A Decoupling Method and System for Risk Characteristics of Earth's Surface Natural Electric Field Based on Order Rules

Abstract

This invention relates to a method and system for decoupling risk characteristics of natural electric fields on the surface based on the principle of order. The method includes: determining the spatial range of an electrode array based on the principle of spatial separability of risk sources and deploying the electrode array; acquiring multi-channel observation time series data of shallow surface natural potential through the electrode array; determining a time window based on the principle of spatial separability of risk sources; calculating the rank correlation coefficient between any two observation channels within the time window; mapping the calculated rank correlation coefficient to the corresponding field source state level according to a preset field source state level classification standard; calculating a decoupling index based on the normalized proportion of each field source state level and its preset risk weight; and directly quantifying the overall orderliness of the field sources through the decoupling index, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field, effectively solving the core problem of identifying risk characteristics.

Description

Technical Field

[0001] This invention relates to the field of mineral exploration technology, specifically to a method and system for decoupling the risk characteristics of the natural electric field on the earth's surface based on the principle of order. Background Technology

[0002] Shallow surface natural electric fields are time-varying fields formed by the dynamic coupling of physical and chemical processes in the subsurface medium. Their signals contain rich information about the properties and dynamic changes of the subsurface medium, making them valuable for applications in contaminated site monitoring, landslide early warning, and reservoir / dam leakage detection. However, changes in natural electric field signals carry multiple geological implications, and abnormal responses do not necessarily equate to risk. Identifying risk characteristics from the dynamic signals of shallow surface natural electric fields remains a core challenge for monitoring technology.

[0003] Existing methods have significant limitations when directly decoupling from risk characteristics: most inversion and filtering methods heavily rely on prior assumptions about medium parameters, source functions, or data noise distribution, which are difficult to meet under complex and variable real-world conditions. Traditional signal analysis methods often focus on extracting amplitude anomalies or specific frequency components, while risk events (such as the early stages of landslide development or trace pollution leaks) may only manifest as subtle disruptions in the synergistic relationship (or order) between multiple observation points in their early stages, with amplitude changes potentially insignificant, easily leading to missed warnings. Finally, existing methods lack comprehensive evaluation indicators that can integrate multi-channel information, intuitively quantify the overall disorder of the field source, and directly correlate with the risk level, resulting in insufficient decision support capabilities for monitoring results. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and system for decoupling risk characteristics of natural electric fields on the earth's surface based on the order rule, in order to solve the problem that the prior art is unable to identify risk characteristics from dynamic signals, and to achieve direct quantification of field source order and effective decoupling of risk characteristics without relying on prior models.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] On the one hand, a method for decoupling the risk characteristics of the natural electric field on the Earth's surface based on the order rule is provided, including the following steps: Based on the principle of spatial separability constraint of risk sources, the spatial range of electrode array layout is determined, and the electrode array is laid out within the spatial range. Multi-channel observation time series data of shallow surface spontaneous potential were obtained using an electrode array. Based on the principle of spatial separability of risk sources, the time window for analyzing the multi-channel observation time series data is determined; Within the time window, for the observation channels within the spatial range of the electrode array, calculate the rank correlation coefficient between any two observation channels; According to the preset field source state level classification standard, the calculated rank correlation coefficient is mapped to the corresponding field source state level; Based on the normalized proportion of each source state level and its preset risk weight, a decoupling index is calculated. The overall orderliness of the source is directly quantified through the decoupling index, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field.

[0007] On the other hand, a decoupling system for the risk characteristics of natural electric fields on the earth's surface based on order principles is provided to implement the aforementioned decoupling method for the risk characteristics of natural electric fields on the earth's surface based on order principles, including: The electrode array module is used to determine the spatial range of the electrode array based on the principle of separability of risk source space, and to deploy the electrode array within the spatial range. The data acquisition module is connected to the electrode array and is used to acquire multi-channel observation time series data of shallow surface spontaneous potential through the electrode array; The parameter configuration module, connected to the data acquisition module, is used to determine the time window for analyzing the multi-channel observation time series data based on the principle of spatial separability constraint of risk sources. The rank correlation calculation module is used to calculate the rank correlation coefficient between any two observation channels within the time window, for the observation channels within the spatial range of the electrode array layout. The field source classification module is used to map the calculated rank correlation coefficient to the corresponding field source state level according to the preset field source state level classification standard. The risk decoupling module is used to calculate the decoupling index based on the normalized proportion of the state level of each field source and its preset risk weight. The decoupling index directly quantifies the overall orderliness of the field sources, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field.

[0008] On the other hand, the present invention provides an electronic device including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the above-described method for decoupling the risk characteristics of the natural electric field on the ground based on the order rule.

[0009] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the above-described method for decoupling the risk characteristics of the natural electric field of the earth's surface based on the order rule.

[0010] On the other hand, the present invention provides a computer program product stored on a computer-readable storage medium and including computer instructions that, when executed by a processor, cause an electronic device to implement the steps of the above-described method for decoupling the risk characteristics of the natural electric field of the earth's surface based on the order rule.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a method for decoupling risk characteristics of surface natural electric fields based on the principle of order. The method includes: determining the spatial range of an electrode array based on the principle of spatial separability of risk sources, and deploying the electrode array within the specified spatial range; acquiring multi-channel observation time-series data of shallow surface natural potential through the electrode array; determining a time window for analyzing the multi-channel observation time-series data based on the principle of spatial separability of risk sources; calculating the rank correlation coefficient between any two observation channels within the spatial range of the electrode array deployment within the time window; mapping the calculated rank correlation coefficient to the corresponding field source state level according to a preset field source state level classification standard; calculating a decoupling index based on the normalized proportion of each field source state level and its preset risk weight; and directly quantifying the overall orderliness of the field source through the decoupling index, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field. This invention effectively solves the core problem in existing shallow surface natural electric field monitoring technologies, where it is difficult to identify risk characteristics due to multi-field coupling and complex components of the signal, and provides a principle for the design and deployment of multi-channel sensors or electrode arrays. This invention avoids reliance on complex prior physical models. By introducing the principle of spatial separability of risk sources and the principle of adaptive risk evolution time windows through order rules, it ensures that observed data can effectively reflect the local disturbance characteristics of risk sources. Simultaneously, it enables the order evaluation to dynamically adjust the analysis window according to the intensity of signal fluctuations, improving the method's responsiveness to rapidly changing risk events and its real-time decoupling efficiency. The decoupling index, constructed based on the normalized proportion of each source state level and its preset risk weights, can fuse multi-channel information, quantitatively characterize the overall order level of the source fields, and effectively distinguish risk characteristics from the background field, significantly improving the accuracy and reliability of risk warning. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1A flowchart of a method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule, as provided in one embodiment; Figure 2 Here is a theoretical model of a uniform soil slope and a time-varying field curve of SP in one embodiment, wherein Figure 2 (a) is a schematic diagram of the slope and point charge distribution in the soil model. Figure 2 (b) is the steady-state point charge simulation SP time series curve. Figure 2 (c) is the simulated SP time series curve after the periodic mutation at point Q3. Figure 2 (d) is the simulated SP time series curve after Q4 is randomly varied; Figure 3 This is a simulation SP timing curve diagram under off-field interference in one embodiment; Figure 4 This is an electrode layout diagram in one embodiment; Figure 5 This is a time series curve of each SP under normal rainfall in one embodiment; Figure 6 This is a time series curve of each SP channel under the influence of destructive factors in one embodiment; Figure 7 This is a time-series curve of each SP channel during Zn contaminant leakage in one embodiment; Figure 8 This is a structural block diagram of an electronic device provided in one embodiment. Detailed Implementation

[0014] The technical solution of the present invention will now be clearly and completely described through specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] Reference Figure 1 In one embodiment, a method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule is provided, including the following steps: Based on the principle of spatial separability constraint of risk sources, the spatial range of electrode array layout is determined, and the electrode array is laid out within the spatial range. Multi-channel observation time series data of shallow surface spontaneous potential were obtained using an electrode array. Based on the principle of spatial separability of risk sources, the time window for analyzing the multi-channel observation time series data is determined; Within the time window, for the observation channels within the spatial range of the electrode array, calculate the rank correlation coefficient between any two observation channels; According to the preset field source state level classification standard, the calculated rank correlation coefficient is mapped to the corresponding field source state level; Based on the normalized proportion of each source state level and its preset risk weight, a decoupling index is calculated. The overall orderliness of the source is directly quantified through the decoupling index, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field.

[0016] The shallow surface natural electric field is essentially a field system in which the physical quantities of the source (charge, ion concentration, stress) dynamically evolve over time under the coupling of multiple factors such as electrochemical dynamics, porous media seepage, and ion migration. The source intensity and spatial distribution change over time. In this invention, the natural potential of the shallow surface is used as observational data.

[0017] Furthermore, the order principle described in this invention includes the risk source spatial separability constraint principle for determining the spatial scope and the risk evolution time window adaptive principle for determining the time window.

[0018] Risk source disturbances exhibit spatial attenuation. When designing and arranging multi-channel electrode arrays, the selection of the spatial range must ensure that the observation array can capture near-field-far-field differences. Specifically, the principle of spatial separability constraint for risk sources is as follows: Suppose the target time-varying field source is located at point in space. At the location, its place of presence Location-generated signal strength Satisfies the decay function:

[0019] in As the reference strength, For time The disturbance value at that time The attenuation coefficient is... , This is a typical value. The absorption coefficient of the medium can be determined by factors such as porosity and water content of the soil and rock mass. Define effective radius of action To attenuate the disturbance signal from the risk source to the detection threshold Critical distance and spatial range Feature scale Must meet:

[0020] in These are all range coefficients. A range coefficient that is too small will lead to overall sensitivity, while a range coefficient that is too large will result in insufficient effective samples, both affecting the evaluation of orderliness. Preferably, spatial range Feature scale To characterize the geometric parameters of the deployment space, the diameter, side length, edge length, face diagonal, or volume diagonal of the deployment space are used to ensure that the electrode array can capture the signal difference between the near field and the far field.

[0021] Based on the above principle of spatial separability constraint of risk sources, the spatial scope is determined. .

[0022] Specifically, the adaptive principle of the risk evolution time window is as follows: Set the reference time window Then the time window for:

[0023] in The variable coefficient is adjusted according to the engineering monitoring requirements. The comprehensive characteristic function of the disturbance signal is given by the standard deviation of the signal amplitude. Rate of change of frequency Rising edge slope parameter The fitting results show that the more drastic the signal fluctuations, the better. The larger the value, the longer the time window. The shorter.

[0024] Signal amplitude standard deviation It is the degree of dispersion or fluctuation of the natural potential amplitude value relative to its average amplitude (mean), and the rate of frequency change. Instantaneous frequency of natural potential Over time The rate of change, the slope of the rising edge It refers to the rate of change of the natural potential over time as it jumps from a low value to a peak value.

[0025]

[0026] in , , These are the standard deviations of the signal amplitude. Rate of change of frequency The weighting coefficients corresponding to the rising edge slope parameter. , , These are the reference values ​​for the signal amplitude standard deviation, frequency change rate, and rising edge slope parameters, respectively, and the statistical values ​​of each parameter are taken during the risk-free period of the site.

[0027] Specifically, the adaptive time window is determined based on the above adaptive principle of risk evolution time window. The principle of dynamically adjusting the evaluation window length based on the intensity of signal fluctuations is called the time window adaptive principle. It transforms the order rule from a static criterion into a dynamic evaluation tool and is a key technology for achieving real-time decoupling of risk characteristics.

[0028] The rank correlation coefficient described in this invention is the Spearman rank correlation coefficient. Within the time window, for observation channels located within the spatial range, the Spearman rank correlation coefficient between any two observation channels in the observation time series data of each observation channel is calculated, including: Suppose that the two observation channels used to calculate the rank correlation coefficient are the first observation channel and the second observation channel, respectively. The observation time series data of the first observation channel are then... Time series data from the second observation channel Sort the natural electric field observation data according to the observation time in ascending order to obtain the rank of each observation time for the first and second observation channels. , ,in The observation times for the first and second observation channels are respectively: The corresponding observation data, , They are respectively The corresponding rank, ; The Spearman rank correlation coefficient is:

[0029] in is the Spearman rank correlation coefficient, which ranges from -1 to 1.

[0030] The Spearman rank correlation coefficient between each observation channel is calculated. The Spearman rank correlation coefficient ranges from -1 to 1. The correlation between the natural electric field observation time series data of different observation channels is quantified based on the magnitude of the Spearman rank correlation coefficient and the differences between different observation channels.

[0031] Furthermore, by setting multiple Spearman rank correlation coefficient thresholds, the Spearman rank correlation coefficient is divided into four levels: no correlation, low correlation, moderate correlation, and significant correlation, which correspond to disordered field sources, disordered field sources, ordered field sources, and ordered field sources, respectively, thereby realizing a quantitative mapping from statistical correlation to physical order.

[0032] Ordered field sources mainly refer to the geophysical characteristics of dynamic equilibrium. The physical essence is that multiple physical field source terms are in dynamic equilibrium, the evolution rate of each source term remains relatively stable, and the main physical processes are characterized by a stable Darcy flow velocity in a replenishment-discharge balance, slow changes in redox potential, and no significant acceleration of creep in the rock and soil structure.

[0033] The secondary field source mainly refers to the secondary disturbance under the master control equilibrium. The physical essence is that the master control field source maintains equilibrium, and local secondary disturbances begin to appear, but are not enough to destroy the overall rank structure. The main physical processes are manifested as changes in shallow water content but the master control field of deep seepage remains stable, surface oxidation-reduction produces frequency differences but the phase remains synchronous, and microcracks in the rock and soil structure expand but the master control structural surface is not connected.

[0034] Disordered field sources mainly refer to local risk sources that disrupt the overall synchronization. The physical essence is that the evolution rate of local risk sources exceeds the regulation capacity of the master field, causing the rank time series of observation points within its influence range to become unstable first. The rank synchronization across subspaces is destroyed, exhibiting obvious near-field-far-field rank time series decoupling characteristics. The main physical processes are manifested as the formation of local strong leakage channels that disrupt the regional seepage balance, the injection of pollutant ion concentrations that produce nonlinear electrochemical polarization, and local shear zones in rock and soil structures.

[0035] Turbulent field sources mainly refer to the critical state of multi-source irregular coupling. The physical essence is that the evolution rates of multiple risk sources are incoherent, the rank time series of the observation point exhibits random walk, the topological structure of the phase space trajectory is destroyed, the predictability of the rank time series is lost, and the main physical processes are manifested as the formation of pipe flow accompanied by multi-source frequency, phase, and amplitude uncorrelated oscillations, competition among multiple mechanisms such as redox reactions, ion convection, and diffusion, and accelerated creep of the soil and rock structure.

[0036] The above four-level sequence reflects four scenarios of field order and also the risk characteristics of field sources. Clearly, the grading standard needs to be optimized according to the actual situation for different projects and different field source environments. The Spearman rank correlation coefficient formula can be used to calculate the rank correlation coefficient between different numbers of observation channels, allowing for a comprehensive examination of the correlation between the observation data of each observation channel. Based on the established field source state level classification standard, the field source order between each observation channel can be quickly obtained.

[0037] Specifically, the field source state levels include disordered field sources, out-of-order field sources, ordered field sources, and ordered field sources, with the following criteria for classifying the field source state levels: when Time is defined as unrelated, corresponding to the source of disordered field, and characterizing the loss of rank temporal predictability; when When the correlation is low, it corresponds to a disordered field source and characterizes the disruption of cross-subspace rank synchronization. when When it is defined as moderately correlated, it corresponds to the order field source and represents that minor perturbations do not disrupt the overall rank structure. when When the correlation is significant, it corresponds to an ordered field source and characterizes the multiphysics field source term as being in dynamic equilibrium. Four Spearman rank correlation coefficient thresholds are set to classify the source state levels, where Without loss of generality, in one embodiment See Table 1 for details: Table 1. Source order standard table based on rank correlation coefficient

[0038] To facilitate the overall evaluation of field sources, this invention further calculates a decoupling index based on the normalized proportion of each field source state level and its preset risk weight, including: Calculate the normalized percentage of the order number of disordered, out-of-order, ordered, and ordered field sources relative to the total order number, and denote them as follows: , , , ; Weights are preset for disordered field sources, out-of-order field sources, ordered field sources, and ordered field sources, respectively, and are calculated as follows: , , , ; The decoupling index is calculated using the following formula:

[0039] Among them, the decoupling index It is a dimensionless, normalized quantitative index used to characterize the level of order in the overall field source. A larger value indicates a more disordered field source order and a higher level of risk. Without loss of generality, as shown in Table 1, the weights corresponding to disordered field sources in one embodiment are... Weights corresponding to disordered field sources 25. Weights corresponding to the order field sources Weights corresponding to ordered field sources .

[0040] Let the number of observation channels be... Then the total ordinal number According to the preset field source state level classification standard, the calculated rank correlation coefficient is mapped to the corresponding field source state level, including disordered field source, out-of-order field source, ordered field source, and ordered field source. The number of channels for the four field source state levels is counted and denoted as follows: , , , And satisfy The normalized percentages are calculated as follows: , , , Defined as Example: Taking 12 observation channels as an example, the total order number... 66. If the statistics show: , =6, =6, Then the normalized proportion is , , , .

[0041] For decoupling index Threshold calibration must adhere to the principle of scenario adaptation. Typically, it requires establishing a threshold system that matches the application requirements based on specific application objectives (landslide early warning, pollution monitoring, or reservoir / dam leakage identification), site environmental characteristics (medium type, hydrological conditions, disturbance levels), and historical monitoring data. Thresholds are determined using statistical quantile methods or other calculation methods based on theoretical models and physical experimental data. The main approach is to first calculate the decoupling index under various theoretical and experimental models. Sample, analysis of decoupling index The cumulative distribution characteristics of the samples are then analyzed, and combined with the verification of the physical state of the field source, to finally determine... <0.06 corresponds to an ordered field (steady-state background), 0.06≤ <0.1 corresponds to the order field (slight perturbation), 0.1≤ <0.2 corresponds to a disordered field (local risk). A value ≥0.2 corresponds to a disordered field (system instability); therefore, the decoupling index... It can be defined as a normalized quantitative evaluation coefficient that decouples multi-channel time-varying natural electric field data into four levels of characteristics—disorder, disorder, order, and order—based on the order rule and through the rank correlation test.

[0042] This invention proposes an order principle, realizing a cognitive shift from qualitative anomaly description to quantitative order assessment. It proposes two major application principles: risk source spatial separability constraints and adaptive risk evolution time windows, solving the problem of selecting evaluation scales in practical monitoring and serving as a methodological basis for engineering deployment. This invention avoids the dependence of traditional methods on data distribution, amplitude calibration, and inversion calculations, constructing a risk decoupling model solely through the rank correlation of time-series change patterns. Based on the rank correlation coefficient threshold, it classifies ordered, sequential, disordered, and chaotic field sources, and introduces a decoupling index to compress complex field states into a single quantitative indicator. This achieves decoupling of risk characteristics under multi-factor coupling, improving the intuitiveness and operability of evaluation results, and has significant implications for advancing the engineering application of natural potential monitoring.

[0043] In another embodiment, a surface natural electric field risk characteristic decoupling system based on order rules is provided, comprising: The electrode array module is used to determine the spatial range of the electrode array based on the principle of separability of risk source space, and to deploy the electrode array within the spatial range. The data acquisition module is connected to the electrode array and is used to acquire multi-channel observation time series data of shallow surface spontaneous potential through the electrode array; The parameter configuration module, connected to the data acquisition module, is used to determine the time window for analyzing the multi-channel observation time series data based on the principle of spatial separability constraint of risk sources. The rank correlation calculation module is used to calculate the rank correlation coefficient between any two observation channels within the time window, for the observation channels within the spatial range of the electrode array layout. The field source classification module is used to map the calculated rank correlation coefficient to the corresponding field source state level according to the preset field source state level classification standard. The risk decoupling module is used to calculate the decoupling index based on the normalized proportion of the state level of each field source and its preset risk weight. The decoupling index directly quantifies the overall orderliness of the field sources, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field.

[0044] The electrode array can be composed of multiple non-polarized reference electrodes, arranged in a two-dimensional or three-dimensional array in the monitoring area. The electrode spacing of the detection area and the electrode array satisfies the principle of spatial separability constraint of risk source. The electrodes are connected to the data acquisition unit through shielded cables.

[0045] The following experiments were conducted to demonstrate the effectiveness of the present invention: Build as Figure 2 The theoretical model of a uniform soil slope shown is as follows, in which Figure 2(a) is a schematic diagram of the slope and point charge distribution in the soil model. The electrochemical units in the slope are simplified into four equivalent point charges with stable changing cycles (Q1, Q2, Q3, Q4). Although this model is an idealized approximation, it can capture the core mechanism of multi-source superposition. Twelve simulated observation channels (P1-P12) are set on the surface. The relative coordinates of the point charges and observation points are shown in Table 2. The spontaneous potential (hereinafter referred to as SP) is used as the observation parameter. The SP value of each observation channel is the superposition of the contributions of the four point charges.

[0046]

[0047] Steady-state field source (Q1-Q4 periodically stable): Assuming the four point charge variations have relatively stable periods and amplitudes, the time-varying fields of each channel are calculated, and the time series curves are shown below. Figure 2 (b), Figure 2 (b) is the time series curve of the steady-state point charge simulation channel SP. The time series data of each point are dominated by low-frequency fluctuations, with no obvious trend changes. The abnormal regularity can be clearly distinguished, and there is a clear correlation between channels. The change trend of this type of field source is predictable and has order, which is called steady-state field.

[0048] Abrupt change in the source period (shortening of the Q3 period): If, at a certain moment, due to external or internal factors, the period of the Q3 point charge suddenly shortens, the electric field at each observation point will change accordingly. Figure 2 (c) shows the SP time series curves for each observation point. The period and phase of the data from each observation channel are significantly different due to the instability of Q3. Obvious quasi-periodic oscillations (such as P7 and P10) appear in the area near Q3. The disturbance spreads to the outer measurement points, but the amplitude is significantly reduced (such as P3). The correlation between the near Q3 channel and the far channel is significantly reduced, and the spatial correlation of the electric field is impaired.

[0049] Random variation of the field source (Q4 periodic randomness): When the mode of action of external or internal factors changes, the period of the Q4 point charge becomes random. Calculate the SP of each observation channel, and the time series curve is as follows: Figure 2 (d) The time series curves exhibit multi-scale non-stationary fluctuations, nonlinear decay, and more prominent decorrelation phenomena. SP shows strong heterogeneity and weak repeatability. For the effective identification of such complex anomalies, traditional inversion methods based on steady-state models face adaptive challenges.

[0050] Continuing with the above model as an example, for the case where Q4 varies randomly, assuming that Q1, Q2, and Q3 are related to normal rainfall, the resulting total field is E. t A and Q4 are related to uncertain media risk factors such as pollution leaks, and the resulting total field is E. t B, Observational Field E t = E tA+ E t B. When the mechanisms of action of the two types of field sources are independent, they will each exhibit a high correlation. Obviously, due to the regional nature of normal rainfall, the time-varying field at each point will have a high rank correlation coefficient, while the scope of action of risk factors is local. The superposition of field sources will lead to a sharp drop in the rank correlation coefficient. Those electrode pairs that suddenly become asynchronous are the fingerprints of the risk occurrence. By calculating the rank correlation coefficient matrix between channels, abrupt changes can be identified, risk characteristics can be extracted, and the spatiotemporal decoupling of risk sources can be achieved.

[0051] Based on the method proposed in this invention, Figure 2 The effectiveness of risk decoupling was verified using the theoretical model of the homogeneous soil slope shown. Steady-state field source ( Figure 2 (b) The orderliness test results show that all 66 groups of field sources have an order level of "□", that is, they are ordered field sources, X4 ’ =100%, S=0.048, and Figure 2 (b) The time-varying field source reflects the relatively stable characteristics.

[0052] Q3 periodic mutation ( Figure 2 (c) The results of the orderliness test are shown in Table 3: 48 groups of ordered field sources, 11 groups of sequential field sources, 6 groups of disordered field sources, and 1 group of chaotic field sources, with S=0.11. Figure 2 (c) The SP field in the corresponding time period is determined to be an disordered field.

[0053] Q4 random variation ( Figure 2 (d) The results of the orderliness test (Table 4) show that there are 26 groups of ordered field sources, 14 groups of sequential field sources, 17 groups of disordered field sources, and 9 groups of chaotic field sources, with S=0.29. Figure 2 (d) The SP field in the corresponding time period is determined to be a disordered field.

[0054] Off-field interference: Figure 2 (b) Based on the steady-state field, multi-feature external interference information is added. Eight different external interference sources with varying frequencies and amplitudes are added at different time periods. The theoretical time-series curves are shown below. Figure 3 The orderliness test results and Figure 2 (b) The situation is completely consistent without the addition of interference, S=0.048. Based on the analysis of the test results, the way in which external interference affects each point in the field is consistent. It does not destroy the stability of the field source in the field, and the correlation between each observation point remains unchanged. The risk decoupling model has good robustness against the influence of external interference factors.

[0055] The results of the orderliness test of the theoretical model are in good agreement with the complexity reflected by the time series curve, indicating that the risk decoupling model can not only effectively evaluate the orderliness characteristics of the field source, but also provide quantitative indicators.

[0056]

[0057]

[0058] Furthermore, an indoor soil slope model (length × width × height = 1200cm × 80cm × 43cm) was constructed, with the front end as the slope bottom and the rear end as the slope top. Baffles were installed on both sides, and a spray-water circulation system was installed. The slope was filled with fresh red soil and saturated with water, then left to stand at an ambient temperature of 25℃ to 35℃ until the average relative humidity of the bottom soil reached a near-natural state of 50%. Twelve observation channels (P0~P11) were arranged in a 3-point X-axis and 4-point Y-axis array using copper sulfate reference electrodes. The electrode layout diagram is shown below. Figure 4 The electrode layout coordinates are shown in Table 5, and data is collected 5 times per second for each channel. To ensure reading accuracy, insulating and waterproof pads are attached to the inner wall of the model body to isolate external electric field interference. Empty bottles are placed on top of the electrodes to reduce the influence of spray water on the electrode potential. Before each test, the status of the non-polarized electrodes is checked, and background values ​​are measured for 3-5 minutes. Soil moisture is measured before each stage of the test, and the relative humidity is controlled at around 50%.

[0059] Statistical analysis of the background value detection results before the three phased experiments yielded S values ​​of 0.049, 0.058, and 0.051, respectively. The relative standard deviation of the three measurements was 8.9%, indicating that the model has good stability and the experimental system has good statistical consistency.

[0060]

[0061] Furthermore, a decoupling experiment of rainfall risk factors was conducted: Four mist nozzles were used to simulate light rain conditions. The initial intensity was set at a basic balance between infiltration and rainfall, with no significant surface water accumulation. Rainfall continued until significant surface or linear flow appeared on the soil surface, and the soil was nearly saturated. Testing was conducted nearly 30 hours after the rainfall ceased and the slope stabilized (ambient temperature 25℃-30℃), with continuous observations for more than 5 hours from 12:00 to 17:00. The SP time-series data for each channel are shown below. Figure 5 Analysis of the time-series curves reveals distinct characteristics for each data point. Particularly during the period of rising ambient temperature before 15:30 (corresponding to 6000 s in the figure), the SP data points exhibited inverse changes due to evaporation, resulting in a complex overall pattern. However, the frequency of change across these data points was largely synchronous. The orderliness test results are shown in Table 6. Only three pairs showed disorder, with the remainder exhibiting at least some order. The overall evaluation factor S was 0.09, indicating that the source field was in an ordered state. The rainfall event caused minor disturbances but did not disrupt the overall rank structure, reflecting the overall stability of the SP source field and the absence of structural changes in the site. This aligns with actual operating conditions, providing a more intuitive and quantitative indicator.

[0062]

[0063] Structural failure risk decoupling experiment: After the soil was allowed to settle to a near-natural state, under an ambient temperature of approximately 25℃, four mist nozzles were used to simulate moderate rainfall. The initial intensity was based on the assumption that infiltration and rainfall were basically balanced. Later, the rainfall intensity was increased and continuous rainfall was maintained to keep the soil surface showing obvious surface and linear flow until the slope soil cracked and slipped. SP values ​​were continuously observed at each level. Figure 6 The graph shows the SP curves of each channel before slippage. As can be seen from the graph, the disordered changes are quite obvious, and the synchronous frequency characteristics of each channel are similar to those of the previous channel. Figure 5 The results show significant differences, indicating a more complex field source order. The order test results are shown in Table 7, which differs significantly from Table 6. The number of ordered pairs decreased to 27, with 16 pairs of disordered field sources and 7 pairs of unordered field sources. S=0.34. Based on the aforementioned criterion of S≥0.2 for a disordered field, this is classified as a disordered field, indicating that simulated rainfall has caused significant changes in soil structure, which perfectly matches the subsequent slippage phenomenon. Experimental studies have found that continuous site SP monitoring, combined with an adaptive sliding window model for continuous assessment of field source order, and integrated with divergent response anomaly evaluation, can accurately capture precursor signals of soil structural failure. This has significant implications for practical applications.

[0064]

[0065] To verify the decoupling capability of this method for heavy metal pollution leakage risk, a heavy metal pollution leakage risk decoupling experiment was conducted. Three soil models (length × width × depth = 80 cm × 40 cm × 40 cm) were constructed. A water circulation tube and a valve controlling the solution infiltration rate were pre-installed at the bottom of each model. An aqueous solution containing heavy metal pollution was injected into the tube. The infiltration time was controlled by switching the tube on and off. The actual infiltration volume was calculated by the difference between the injected and recovered volumes. The average relative humidity of the soil was controlled at approximately 50%, and the indoor ambient temperature was controlled at 25℃. An aqueous solution with a concentration of 1000 mg / L was prepared and tested in the three soil models. The experiment included background value observation and infiltration of the polluted aqueous solution. Each infiltration time was 3 minutes, and the infiltration rate was approximately 110 mL / h, simulating a slow leakage process. The experimental data are shown in Table 8. Experimental results show that background observations conducted before pollution infiltration did not reveal any disordered or chaotic field sources, with S-values ​​all below 0.05. After pollution infiltration, the field source risk became significantly abnormal, with different elements exhibiting distinct decoupling characteristics. S-values ​​all jumped from below 0.05 to above 0.13, reaching the level of disordered risk, which can serve as early warning information for pollution leaks. The experiment confirms that the risk decoupling model can effectively distinguish between pollution risk and the background field.

[0066] To further illustrate the effectiveness of the method, Figure 7 The SP time series curves of each channel during the Zn contaminant leak are shown. A Zn contaminant leak occurred in the period from 99000 to 100500. The above figure is the S-curve. The peak area has a clear correlation with the Zn contaminant leak time period. The risk decoupling model effectively monitored the occurrence of the contaminant leak event.

[0067] Table 8 Results of the decoupling experiment for heavy metal pollution leakage risk

[0068] In summary, this invention proposes two major application principles: spatial separability constraints of risk sources and adaptive risk evolution time windows. These principles solve the problem of selecting evaluation scales in practical monitoring and can serve as a methodological basis for engineering deployment. This invention avoids the reliance of traditional methods on data distribution, amplitude calibration, and inversion calculations. It constructs a risk decoupling model solely through the rank correlation of time-series variation patterns. Based on the rank correlation coefficient threshold, it classifies field sources into ordered, sequential, disordered, and chaotic types, and introduces a decoupling index to compress complex field states into a single quantitative indicator. This achieves decoupling of risk characteristics under multi-factor coupling, improving the intuitiveness and operability of the evaluation results, and is of great significance in promoting the engineering application of natural potential monitoring. In the experimental verification phase, the correspondence between the risk decoupling model and the complexity of the field sources was verified by designing three typical operating conditions: steady state, periodic abrupt changes, and random changes. Under steady-state conditions and external disturbances, S=0.048; under periodic abrupt changes in Q3, S=0.11; and under random changes in Q4, S=0.29. The S-index accurately characterizes the complexity of the source. Physical experiments were conducted using a soil model. Under normal conditions, the S-index was less than 0.06. Under destructive rainfall, the source S jumped to 0.34, and under heavy metal pollution leakage, the source S jumped to a maximum of 0.163. This accurately captured precursor signals of soil structural damage and early warning signals of pollution occurrence, verifying the early warning potential of the method in engineering monitoring.

[0069] Figure 8 A block diagram of an embodiment of an electronic device is shown, such as Figure 8As shown, the electronic device includes one or more processors and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for decoupling the risk characteristics of the natural electric field of the earth's surface based on the order rule provided in any of the above embodiments. The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0070] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0071] Of course, for the sake of simplicity, Figure 8 Only some of the components of the electronic device relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0072] Embodiments of the present invention may also be computer-readable storage media storing a computer program thereon, which, when executed by a processor, implements the steps of the method for decoupling the risk characteristics of the natural electric field of the earth's surface based on the order rule provided in any of the above embodiments. The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0073] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0074] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0075] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0076] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application should not be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0077] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for decoupling the risk characteristics of the natural electric field on the Earth's surface based on the order rule, characterized in that, Includes the following steps: Based on the principle of spatial separability constraint of risk sources, the spatial range of electrode array layout is determined, and the electrode array is laid out within the spatial range. Multi-channel observation time series data of shallow surface spontaneous potential were obtained using an electrode array. Based on the adaptive principle of risk evolution time window, the time window for analyzing the multi-channel observation time series data is determined, including setting a baseline time window. Then the time window for: in The variable coefficient is adjusted according to the engineering monitoring requirements. The comprehensive characteristic function of the perturbation signal is obtained by fitting parameters such as the standard deviation of the signal amplitude, the rate of change of frequency, and the rise slope. The more severe the signal fluctuation, the more pronounced the characteristic function. The larger the value, the longer the time window. The shorter; Within the time window, for the observation channels within the spatial range of the electrode array, calculate the Spearman rank correlation coefficient between any two observation channels, including: Suppose that the two observation channels used to calculate the rank correlation coefficient are the first observation channel and the second observation channel, respectively. The observation time series data of the first observation channel are then... Time series data from the second observation channel Sort the natural electric field observation data according to the observation time in ascending order to obtain the rank of each observation time for the first and second observation channels. , ,in The observation times for the first and second observation channels are respectively: The corresponding observation data, , They are respectively The corresponding rank, ; The Spearman rank correlation coefficient is: in This is the Spearman rank correlation coefficient, which ranges from -1 to 1. According to the preset field source state level classification standard, the calculated rank correlation coefficient is mapped to the corresponding field source state level; Based on the normalized proportion of each source state level and its preset risk weight, a decoupling index is calculated. The overall orderliness of the source is directly quantified through the decoupling index, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field.

2. The method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule as described in claim 1, characterized in that, Based on the principle of spatial separability constraint of risk sources, the spatial range for the electrode array is determined, including: Suppose the target time-varying field source is located at point in space. At the location, its place of presence Location-generated signal strength Satisfies the decay function: in As the reference strength, For time The disturbance value at that time The attenuation coefficient is... , The medium absorption coefficient; Define effective radius of action To attenuate the disturbance signal from the risk source to the detection threshold Critical distance and spatial range Feature scale Must meet: in All are range coefficients. spatial range Feature scale To characterize the geometric parameters of the deployment space, the diameter, side length, edge length, face diagonal, or volume diagonal of the deployment space are used to ensure that the electrode array can capture the signal difference between the near field and the far field.

3. The method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule as described in claim 1 or 2, characterized in that, The state levels of field sources include disordered field sources, out-of-order field sources, ordered field sources, and ordered field sources; the criteria for classifying field source state levels are as follows: when Time is defined as unrelated, corresponding to the source of disordered field, and characterizing the loss of rank temporal predictability; when When the correlation is low, it corresponds to a disordered field source and characterizes the disruption of cross-subspace rank synchronization. when When it is defined as moderately correlated, it corresponds to the order field source and represents that minor perturbations do not disrupt the overall rank structure. when When the correlation is significant, it corresponds to an ordered field source and characterizes the multiphysics field source term as being in dynamic equilibrium. in .

4. The method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule as described in claim 3, characterized in that, 。 5. The method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule as described in claim 3, characterized in that, Based on the normalized proportion of each source state level and its preset risk weight, the decoupling index is calculated, including: Calculate the normalized percentage of the order number of disordered, out-of-order, ordered, and ordered field sources relative to the total order number, and denote them as follows: , , , ; Weights are preset for disordered field sources, out-of-order field sources, ordered field sources, and ordered field sources, respectively, and are calculated as follows: , , , ; The decoupling index is calculated using the following formula: Among them, the decoupling index It is a dimensionless, normalized quantitative index used to characterize the level of order in the overall field source. A larger value indicates a more disordered field source order and a higher level of risk.

6. The method for decoupling the risk characteristics of the natural electric field on the earth's surface based on the order rule as described in claim 5, characterized in that, Weights corresponding to disordered field sources Weights corresponding to disordered field sources 25. Weights corresponding to the order field sources Weights corresponding to ordered field sources .

7. A decoupling system for the risk characteristics of natural electric fields on the Earth's surface based on order principles, used to implement the decoupling method for the risk characteristics of natural electric fields on the Earth's surface based on order principles as described in claim 1, characterized in that, include: The electrode array module is used to determine the spatial range of the electrode array based on the principle of separability of risk source space, and to deploy the electrode array within the spatial range. The data acquisition module is connected to the electrode array and is used to acquire multi-channel observation time series data of shallow surface spontaneous potential through the electrode array; The parameter configuration module, connected to the data acquisition module, is used to determine the time window for analyzing the multi-channel observation time series data according to the risk evolution time window adaptive principle. The rank correlation calculation module is used to calculate the rank correlation coefficient between any two observation channels within the time window, for the observation channels within the spatial range of the electrode array layout. The field source classification module is used to map the calculated rank correlation coefficient to the corresponding field source state level according to the preset field source state level classification standard. The risk decoupling module is used to calculate the decoupling index based on the normalized proportion of the state level of each field source and its preset risk weight. The decoupling index directly quantifies the overall orderliness of the field sources, thereby decoupling and identifying risk characteristics from the dynamic signal of the shallow surface natural electric field.

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