is reached, the electromagnetic release mechanism is triggered to release the hammer. The response time t of the electromagnetic release mechanism can be less than or equal to 0.01s, and the release time accuracy error can be within the range of -0.1us and 0.1us.

[0148] In step S24, a sensor array is used to acquire the first seismic signal excited by the ground of the seismic exploration area by each hammer in the hammer set, and the first seismic signal is processed to acquire the second seismic signal.

[0149] The relevant descriptions can be as described above, and will not be repeated here.

[0150] According to some embodiments, a sensor array may be, for example, a collection of at least one sensor. The sensor array may, for example, include different types of sensors. The sensor array may also be referred to as a sensor set, and this disclosure does not limit it.

[0151] According to some embodiments, a sensor array is used to acquire the first seismic signal excited by the ground when each hammer in the set of hammers impacts the seismic exploration area, and the first seismic signal is processed to acquire the second seismic signal, including:

[0152] The vibration signals excited by the ground when each hammer in the set of hammers impacts the seismic exploration area are obtained by using a three-component ground geophone subarray in the sensor array.

[0153] The ring microphone subarray in the sensor array is used to acquire the acoustic wave signals excited by the ground when each hammer in the set of hammers impacts the seismic exploration area. The acoustic wave signals include at least one of airborne acoustic wave signals and ground-transmitted acoustic wave signals.

[0154] The fiber optic grating sensor subarray in the sensor array is used to acquire the physical quantity signals excited by the ground of each hammer impacting the seismic exploration area in the hammer set.

[0155] Time-domain alignment processing is performed on vibration signals, acoustic signals, and physical quantity signals to obtain a third seismic signal;

[0156] A combined algorithm of wavelet threshold denoising and adaptive filtering was used to denoise the third seismic signal to obtain the second seismic signal. Therefore, the accuracy of obtaining the second seismic signal and the wave impedance profile can be improved.

[0157] According to some embodiments, the sensor array integrates a high-precision three-component ground geophone subarray, a ring microphone subarray, and a fiber Bragg grating sensor subarray to simultaneously acquire hammer impact vibration signals and acoustic signals. The three-component ground geophone subarray may, for example, include 200 geophones arranged in a straight line, shallowly buried on the ground surface with a spacing of 30 meters; the ring microphone subarray may, for example, include 16-channel sensors distributed in a ring 10 meters from the seismic source to capture airborne sound waves and ground-transmitted sound waves; the fiber Bragg grating sensor subarray may, for example, include a 10-kilometer, 6-core armored optical fiber and a fiber optic demodulator, with the fiber optic cable shallowly buried in an S-shape on the ground surface of the area to be explored. All three types of sensor subarrays may, for example, use the same high-precision GPS or BeiDou dual-mode timing module (time synchronization error ≤0.1μs) to trigger synchronous acquisition by all sensors.

[0158] According to some embodiments, seismic data acquired can be transmitted in real time to electronic devices via dual-link redundant transmission with satellite communication, for example, using 4G or 5G mobile communication technology, with bandwidth ≥20Mbps and latency <50ms. Time-domain alignment is achieved by correcting the time difference between multi-sensor array systems using a cross-correlation algorithm (accuracy ±0.1ms). Noise suppression of the seismic signal can be achieved using a joint algorithm of wavelet threshold denoising and adaptive filtering. The wavelet threshold denoising algorithm can be, for example, an 8th-order Symlet (sym8) wavelet basis algorithm.

[0159] In step S25, the time-frequency characteristics corresponding to the second seismic signal are obtained by wavelet packet energy entropy and short-time Fourier transform algorithm;

[0160] In some embodiments, the wavelet packet energy entropy and short-time Fourier transform algorithms can be used to obtain the time-frequency characteristics of the vibration signal in the second seismic signal. These wavelet packet energy entropy and short-time Fourier transform algorithms are not specifically defined by any particular algorithm. For example, any algorithm capable of obtaining time-frequency characteristics can be used. For instance, when the algorithm parameters of any one of the wavelet packet energy entropy and short-time Fourier transform algorithms change, the parameters of that algorithm can also change accordingly.

[0161] According to some embodiments, the time-frequency characteristic can be, for example, the dynamic characteristics of the vibration signal in the joint time-frequency domain. This time-frequency characteristic does not specifically refer to a single fixed characteristic. For example, when the second seismic signal changes, the time-frequency characteristic can also change accordingly.

[0162] In step S26, the spectral characteristics of the second seismic signal are obtained by using the Mel frequency cepstral coefficients and the spectral centroid.

[0163] According to some embodiments, spectral characteristics refer to stable, quantifiable statistical or physical properties exhibited by a signal in the frequency domain, used to describe the distribution of the signal's frequency components. These spectral characteristics are not specifically defined by a single fixed feature. For example, when the second seismic signal changes, these spectral characteristics may also change accordingly.

[0164] In some embodiments, the Mel frequency cepstral coefficients and spectral centroids do not specifically refer to a particular fixed algorithm. This disclosure does not limit the method of obtaining spectral features.

[0165] In step S27, the dynamic response characteristics corresponding to the second seismic signal are obtained through the strain gradient algorithm;

[0166] According to some embodiments, the strain gradient algorithm may be, for example, an algorithm that introduces a “strain gradient” or “higher-order strain derivative” into a class of numerical solution techniques.

[0167] In some embodiments, dynamic response features can be used, for example, to indicate quantifiable behavior exhibited when the ground is impacted, specifically including all measurable fingerprints of how the ground vibrates, decays, and deforms over time when impacted.

[0168] In step S28, an adaptive attention weighting algorithm is used to fuse time-frequency features, spectral features, and dynamic response features to obtain the processed features;

[0169] In some implementations, Adaptive Attention Weighting (AAW) is a mechanism that dynamically calculates attention weights based on input data or training state. It can improve the accuracy of feature fusion by determining the weights corresponding to time-frequency features, spectral features, and dynamic response features.

[0170] In step S29, the processed features are inferred using geophysical inversion methods to obtain the wave impedance profile map corresponding to the seismic exploration area.

[0171] In some embodiments, geophysical inversion methods are a type of mathematical-physical technique that uses surface or well observation data (seismic waves, gravity, magnetism, electromagnetic, etc.) to infer the distribution of physical parameters of subsurface media. It is the "core engine" of modern geophysical exploration. For example, the geophysical inversion method in this disclosure can infer the corresponding geological structure of the ground surface based on seismic signals generated when the ground is impacted.

[0172] According to some embodiments, a wave-impedance section is a visualization of the wave impedance obtained from seismic or acoustic data inversion, plotted as a two-dimensional cross-section along a survey line or wellbore. The wave-impedance section corresponding to the seismic exploration area does not refer to a specific fixed section. For example, the wave-impedance section may change accordingly when the seismic signal acquisition method changes. Similarly, the wave-impedance section may change accordingly when the generation method changes.

[0173] According to some embodiments, it can also be determined whether the obtained wave impedance profile corresponding to the seismic exploration area achieves the expected effect. If it does, the active seismic source excitation by the UAV-dropped heavy hammer is completed. If it does not, the technical solution of this disclosure is re-executed.

[0174] In one or related embodiments, a sensor array is used to acquire the first seismic signal excited by the impact of each hammer in the seismic exploration area on the ground. The first seismic signal is then processed to acquire the second seismic signal. The time-frequency characteristics of the second seismic signal are obtained using wavelet packet energy entropy and short-time Fourier transform algorithms. The spectral characteristics of the second seismic signal are obtained using Mel frequency cepstral coefficients and spectral centroids. The dynamic response characteristics of the second seismic signal are obtained using a strain gradient algorithm. An adaptive attention weighting algorithm is used to fuse the time-frequency characteristics, spectral characteristics, and dynamic response characteristics to obtain the processed features. Geophysical inversion methods are used to infer the processed features to obtain the wave impedance profile corresponding to the seismic exploration area. Therefore, corresponding features can be obtained through appropriate algorithms, and feature fusion using the adaptive attention weighting algorithm can improve the accuracy of feature fusion, the accuracy of seismic signal analysis, and the accuracy of obtaining the wave impedance profile.

[0175] A block diagram illustrating a seismic source excitation device for airdropping a heavy hammer by a drone, according to an exemplary embodiment. (Refer to...) Figure 7 The device 700 includes:

[0176] The data acquisition unit 701 is used to acquire the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area through images collected by the first UAV set targeting the seismic exploration area.

[0177] The hammer drop point acquisition unit 702 is used to acquire the hammer drop point corresponding to each drone in the second drone set through the image, wherein each drone in the second drone set corresponds one-to-one with each hammer, and each drone is used to release each hammer.

[0178] The hammer release unit 703 is used to send release commands to each UAV. The release commands are used to instruct each UAV to synchronize its time and then release the hammer corresponding to each UAV according to the hammer drop point, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground in the seismic exploration area to generate the first seismic signal.

[0179] According to some embodiments, the data acquisition unit 701, when acquiring the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area using images collected by the first UAV set targeting the seismic exploration area, is specifically used for:

[0180] The hammering energy of each hammer in the hammer set corresponding to the seismic exploration area is obtained by using images collected by the first set of drones targeting the seismic exploration area.

[0181] Based on the impact energy of each hammer, the relative velocity error of each hammer, and the energy conversion efficiency, the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained.

[0182] According to some embodiments, when the data acquisition unit 701 acquires the impact source energy of each hammer in the hammer set corresponding to the seismic exploration area using images collected by the first UAV set targeting the seismic exploration area, it is specifically used for:

[0183] By using the first UAV to collect images of the seismic exploration area, the ground sampling distance corresponding to each pixel in the image is obtained;

[0184] The radius of the seismic exploration area is obtained based on the number of pixels between the two farthest pixels in the image and the ground sampling distance of each pixel.

[0185] Based on the radius corresponding to the seismic exploration area, the sensitivity threshold of the seismic detector, and the formation cutoff absorption coefficient, the impact source energy of each hammer in the set of hammers corresponding to the seismic exploration area is obtained.

[0186] According to some embodiments, when the hammer landing point acquisition unit 702 acquires the hammer landing point corresponding to each drone in the second drone set through an image, it is specifically used for:

[0187] Find the two farthest pixels in the image;

[0188] Based on the line segment between two pixels, obtain the first landing point corresponding to the first drone in the second drone set, and based on the first landing point, obtain the second landing point of the second drone in the second drone set. Here, the first drone is the first drone in the second drone set to determine the landing point, and the second drone is any drone in the second drone set other than the first drone.

[0189] The shortest path planning algorithm is used to obtain the path from the starting point of each drone to the landing point of each drone in the second drone set;

[0190] Control each drone to hover along the path to the corresponding height of each drone directly above the landing point of each drone's hammer. The corresponding height of each drone is the release height of the hammer corresponding to each drone.

[0191] According to some embodiments, the hammer landing point acquisition unit 702 is used to acquire the first hammer landing point corresponding to the first drone in the second drone set based on the line segment between two pixels, and to acquire the second hammer landing point of the second drone in the second drone set based on the first hammer landing point, specifically for:

[0192] The latitude and longitude coordinates of the midpoint of the line segment between two pixels are used as the first hammer landing point corresponding to the first drone in the second drone set.

[0193] The second landing point of the second UAV in the second UAV set is symmetrically set on both sides of the first landing point, and the excitation source array corresponding to the second UAV set is evenly distributed on the line segment between the two pixels. The distance between any two adjacent UAVs in the second UAV set is the same.

[0194] According to some embodiments, the counterweight release unit 703 is also specifically used for:

[0195] A sensor array is used to acquire the first seismic signal excited by the ground of the seismic exploration area by each hammer in the set of hammers, and the first seismic signal is processed to acquire the second seismic signal.

[0196] The time-frequency characteristics of the second seismic signal were obtained by using wavelet packet energy entropy and short-time Fourier transform algorithms.

[0197] The spectral characteristics of the second seismic signal were obtained by using the Mel frequency cepstral coefficients and the spectral centroid.

[0198] The dynamic response characteristics corresponding to the second seismic signal are obtained by using the strain gradient algorithm;

[0199] An adaptive attention weighting algorithm is used to fuse time-frequency features, spectral features, and dynamic response features to obtain the processed features;

[0200] Geophysical inversion methods are used to infer the processed features and obtain the wave impedance profile corresponding to the seismic exploration area.

[0201] According to some embodiments, the hammer release unit 703 is used to acquire the first seismic signal excited by the ground of the seismic exploration area by each hammer in the hammer set impacting the seismic exploration area using a sensor array, and to process the first seismic signal to acquire the second seismic signal, specifically for:

[0202] The vibration signals excited by the ground when each hammer in the set of hammers impacts the seismic exploration area are obtained by using a three-component ground geophone subarray in the sensor array.

[0203] The ring microphone subarray in the sensor array is used to acquire the acoustic wave signals excited by the ground when each hammer in the set of hammers impacts the seismic exploration area. The acoustic wave signals include at least one of airborne acoustic wave signals and ground-transmitted acoustic wave signals.

[0204] The fiber optic grating sensor subarray in the sensor array is used to acquire the physical quantity signals excited by the ground of each hammer impacting the seismic exploration area in the hammer set.

[0205] Time-domain alignment processing is performed on vibration signals, acoustic signals, and physical quantity signals to obtain a third seismic signal;

[0206] A combined algorithm of wavelet threshold denoising and adaptive filtering was used to denoise the third seismic signal to obtain the second seismic signal.

[0207] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0208] In some or related embodiments, a data acquisition unit is used to acquire the mass and release height of each hammer in the hammer set corresponding to the seismic exploration area based on images collected by the first set of drones targeting the seismic exploration area; a hammer landing point acquisition unit is used to acquire the hammer landing point corresponding to each drone in the second set of drones based on the images, wherein each drone in the second set of drones corresponds one-to-one with each hammer, and each drone is used to release each hammer; a hammer release unit is used to send a release command to each drone, the release command instructing each drone to synchronize its time and then release the hammer corresponding to the drone based on the hammer landing point, the release height of each hammer, and the release time point corresponding to the hammer set, and each hammer is used to impact the ground in the seismic exploration area to generate a first seismic signal. Therefore, by acquiring images of the seismic exploration area, information on the hammers and the drones releasing each hammer can be obtained. By using drones to release the hammers at the impact point, the excitation point, height, and energy can be precisely controlled and repeated. This eliminates the need for complex approval procedures and poor safety associated with explosive seismic sources, improving the safety of seismic source excitation and meeting green mining requirements. It also reduces the need for large equipment sizes and terrain-required conditions for controllable seismic sources. This disclosed technical solution is independent of roads or terrain conditions, increasing its applicability. It is suitable for areas difficult to access with traditional seismic sources, such as mountains, subsidence areas, and densely vegetated areas. The weight and height of the hammers can be adjusted to generate different impact forces, reducing the limited energy and impact force limitations of manual hammering, which hinders mobility. This disclosed technical solution is low-cost, fast-deployment, and highly automated, making it applicable to high-resolution seismic exploration in complex environments. It improves the convenience and accuracy of seismic exploration, increases seismic source deployment efficiency, enhances the spatial distribution density of exploration data, and meets the needs of green, efficient, and intelligent seismic exploration.

[0209] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device 800 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0210] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0211] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0212] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, the above methods can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the above methods by any other suitable means (e.g., by means of firmware).

[0213] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0214] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0215] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0216] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0217] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0218] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the management difficulties and weak business scalability inherent in traditional physical hosts and VPS (Virtual Private Server) services. Servers can also be servers for distributed systems or servers integrated with blockchain technology.

[0219] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0220] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. 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 principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for source excitation of a drone-dropped heavy hammer, characterized in that, include: The mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area are obtained by using images collected by the first set of drones targeting the seismic exploration area. The image is used to obtain the hammer landing point corresponding to each drone in the second drone set, wherein each drone in the second drone set corresponds one-to-one with each hammer, and each drone is used to release each hammer. A release command is sent to each of the UAVs. The release command is used to instruct each UAV to synchronize its time and then release the hammer corresponding to each UAV according to the hammer drop point, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground of the seismic exploration area to generate the first seismic signal.

2. The method according to claim 1, characterized in that, The step of obtaining the mass of each hammer and the release height of each hammer in the hammer set corresponding to the seismic exploration area by acquiring images of the seismic exploration area using images collected by the first set of drones includes: The hammering energy of each hammer in the hammer set corresponding to the seismic exploration area is obtained by using images collected by the first set of drones targeting the seismic exploration area. Based on the impact energy of each hammer, the relative velocity error of each hammer, and the energy conversion efficiency, the mass of each hammer in the set of hammers corresponding to the seismic exploration area and the release height of each hammer are obtained.

3. The method according to claim 2, characterized in that, The step of obtaining the impact energy of each hammer in the set of hammers corresponding to the seismic exploration area by acquiring images of the seismic exploration area using images collected by the first set of drones includes: By using images collected by the first UAV targeting the seismic exploration area, the ground sampling distance corresponding to each pixel in the image is obtained; The radius corresponding to the seismic exploration area is obtained based on the number of pixels between the two farthest pixels in the image and the ground sampling distance of each pixel; Based on the radius corresponding to the seismic exploration area, the sensitivity threshold of the seismic detector, and the formation cutoff absorption coefficient, the impact source energy of each hammer in the set of hammers corresponding to the seismic exploration area is obtained.

4. The method according to claim 1, characterized in that, The step of obtaining the landing point corresponding to each drone in the second drone set through the image includes: Obtain the two farthest pixels in the image; Based on the line segment between the two pixels, the first landing point corresponding to the first drone in the second drone set is obtained, and based on the first landing point, the second landing point of the second drone in the second drone set is obtained, wherein the first drone is the first drone in the second drone set to determine the landing point, and the second drone is any drone in the second drone set other than the first drone. The shortest path planning algorithm is used to obtain the path from the starting point of each drone to the landing point of each drone in the second drone set; Control each drone to hover along the path to the height corresponding to the landing point of each drone, where the height of each drone is the release height of the hammer corresponding to each drone.

5. The method according to claim 4, characterized in that, The step of obtaining the first landing point corresponding to the first drone in the second drone set based on the line segment between the two pixels, and obtaining the second landing point of the second drone in the second drone set based on the first landing point, includes: The latitude and longitude coordinates of the midpoint of the line segment between the two pixels are used as the first hammer landing point corresponding to the first drone in the second drone set; The second landing point of each second drone in the second drone set is symmetrically set on both sides of the first landing point, and the excitation source array corresponding to the second drone set is evenly distributed on the line segment between the two pixels, wherein the distance between any two adjacent drones in the second drone set is the same.

6. The method according to claim 1, characterized in that, The method further includes: A sensor array is used to acquire the first seismic signal generated by each hammer in the set striking the ground in the seismic exploration area, and the first seismic signal is processed to acquire the second seismic signal. The time-frequency characteristics of the second seismic signal are obtained by using wavelet packet energy entropy and short-time Fourier transform algorithms. The spectral characteristics of the second seismic signal are obtained by using the Mel frequency cepstral coefficients and the spectral centroid. The dynamic response characteristics corresponding to the second seismic signal are obtained by using the strain gradient algorithm; An adaptive attention weighting algorithm is used to fuse the time-frequency features, the spectral features, and the dynamic response features to obtain the processed features; The processed features are inferred using geophysical inversion methods to obtain the wave impedance profile corresponding to the seismic exploration area.

7. The method according to claim 6, characterized in that, The process involves using a sensor array to acquire the first seismic signal generated by each hammer in the set impacting the ground in the seismic exploration area, and processing the first seismic signal to acquire the second seismic signal, including: The vibration signals excited by each hammer in the set of hammers striking the ground in the seismic exploration area are obtained by using a three-component ground geophone subarray in the sensor array. Using a ring microphone subarray in a sensor array, acoustic signals excited by each hammer in the set of hammers striking the ground in the seismic exploration area are acquired. The acoustic signals include at least one of airborne acoustic signals and ground-transmitted acoustic signals. The physical quantity signals excited by each hammer in the set of hammers striking the ground in the seismic exploration area are obtained by using the fiber optic grating sensor subarray in the sensor array. The vibration signal, sound wave signal, and physical quantity signal are time-domain aligned to obtain a third seismic signal. The third seismic signal was denoised using a combined wavelet threshold denoising and adaptive filtering algorithm to obtain the second seismic signal.

8. A seismic source excitation device for airdropping a heavy hammer by a drone, characterized in that, include: The data acquisition unit is used to acquire the mass of each hammer in the hammer set corresponding to the seismic exploration area and the release height of each hammer by using images collected by the first set of drones targeting the seismic exploration area. The hammer drop point acquisition unit is used to acquire the hammer drop point corresponding to each drone in the second drone set through the image, wherein each drone in the second drone set corresponds one-to-one with each hammer, and each drone is used to release each hammer. The hammer release unit is used to send a release command to each UAV. The release command is used to instruct each UAV to synchronize its time and then release the hammer corresponding to each UAV according to the hammer drop point, the release height of each hammer and the release time point corresponding to the set of hammers. Each hammer is used to strike the ground of the seismic exploration area to generate the first seismic signal.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for generating a seismic source for airdropping a heavy hammer by a drone as described in any one of claims 1 to 7.

10. A storage medium storing instructions, characterized in that, When the instruction is executed on the electronic device, the electronic device performs the source excitation method for airdropping a heavy hammer by a drone as described in any one of claims 1 to 7.

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