Time-shifting resistivity inversion imaging method based on time sequence gradient structure constraint

Abstract

The invention discloses a time sequence gradient structure constraint-based time shift resistivity inversion imaging method, which comprises the following steps of: firstly, acquiring apparent resistivity data at a plurality of moments, and performing independent inversion imaging by using a conventional electrical method at the initial stage of monitoring; and at the subsequent moment, gradient structure constraints of the previous moment are introduced into a traditional electrical method regularization inversion objective function, that is, gradient structure information of an inversion result of the previous moment is introduced into the inversion objective function for constraint, so that an inversion imaging result of the next moment based on the gradient structure constraints of the model of the previous moment is obtained. The electrical difference at the next moment is effectively regulated and controlled, so that the resolution of gradient structure constraint inversion is higher, the capability of an inversion model on boundary analysis is enhanced, the change process of the geological condition is accurately captured, the formation process of the geological anomalous body can be effectively distinguished finally, the resolution of the time shift resistivity method on the anomalous body boundary is improved, and the accuracy of the time shift resistivity method is improved. Accurate monitoring of the development process of the geological anomalous body is realized.

Description

Technical Field

[0001] The invention belongs to the field of geological monitoring, and in particular to a time-lapse resistivity inversion imaging method based on time series gradient structure constraints. Background Art

[0002] Time-lapse resistivity monitoring is a geophysical method that detects dynamic changes in underground structures by measuring the resistivity of underground media over time. There are currently a variety of methods for time-lapse resistivity monitoring: 1. Independent inversion method: The apparent resistivity data at each time point is inverted independently without considering the correlation between different time points. This method ignores the continuity of the development of geological anomalies and cannot capture the gradual change process of underground results. 2. Differential imaging method: The apparent resistivity data at different time points are differentiated to highlight the apparent resistivity values ​​of the changing parts, and then the changing apparent resistivity values ​​are inverted. The limitation of this method is that the differential operation will amplify the data noise, resulting in instability in the inversion results, which may lead to non-convergence of the inversion and serious artifacts. 3. Time-constrained inversion method: Time constraints are introduced in the inversion process, but the current method only involves the inversion model itself and is a simple linear smoothing constraint, that is, the constraint imposed is the model m inverted at that moment. i and the model result m obtained by inversion at the previous moment i-1 This method encourages the inverted model to change smoothly in space without large mutations. Therefore, the newly emerged abnormal bodies will be overly smooth, resulting in the inability to clearly reflect the boundaries between the new and old abnormal bodies, and thus the inability to accurately obtain the development process of the abnormal bodies.

[0003] Therefore, how to provide a new method that can accurately capture the changing trend of anomalies, effectively distinguish the formation process of geological anomalies, improve the boundary resolution of the time-lapse resistivity method for the development process of anomalies, and ultimately accurately obtain the development process of anomalies is the research direction required by the present invention. Summary of the invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention provides a time-lapse resistivity inversion imaging method based on time series gradient structure constraints, which can accurately capture the changing trend of the anomaly body, effectively distinguish the formation process of the geological anomaly body, improve the boundary resolution of the time-lapse resistivity method for the anomaly body development process, and finally accurately obtain the anomaly body development process.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a time-lapse resistivity inversion imaging method based on time series gradient structure constraints, the specific steps are:

[0006] Step 1: Determine the acquisition method and acquisition time: first determine the geological conditions of the abnormal body to be monitored, and design the corresponding time series electrical resistivity data acquisition method, and at the same time determine the acquisition time, a total of Tn, n = 1, 2, 3...n;

[0007] Step 2: First inversion imaging: According to the acquisition method determined in step 1, the first apparent resistivity data acquisition is performed at time T1, which is recorded as d obs，1 , using the acquired apparent resistivity data to invert the electrical characteristics of the underground medium at time T1, obs,1 Perform separate electrical inversion imaging, specifically:

[0008] At time T1, the apparent resistivity data d obs，1 The objective function is:

[0009]

[0010] Among them, m T1 is the inversion imaging result at time T1, W d is the covariance matrix of the data fitting term, where d is the forward operator and d obs,1 is the apparent resistivity data collected at time T1, β is the regularization parameter of the model smoothing term, W m is the covariance matrix of the model smoothing term, where the first-order derivative operator is selected, m ref,1 It is the reference model of the smoothing term of the model inverted separately at time T1, which is the mean model;

[0011] Through the above inversion, we can get the inversion imaging result m at time T1. T1 ;

[0012] Step 3: Second inversion imaging: The second apparent resistivity data collection is performed at time T2, recorded as d obs,2 The electrical characteristics of the underground medium at time T2 are inverted using the collected apparent resistivity data. The Gauss-Newton iterative inversion method is used to invert the collected apparent resistivity data d obs,2 Perform inversion and introduce the inversion imaging result m at time T1 into the objective function during the inversion process T1 The gradient structure constraints are as follows:

[0013] At time T2, the apparent resistivity data d obs,2 Introduce m T1 The objective function of the gradient structure constraint is:

[0014]

[0015] Among them, m T2 is the inversion imaging result at time T2, d obs,2is the apparent resistivity data collected at time T2, m ref,2 is the reference model of the smoothing term of the model inverted separately at time T2, which is a mean model, λ is the gradient structure constraint regularization parameter, W g is the covariance matrix of the gradient structure constraint, is the gradient operator, Represents the gradient cross product result of the inversion imaging results at time T2 and T1;

[0016] Through the above inversion, the inversion imaging result m at time T2 is obtained T2 ;

[0017] Step 4: The third inversion imaging: The third apparent resistivity data collection is performed at time T3, recorded as d obs，3 The electrical characteristics of the underground medium at time T3 are inverted using the collected apparent resistivity data. The Gauss-Newton iterative inversion method is used to invert the collected apparent resistivity data d obs,3 Perform inversion and introduce the inversion imaging result m at time T2 into the objective function during the inversion process T2 The gradient structure constraints are as follows:

[0018] At time T3, the apparent resistivity data d obs,3 Introduce m T2 The objective function of the gradient structure constraint is:

[0019]

[0020] Among them, m T3 is the inversion imaging result at time T3, d obs,3 is the apparent resistivity data at time T3, m ref,3 is the reference model of the smoothing term of the model inverted separately at time T3, which is a mean model. It represents the gradient cross product result of the inversion imaging results at time T3 and T2;

[0021] Through the above inversion, the inversion imaging result m at time T3 is obtained T3 ;

[0022] Step 5: Monitoring the development process of abnormal bodies: The apparent resistivity data collected at each acquisition moment are processed according to the processing process of step 4, so as to obtain the inversion imaging results at different moments, and the inversion imaging results at different moments are compared and analyzed, so as to realize the monitoring of the development process of abnormal bodies.

[0023] Furthermore, the abnormal body in step 1 is a water channel or a fault. As time goes by, the scope of these two abnormal bodies is easy to grow and expand due to external influences. Therefore, monitoring them can timely know the development of the abnormal body and facilitate timely measures.

[0024] Furthermore, the time series electrical resistivity data acquisition method in step 1 is the Wenner running electrode method.

[0025] Furthermore, the intervals between the various acquisition moments in step 1 are the same. By setting the same sampling interval, the changes in the abnormal body imaging at different moments in the same time period can be further observed, thereby determining whether the abnormal body is developing faster.

[0026] Furthermore, the number of acquisition moments set in step 1 is at least 3. Setting at least 3 acquisition moments can ensure that the method of the present invention achieves the required accuracy.

[0027] Due to the volume effect of electrical imaging and the current situation of multiple solutions in geophysical imaging, the formation process of geological anomalies cannot be accurately analyzed by electrical inversion alone, and the ability to analyze the boundaries is relatively limited, and artifacts may exist. Based on this, the present invention first collects apparent resistivity data at multiple times, and in the early stage of monitoring, uses conventional electrical methods to perform single inversion imaging; in the subsequent moments, time-lapse resistivity inversion monitoring imaging with time series gradient structure constraints is performed, that is, the gradient structure constraints of the previous moment are introduced into the regularized inversion objective function of the conventional electrical method, and the gradient structure information of the inversion result of the previous moment is introduced into the inversion objective function to constrain, so as to obtain the inversion imaging result of the next moment based on the gradient structure constraints of the model at the previous moment, effectively regulate the electrical difference at the next moment, make the resolution of the gradient structure constraint inversion higher, enhance the ability of the inversion model in boundary analysis, so as to accurately capture the change process of geological conditions, and finally effectively distinguish the formation process of geological anomalies, improve the resolution of the time-lapse resistivity method for the boundary of the anomaly, and realize the accurate monitoring of the development process of geological anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the process of the present invention;

[0029] Figure 2 It is the location distribution map of the water diversion channels laid out in the experimental proof;

[0030] Figure 3 This is the result of the single inversion imaging at time T1 in the experimental proof;

[0031] Figure 4 It is the location distribution map of the water-conducting channel after expansion as proved by the experiment;

[0032] Figure 5 This is the result of the single inversion imaging at time T2 in the experimental proof;

[0033] Figure 6 This is the inversion imaging result diagram of the present invention at time T2 in the experimental proof;

[0034] Figure 7 This is the experimental proof of the location distribution of the water channel after it is expanded again;

[0035] Figure 8 This is the result of the single inversion imaging at time T3 in the experimental proof;

[0036] Fig. 9 It is the inversion imaging result diagram of the present invention at time T3 in the experimental proof. DETAILED DESCRIPTION

[0037] The present invention will be further described below.

[0038] like Figure 1 As shown, the specific steps of this embodiment are:

[0039] Step 1: Determine the acquisition method and acquisition time. First determine the geological conditions of the abnormal body to be monitored, and design the corresponding time series electrical resistivity data acquisition method. This embodiment adopts the Wenner running pole method, and determines a total of 3 acquisition times, namely T1, T2 and T3; the interval time between each acquisition time is the same. By setting the same sampling interval, it is possible to further observe the changes in the abnormal body imaging at different times in the same time period, so as to determine whether the abnormal body development is accelerated; the abnormal body is a water channel or a fault. As time goes by, the scope of these two abnormal bodies is more likely to expand due to external influences. Therefore, monitoring them can timely know the development of the abnormal body, so as to facilitate timely measures.

[0040] Step 2: First inversion imaging: According to the acquisition method determined in step 1, the first apparent resistivity data acquisition is performed at time T1, which is recorded as d obs，1 , using the acquired apparent resistivity data to invert the electrical characteristics of the underground medium at time T1, obs，1 Perform separate electrical inversion imaging, specifically:

[0041] At time T1, the apparent resistivity data d obs，1 The objective function is:

[0042]

[0043] Among them, m T1 is the inversion imaging result at time T1, W d is the covariance matrix of the data fitting term, where d is the forward operator and d obs，1 is the apparent resistivity data collected at time T1, β is the regularization parameter of the model smoothing term, W m is the covariance matrix of the model smoothing term, where the first-order derivative operator is selected, m ref，1is the reference model of the model smoothing term inverted separately at time T1, which is a mean model. This embodiment adopts the mean model of the apparent resistivity data at time T1;

[0044] Through the above inversion, we can get the inversion imaging result m at time T1. T1 ;

[0045] Step 3: Second inversion imaging: The second apparent resistivity data collection is performed at time T2, recorded as d obs,2 The electrical characteristics of the underground medium at time T2 are inverted using the collected apparent resistivity data. The Gauss-Newton iterative inversion method is used to invert the collected apparent resistivity data d obs,2 Perform inversion and introduce the inversion imaging result m at time T1 into the objective function during the inversion process T1 The gradient structure constraints are as follows:

[0046] At time T2, the apparent resistivity data d obs，2 Introduce m T1 The objective function of the gradient structure constraint is:

[0047]

[0048] Among them, m T2 is the inversion imaging result at time T2, d obs,2 is the apparent resistivity data collected at time T2, m ref,2 is the reference model of the model smoothing term of the single inversion at time T2, which is a mean model. This embodiment adopts the mean model of the apparent resistivity data at time T2, λ is the gradient structure constraint regularization parameter, W g is the covariance matrix of the gradient structure constraint, is the gradient operator, Represents the gradient cross product result of the inversion imaging results at time T2 and T1;

[0049] Through the above inversion, the inversion imaging result m at time T2 is obtained T2 ;

[0050] Step 4: The third inversion imaging: The third apparent resistivity data collection is performed at time T3, recorded as d obs，3 The electrical characteristics of the underground medium at time T3 are inverted using the collected apparent resistivity data. The Gauss-Newton iterative inversion method is used to invert the collected apparent resistivity data d obs,3 Perform inversion and introduce the inversion imaging result m at time T2 into the objective function during the inversion process T2 The gradient structure constraints are as follows:

[0051] At time T3, the apparent resistivity data d obs,3 Introduce m T2The objective function of the gradient structure constraint is:

[0052]

[0053] Among them, m T3 is the inversion imaging result at time T3, d obs,3 is the apparent resistivity data at time T3, m ref,3 is the reference model of the model smoothing term inverted separately at time T3, which is a mean model. This embodiment adopts the mean model of the apparent resistivity data at time T3. It represents the gradient cross product result of the inversion imaging results at time T3 and T2;

[0054] Through the above inversion, the inversion imaging result m at time T3 is obtained T3 ;

[0055] Step 5: Monitoring the development process of the abnormal body: Based on the inversion imaging results at different times from T1 to T3 obtained above, the inversion imaging results at different times are compared and analyzed, so as to realize the monitoring of the development process of the abnormal body.

[0056] Experimental proof:

[0057] In order to verify the effect of the inversion imaging of the present invention, a simulation experiment was conducted. The formation and development of the water-conducting channel was used as the experimental scheme to determine the abnormal body. The setting scheme of the abnormal body was as follows: the resistivity of the surrounding rock was set to 100Ωm, the water was set to 20Ωm, and the position of the abnormal body in the simulated rock body was determined. The specific geological model is as follows: Figure 2 As shown in the figure, the electrical survey line is then laid out. The total length of the electrical survey line is 100m, the electrode spacing is set to 3m, and a total of 33 electrodes are laid out. The Wenner running electrode method is used to collect time series electrical apparent resistivity data. The water channel is inverted and imaged using the method of this embodiment. The inversion imaging of the water channel at time T1 is obtained separately. T1 like Figure 3 As shown, Figure 1 By comparing with the actual geological model position, it can be seen that the inverted central resistance value is located at the center of the water channel at the actual time T1, and the imaging resolution of the inversion result alone at the time T1 is better.

[0058] Then the scope of the water channel is expanded to simulate the expansion of the abnormal body. Figure 4 As shown, after the apparent resistivity data is collected at time T2, the existing single inversion method and the gradient structure constraint method introduced in this embodiment are used to perform inversion imaging. Figure 5 and 6 As shown, first Figure 5 and Figure 4By comparing and analyzing, we can see that the central resistance value of the existing single inversion imaging results is located at the center of the overall water channel at T1 and T2, indicating that the single inversion image reflects more like the process of water migration rather than the process of diffusion, which is inconsistent with the abnormal body model set up in the experiment, and the resolution of the boundary between the new and old abnormal bodies (i.e. the abnormal bodies before and after expansion) is also poor. Figure 6 and Figure 4 For comparative analysis, the method of this embodiment is Figure 6 The development and diffusion of the water-conducting channel can be clearly distinguished, that is, the process of the water-conducting channel developing from time T1 to time T2 and Figure 4 The experimental setup is consistent with the anomaly model, which significantly improves the boundary resolution of the water-conducting channel.

[0059] Then the scope of the water channel is further expanded to simulate the further expansion of the abnormal body. Figure 7 As shown, after the apparent resistivity data is collected at time T3, the existing single inversion method and the gradient structure constraint method introduced in this embodiment are used to perform inversion imaging. Figure 8 and 9 As shown, first Figure 8 and Figure 7 Comparative analysis shows that the central resistance of the single inversion imaging result is located to the right of the center of the overall water channel at T1, T2, and T3, making it more difficult to analyze the development process of the water channel. It is quite different from the expansion process of the abnormal body model set in the experiment, and the boundary resolution of the new and old abnormal bodies is also poor. Fig. 9 and Figure 7 For comparative analysis, the method of this embodiment is Fig. 9 The development process of the water channel from time T1 to time T3 can be clearly distinguished, and the resolution of the interface between the new and old anomalies and the boundary between the overall anomaly and the surrounding rock are relatively high. The positions of the new and old anomalies are accurately analyzed, and the changing process of the geological conditions is accurately captured. This shows that the present invention can accurately capture the changing trend of the anomaly, effectively distinguish the formation process of the geological anomaly, improve the boundary resolution of the time-lapse resistivity method for the anomaly development process, and finally accurately obtain the development process of the anomaly.

[0060] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A time-lapse resistivity inversion imaging method based on time series gradient structure constraints, characterized in that: The specific steps are: Step 1: Determine the acquisition method and acquisition time: First determine the geological conditions of the abnormal body to be monitored, and design the corresponding time series electrical resistivity data acquisition method, and at the same time determine the acquisition time, a total of Tn, n = 1, 2, 3...n; Step 2: First inversion imaging: According to the acquisition method determined in step 1, the first apparent resistivity data is collected at time T1, recorded as dobs,1, and the electrical characteristics of the underground medium at time T1 are inverted using the collected apparent resistivity data. A separate electrical inversion imaging is performed on the collected apparent resistivity data dobs,1, specifically: The objective function for the apparent resistivity data dobs,1 at time T1 is: Among them, m T1 is the inversion imaging result at time T1, W d is the covariance matrix of the data fitting term, where d is the forward operator and d obs，1 is the apparent resistivity data collected at time T1, β is the regularization parameter of the model smoothing term, W m is the covariance matrix of the model smooth terms, m ref,1 It is the reference model of the smoothing term of the model inverted separately at time T1, which is the mean model; Through the above inversion, we can get the inversion imaging result m at time T1. T1 ; Step 3: Second inversion imaging: The second apparent resistivity data collection is performed at time T2, recorded as d obs,2 The electrical characteristics of the underground medium at time T2 are inverted using the collected apparent resistivity data. The Gauss-Newton iterative inversion method is used to invert the collected apparent resistivity data d obs,2 Perform inversion and introduce the inversion imaging result m at time T1 into the objective function during the inversion process T1 The gradient structure constraints are as follows: At time T2, the apparent resistivity data d obs,2 Introduce m T1 The objective function of the gradient structure constraint is: Among them, m T2 is the inversion imaging result at time T2, d obs，2 is the apparent resistivity data collected at time T2, m ref，2 is the reference model of the smoothing term of the model inverted separately at time T2, which is a mean model, λ is the gradient structure constraint regularization parameter, W g is the covariance matrix of the gradient structure constraint, is the gradient operator, Represents the gradient cross product result of the inversion imaging results at time T2 and T1; Through the above inversion, the inversion imaging result m at time T2 is obtained T2 ; Step 4: The third inversion imaging: The third apparent resistivity data collection is performed at time T3, recorded as d obs,3 The electrical characteristics of the underground medium at time T3 are inverted using the collected apparent resistivity data. The Gauss-Newton iterative inversion method is used to invert the collected apparent resistivity data d obs,3 Perform inversion and introduce the inversion imaging result m at time T2 into the objective function during the inversion process T2 The gradient structure constraints are as follows: At time T3, the apparent resistivity data d obs,3 Introduce m T2 The objective function of the gradient structure constraint is: Among them, m T3 is the inversion imaging result at time T3, d obs，3 is the apparent resistivity data at time T3, m ref，3 is the reference model of the smoothing term of the model inverted separately at time T3, which is a mean model. It represents the gradient cross product result of the inversion imaging results at time T3 and T2; Through the above inversion, the inversion imaging result m at time T3 is obtained T3 ; Step 5: Monitoring the development process of abnormal bodies: The apparent resistivity data collected at each acquisition moment are processed according to the processing process of step 4, so as to obtain the inversion imaging results at different moments, and the inversion imaging results at different moments are compared and analyzed, so as to realize the monitoring of the development process of abnormal bodies.

2. The time-lapse resistivity inversion imaging method based on time series gradient structure constraint according to claim 1, characterized in that: The abnormal body in step 1 is a water-conducting channel or a fault.

3. The time-lapse resistivity inversion imaging method based on time series gradient structure constraint according to claim 1, characterized in that: The time series electrical resistivity data acquisition method in step 1 is the Wenner running electrode method.

4. The time-lapse resistivity inversion imaging method based on time series gradient structure constraint according to claim 1, characterized in that: The intervals between the various collection moments in step 1 are the same.

5. The time-lapse resistivity inversion imaging method based on time series gradient structure constraint according to claim 1, characterized in that: The number of collection moments set in step 1 is at least 3.

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