Geophysical prospecting method and apparatus based on remote sensing frequency resonance

Abstract

The embodiment of the application provides a geophysical exploration method and device based on remote sensing frequency resonance, which comprises the following steps: after pre-processing of multi-source satellite remote sensing image data is obtained, the pixel intensity value of the pre-processed image data is converted from the spatial domain to the frequency domain through Fourier transform, a target material filter is constructed to perform resonance filtering on the frequency domain, target material frequency values are obtained, and the target material frequency values are inversely Fourier transformed to obtain a target material spatial domain image; based on a statistical threshold method, an abnormal mapping is performed on the spatial domain image, and based on the abnormal mapping result, an average pore fluid pressure value and a predicted depth value are calculated, a probability level of containing target material is obtained, an abnormal area verification is performed according to the probability level of containing target material in combination with preset ground electromagnetic measurement data, and a corresponding drilling recommendation map is determined. The application can improve the accuracy and efficiency of geophysical exploration.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a geophysical exploration method and apparatus based on remote sensing frequency resonance. Background Technology

[0002] In geophysical research, traditional geophysical research is centered on "tectonic orientation," indirectly inferring the distribution of underground resources through methods such as gravity, magnetism, seismicity, and electromagnetics. It relies on surface reflection characteristics or shallow physical signals, combined with geological structural analysis, to locate resources, but suffers from problems such as shallow detection depth, weak resistance to interference, and multiple interpretations.

[0003] Traditional methods have significant drawbacks in resource exploration:

[0004] First, the detection depth is limited, mainly relying on surface reflection or shallow signals (such as gravity and magnetism), making it difficult to identify deep (>100 meters) resources and insufficient for detecting concealed structures.

[0005] Secondly, indirect inference leads to high ambiguity. Indirect analysis based on spectra or geological structures is easily affected by vegetation, clouds, and soil moisture. Different minerals may produce similar anomalies, increasing the uncertainty of interpretation.

[0006] Third, the disadvantages of efficiency and cost are prominent. 3D seismic exploration of a single block takes several weeks to several months and costs up to several million yuan. Moreover, construction is difficult and time-consuming in complex terrain (such as swamps and mountains).

[0007] Fourth, it has weak anti-interference ability, the spectroscopic technology is significantly affected by weather, and the electromagnetic method is sensitive to local noise and requires complex correction.

[0008] Fifth, traditional methods have poor environmental adaptability, relying on exposed surface areas. Exploration of resources in deep or covered areas requires verification using multiple methods, resulting in a cumbersome process and limited accuracy. These shortcomings restrict the ability of traditional methods to discover resources in deep and complex environments.

[0009] Based on the above problems, there is an urgent need for a geophysical exploration method based on remote sensing frequency resonance, which can promote the transformation of geophysical research from "tectonic-oriented" to "material-oriented" and improve the accuracy and efficiency of geophysical exploration. Summary of the Invention

[0010] To address the problems in the existing technology, this application provides a geophysical exploration method and apparatus based on remote sensing frequency resonance, which can improve the accuracy and efficiency of geophysical exploration.

[0011] To solve at least one of the above problems, this application provides the following technical solution:

[0012] In a first aspect, this application provides a geophysical exploration method based on remote sensing frequency resonance, comprising:

[0013] Acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0014] The preprocessed image data is subjected to Fourier transform to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; a target material filter is constructed, and the frequency domain is subjected to resonant filtering processing according to the target material filter to determine the target material frequency data after the resonant filtering; the target material frequency data is subjected to inverse Fourier transform to determine the corresponding target material spatial domain image.

[0015] Anomaly mapping is performed on the spatial domain image of the target material using a statistical threshold method to identify corresponding anomaly regions. The average pore fluid pressure is calculated based on the image pixel intensity of the anomaly regions. Depth prediction is performed based on an empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. The probability level of containing the target material is determined based on the average pore fluid pressure value and the predicted depth value. The anomaly region is verified based on the probability level of containing the target material and preset ground electromagnetic measurement data to determine the corresponding recommended drilling map.

[0016] Further, the step of preprocessing the multi-source satellite remote sensing image data to determine the corresponding preprocessed image data includes:

[0017] Geometric correction is performed on satellite remote sensing images using ground control points and digital elevation models. This geometric correction is used to eliminate systematic and non-systematic geometric distortions.

[0018] Based on the illumination model and slope and aspect information, terrain compensation is performed on the thermal infrared data and the multispectral data. The terrain compensation is used to reduce radiation distortion caused by terrain undulation.

[0019] Based on the image data after geometric correction and terrain compensation, the corresponding preprocessed image data is determined.

[0020] Furthermore, the construction of the target material filter includes:

[0021] The characteristic resonance frequencies of the target material are obtained from a preset material-frequency fingerprint database, which is used to call the corresponding resonance frequencies for different exploration targets.

[0022] A Gaussian bandpass filter function is constructed based on the characteristic resonant frequency of the target material, and the corresponding target material filter is determined.

[0023] Further, the step of performing resonant filtering on the frequency domain based on the target material filter to determine the target material frequency data after resonant filtering includes:

[0024] The target material filter performs a pixel-by-pixel multiplication operation in the frequency domain;

[0025] The reference station signal is acquired synchronously, and the frequency domain data after the dot product operation is corrected by the transfer function based on the reference station signal to determine the corresponding target material frequency data.

[0026] Further, the step of performing anomaly mapping on the spatial domain image of the target material according to the statistical threshold method to determine the corresponding anomaly region includes:

[0027] Based on the local statistical characteristics of the target material spatial domain image, the target material spatial domain image is divided into several sub-regions, and the local statistics of each sub-region are calculated independently, wherein the local statistics include the local mean and the local standard deviation.

[0028] The anomaly determination threshold of each sub-region is dynamically calculated based on the local statistics, and the anomaly determination of each sub-region is performed based on the anomaly determination threshold to determine the corresponding abnormal region.

[0029] Further, the step of calculating the average pore fluid pressure based on the image pixel intensity of the abnormal region, and performing depth prediction based on an empirical linear model and the average pore fluid pressure value obtained after the calculation, to determine the corresponding predicted depth value, includes:

[0030] The average pore fluid pressure value is determined based on the ratio of the integral of the image pixel intensity of the abnormal region to the area of ​​the abnormal region.

[0031] Regression analysis is performed on the actual drilling data and remote sensing inverted pore fluid pressure data of the preset geological exploration area to construct a corresponding empirical linear model. The average pore fluid pressure value is then input into the empirical linear model to determine the corresponding predicted depth value.

[0032] Furthermore, the step of verifying abnormal areas based on the probability level of containing the target substance and combining it with preset ground electromagnetic measurement data to determine the corresponding drilling recommendation map includes:

[0033] Acquire preset ground electromagnetic measurement data, which includes resistivity and polarizability information obtained by SKIP-VERS electrical method measurement;

[0034] The frequency resonance anomaly intensity is extracted from the probability level containing the target material. The frequency resonance anomaly intensity, resistivity information, and polarizability information are fused by Bayesian inference to determine the corresponding posterior probability distribution containing the target material. The anomaly region is verified based on the posterior probability distribution, and the corresponding drilling recommendation map is determined.

[0035] Secondly, this application provides a geophysical detection device based on remote sensing frequency resonance, comprising:

[0036] The data acquisition module is used to acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0037] The target substance extraction module is used to perform Fourier transform on the preprocessed image data, converting the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target substance filter, perform resonant filtering on the frequency domain according to the target substance filter, determine the target substance frequency data after the resonant filtering, and perform inverse Fourier transform on the target substance frequency data to determine the corresponding target substance spatial domain image.

[0038] The target mapping and determination module is used to perform anomaly mapping on the spatial domain image of the target material according to the statistical threshold method, determine the corresponding anomaly region, calculate the average pore fluid pressure based on the image pixel intensity of the anomaly region, and perform depth prediction based on the empirical linear model and the average pore fluid pressure value obtained after the calculation to determine the corresponding predicted depth value. Based on the average pore fluid pressure value and the predicted depth value, the probability level of containing the target material is determined. Based on the probability level of containing the target material and the preset ground electromagnetic measurement data, the anomaly region is verified to determine the corresponding drilling recommendation map.

[0039] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the geophysical exploration method based on remote sensing frequency resonance.

[0040] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the geophysical exploration method based on remote sensing frequency resonance.

[0041] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the geophysical exploration method based on remote sensing frequency resonance.

[0042] As can be seen from the above technical solution, this application provides a geophysical exploration method and device based on remote sensing frequency resonance. After acquiring and preprocessing multi-source satellite remote sensing image data, the pixel intensity values ​​of the preprocessed image data are converted from the spatial domain to the frequency domain through Fourier transform. A target material filter is constructed to perform resonance filtering in the frequency domain to obtain the target material frequency value. The target material frequency value is then subjected to inverse Fourier transform to obtain the target material spatial domain image. Anomaly mapping is performed on the spatial domain image based on the statistical threshold method, and the average pore fluid pressure value and predicted depth value are calculated based on the anomaly mapping results to obtain the probability level of containing the target material. Based on the probability level of containing the target material and the preset ground electromagnetic measurement data, the anomaly area is verified to determine the corresponding drilling recommendation map, thereby improving the accuracy and efficiency of geophysical exploration. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is one of the flowcharts illustrating the geophysical exploration method based on remote sensing frequency resonance in the embodiments of this application;

[0045] Figure 2 This is a structural diagram of the geophysical exploration device based on remote sensing frequency resonance in the embodiments of this application;

[0046] Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0047] Figure label:

[0048] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0051] Traditional geophysical research, centered on "tectonic orientation" and relying on geological structural analysis for resource location, suffers from limitations such as shallow detection depth, weak anti-interference capabilities, and multiple interpretations. This application provides a geophysical exploration method and apparatus based on remote sensing frequency resonance. After preprocessing multi-source satellite remote sensing image data, Fourier transform is used to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain. A target material filter is constructed to perform resonance filtering in the frequency domain, obtaining the target material's frequency value. An inverse Fourier transform is then performed on the target material's frequency value to obtain a spatial domain image of the target material. Anomaly mapping is performed on the spatial domain image using a statistical thresholding method. Based on the anomaly mapping results, the average pore fluid pressure and predicted depth are calculated to obtain the probability level of containing the target material. Based on the probability level of containing the target material and pre-set ground electromagnetic measurement data, anomaly area verification is performed to determine the corresponding drilling recommendation map, thereby improving the accuracy and efficiency of geophysical exploration.

[0052] To improve the accuracy and efficiency of geophysical exploration, this application provides an embodiment of a geophysical exploration method based on remote sensing frequency resonance, see [link to embodiment]. Figure 1 The geophysical exploration method based on remote sensing frequency resonance specifically includes the following:

[0053] Step S101: Acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0054] Optionally, in this embodiment, this step is the stage of acquiring remote sensing image data of different bands from multiple satellite platforms and performing preprocessing to lay the data foundation.

[0055] First, the system acquires multi-source remote sensing imagery data from various satellite sensors. This data primarily includes three categories: multispectral data, thermal infrared data, and microwave data. Multispectral data, sourced from sensors such as Sentinel-2MSI, provides information across multiple bands including visible and near-infrared light, with a spatial resolution of at least 10 meters, reflecting the spectral characteristics of surface materials. Thermal infrared data, from satellites such as Landsat-8, has a spatial resolution of at least 30 meters and is used to capture the thermal radiation characteristics of the surface. Microwave data, from synthetic aperture radar (SAR) systems such as Sentinel-1, has a spatial resolution of up to 5 meters, penetrating clouds and some surface cover, and is sensitive to the dielectric properties and structure of ground features. This data is input into the system in digital image format, with each pixel containing radiance values ​​or backscattering coefficients for different bands.

[0056] After acquiring the raw data, geometric correction is performed first. This process aims to eliminate geometric distortions in the image, ensuring an accurate spatial correspondence between each pixel and its corresponding geographic coordinates. Geometric distortions primarily arise from factors such as sensor attitude variations, terrain undulations, Earth's curvature, and atmospheric refraction. In this process, the system utilizes ground control points (GCPs) and a digital elevation model (DEM) to resample the image through affine transformations or polynomial correction models. Specifically, control points provide reference positions with known geographic coordinates, the DEM provides elevation information, and the correction model calculates the correct position of each pixel based on this information, generating new image data through interpolation methods (such as nearest neighbor, bilinear interpolation, or cubic convolution). The effect of geometric correction is to generate images with consistent spatial references and accurate geographic positioning, laying the foundation for the registration and fusion of multi-temporal and multi-sensor data, and avoiding positional biases in subsequent analysis.

[0057] Next, terrain compensation is performed, primarily for multispectral and thermal infrared data. In mountainous or undulating terrain, surface slope and aspect lead to differences in illumination conditions, causing similar features to exhibit different radiance values ​​at different locations. This distortion is called the terrain effect. The purpose of terrain compensation is to eliminate radiation distortion caused by terrain and restore the true reflection or emission characteristics of features. During processing, the system uses DEM data to calculate the slope, aspect, and solar incidence angle of each pixel, and performs radiometric correction using an illumination model (such as the C model or Minnaert model). This model, based on the Lambertian assumption or non-Lambertian correction, adjusts pixel brightness values ​​to reduce the impact of terrain shadows and excessive illumination. The effect of terrain compensation is to significantly improve image quality and comparability, ensuring that similar features exhibit consistent radiation characteristics under different terrain conditions, thereby enhancing the reliability of subsequent spectral analysis and anomaly extraction.

[0058] After the above processing, the system generates preprocessed image data. This data is geometrically precisely registered and radiometrically free of major distortions, resulting in high-quality, standardized image products. The preprocessed multispectral data more realistically reflects the surface spectral reflectance characteristics, the thermal infrared data more accurately represents the surface temperature distribution, and the microwave data maintains good geometric consistency. This image data serves as input for subsequent in-depth analyses such as frequency resonance filtering and anomaly extraction; its quality directly impacts the accuracy and reliability of the entire mineral exploration process.

[0059] Step S101 transforms the raw satellite remote sensing data containing various distortions into high-quality image data that is geometrically accurate and radiometrically consistent through systematic preprocessing operations.

[0060] Step S102: Perform Fourier transform on the preprocessed image data to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target material filter, perform resonant filtering on the frequency domain according to the target material filter, determine the target material frequency data after the resonant filtering, and perform inverse Fourier transform on the target material frequency data to determine the corresponding target material spatial domain image.

[0061] Optionally, in this embodiment, this step is the core of the frequency resonance filtering process, which aims to directly identify and enhance the abnormal information related to the target substance (such as oil and gas, specific minerals, etc.) from the remote sensing image, and realize direct detection based on physical characteristics.

[0062] The core principle of this step is to directly detect matter through frequency resonance: substances such as hydrocarbons have unique resonance frequencies, which appear as spectral / energy anomalies in remote sensing data. This method amplifies these anomalies by filtering them with resonance frequencies (e.g., hydrocarbon alteration bands are in the 350–2,500 nm range), treating the satellite as a "giant spectrometer."

[0063] First, a Fourier transform is performed on the preprocessed image data. The preprocessed image has undergone geometric correction, radiometric calibration, and noise suppression, ensuring the data's consistency in both spatial and radiometric senses. The Fourier transform converts the image from the spatial domain (i.e., using pixel position (x,y) and wavelength λ as variables) to the frequency domain (using spatial frequency (u,v) and frequency f as variables). Essentially, this transformation decomposes the image into sinusoidal components of different frequencies and directions, thereby revealing the implicit periodic structure and frequency characteristics within the image. In the frequency domain, the image's global texture, edge details, and noise are often distributed across different frequency ranges, laying the foundation for identifying the resonant frequencies of the target material.

[0064] Fourier transform:

[0065] Next, a dedicated filter is constructed based on the resonance characteristics of the target material. Different substances, due to differences in their molecular structure and chemical bond vibration modes, will exhibit resonant responses to electromagnetic waves of specific frequencies. For example, hydrocarbons exhibit characteristic absorption or scattering behaviors in the mid-infrared and microwave bands. The target material filter is designed to operate at the material's resonant frequency f. k A bandpass filter centered on the target material, commonly in the form of a Gaussian function. This filter highlights components with frequencies close to the resonant frequency of the target material in the frequency domain, while suppressing irrelevant frequency information, including high-frequency noise and low-frequency background radiation. The filter bandwidth parameter σ controls the tolerance range for frequency selection; its value must balance sensitivity and anti-interference capability, and is usually determined based on prior knowledge or experimental calibration results.

[0066] Bandpass filtering: f k It is the resonance frequency of the matter.

[0067] After constructing the filter, resonant filtering is applied to the frequency domain data. Specifically, the frequency domain representation obtained from the Fourier transform is multiplied by the filter. This operation is equivalent to weighting the image in the frequency domain, strengthening components with resonant frequencies consistent with the target substance, and weakening other frequency responses. The result is a set of resonant-enhanced frequency domain data, which significantly highlights signal components related to the presence of the target substance.

[0068] Resonance image:

[0069] Next, an inverse Fourier transform is performed on the filtered frequency domain data to convert it back to the spatial domain. The inverse transform process reconstructs the spatial distribution image from the resonance enhancement results in the frequency domain, i.e., the spatial domain image of the target material. Each pixel value in this image reflects the response intensity at the corresponding location's resonance frequency of the target material; high-value regions indicate a higher probability of the target material's presence. Because the filtering operation is performed in the frequency domain, the reconstructed image not only enhances signals directly related to the material but also, to some extent, suppresses incoherent noise and surface variations unrelated to the material.

[0070] Inverse Fourier Transform: I res (x, y)H(f)

[0071] This step achieves the transformation from "image intensity" to "material response." Traditional remote sensing methods rely on differences in surface reflection or emission for indirect inference, while this step directly correlates the physical properties of underground materials through a frequency resonance mechanism.

[0072] Its advantages are mainly reflected in three aspects: First, it significantly improves the detection depth and capability, because the resonance response is not completely shielded by the surface cover, and can reveal the anomalous information of deep materials; second, it enhances anti-interference ability, as frequency filtering can effectively separate material signals from environmental noise and reduce interference from factors such as vegetation and soil moisture; third, it provides a quantitative basis, as the filtered image data can be directly used for anomaly intensity calculation, depth inversion and probability assessment, supporting subsequent automated and refined interpretation.

[0073] Step S103: Perform anomaly mapping on the spatial domain image of the target material using the statistical threshold method to determine the corresponding anomaly region. Calculate the average pore fluid pressure based on the image pixel intensity of the anomaly region. Perform depth prediction based on the empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. Determine the probability level of containing the target material based on the average pore fluid pressure value and the predicted depth value. Verify the anomaly region based on the probability level of containing the target material combined with preset ground electromagnetic measurement data to determine the corresponding recommended drilling map.

[0074] Optionally, in this embodiment, the statistical threshold method is used to identify and extract abnormal regions from the spatial domain image of the target material.

[0075] First, global statistics are calculated on the spatial domain resonance intensity image obtained after frequency resonance filtering and inverse Fourier transform, including the mean (μ) and standard deviation (σ) of the intensity of all pixels in the entire image. Each pixel value in this image represents the resonance response intensity of the target material that may exist at the corresponding geographical location.

[0076] Define a dynamic threshold calculation formula, with the abnormal threshold being: Ires > μ + 3σ.

[0077] This threshold means that areas with pixel values ​​more than three standard deviations above the mean are considered significant anomalies. This stringent standard effectively suppresses weak responses caused by random noise or background features, highlighting truly significant strong anomaly signals.

[0078] Subsequently, the image is binarized and segmented using this threshold. Each pixel is traversed, and its intensity is compared with the threshold. Pixels with an intensity greater than the threshold are marked as candidate anomalies, while pixels with an intensity lower than the threshold are considered background. Based on this, spatially continuous or adjacent anomaly pixels are clustered and region growing is performed to form connected anomaly zones (ATZs), and sporadic noise points are removed to improve spatial continuity.

[0079] In the physical property parameter estimation step, based on the image pixel intensity information of the abnormal area, the pressure, burial depth and existence probability of the underground target fluid are estimated step by step through quantitative calculation and model inference.

[0080] Specifically, the system performs pixel intensity integration on the extracted anomaly target zone (ATZ). This zone is a spatial range significantly correlated with the resonance response of the target material (such as hydrocarbons or specific minerals), determined after frequency resonance filtering and anomaly mapping. During calculation, the intensity value of each pixel within the ATZ after resonance filtering is spatially integrated to obtain the total energy response value. This value is then divided by the total area of ​​the anomaly zone to obtain the average energy intensity per unit area, which is the estimated average pore fluid pressure. Essentially, this calculation treats remotely sensed anomaly intensity as an energy representation of fluid pressure or seepage effects on the Earth's surface. Its function is to convert grayscale or radiometric values ​​in the image into physically meaningful pressure parameters, thereby providing a quantitative indicator of the fluid activity level in the target area. Higher pressure values ​​generally indicate stronger underground fluid migration or enrichment, further suggesting a greater likelihood of oil, gas, or mineral deposits in the area.

[0081] Pore ​​fluid pressure estimation (P):

[0082] Next, an empirical linear model is introduced, using the average pore fluid pressure value obtained in the previous step as input, to predict the burial depth of the top boundary or enrichment center of the target body.

[0083] Deep linear model: d = a·P + b

[0084] This model is an empirical model, where a and b are model coefficients determined by performing regional-specific corrections on known geological backgrounds, drilling calibration data, or historical exploration results, thus adapting to the actual geological conditions of different basins or mineralized areas. For example, the correction coefficient for a certain region is 700-2,100 meters.

[0085] This step enables the conversion from surface anomaly intensity to subsurface depth, overcoming the limitation of traditional remote sensing methods in inferring deep information. Through this model, the system can output predicted depth values ​​in meters. Its reliability is closely related to the quality and representativeness of the data used for model calibration. This step significantly enhances the vertical detection capability of the technology, providing crucial depth information for subsequent drilling deployments.

[0086] Then, based on the average pore fluid pressure and predicted depth values ​​obtained from the above calculations, and according to the classification rules, the probability of mineral (or oil and gas) content in each anomalous area is assessed.

[0087] Generally, anomaly zones with higher pressure values ​​and depth values ​​within favorable ranges are assigned a high probability rating; conversely, they are rated as medium or low probability. The probability rating is output as a discrete category (e.g., high, medium, low) or a probability percentage. This step serves to classify and prioritize exploration targets based on risk. Its effect is to transform massive amounts of anomaly information into exploration target areas with varying degrees of confidence, directly guiding decision-making. For example, high-probability target areas can be prioritized for drilling verification, while low-probability areas can be postponed, thereby significantly improving exploration efficiency and reducing the economic risks of blind drilling.

[0088] Next, based on the pre-acquired ground electromagnetic measurement data, the remote sensing anomaly areas will be verified and analyzed in detail.

[0089] First, the probability level map obtained by frequency resonance processing is spatially gridded, and the anomalous zones (ATZs) with high probability values ​​are extracted as target areas to be verified. These areas are usually represented as continuous or isolated high-probability patches in the map, indicating the possible existence of target ore bodies or oil and gas reservoirs underground.

[0090] Next, pre-set ground electromagnetic measurement data is introduced. Acquired through a distributed electromagnetic acquisition system deployed on the ground (such as the SKIP-VERS electrical resistivity meter), this data includes physical properties such as resistivity and polarizability, and possesses high vertical resolution and reliability. The ground electromagnetic measurement points are then spatially registered with remote sensing anomaly areas with high precision to ensure comparability between the two types of data in the same coordinate system.

[0091] Subsequently, joint inversion and anomaly verification were conducted. A multi-source data fusion model was established, using probability levels as prior information and ground electromagnetic parameters as observational data. Probabilistic inference algorithms (such as Bayesian fusion or co-kriging) were employed to calculate the posterior probability of ore-bearing bodies within each anomaly area. This process effectively suppressed the ambiguity of single remote sensing methods and improved the reliability of target identification.

[0092] Based on this, each candidate area is comprehensively classified and ranked according to the fused probability level, resistivity anomaly morphology, and burial depth information obtained through inversion, thus clarifying the drilling priority. The final generated drilling recommendation map is a thematic map, which clearly marks the recommended target area boundaries, confidence levels, expected depths, and suggested drilling sequences.

[0093] This step, through the integration of space, ground, and ground data, combines broad-area screening with refined verification, significantly reducing exploration risks. It improves the efficiency and accuracy of anomaly verification, reducing invalid drilling; the output results directly support engineering deployment, providing a reliable basis for mineral rights assessment and drilling decisions; and it performs particularly well in areas with complex surface conditions and low exploration levels.

[0094] This example demonstrates how this embodiment performs underground mineral inversion based on the physical properties of the material.

[0095] As described above, the geophysical exploration method based on remote sensing frequency resonance provided in this application can acquire multi-source satellite remote sensing image data, preprocess it, and then convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain through Fourier transform. A target material filter is constructed to perform resonance filtering on the frequency domain to obtain the target material frequency value. The target material frequency value is then subjected to inverse Fourier transform to obtain the target material spatial domain image. Anomaly mapping is performed on the spatial domain image based on the statistical threshold method, and the average pore fluid pressure value and predicted depth value are calculated based on the anomaly mapping results to obtain the probability level of containing the target material. Based on the probability level of containing the target material and the preset ground electromagnetic measurement data, the anomaly area is verified, and the corresponding drilling recommendation map is determined. This can improve the accuracy and efficiency of geophysical exploration.

[0096] In one embodiment of the geophysical exploration method based on remote sensing frequency resonance of this application, it may further include the following:

[0097] Step S201: Geometric correction is performed on the satellite remote sensing image using ground control points and digital elevation model. The geometric correction is used to eliminate systematic and non-systematic geometric distortions.

[0098] Step S202: Based on the illumination model and slope and aspect information, perform terrain compensation on the thermal infrared data and the multispectral data. The terrain compensation is used to reduce radiation distortion caused by terrain undulation.

[0099] Step S203: Determine the corresponding preprocessed image data based on the image data after geometric correction and terrain compensation.

[0100] Optionally, in this embodiment, a geometric correction step is performed. This step utilizes ground control points (GCPs) and a digital elevation model (DEM) to accurately correct systematic and non-systematic geometric distortions in the original satellite remote sensing imagery through affine transformation or polynomial correction methods. Systematic distortions originate from factors such as sensor attitude, Earth curvature, and atmospheric refraction, while non-systematic distortions are caused by terrain undulations. Through this step, image pixels are accurately repositioned to their true geographic coordinates, significantly improving the spatial geometric accuracy of the image and laying a reliable geometric foundation for subsequent multi-source data registration and information extraction.

[0101] Optionally, in this embodiment, terrain compensation is performed. This step mainly targets thermal infrared and multispectral data, and based on the illumination model and combined with terrain factors such as slope and aspect derived from the DEM, physically corrects the surface radiation values. Since the solar incidence angle varies with terrain, the phenomena of different spectra for the same object and different objects with the same spectrum are particularly significant in mountainous areas. This compensation process effectively eliminates the influence of terrain shading and irradiance differences on the radiation signals of ground objects, significantly reducing radiation distortion caused by terrain undulations. This allows the corrected image to more accurately reflect the inherent spectral reflectance or emission characteristics of ground objects, improving the comparability and classification accuracy of ground object information in areas with different slope aspects.

[0102] Optionally, in this embodiment, the system integrates and outputs the image data that has undergone the aforementioned geometric correction and topographic radiometric compensation processing, generating high-quality preprocessed image data that can be used for subsequent frequency resonance analysis. This ensures that all input data meet consistent high standards in both geometric and radiometric dimensions, eliminating non-target noise introduced by sensors, atmosphere, and terrain in the original data. This is a crucial step in ensuring the accuracy of the results throughout the entire data processing workflow.

[0103] Through step S203, this embodiment obtains high-quality preprocessed image data that can be used for subsequent frequency resonance analysis, providing a reliable and consistent data foundation for subsequent anomaly extraction.

[0104] In one embodiment of the geophysical exploration method based on remote sensing frequency resonance of this application, it may further include the following:

[0105] Step S301: Obtain the characteristic resonance frequency of the target material from the preset material-frequency fingerprint database, wherein the material-frequency fingerprint database is used to call the corresponding resonance frequency for different exploration targets;

[0106] Step S302: Construct a Gaussian bandpass filter function based on the characteristic resonant frequency of the target material, and determine the corresponding target material filter.

[0107] Optionally, in this embodiment, the characteristic resonance frequency corresponding to the current exploration target material is obtained by accessing a preset material-frequency fingerprint database. The material-frequency fingerprint database is based on the inherent physical properties of different materials (such as hydrocarbons, metallic minerals, or groundwater) and systematically records the resonance response frequencies of various materials under the action of electromagnetic waves.

[0108] In practice, the system queries pre-stored frequency values ​​in the database based on the user-specified exploration target (e.g., "oil and gas" or "copper ore"). These frequency values, obtained through experimental measurement or theoretical calculation, possess clear material specificity and repeatability, accurately linking material types with resonant frequencies. This provides accurate frequency filtering criteria for subsequent signal processing, ensuring that the filtering operation has clear physical directionality and targeted detection.

[0109] Optionally, in this embodiment, after obtaining the characteristic resonance frequency of the target material, a Gaussian bandpass filter function is constructed based on this frequency.

[0110] Gaussian bandpass filter function:

[0111] This filter centers on a characteristic frequency and controls the frequency selectivity by adjusting the bandwidth parameter. Its function exhibits a Gaussian distribution, maximizing the transmission coefficient near the center frequency and gradually attenuating towards both sides, effectively highlighting the target frequency band and suppressing irrelevant noise. During construction, the passband width and attenuation steepness must be appropriately set to balance frequency selectivity and anti-interference capability. The filter's function is to accurately extract frequency components related to the target substance from the complex remote sensing spectrum, enhancing the signal-to-noise ratio and highlighting weak substance responses that were previously submerged in background noise, providing clear and reliable frequency domain feature information for subsequent anomaly identification.

[0112] Through step S302, this embodiment successfully determined the resonance wave of the target material, laying the foundation for subsequent extraction of abnormal areas.

[0113] In one embodiment of the geophysical exploration method based on remote sensing frequency resonance of this application, it may further include the following:

[0114] Step S401: Perform pixel-by-pixel multiplication operation on the target material filter in the frequency domain;

[0115] Step S402: Synchronously acquire reference station signals, and perform transfer function correction on the frequency domain data after the dot multiplication operation based on the reference station signals to determine the corresponding target material frequency data.

[0116] Optionally, in this embodiment, a dot product operation is performed on each pixel in the frequency domain according to a pre-constructed target material filter. This filter is designed based on the inherent resonant frequency characteristics of the target material (such as a specific mineral or hydrocarbon), and is represented as a Gaussian or other form of bandpass function with the center frequency as its core. During the operation, the filter function is multiplied by the complex spectral value of each pixel position in the frequency domain; essentially, this is a weighted operation in the frequency domain. This can enhance frequency components that match the resonant frequency of the target material while suppressing irrelevant frequency signals, thereby highlighting the abnormal response of the target object in the frequency domain and providing a purified frequency data foundation for subsequent anomaly identification.

[0117] Optionally, in this embodiment, transfer function correction is performed on the frequency domain data after dot product operation. This correction relies on the synchronously acquired reference station signal. The reference station is deployed in an area with a known stable geological background or no target object anomalies, and is used to continuously record background changes in the electromagnetic field from natural or artificial sources. During processing, the system first calculates the transfer function in the frequency domain between the rover (measuring point) and the reference station signals. This function characterizes the combined effects of environmental electromagnetic interference, system response, and propagation path effects.

[0118] Transfer function correction: S corr =S meas / T(f), where T(f) is the reference-movement ratio;

[0119] Using this transfer function, the frequency domain data after dot product is corrected, usually by dividing the measured signal by the transfer function, thereby effectively eliminating regional background noise and common mode interference.

[0120] The core function of this step is to separate out the anomalous signals that are truly caused by local target materials, which greatly improves the signal-to-noise ratio and reliability of the data, ensures that the anomalies identified subsequently have clear locality and geological significance, and ultimately improves the accuracy of depth estimation and material identification.

[0121] Through step S402, this embodiment successfully performed frequency domain correction using a transfer function to obtain the target material frequency, laying the foundation for subsequent extraction of outliers.

[0122] In one embodiment of the geophysical exploration method based on remote sensing frequency resonance of this application, it may further include the following:

[0123] Step S501: Based on the local statistical characteristics of the target material spatial domain image, the target material spatial domain image is divided into several sub-regions, and the local statistics of each sub-region are calculated independently, wherein the local statistics include the local mean and the local standard deviation.

[0124] Step S502: Dynamically calculate the anomaly determination threshold of each sub-region based on the local statistics, and determine the anomaly of each sub-region based on the anomaly determination threshold to identify the corresponding abnormal region.

[0125] Optionally, in this embodiment, the spatial domain image of the target material is divided into regions based on the local statistical characteristics of the image.

[0126] Specifically, a sliding window or image segmentation algorithm is used to divide the entire image into multiple appropriately sized, potentially partially overlapping sub-regions. During segmentation, computational efficiency and local feature fidelity must be balanced to ensure relatively consistent statistical characteristics within each sub-region. After segmentation, local statistics for each sub-region are calculated independently, primarily including the local mean and local standard deviation. The local mean reflects the average level of pixel intensity in that region, while the local standard deviation characterizes the dispersion and fluctuation range of pixel values. The core objective is to eliminate the influence of global image non-uniformity (such as uneven distribution of illumination gradients, background trends, or noise) through localization, laying the foundation for subsequent adaptive threshold calculation.

[0127] The anomaly detection threshold is dynamically determined based on the local statistics calculated for each sub-region.

[0128] Abnormal threshold: Ires > μ + 3σ

[0129] This model enables the threshold to adaptively follow changes in the local background of the image: in areas with uniform background and minimal fluctuations, the threshold is lower, which is beneficial for detecting weak anomalies; in areas with complex backgrounds or high noise, the threshold automatically increases, effectively suppressing false positives. After determining the threshold for each sub-region, the system applies it to the corresponding region, performing anomaly detection pixel by pixel. Pixels with values ​​exceeding the threshold of their respective sub-regions are marked as anomalies, ultimately generating a refined distribution map of anomaly regions.

[0130] Through step S502, this embodiment successfully overcomes the detection bias caused by uneven global grayscale distribution in the image by using a local adaptive threshold strategy, and significantly improves the detection capability of weak and small abnormal targets in complex backgrounds.

[0131] In one embodiment of the geophysical exploration method based on remote sensing frequency resonance of this application, it may further include the following:

[0132] Step S601: Determine the corresponding average pore fluid pressure value based on the ratio of the image pixel intensity integral of the abnormal region to the area of ​​the abnormal region;

[0133] Step S602: Based on the actual drilling data of the preset geological exploration area and the remote sensing inverted pore fluid pressure data, perform regression analysis and fitting to construct the corresponding empirical linear model, input the average pore fluid pressure value into the empirical linear model, and determine the corresponding predicted depth value.

[0134] Optionally, in this embodiment, the formula for estimating pore fluid pressure is as follows:

[0135] pressure:

[0136] The average pore fluid pressure is determined by calculating the ratio of the integral of the image pixel intensity within the abnormal region to the total area of ​​that region.

[0137] First, spatial domain integration is performed on the anomalous image after frequency resonance filtering to obtain a value representing the total energy intensity of the anomalous region. Then, this integral value is divided by the total area of ​​pixels covered by the anomalous region to obtain the average intensity per unit area, which is the retrieved average pore fluid pressure. This calculation is based on the physical principle that the presence of underground hydrocarbons or mineralized fluids enhances the surface electromagnetic response, and there is a significant correlation between the response intensity and pore fluid pressure. Therefore, this ratio can serve as an effective indicator for assessing the potential for underground mineralized fluid occurrence.

[0138] Optionally, in this embodiment, the formula for calculating the predicted depth is as follows:

[0139] Depth: d = a·P + b

[0140] By using depth data obtained from actual drilling in a specific geological exploration area and pore fluid pressure data obtained from remote sensing inversion, regression analysis is performed to establish the statistical relationship between the two, thereby constructing an empirical linear model.

[0141] Specifically, the model uses remotely sensed pressure as the independent variable and actual drilling depth as the dependent variable, and determines the linear coefficients using fitting methods such as the least squares method.

[0142] In practical applications, the average pore fluid pressure value calculated in step S601 is input into the linear model to output the corresponding predicted depth value. This enables the inference from two-dimensional surface anomalies to three-dimensional subsurface structures, overcoming the shortcomings of traditional remote sensing methods in depth estimation and providing crucial basis for subsequent drilling target area selection and resource assessment.

[0143] Through step S602, this embodiment successfully transforms the physical quantities retrieved from remote sensing into depth estimates with clear geological significance, significantly improving the practical usability and accuracy of the prediction results.

[0144] In one embodiment of the geophysical exploration method based on remote sensing frequency resonance of this application, it may further include the following:

[0145] Step S701: Obtain preset ground electromagnetic measurement data, which includes resistivity and polarizability information obtained by SKIP-VERS electrical method measurement;

[0146] Step S702: Extract the frequency resonance anomaly intensity from the probability level containing the target material, and determine the corresponding posterior probability distribution containing the target material by fusing the frequency resonance anomaly intensity, the resistivity information, and the polarizability information through Bayesian inference. Verify the anomaly region based on the posterior probability distribution and determine the corresponding drilling recommendation map.

[0147] Optionally, in this embodiment, ground electromagnetic measurement data is acquired using the SKIT-VERS electrical resistivity system. Measurement points are laid out in a grid pattern, typically at intervals of 50 to 200 meters, to obtain geophysical parameters including resistivity and polarizability. Resistivity information reflects the electrical conductivity of the subsurface medium, while polarizability indicates the presence of polarizable substances (such as metallic minerals or hydrocarbons) in the medium. This data has high vertical resolution and local accuracy, serving as an important supplement and verification basis for satellite remote sensing anomalies.

[0148] Optionally, in this embodiment, firstly, the anomalous intensity value for each spatial location is extracted from the anomalous map generated by frequency resonance processing. This intensity reflects the prior probability of the presence of the target substance. Then, using a Bayesian inference framework, a multi-parameter joint likelihood function is established, with the frequency resonance anomalous intensity, ground resistivity, and polarizability information as input evidence. By fusing the prior probability with the likelihood values ​​from ground observation data, the posterior probability distribution of the presence of the target substance in each spatial unit is calculated. This posterior probability not only quantifies the credibility of the anomalous event but also significantly reduces the ambiguity of a single data source.

[0149] Based on the posterior probability distribution, the initially identified abnormal regions are verified and reclassified:

[0150] High-posterior-probability areas are confirmed as reliable anomalies, while low-probability areas are removed or downgraded. Finally, the system generates a drilling recommendation map, which spatially and explicitly marks the verified anomaly areas, their probability levels, and the recommended drilling sequence.

[0151] Through step S702, this embodiment successfully improved the objectivity and accuracy of anomaly interpretation by introducing ground-measured data and a probability model, effectively reduced exploration risks, and provided a quantitative and highly reliable scientific basis for drilling decisions.

[0152] To improve the accuracy and efficiency of geophysical exploration, this application provides an embodiment of a geophysical exploration device based on remote sensing frequency resonance for implementing all or part of the aforementioned geophysical exploration method based on remote sensing frequency resonance. See [link to embodiment]. Figure 2 The geophysical exploration device based on remote sensing frequency resonance specifically includes the following components:

[0153] The data acquisition module 10 is used to acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0154] The target substance extraction module 20 is used to perform Fourier transform on the preprocessed image data, converting the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target substance filter, perform resonant filtering on the frequency domain according to the target substance filter, determine the target substance frequency data after the resonant filtering, and perform inverse Fourier transform on the target substance frequency data to determine the corresponding target substance spatial domain image.

[0155] The target mapping and determination module 30 is used to perform anomaly mapping on the spatial domain image of the target material according to the statistical threshold method, determine the corresponding anomaly region, calculate the average pore fluid pressure based on the image pixel intensity of the anomaly region, and perform depth prediction based on the empirical linear model and the average pore fluid pressure value obtained after the calculation to determine the corresponding predicted depth value. Based on the average pore fluid pressure value and the predicted depth value, the probability level of containing the target material is determined. Based on the probability level of containing the target material and the preset ground electromagnetic measurement data, the anomaly region is verified to determine the corresponding drilling recommendation map.

[0156] As described above, the geophysical exploration device based on remote sensing frequency resonance provided in this application can acquire multi-source satellite remote sensing image data, preprocess it, and then convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain through Fourier transform. A target material filter is constructed to perform resonance filtering on the frequency domain to obtain the target material frequency value. An inverse Fourier transform is then performed on the target material frequency value to obtain the target material spatial domain image. Anomaly mapping is performed on the spatial domain image based on the statistical threshold method, and the average pore fluid pressure value and predicted depth value are calculated based on the anomaly mapping results to obtain the probability level of containing the target material. Based on the probability level of containing the target material and pre-set ground electromagnetic measurement data, anomaly area verification is performed to determine the corresponding drilling recommendation map, thereby improving the accuracy and efficiency of geophysical exploration.

[0157] From a hardware perspective, in order to improve the accuracy and efficiency of geophysical exploration, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned geophysical exploration method based on remote sensing frequency resonance. The electronic device specifically includes the following components:

[0158] The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the geophysical exploration method based on remote sensing frequency resonance and core business systems, user terminals, and related databases and other related equipment; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the geophysical exploration method based on remote sensing frequency resonance in the present embodiment, and the contents of the embodiments of the geophysical exploration method based on remote sensing frequency resonance are incorporated herein, and repeated details will not be described again.

[0159] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0160] In practical applications, parts of the geophysical exploration method based on remote sensing frequency resonance can be executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0161] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0162] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0163] In one embodiment, the geophysical exploration method based on remote sensing frequency resonance can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0164] Step S101: Acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0165] Step S102: Perform Fourier transform on the preprocessed image data to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target material filter, perform resonant filtering on the frequency domain according to the target material filter, determine the target material frequency data after the resonant filtering, and perform inverse Fourier transform on the target material frequency data to determine the corresponding target material spatial domain image.

[0166] Step S103: Perform anomaly mapping on the spatial domain image of the target material using the statistical threshold method to determine the corresponding anomaly region. Calculate the average pore fluid pressure based on the image pixel intensity of the anomaly region. Perform depth prediction based on the empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. Determine the probability level of containing the target material based on the average pore fluid pressure value and the predicted depth value. Verify the anomaly region based on the probability level of containing the target material combined with preset ground electromagnetic measurement data to determine the corresponding recommended drilling map.

[0167] As described above, the electronic device provided in this application preprocesses multi-source satellite remote sensing image data, then converts the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain using Fourier transform. A target material filter is constructed to perform resonant filtering in the frequency domain to obtain the target material frequency value. An inverse Fourier transform is then performed on the target material frequency value to obtain a spatial domain image of the target material. Anomaly mapping is performed on the spatial domain image based on a statistical threshold method, and the average pore fluid pressure value and predicted depth value are calculated based on the anomaly mapping results to obtain the probability level of containing the target material. Based on the probability level of containing the target material and pre-set ground electromagnetic measurement data, anomaly area verification is performed to determine the corresponding recommended drilling map, thereby improving the accuracy and efficiency of geophysical exploration.

[0168] In another embodiment, the geophysical exploration method based on remote sensing frequency resonance can be configured separately from the central processing unit 9100. For example, the geophysical exploration method based on remote sensing frequency resonance can be configured as a chip connected to the central processing unit 9100, and the function of the geophysical exploration method based on remote sensing frequency resonance can be realized through the control of the central processing unit.

[0169] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 3 All components shown; in addition, the electronic device 9600 may also include Figure 3 For components not shown, please refer to existing technology.

[0170] like Figure 3 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0171] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0172] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0173] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0174] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0175] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0176] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0177] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the geophysical exploration method based on remote sensing frequency resonance, where the execution subject is a server or client, as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the geophysical exploration method based on remote sensing frequency resonance, where the execution subject is a server or client, as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0178] Step S101: Acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0179] Step S102: Perform Fourier transform on the preprocessed image data to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target material filter, perform resonant filtering on the frequency domain according to the target material filter, determine the target material frequency data after the resonant filtering, and perform inverse Fourier transform on the target material frequency data to determine the corresponding target material spatial domain image.

[0180] Step S103: Perform anomaly mapping on the spatial domain image of the target material using the statistical threshold method to determine the corresponding anomaly region. Calculate the average pore fluid pressure based on the image pixel intensity of the anomaly region. Perform depth prediction based on the empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. Determine the probability level of containing the target material based on the average pore fluid pressure value and the predicted depth value. Verify the anomaly region based on the probability level of containing the target material combined with preset ground electromagnetic measurement data to determine the corresponding recommended drilling map.

[0181] As described above, the computer-readable storage medium provided in this application preprocesses multi-source satellite remote sensing image data, then converts the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain using Fourier transform. A target material filter is constructed to perform resonant filtering in the frequency domain to obtain the target material frequency value. An inverse Fourier transform is then performed on the target material frequency value to obtain a spatial domain image of the target material. Anomaly mapping is performed on the spatial domain image based on a statistical threshold method, and the average pore fluid pressure value and predicted depth value are calculated based on the anomaly mapping results to obtain the probability level of containing the target material. Based on the probability level of containing the target material and pre-set ground electromagnetic measurement data, anomaly area verification is performed to determine the corresponding drilling recommendation map, thereby improving the accuracy and efficiency of geophysical exploration.

[0182] Embodiments of this application also provide a computer program product capable of implementing all steps of the geophysical exploration method based on remote sensing frequency resonance, where the execution subject is a server or client as described in the above embodiments. When executed by a processor, this computer program / instruction implements the steps of the geophysical exploration method based on remote sensing frequency resonance. For example, the computer program / instruction implements the following steps:

[0183] Step S101: Acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation.

[0184] Step S102: Perform Fourier transform on the preprocessed image data to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target material filter, perform resonant filtering on the frequency domain according to the target material filter, determine the target material frequency data after the resonant filtering, and perform inverse Fourier transform on the target material frequency data to determine the corresponding target material spatial domain image.

[0185] Step S103: Perform anomaly mapping on the spatial domain image of the target material using the statistical threshold method to determine the corresponding anomaly region. Calculate the average pore fluid pressure based on the image pixel intensity of the anomaly region. Perform depth prediction based on the empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. Determine the probability level of containing the target material based on the average pore fluid pressure value and the predicted depth value. Verify the anomaly region based on the probability level of containing the target material combined with preset ground electromagnetic measurement data to determine the corresponding recommended drilling map.

[0186] As described above, the computer program product provided in this application preprocesses multi-source satellite remote sensing image data, then converts the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain using Fourier transform. A target material filter is constructed to perform resonant filtering in the frequency domain to obtain the target material frequency value. An inverse Fourier transform is then performed on the target material frequency value to obtain the target material spatial domain image. Anomaly mapping is performed on the spatial domain image based on a statistical threshold method, and the average pore fluid pressure value and predicted depth value are calculated based on the anomaly mapping results to obtain the probability level of containing the target material. Based on the probability level of containing the target material and pre-set ground electromagnetic measurement data, anomaly area verification is performed to determine the corresponding drilling recommendation map, thereby improving the accuracy and efficiency of geophysical exploration.

[0187] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0189] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0190] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0191] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A geophysical exploration method based on remote sensing frequency resonance, characterized in that, The method includes: Acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation. The preprocessed image data is subjected to Fourier transform to convert the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; a target material filter is constructed, and the frequency domain is subjected to resonant filtering processing according to the target material filter to determine the target material frequency data after the resonant filtering; the target material frequency data is subjected to inverse Fourier transform to determine the corresponding target material spatial domain image. Anomaly mapping is performed on the spatial domain image of the target material using the statistical threshold method to identify corresponding anomaly regions. The average pore fluid pressure value is calculated based on the image pixel intensity of the anomaly regions. Depth prediction is performed based on an empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. The probability level of containing the target material is determined based on the average pore fluid pressure value and the predicted depth value. The anomaly region is verified based on the probability level of containing the target material and preset ground electromagnetic measurement data to determine the corresponding recommended drilling map.

2. The geophysical exploration method based on remote sensing frequency resonance according to claim 1, characterized in that, The step of preprocessing the multi-source satellite remote sensing image data to determine the corresponding preprocessed image data includes: Geometric correction is performed on satellite remote sensing images using ground control points and digital elevation models. This geometric correction is used to eliminate systematic and non-systematic geometric distortions. Based on the illumination model and slope and aspect information, terrain compensation is performed on the thermal infrared data and the multispectral data. The terrain compensation is used to reduce radiation distortion caused by terrain undulation. Based on the image data after geometric correction and terrain compensation, the corresponding preprocessed image data is determined.

3. The geophysical exploration method based on remote sensing frequency resonance according to claim 1, characterized in that, The construction of the target material filter includes: The characteristic resonance frequencies of the target material are obtained from a preset material-frequency fingerprint database, which is used to call the corresponding resonance frequencies for different exploration targets. A Gaussian bandpass filter function is constructed based on the characteristic resonant frequency of the target material, and the corresponding target material filter is determined.

4. The geophysical exploration method based on remote sensing frequency resonance according to claim 1, characterized in that, The step of performing resonant filtering on the frequency domain according to the target material filter to determine the target material frequency data after resonant filtering includes: The target material filter performs a pixel-by-pixel multiplication operation in the frequency domain; The reference station signal is acquired synchronously, and the frequency domain data after the dot product operation is corrected by the transfer function based on the reference station signal to determine the corresponding target material frequency data.

5. The geophysical exploration method based on remote sensing frequency resonance according to claim 1, characterized in that, The step of performing anomaly mapping on the spatial domain image of the target material according to the statistical threshold method to determine the corresponding anomaly region includes: Based on the local statistical characteristics of the target material spatial domain image, the target material spatial domain image is divided into several sub-regions, and the local statistics of each sub-region are calculated independently, wherein the local statistics include the local mean and the local standard deviation. The anomaly threshold for each sub-region is dynamically calculated based on the local statistics, and anomalies are determined for each sub-region based on the anomaly threshold to identify the corresponding abnormal region.

6. The geophysical exploration method based on remote sensing frequency resonance according to claim 1, characterized in that, The step of calculating the average pore fluid pressure value based on the image pixel intensity of the abnormal region, and performing depth prediction based on an empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value includes: The average pore fluid pressure value is determined based on the ratio of the integral of the image pixel intensity of the abnormal region to the area of ​​the abnormal region. Regression analysis is performed on the actual drilling data and remote sensing inverted pore fluid pressure data of the preset geological exploration area to construct a corresponding empirical linear model. The average pore fluid pressure value is then input into the empirical linear model to determine the corresponding predicted depth value.

7. The geophysical exploration method based on remote sensing frequency resonance according to claim 1, characterized in that, The step of verifying abnormal areas based on the probability level of containing the target substance and combining it with preset ground electromagnetic measurement data to determine the corresponding drilling recommendation map includes: Acquire preset ground electromagnetic measurement data, which includes resistivity and polarizability information obtained by SKIP-VERS electrical method measurement; The frequency resonance anomaly intensity is extracted from the probability level containing the target material. The frequency resonance anomaly intensity, resistivity information, and polarizability information are fused by Bayesian inference to determine the corresponding posterior probability distribution containing the target material. The anomaly region is verified based on the posterior probability distribution, and the corresponding drilling recommendation map is determined.

8. A geophysical exploration device based on remote sensing frequency resonance, characterized in that, The device includes: The data acquisition module is used to acquire multi-source satellite remote sensing image data, perform data preprocessing on the multi-source satellite remote sensing image data, and determine the corresponding preprocessed image data. The multi-source satellite remote sensing image data includes multispectral data, thermal infrared data, and microwave band data. The data preprocessing includes geometric correction and terrain compensation. The target substance extraction module is used to perform Fourier transform on the preprocessed image data, converting the pixel intensity values ​​of the preprocessed image data from the spatial domain to the frequency domain; construct a target substance filter, perform resonant filtering on the frequency domain according to the target substance filter, determine the target substance frequency data after the resonant filtering, and perform inverse Fourier transform on the target substance frequency data to determine the corresponding target substance spatial domain image. The target mapping and determination module is used to perform anomaly mapping on the spatial domain image of the target material according to the statistical threshold method, determine the corresponding anomaly region, calculate the average pore fluid pressure value based on the image pixel intensity of the anomaly region, and perform depth prediction based on the empirical linear model and the calculated average pore fluid pressure value to determine the corresponding predicted depth value. Based on the average pore fluid pressure value and the predicted depth value, the probability level of containing the target material is determined. Based on the probability level of containing the target material and the preset ground electromagnetic measurement data, the anomaly region is verified, and the corresponding drilling recommendation map is determined.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the geophysical exploration method based on remote sensing frequency resonance as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the geophysical exploration method based on remote sensing frequency resonance as described in any one of claims 1 to 7.

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