Dynamic Monitoring System and Method for Subsurface Multi-Component Pollutants Based on Tomographic Data Inversion

Abstract

This invention discloses a dynamic monitoring system and method for underground multi-component pollutants based on tomographic data inversion, belonging to the field of environmental monitoring and environmental information processing technology. The method includes the following steps: acquiring underground electrical variation records at a continuous time scale, constructing an electrical variation time series, and simultaneously extracting the instantaneous amplitude fluctuation trajectory of the electrical variation time series. Based on the instantaneous amplitude fluctuation trajectory, short-term fluctuation segments are marked in the electrical variation time series to characterize the rhythm of abnormal disturbances. This invention effectively distinguishes between instantaneous disturbances and pollution migration rhythms through short-term fluctuation segment marking, time-period stretching, and continuous variation trajectory construction. Combined with a directional consistency comparison mechanism, it improves the stability and accuracy of migration trends, avoiding misjudgments caused by short-period electromagnetic disturbances. Through inverse time stretching and amplitude fading processing, migration expressions are rearranged to eliminate trend bias, making pollution diffusion prediction results closer to reality and enhancing the accuracy of dynamic monitoring and the reliability of decision-making.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and environmental information processing technology, specifically to a dynamic monitoring system and method for underground multi-component pollutants based on chromatographic data inversion. Background Technology

[0002] Dynamic monitoring of subsurface multi-component pollutants based on tomographic data inversion refers to the use of physical data collected by tomographic techniques (such as resistivity tomography and electromagnetic tomography) in the subsurface environment, followed by inversion processing, to deduce the spatial distribution and concentration changes of various subsurface pollutants (such as heavy metals and organic matter). Specifically, tomographic techniques acquire electromagnetic or resistivity response data of the subsurface medium through multiple sensors, and then use inversion algorithms to transform this data into a concentration field and distribution pattern of pollutants. Dynamic monitoring refers to tracking the changes in subsurface pollutants in real time or periodically, reflecting the migration, diffusion, or attenuation trends of pollutants over time, thereby providing decision support for groundwater resource management, soil remediation, and environmental protection. This method combines geophysical exploration technology and environmental monitoring technology, offering advantages such as high efficiency, real-time performance, and accuracy, enabling precise pollution source tracking and pollution trend prediction in the subsurface environment.

[0003] The existing technology has the following shortcomings: In existing technologies, when conducting dynamic monitoring of multi-component pollutants in underground environments based on chromatographic electrical response data, the data processing typically involves migration inversion analysis based on the electrical change trends in continuous time series. However, in actual operation, the underground environment is affected by short-period electromagnetic disturbances, such as instantaneous power load fluctuations or electric field anomalies caused by the start-up and shutdown of surrounding equipment. When such short-term disturbance signals enter the data processing flow, the system tends to interpret them as real electrical changes caused by the continuous migration of pollutants, and this manifests as an accelerated trend of the pollution plume in the inversion results. This trend judgment is inconsistent with the actual diffusion state, easily leading to prediction deviations, which may trigger excessive containment or premature intervention, affecting the rationality of pollution control decisions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic monitoring system and method for underground multi-component pollutants based on chromatographic data inversion, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic monitoring of underground multi-component pollutants based on chromatographic data inversion, comprising the following steps: Records of underground electrical changes under continuous time scale are obtained, electrical change time series are constructed, and the instantaneous amplitude fluctuation trajectory of the electrical change time series is extracted simultaneously. Short-term fluctuation segments are marked in the electrical change time series according to the instantaneous amplitude fluctuation trajectory to characterize the rhythm of abnormal disturbances. The time series of electrical changes is stretched around short-term fluctuation segments, and the short-term fluctuation segments are embedded into the time series of electrical changes for continuous expansion. The continuous change trajectory is formed in the expansion result to distinguish between the instantaneous fluctuation rhythm and the pollution migration rhythm. Based on the continuous change trajectory, the pollution migration inversion results are compared for directional consistency. Migration advancement segments that are inconsistent with the direction of the continuous change trajectory are extracted, and abnormal advancement markers are generated in the pollution migration inversion results. Based on the abnormal progression markers, the corresponding electrical change time series segments are traced back to identify abnormal segments that show a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window, thus determining the segment to which the disturbance belongs. The time series of pollution migration inversion is adjusted by rhythm drift around the disturbance attribution segment. Inverse time scaling is introduced before and after the disturbance attribution segment, and amplitude fading is superimposed. Pollution migration expression is rearranged by progressive time difference misalignment to eliminate the misjudgment trend caused by short-period electromagnetic disturbance.

[0007] The preferred method for annotating short-term fluctuation segments is as follows: The underground electrical response records were acquired and time-calibrated to establish a time index sequence. The electrical response values ​​were then bound to the time scale to form a time series of electrical changes. The amplitude difference between adjacent time scales is calculated on a scale around the time series of electrical changes to form a sequence of amplitude changes and construct the instantaneous amplitude fluctuation trajectory; Based on the instantaneous fluctuation trajectory of amplitude, a time window is set within a continuous time scale, and candidate abnormal segments are divided by sliding and written into the additional identification area of ​​the electrical change time series. By analyzing the duration and amplitude change sequences of candidate abnormal segments, candidate abnormal segments that meet the preset short period range and whose amplitude changes deviate from the continuous change trajectory range are marked as short-term fluctuation segments.

[0008] Preferably, the steps for forming a continuously changing trajectory are as follows: Extract the start and end time scales of short-term fluctuation segments in the time series of electrical changes, and divide the time series into forward correlation segments and backward correlation segments around the start and end time scales to form reconstructed time segments; Based on the reconstructed time interval, the time scales within the short-time fluctuation segment are rearranged according to a fixed time increment, while maintaining the order of electrical response values ​​and adjusting the intervals between adjacent time scales. Embedding the rearranged short-term fluctuation segments into the corresponding positions of the electrical change time series, the time scale of the backward correlation segment is shifted as a whole to form a continuous unfolding structure; Based on the continuous unfolding structure, the amplitude changes between adjacent time scales are connected according to the new time scale order to form a continuously changing trajectory.

[0009] Preferably, the abnormal advancement marker generation steps are as follows: The electrical response values ​​were read segment by segment according to the time scale around the continuously changing trajectory, and the positive change interval, the negative change interval, and the stable change interval were marked to form a sequence of continuously changing directions. Based on the continuously changing direction sequence, the pollution migration inversion results are read in the same time scale order to read the migration advancement expression value and mark the forward advancement interval, reverse advancement interval and steady advancement interval to form the migration advancement direction sequence; The migration and propulsion segments are formed by comparing the continuously changing direction sequence and the migration and propulsion direction sequence on a time scale and merging the intervals of continuous inconsistency in direction. Based on the migration progression segment, start and end time scales are written into the pollution migration inversion results, and abnormal progression markers are generated.

[0010] Preferably, the continuously changing direction sequence and the migration and advancement direction sequence are compared segment by segment within the corresponding time scale around the migration and advancement segment, and time merging and segment recording are only performed on the intervals where the continuous directions are inconsistent, and the start time scale and end time scale of the migration and advancement segment are consistent with the abnormal advancement mark.

[0011] Preferably, the steps for determining the disturbance attribution zone are as follows: Read the start and end time scales corresponding to the abnormal advancement markers and map them to the electrical change time series to form a backtracking segment; A preset time window is divided around the backtracking section, and the electrical response values ​​are read on a scale to form an amplitude change trajectory. Based on amplitude change trajectory identification, candidate abnormal segments are identified that show a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window; Match the candidate anomaly segment with the time segment corresponding to the anomaly advancement marker and write the result into the disturbance attribution identifier field to determine the disturbance attribution segment.

[0012] Preferably, the preset time window is symmetrically divided around the start and end time scales of the backtracking segment, and the amplitude change trajectory is compared with the benchmark interval range step by step in chronological order. The candidate abnormal segment is limited to the time segment that corresponds continuously to the amplitude increase segment and the amplitude return segment.

[0013] Preferably, rhythm drift adjustment is applied to the pollution migration inversion time series around the perturbation attribution segment. Inverse time scaling is introduced before and after the perturbation attribution segment, and amplitude fading is superimposed. The pollution migration expression steps are rearranged through progressive time difference misalignment as follows: Read the start and end time scales of the disturbance attribution segment and divide the forward and backward time segments around the start and end time scales to form a time adjustment interval; The time interval of the forward time segment is compressed and the time interval of the backward time segment is extended around the time adjustment interval to form a reverse time stretching process. Based on the perturbation attribution segment, the migration expression value is adjusted by decreasing and increasing in chronological order to form a gradual fading of amplitude; The migration expression values ​​are rearranged according to the new time interval around the time adjustment interval to form a progressive time difference misalignment rearrangement to eliminate the tendency of misjudgment.

[0014] A dynamic monitoring system for underground multi-component pollutants based on tomographic data inversion includes an electrical rhythm annotation module, a time-period stretching analysis module, a migration direction comparison module, a disturbance zone identification module, and a rhythm drift adjustment module. The electrical rhythm annotation module acquires underground electrical change records under a continuous time scale, constructs an electrical change time series, and simultaneously extracts the instantaneous amplitude fluctuation trajectory of the electrical change time series. Based on the instantaneous amplitude fluctuation trajectory, short-term fluctuation segments are annotated in the electrical change time series to characterize the abnormal disturbance rhythm. The time-period stretching analysis module stretches the electrical change time series around short-term fluctuation segments, embeds the short-term fluctuation segments into the electrical change time series for continuous expansion, and forms a continuous change trajectory in the expansion result to distinguish between instantaneous fluctuation rhythm and pollution migration rhythm. The migration direction comparison module compares the direction consistency of the pollution migration inversion results based on the continuously changing trajectory, extracts the migration advancement segments that are inconsistent with the direction of the continuously changing trajectory, and generates abnormal advancement markers in the pollution migration inversion results. The disturbance segment identification module traces back the corresponding electrical change time series segment based on the abnormal advancement marker, identifies the abnormal segment that shows a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window, and determines the segment to which the disturbance belongs. The rhythm drift adjustment module applies rhythm drift adjustment to the pollution migration inversion time series around the disturbance attribution segment. It introduces reverse time scaling processing and superimposes amplitude fading processing before and after the disturbance attribution segment. It rearranges the pollution migration expression through progressive time difference misalignment to eliminate the misjudgment trend caused by short-period electromagnetic disturbance.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention, by annotating short-term fluctuation segments, stretching and expanding time-series data, and constructing continuous change trajectories, enables a structured rhythmic expression of the electrical response over time, effectively distinguishing between instantaneous disturbance rhythms and pollution migration rhythms. By introducing a directional consistency comparison mechanism, a temporal correspondence is established between pollution migration inversion results and continuous change trajectories. This allows for the identification of directional shift segments at the trend level, preventing short-period electromagnetic disturbances from being misjudged as pollution plume propulsion behavior, thereby improving the stability and temporal continuity of migration trend interpretation.

[0016] This invention employs reverse time scaling and amplitude fading processing around the disturbance attribution zone, and uses a progressive time-difference misalignment rearrangement of the pollution migration expression to redistribute and mitigate the local anomaly progression trend along the time axis, thus eliminating the trend amplification effect caused by short-period electromagnetic disturbances. Through rhythm drift adjustment, the pollution migration inversion time series restores an expression structure consistent with the continuous change trajectory, making the pollution diffusion prediction results closer to the actual migration process, reducing the interference of misjudged trends on governance decisions, and improving the accuracy of the time expression and the reliability of decision-making references for dynamic monitoring of underground multi-component pollutants. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the method for dynamic monitoring of underground multi-component pollutants based on chromatographic data inversion according to the present invention.

[0019] Figure 2 This is a schematic diagram of the module of the dynamic monitoring system for underground multi-component pollutants based on chromatographic data inversion according to the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The method for dynamic monitoring of subsurface multi-component pollutants based on chromatographic data inversion, as shown, includes the following steps: Records of underground electrical changes under continuous time scale are obtained, electrical change time series are constructed, and the instantaneous amplitude fluctuation trajectory of the electrical change time series is extracted simultaneously. Short-term fluctuation segments are marked in the electrical change time series according to the instantaneous amplitude fluctuation trajectory to characterize the rhythm of abnormal disturbances. To fully represent the trajectory of underground electrical variations over continuous time, and to structurally annotate the rhythm of anomalous disturbances, the following implementation steps are outlined, focusing on temporal continuity, amplitude variation trajectory construction, and short-term fluctuation segment identification: The underground electrical response records acquired within the observation area according to a fixed sampling period are subjected to unified time calibration. Each sampling generates an electrical response value, and the time scale of the sampling moment is recorded simultaneously. All sampling moments are arranged in chronological order to establish a time index sequence. Subsequently, each electrical response value is bound to its corresponding time scale, forming a one-to-one correspondence, ensuring that each electrical response value has a unique position in the time dimension. During the arrangement process, all time scales are mapped at equal intervals to maintain a consistent time interval expression between time scales and avoid time offsets between different sampling batches. Next, all electrical response values ​​undergo unified benchmark correction processing. The electrical response value at the initial sampling moment is used as the benchmark reference value, and subsequent electrical response values ​​are all recalibrated based on this benchmark reference value, ensuring that the electrical change time series can truly reflect the electrical change trend without including the influence of the initial background bias. After completing the above processing, an electrical change time series is obtained, arranged in chronological order, with unified time intervals and consistent amplitude benchmarks.

[0022] Based on the established electrical change time series, the amplitude change process between adjacent time scales is analyzed point by point. Specifically, starting from the first time scale of the electrical change time series, the amplitude difference between the electrical response value at the current time scale and the electrical response value at the previous time scale is calculated sequentially. The amplitude differences between each set of adjacent time scales are recorded chronologically, forming a continuous sequence of amplitude changes. After forming the amplitude change sequence, this sequence is remapped according to the time scale, so that each time scale corresponds to an amplitude change. The amplitude changes from multiple consecutive time scales are then sequentially concatenated to construct an instantaneous amplitude fluctuation trajectory reflecting the rhythm of electrical response changes over time. During this process, the positive and negative changes in amplitude are directionally identified, ensuring that the instantaneous amplitude fluctuation trajectory simultaneously includes information on both the direction and magnitude of change. Through this point-by-point calculation and sequential concatenation method, the instantaneous amplitude fluctuation trajectory completely covers the entire electrical change time series, thereby establishing a mapping relationship between the electrical change trend and transient fluctuations.

[0023] After the instantaneous amplitude fluctuation trajectory is formed, its continuous change pattern in the time dimension is segmented. The specific process is as follows: a fixed-length time window is set within a continuous time scale. Starting from the beginning of the electrical change time series, each time window is slid forward, and the distribution of amplitude changes is statistically analyzed within each time window. When a segment of amplitude deviates from the baseline change range within a certain time window, and the amplitude change returns to the previous continuous change range in the next time window, the time scale range corresponding to that time window is identified as a candidate abnormal segment. Subsequently, based on the start and end time scales of the candidate abnormal segment, the time range is recorded in the electrical change time series, and the time boundary of the candidate abnormal segment is written into the additional identifier area of ​​the electrical change time series. This allows the electrical change time series to carry additional segment identification information while maintaining the original data arrangement. Through this time window sliding and segment locking method, abnormal fluctuations are clearly separated in the time dimension.

[0024] After writing the time boundaries for all candidate anomaly segments, the duration and amplitude distribution characteristics of each segment are analyzed. The duration of each candidate anomaly segment and its corresponding amplitude change sequence are arranged segment by segment, and the number of time scales showing concentrated amplitude changes within the anomaly segment is counted. Simultaneously, the number of time scales corresponding to the amplitude changes returning to a continuous trajectory after the anomaly segment ends is also counted. When a candidate anomaly segment meets the criteria of its time span being within a preset short period, and its amplitude changes showing concentrated deviations within the segment and returning to a continuous trajectory after the segment ends, the candidate anomaly segment is formally identified as a short-term fluctuation segment and marked as an anomalous disturbance rhythm segment in the electrical change time series. After marking, the electrical change time series simultaneously contains a continuous time expression structure and a short-term fluctuation segment location identifier, enabling the electrical change time series to not only reflect the trend of underground electrical changes but also possess a structured expression capability for anomalous disturbance rhythms. This provides clear time boundaries and rhythmic basis for subsequent time-period stretching processing around short-term fluctuation segments.

[0025] The time series of electrical changes is stretched around short-term fluctuation segments, and the short-term fluctuation segments are embedded into the time series of electrical changes for continuous expansion. The continuous change trajectory is formed in the expansion result to distinguish between the instantaneous fluctuation rhythm and the pollution migration rhythm. To fully expand and represent the labeled short-term fluctuation segments in the time dimension, and to form a continuous change trajectory that reflects the continuity of pollution migration rhythm, time reconstruction and continuous expansion processing are performed on the electrical change time series. The specific implementation steps are as follows: For each short-term fluctuation segment in the electrical change time series that has been labeled, time boundaries are extracted. The start and end time scales of each short-term fluctuation segment are read and marked on the original time axis. After the time boundary extraction, a fixed number of consecutive time scales are selected forward from the start time scale of the short-term fluctuation segment as the forward correlation segment, and a fixed number of consecutive time scales are selected backward from the end time scale of the short-term fluctuation segment as the backward correlation segment, so that the forward correlation segment, short-term fluctuation segment, and backward correlation segment form a continuous arrangement on the time axis. Subsequently, the electrical response values ​​in the forward and backward correlation segments are completely preserved in their original time order without changing their time scale intervals. Only the short-term fluctuation segment is divided into an independent time segment, thus forming a reconstructed time segment containing three parts: the forward correlation segment, the short-term fluctuation segment, and the backward correlation segment, providing a complete time context for subsequent time stretching processing.

[0026] Based on the reconstructed time segments, the time scale within short-term fluctuation segments is redistributed. Specifically, the original continuous time scales of the short-term fluctuation segments are read one by one and rearranged according to fixed time increments, so that the time scales, originally arranged continuously within a finite time span, are remapped to an expanded time range. During the remapping process, the sequential order of the electrical response values ​​remains unchanged, while the time intervals between adjacent electrical response values ​​are adjusted to a uniform interval, ensuring that the amplitude changes within the short-term fluctuation segments unfold linearly in the time dimension. Through this time scale rearrangement process, the amplitude changes within the short-term fluctuation segments are transformed from a concentrated expression to a dispersed temporal expression, thereby eliminating the rhythm concentration phenomenon caused by time compression in the original timeline.

[0027] After redistributing the time scale within the short-term fluctuation segments, the redistributed short-term fluctuation segments are embedded into their corresponding positions in the original electrical change time series. The embedding process involves: preserving the original positions of the forward-correlated segments on the time axis; arranging the redistributed short-term fluctuation segments immediately following the forward-correlated segments according to the new time scale; and shifting the backward-correlated segments backward as a whole, thus creating a continuous unfolding structure in the time dimension of the entire electrical change time series. During the shifting process, the time scale of the backward-correlated segments is shifted as a whole, ensuring that its starting time scale remains continuous with the ending time scale of the short-term fluctuation segments, while maintaining the time intervals within the backward-correlated segments unchanged. Through this embedding and shifting process, the overall time axis of the electrical change time series is extended, while the short-term fluctuation segments gain sufficient unfolding space in the time dimension.

[0028] After embedding the short-term fluctuation segments, a continuous change trajectory is constructed for the expanded electrical change time series. Specifically, all electrical response values ​​are rearranged according to the new time scale order, and the amplitude changes between adjacent time scales are continuously connected in chronological order, forming an amplitude change path that runs through the entire time axis. In this amplitude change path, the short-term fluctuation segments, after time-stretching, appear as segments with expanded time spans and consistent amplitude change order, while forward-correlated and backward-correlated segments retain their original continuous change characteristics. By integrating these three parts into a single continuous time expression structure, a continuous change trajectory covering the entire time scale is formed. This allows the instantaneous fluctuation rhythm to appear as locally densely changing segments after expansion, while the pollution migration rhythm appears as continuously advancing segments across time intervals. Thus, the instantaneous fluctuation rhythm and the pollution migration rhythm are distinguished within the same time expression framework, providing a continuous time basis and a complete rhythm expression structure for subsequent directional comparisons based on the continuous change trajectory.

[0029] Based on the continuous change trajectory, the pollution migration inversion results are compared for directional consistency. Migration advancement segments that are inconsistent with the direction of the continuous change trajectory are extracted, and abnormal advancement markers are generated in the pollution migration inversion results. Based on the established continuous evolution trajectory and pollution migration inversion results with perfectly corresponding time scales, in order to identify migration progression segments in the pollution migration inversion results that are inconsistent with the rhythm direction of the continuous evolution trajectory, and to generate abnormal progression markers in the pollution migration inversion results, a process of comparing directional consistency is specifically implemented. The specific implementation steps are as follows: The continuously changing trajectory is read segment by segment according to the time scale, and a complete directional expression structure is constructed. Specifically, starting from the initial time scale of the continuously changing trajectory, the electrical response values ​​corresponding to the current and previous time scales are read sequentially, and the relationship between the two time scales is determined. When the electrical response value corresponding to the current time scale is higher than that corresponding to the previous time scale, this time scale interval is recorded as a positive change interval; when the electrical response value corresponding to the current time scale is lower than that corresponding to the previous time scale, this time scale interval is recorded as a negative change interval; when the electrical response values ​​corresponding to the two time scales are consistent, this time scale interval is recorded as a stable change interval. This directional marking process, performed segment by segment, is applied to the entire continuously changing trajectory, ensuring that each time scale interval corresponds to a unique directional attribute, forming a continuously changing direction sequence covering all time scales, thus giving the continuously changing trajectory a complete directional expression capability in the time dimension.

[0030] After the continuous change direction sequence is formed, the pollution migration inversion results are expanded according to the same time scale order, and a migration progression direction sequence corresponding to the continuous change direction sequence is constructed. Specifically, the migration progression expression values ​​corresponding to each time scale in the pollution migration inversion results are arranged chronologically. Starting from the initial time scale, the changes in migration progression expression values ​​between the current time scale and the previous time scale are read sequentially. When the migration progression expression value corresponding to the current time scale shows an expanding state relative to the previous time scale, this time scale interval is recorded as a positive progression interval; when the migration progression expression value corresponding to the current time scale shows a contraction state relative to the previous time scale, this time scale interval is recorded as a reverse progression interval; when the migration progression expression values ​​between two time scales remain consistent, this time scale interval is recorded as a stable progression interval. After completing the above directional attribute extraction, the pollution migration inversion results form a complete migration progression direction sequence in the time dimension, ensuring that the migration progression direction sequence completely corresponds to the continuous change direction sequence in terms of time scale.

[0031] After establishing the correspondence between the continuously changing direction sequence and the migration and advancement direction sequence, the two direction sequences are compared segment by segment according to the time scale. Specifically, starting from the first time scale interval, the direction attributes in both the continuously changing direction sequence and the migration and advancement direction sequence are read one by one, and the two direction attributes are compared one by one. When the continuously changing direction attribute and the migration and advancement direction attribute are consistent, the time scale interval is recorded as a consistent direction interval; when they are inconsistent, the time scale interval is recorded as an inconsistent direction interval. When recording inconsistent direction intervals, adjacent consecutive inconsistent direction intervals are merged into continuous migration and advancement segments, and the start and end time scales of each continuous migration and advancement segment are recorded, so that inconsistent migration and advancement segments form a complete continuous segment representation on the time axis.

[0032] After extracting migration segments with inconsistent directions, abnormal progression markers are generated in the temporal representation structure of the pollution migration inversion results. Specifically, an identifier field is added to the corresponding time scale interval of the pollution migration inversion results. The start and end time scales for each migration segment with inconsistent directions are written into the identifier field, and an abnormal progression status identifier is assigned to that time segment. Simultaneously, multiple abnormal progression segments are numbered sequentially according to time, ensuring that the abnormal progression markers form a continuous and traceable record in the pollution migration inversion results. Through this processing, the pollution migration inversion results, while maintaining the original migration progression representation, additionally form an abnormal progression marker structure for inconsistent direction segments. This allows subsequent steps to accurately trace back the corresponding electrical change time series segments based on the abnormal progression markers, thereby locating the source of misjudgment.

[0033] Based on the abnormal progression markers, the corresponding electrical change time series segments are traced back to identify abnormal segments that show a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window, thus determining the segment to which the disturbance belongs. After the abnormal progression markers have been written and corresponding time segments have been formed in the pollution migration inversion results, in order to accurately determine the source of the disturbance, the electrical change time series is subjected to backtracking and segment identification processing. The specific implementation steps are as follows: The start and end time scales corresponding to each anomaly advancement marker in the pollution migration inversion results are read one by one, and an anomaly advancement time segment record table is formed in chronological order. During the reading process, the time scale number corresponding to the anomaly advancement marker is perfectly aligned and mapped with the original time scale in the pollution migration inversion results, so that the anomaly advancement time segment has a clear boundary on the time axis. Subsequently, each time segment in the anomaly advancement time segment record table is mapped to the electrical change time series. By searching through the scale one by one, the time scale range with the same time number is locked in the electrical change time series, so that a backtracking segment corresponding to the anomaly advancement marker is formed in the electrical change time series. Through this time mapping process, the anomaly advancement marker obtains a precise temporal positioning basis within the electrical change time series.

[0034] A pre-defined time window is constructed around each backtracking segment in the electrical change time series. Specifically, a fixed number of consecutive time scales are selected forward from the starting time scale of the backtracking segment, and a fixed number of consecutive time scales are selected backward from the ending time scale of the backtracking segment. These forward, backtracking, and backward time scales are combined to form a complete pre-defined time window. Within this window, electrical response values ​​are read sequentially, scale by scale, and a corresponding time-amplitude mapping table is established. Simultaneously, the amplitude change at each time scale relative to the previous time scale is recorded, creating a continuous amplitude change trajectory representation structure within the pre-defined time window. This fixed-time-span window division method places the electrical change segment corresponding to the abnormal progression within a complete temporal context.

[0035] Within a preset time window, the amplitude change trajectory is segmented for identification. Specifically, within the preset time window, the amplitude change is scanned sequentially, scale by scale. When an amplitude continuously increases relative to the previous time scale and exceeds the baseline change range within a certain continuous time interval, the starting scale of that continuous time interval is recorded. When the amplitude change gradually decreases and re-enters the baseline change range in an adjacent time window after that continuous time interval, the recovery scale is terminated. All time intervals between the starting and ending scales are designated as candidate anomalous segments. Candidate anomalous segments must meet the condition that amplitude changes occur concentratedly within the preset time window and return to the baseline range within the immediately adjacent time window. Through the above-described scale-by-scale scanning and starting / ending scale recording, candidate anomalous segments possess clear time boundaries and amplitude change trajectory characteristics.

[0036] After identifying candidate anomalous segments, the time range of each candidate anomalous segment is compared with the anomalous advancement time segment. When the time scale of a candidate anomalous segment is entirely contained within the anomalous advancement time segment, or when the candidate anomalous segment overlaps with the anomalous advancement time segment on the time axis, the candidate anomalous segment is identified as the disturbance attribution segment, and a disturbance attribution identifier field is written into the electrical change time series. This is done by appending disturbance attribution status information to the corresponding time scale position, thus adding a disturbance attribution segment expression structure to the electrical change time series while maintaining the integrity of the original time and amplitude data. Through the aforementioned time mapping, preset time window construction, amplitude change trajectory identification, and segment matching processing, the anomalous advancement marker obtains a clear disturbance attribution segment expression in the electrical change time series, thereby providing accurate time boundaries and amplitude change basis for subsequent rhythm drift adjustment around the disturbance attribution segment.

[0037] The rhythm drift adjustment of the pollution migration inversion time series is implemented around the disturbance attribution segment. Inverse time stretching processing is introduced before and after the disturbance attribution segment, and amplitude fading processing is superimposed. The pollution migration expression is rearranged by progressive time difference misalignment to eliminate the misjudgment trend caused by short-period electromagnetic disturbance. After the disturbance attribution period has been located and a clear time boundary has been formed in the pollution migration inversion time series, in order to resolve the misjudgment trend caused by short-period electromagnetic disturbances, a rhythm drift adjustment operation is performed on the pollution migration inversion time series. The specific implementation steps are as follows: A complete time adjustment interval is established around the disturbance attribution segment. Specifically, the start and end time scales of the disturbance attribution segment are read, and all migration expression values ​​corresponding to this time interval are identified in the pollution migration inversion time series. Then, a fixed number of consecutive time scales are traced backward from the start time scale of the disturbance attribution segment to form a forward time segment. Simultaneously, a fixed number of consecutive time scales are extended backward from the end time scale of the disturbance attribution segment to form a backward time segment, ensuring that the forward, disturbance attribution, and backward time segments are arranged continuously on the time axis. The time scale numbers in the forward and backward time segments maintain their original order and intervals, with only the adjustment range logically defined, thus forming a time adjustment interval covering the complete time expression structure before and after the disturbance attribution segment.

[0038] Within the time adjustment interval, reverse time scaling is applied before and after the disturbance attribution segment. Specifically, within the forward time segment, migration expression values ​​are read sequentially, scale by scale, and the intervals between adjacent time scales are compressed by a fixed ratio, resulting in a tightly packed arrangement of multiple time scales within the forward time segment in the new time expression framework. Simultaneously, within the backward time segment, migration expression values ​​are read sequentially, scale by scale, and the intervals between adjacent time scales are extended by a fixed ratio, resulting in an expanded arrangement of multiple time scales within the backward time segment in the new time expression framework. During the reverse time scaling process, the order of migration expression values ​​corresponding to each time scale remains unchanged; only the spacing between time scales is adjusted. This creates a rhythmic counterbalancing structure centered on the disturbance attribution segment on the time axis, thereby weakening the trend amplification effect caused by time concentration.

[0039] After completing the inverse time scaling process, amplitude fading is applied within the perturbation attribution segment. Specifically, an amplitude adjustment start point is set at the beginning time scale of the perturbation attribution segment. Starting from this time scale, the migration expression value is adjusted decreasing sequentially over time, causing the migration expression value to gradually weaken in the first half of the perturbation attribution segment. An amplitude recovery end point is set at the end time scale of the perturbation attribution segment. In the second half of the perturbation attribution segment, the migration expression value is adjusted increasing sequentially over time, causing the migration expression value to gradually recover to its pre-adjustment level. Throughout the amplitude fading process, a continuous transition relationship between migration expression values ​​is maintained, resulting in a smooth, progressive change in migration expression over time without abrupt changes, thereby reducing the concentrated impact of the perturbation attribution segment on the overall migration trend.

[0040] After the reverse time scaling and amplitude fading processing are completed, a progressive time-difference misalignment rearrangement is performed on the entire time adjustment interval. Specifically, starting from the initial time scale of the forward time segment, all migration expression values ​​are rearranged according to the new time interval order, ensuring that the time scale of the forward time segment after compression, the time scale of the disturbance attribution segment after amplitude fading processing, and the time scale of the backward time segment after extension processing form a continuous arrangement on the same time axis. During the rearrangement process, the time scale numbers are continuously incremented to maintain the integrity of the time axis and ensure that the migration expression values ​​maintain a continuous transition relationship at the new time scale positions. Through this progressive time-difference misalignment rearrangement, the sudden advancement expression originally caused by short-period electromagnetic disturbances is redistributed in the time dimension and forms a continuous rhythmic structure with the preceding and following migration expressions. This resolves the misjudgment trend caused by short-period electromagnetic disturbances and restores the migration expression pattern of the pollution migration inversion time series to be consistent with the continuous change trajectory.

[0041] This invention, by annotating short-term fluctuation segments, stretching and expanding time-series data, and constructing continuous change trajectories, enables a structured rhythmic expression of the electrical response over time, effectively distinguishing between instantaneous disturbance rhythms and pollution migration rhythms. By introducing a directional consistency comparison mechanism, a temporal correspondence is established between pollution migration inversion results and continuous change trajectories. This allows for the identification of directional shift segments at the trend level, preventing short-period electromagnetic disturbances from being misjudged as pollution plume propulsion behavior, thereby improving the stability and temporal continuity of migration trend interpretation.

[0042] This invention employs reverse time scaling and amplitude fading processing around the disturbance attribution zone, and uses a progressive time-difference misalignment rearrangement of the pollution migration expression to redistribute and mitigate the local anomaly progression trend along the time axis, thus eliminating the trend amplification effect caused by short-period electromagnetic disturbances. Through rhythm drift adjustment, the pollution migration inversion time series restores an expression structure consistent with the continuous change trajectory, making the pollution diffusion prediction results closer to the actual migration process, reducing the interference of misjudged trends on governance decisions, and improving the accuracy of the time expression and the reliability of decision-making references for dynamic monitoring of underground multi-component pollutants.

[0043] This invention provides, for example Figure 2 The dynamic monitoring system for underground multi-component pollutants based on tomographic data inversion shown includes an electrical rhythm annotation module, a time-period stretching analysis module, a migration direction comparison module, a disturbance zone identification module, and a rhythm drift adjustment module. The electrical rhythm annotation module acquires underground electrical change records under a continuous time scale, constructs an electrical change time series, and simultaneously extracts the instantaneous amplitude fluctuation trajectory of the electrical change time series. Based on the instantaneous amplitude fluctuation trajectory, short-term fluctuation segments are annotated in the electrical change time series to characterize the abnormal disturbance rhythm. The time-period stretching analysis module stretches the electrical change time series around short-term fluctuation segments, embeds the short-term fluctuation segments into the electrical change time series for continuous expansion, and forms a continuous change trajectory in the expansion result to distinguish between instantaneous fluctuation rhythm and pollution migration rhythm. The migration direction comparison module compares the direction consistency of the pollution migration inversion results based on the continuously changing trajectory, extracts the migration advancement segments that are inconsistent with the direction of the continuously changing trajectory, and generates abnormal advancement markers in the pollution migration inversion results. The disturbance segment identification module traces back the corresponding electrical change time series segment based on the abnormal advancement marker, identifies the abnormal segment that shows a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window, and determines the segment to which the disturbance belongs. The rhythm drift adjustment module applies rhythm drift adjustment to the pollution migration inversion time series around the disturbance attribution segment. It introduces reverse time scaling processing and superimposes amplitude fading processing before and after the disturbance attribution segment. It rearranges the pollution migration expression through progressive time difference misalignment to eliminate the misjudgment trend caused by short-period electromagnetic disturbance.

[0044] The dynamic monitoring method for underground multi-component pollutants based on tomographic data inversion provided in this embodiment of the invention is implemented through the above-mentioned dynamic monitoring system for underground multi-component pollutants based on tomographic data inversion. For details of the specific methods and procedures of the dynamic monitoring system for underground multi-component pollutants based on tomographic data inversion, please refer to the embodiments of the above-mentioned dynamic monitoring method for underground multi-component pollutants based on tomographic data inversion, which will not be repeated here.

[0045] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring the dynamics of multi-component pollutants in the subsurface based on inversion of chromatographic data, characterized in that, Includes the following steps: Records of underground electrical changes under continuous time scale are obtained, electrical change time series are constructed, and the instantaneous amplitude fluctuation trajectory of the electrical change time series is extracted simultaneously. Short-term fluctuation segments are marked in the electrical change time series according to the instantaneous amplitude fluctuation trajectory to characterize the rhythm of abnormal disturbances. The time series of electrical changes is stretched around short-term fluctuation segments, and the short-term fluctuation segments are embedded into the time series of electrical changes for continuous expansion. The continuous change trajectory is formed in the expansion result to distinguish between the instantaneous fluctuation rhythm and the pollution migration rhythm. Based on the continuous change trajectory, the pollution migration inversion results are compared for directional consistency. Migration advancement segments that are inconsistent with the direction of the continuous change trajectory are extracted, and abnormal advancement markers are generated in the pollution migration inversion results. Based on the abnormal progression markers, the corresponding electrical change time series segments are traced back to identify abnormal segments that show a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window, thus determining the segment to which the disturbance belongs. The time series of pollution migration inversion is adjusted by rhythm drift around the disturbance attribution segment. Inverse time scaling is introduced before and after the disturbance attribution segment, and amplitude fading is superimposed. Pollution migration expression is rearranged by progressive time difference misalignment to eliminate the misjudgment trend caused by short-period electromagnetic disturbance.

2. The method for monitoring the dynamics of multi-component pollutants in the subsurface based on inversion of chromatographic data according to claim 1, characterized in that, The steps for annotating short-term fluctuation segments are as follows: The underground electrical response records were acquired and time-calibrated to establish a time index sequence. The electrical response values ​​were then bound to the time scale to form a time series of electrical changes. The amplitude difference between adjacent time scales is calculated on a scale around the time series of electrical changes to form a sequence of amplitude changes and construct the instantaneous amplitude fluctuation trajectory; Based on the instantaneous fluctuation trajectory of amplitude, a time window is set within a continuous time scale, and candidate abnormal segments are slidably divided and written into the additional identification area of ​​the electrical change time series. By analyzing the duration and amplitude change sequences of candidate abnormal segments, candidate abnormal segments that meet the preset short period range and whose amplitude changes deviate from the continuous change trajectory range are marked as short-term fluctuation segments.

3. The method for monitoring subsurface multi-component contaminant dynamics based on inversion of chromatographic data according to claim 2, wherein, The steps involved in forming a continuously changing trajectory are as follows: Extract the start and end time scales of short-term fluctuation segments in the time series of electrical changes, and divide the time series into forward correlation segments and backward correlation segments around the start and end time scales to form reconstructed time segments; Based on the reconstructed time interval, the time scales within the short-time fluctuation segment are rearranged according to a fixed time increment, while maintaining the order of electrical response values ​​and adjusting the intervals between adjacent time scales. Embedding the rearranged short-term fluctuation segments into the corresponding positions of the electrical change time series, the time scale of the backward correlation segment is shifted as a whole to form a continuous unfolding structure; Based on the continuous unfolding structure, the amplitude changes between adjacent time scales are connected according to the new time scale order to form a continuously changing trajectory.

4. The method for monitoring subsurface multi-component contaminant dynamics based on inversion of chromatographic data according to claim 3, wherein, The steps for generating anomaly propagation markers are as follows: The electrical response values ​​were read segment by segment according to the time scale around the continuously changing trajectory, and the positive change interval, the negative change interval, and the stable change interval were marked to form a sequence of continuously changing directions. Based on the continuously changing direction sequence, the pollution migration inversion results are read in the same time scale order to read the migration advancement expression value and mark the forward advancement interval, reverse advancement interval and steady advancement interval to form the migration advancement direction sequence; The migration and propulsion segments are formed by comparing the continuously changing direction sequence and the migration and propulsion direction sequence on a time scale and merging the intervals of continuous inconsistency in direction. Based on the migration progression segment, start and end time scales are written into the pollution migration inversion results, and abnormal progression markers are generated.

5. The method for monitoring the dynamics of multi-component pollutants in the subsurface based on inversion of chromatographic data according to claim 4, characterized in that, The continuously changing direction sequence and the migration and advancement direction sequence are compared segment by segment within the corresponding time scale around the migration and advancement segment. Only the intervals with inconsistent continuous directions are merged and segmented. The start time scale and end time scale of the migration and advancement segment are consistent with the abnormal advancement mark.

6. The method for monitoring subsurface multi-component contaminant dynamics based on inversion of chromatographic data according to claim 4, wherein, The steps for determining the disturbance attribution zone are as follows: Read the start and end time scales corresponding to the abnormal advancement markers and map them to the electrical change time series to form a backtracking segment; A preset time window is divided around the backtracking section, and the electrical response values ​​are read on a scale to form an amplitude change trajectory. Based on amplitude change trajectory identification, candidate abnormal segments are identified that show a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window; Match the candidate anomaly segment with the time segment corresponding to the anomaly advancement marker and write the result into the disturbance attribution identifier field to determine the disturbance attribution segment.

7. The method for monitoring subsurface multi-component contaminant dynamics based on inversion of chromatographic data according to claim 6, wherein, The preset time window is symmetrically divided around the start and end time scales of the backtracking section. The amplitude change trajectory is compared with the benchmark interval range step by step in chronological order. The candidate abnormal section is limited to the time segment that corresponds continuously to the amplitude increase section and the amplitude return section.

8. The method for monitoring subsurface multi-component contaminant dynamics based on inversion of chromatographic data according to claim 6, wherein, The pollution migration inversion time series is adjusted by rhythm drift around the perturbation attribution segment. Inverse time scaling is introduced before and after the perturbation attribution segment, and amplitude fading is superimposed. The pollution migration expression steps are as follows through progressive time difference misalignment rearrangement: Read the start and end time scales of the disturbance attribution segment and divide the forward and backward time segments around the start and end time scales to form a time adjustment interval; The time interval of the forward time segment is compressed and the time interval of the backward time segment is extended around the time adjustment interval to form a reverse time scaling process. Based on the perturbation attribution segment, the migration expression value is adjusted by decreasing and increasing in chronological order to form a gradual fading of amplitude; The migration expression values ​​are rearranged according to the new time interval around the time adjustment interval to form a progressive time difference misalignment rearrangement to eliminate the tendency of misjudgment.

9. A subsurface multi-component pollutant dynamic monitoring system based on chromatographic data inversion for implementing the method of subsurface multi-component pollutant dynamic monitoring based on chromatographic data inversion according to any one of claims 1 to 8, characterized in that, It includes an electrical rhythm annotation module, a time-span stretching analysis module, a migration direction comparison module, a disturbance segment identification module, and a rhythm drift adjustment module: The electrical rhythm annotation module acquires underground electrical change records under a continuous time scale, constructs an electrical change time series, and simultaneously extracts the instantaneous amplitude fluctuation trajectory of the electrical change time series. Based on the instantaneous amplitude fluctuation trajectory, short-term fluctuation segments are annotated in the electrical change time series to characterize the abnormal disturbance rhythm. The time-period stretching analysis module stretches the electrical change time series around short-term fluctuation segments, embeds the short-term fluctuation segments into the electrical change time series for continuous expansion, and forms a continuous change trajectory in the expansion result to distinguish between instantaneous fluctuation rhythm and pollution migration rhythm. The migration direction comparison module compares the direction consistency of the pollution migration inversion results based on the continuously changing trajectory, extracts the migration advancement segments that are inconsistent with the direction of the continuously changing trajectory, and generates abnormal advancement markers in the pollution migration inversion results. The disturbance segment identification module traces back the corresponding electrical change time series segment based on the abnormal advancement marker, identifies the abnormal segment that shows a sudden increase in amplitude within a preset time window and whose amplitude returns to the baseline interval within an adjacent time window, and determines the segment to which the disturbance belongs. The rhythm drift adjustment module applies rhythm drift adjustment to the pollution migration inversion time series around the disturbance attribution segment. It introduces reverse time scaling processing and superimposes amplitude fading processing before and after the disturbance attribution segment. It rearranges the pollution migration expression through progressive time difference misalignment to eliminate the misjudgment trend caused by short-period electromagnetic disturbance.

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