A method for detecting wind-oxidized zones in coal seams based on dense array background noise imaging

Abstract

The present invention discloses a method for detecting coal seam wind oxidation zones based on dense array background noise imaging. First, the geological information of the mining area is investigated, and a dense array observation system is designed according to the coal seam occurrence state and mining plan of the mining area, and the background noise data for about one week is recorded; secondly, the underground S wave velocity structure is obtained based on the surface wave dispersion inversion; finally, the lower limit of the coal seam wind oxidation zone is determined in combination with the calibration result of the logging curve. This method reconstructs the seismic surface wave signal that can reflect the underground structure from the background noise data, and then obtains the underground S wave velocity structure by inverting the surface wave dispersion curve, and combines the calibration of the logging curve to realize the detection of the coal seam wind oxidation zone. This method has the advantages of high resolution, short construction period, low cost, and environmental friendliness.

Description

Technical Field

[0001] The invention relates to the technical field of background noise imaging and coal seam wind oxidation zone detection, and in particular to a coal seam wind oxidation zone detection method based on dense array background noise imaging. Background Art

[0002] Due to crustal movement, coal seams buried deep in the crust are exposed to the surface or buried in the shallow crust. Due to temperature changes, the interaction of gas, water and biological activities, various inherent physical and chemical characteristics of coal seams may change, resulting in weathering and oxidation zones. The weathering and oxidation zones of coal seams are generally developed in outcrops or shallow buried areas. Due to the influence of various factors such as topography, rock properties, structure, and hydrogeological conditions, the depth of weathering and oxidation zones is also different. Wind oxidation will cause the development of cracks in coal rock strata and increase the porosity of rock mass. Its geophysical characteristics are manifested as reduced resistivity, reduced seismic wave velocity, and enhanced dispersion attenuation. The weathering and oxidation of coal seams greatly affects the effective mining of coal seams, resulting in direct economic losses and waste of resources. It may even lead to spontaneous combustion of coal seams, gas and coal dust explosions, smoke poisoning or casualties.

[0003] At present, the exploration methods for coal seam wind oxidation zones at home and abroad are relatively simple, relying only on sparse drilling projects to delineate their scope, which relies heavily on experience, resulting in inaccurate delineation of the scope of the wind oxidation zone, ultimately reducing the economic benefits of coal mines and even affecting mining safety. At present, in practical applications, the main ground geophysical methods are transient electromagnetic method and three-dimensional seismic method. The transient electromagnetic method delineates the coal seam wind oxidation zone based on the low-resistance anomaly area, but the characteristics are not obvious and the resolution is low. Three-dimensional seismic delineates the coal seam wind oxidation zone based on the change of wave impedance, with obvious characteristics and high resolution, but it requires artificial seismic sources, long construction period, large engineering volume and high cost.

[0004] Background noise imaging is a new passive source seismic imaging method that can reconstruct seismic surface wave signals from background noise data using a cross-correlation algorithm, and then image the underground S-wave velocity structure through surface wave dispersion inversion. In recent years, with the emergence of dense array observation technology, higher-frequency surface wave signals can be extracted through background noise cross-correlation to study the fine velocity structure of the shallow crust. Its application range has expanded from the lithosphere scale of hundreds of kilometers to the mine scale of tens or hundreds of meters in the shallow crust. This method does not require artificial seismic sources and has the characteristics of short construction period, low cost, and environmental friendliness. However, this method has not yet been applied to the detection of coal seam wind oxidation zones. Summary of the invention

[0005] In view of the technical difficulties and needs of coal seam wind oxidation zone detection, the present invention provides a coal seam wind oxidation zone detection method based on dense array background noise imaging. The method reconstructs seismic surface wave signals that can reflect underground structures from background noise data, and combines well logging information calibration to achieve the detection of coal seam wind oxidation zones. The method has the characteristics of high resolution, short construction period, low cost, and environmental friendliness.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present invention is as follows: a method for detecting coal seam wind oxidation zone based on dense array background noise imaging. First, the geological information of the mining area is investigated, and a dense array observation system is designed according to the coal seam occurrence status and mining plan of the mining area, and the background noise data is recorded; secondly, the underground S-wave velocity structure is obtained based on surface wave dispersion inversion; finally, the lower limit of the coal seam wind oxidation zone is determined in combination with the calibration results of the logging curve.

[0007] The specific steps include:

[0008] Step 1: Determine the detection area and design a dense array observation system based on the geological information and mining plan of the mining area;

[0009] Step 2: Conduct field construction, deploy short-period three-component seismic detectors, form a dense array observation system, and record background noise data for about a week;

[0010] Step 3, preprocessing the background noise data;

[0011] Step 4: reconstruct the surface wave data from the preprocessed background noise data based on the cross-correlation algorithm, perform surface wave dispersion inversion, and obtain the underground S-wave velocity structure;

[0012] Step 5: Collect the existing logging data in the mining area, and statistically analyze the logging curve characteristics of the wind-oxidized coal seams and normal coal seams according to the mining history to obtain the logging curve calibration results;

[0013] Step 6, based on the underground S-wave velocity structure obtained in step 4 and combined with the calibration result of the well logging curve obtained in step 5, the lower limit of the wind oxidation zone of the target coal seam is given.

[0014] Preferably, the preprocessing in step 3 includes: removing instrument response, removing mean, removing trend, bandpass filtering, data truncation, time domain regularization and spectrum equalization.

[0015] Preferably, the surface wave dispersion inversion in step 4 includes the steps of surface wave dispersion curve extraction and dispersion curve inversion.

[0016] Preferably, the logging curves in step 5 include lateral resistivity, density, natural gamma, short-distance gamma, natural potential and well diameter.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. The present invention proposes a coal seam wind oxidation zone detection method based on dense array background noise imaging. The method reconstructs the seismic surface wave signal that can reflect the underground structure from the background noise data, and then obtains the underground S wave velocity structure through the surface wave dispersion curve inversion, and combines the logging curve calibration to realize the detection of the coal seam wind oxidation zone. The method has the advantages of high resolution, short construction period, low cost, and environmental friendliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the implementation of the present invention.

[0020] Figure 2 It is a schematic diagram of the dense array observation system and coal seam wind oxidation zone in the exploration area.

[0021] Figure 3 It is a schematic cross-sectional diagram of the inversion results of the surface wave dispersion curve.

[0022] Figure 4 It is a schematic diagram of the logging curve.

[0023] Figure 5 It is a schematic cross-section diagram of the wind-oxidized zone of the coal seam in the exploration area. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] This embodiment describes a method for detecting coal seam wind oxidation zones based on dense array background noise imaging. The workflow is as follows: Figure 1 As shown, it includes the following main steps:

[0026] The first step is to determine the detection area and design a dense array observation system based on the geological data and mining plan of the work area. Figure 2 This is a schematic diagram of the observation system, and the points are shown as red dots in the figure. The observation system parameters are determined according to the depth of the exploration target. Taking the exploration depth of 100-300m as an example, the point spacing can be set to 10m and the line spacing to 100m. Figure 2 The medium to dark green line is the coal seam outcrop line detected based on geological data and previous exploration work.

[0027] The second step is to carry out field construction, deploy short-period three-component seismic detectors to form a dense array observation system, and then continuously record seismic background noise data for several days (for example, 7 days).

[0028] The third step is to complete the data preprocessing according to the method given by Bensen et al., (2007), including removing instrument response, removing mean, removing trend, bandpass filtering, data truncation, time domain regularization and spectrum equalization.

[0029] The fourth step is to complete the underground S-wave structure imaging based on the pre-processed data in the third step using the surface wave dispersion inversion method. The surface wave dispersion inversion mainly includes key steps such as surface wave dispersion curve extraction and dispersion curve inversion. Finally, the three-dimensional S-wave velocity structure below the work area is obtained after interpolation. The schematic diagram of the S-wave velocity profile is shown in Figure 3 shown.

[0030] Here, we take the spatial autocorrelation method as an example. The spatial autocorrelation between stations A and B with a distance r can be expressed in the form of a Bessel function, where |F(ω)| 2 is the average spectral density of the wave field:

[0031]

[0032] The Green function of the Rayleigh surface wave in the frequency domain can be written in the form of Neumann function (Y0) and Bessel function

[0033]

[0034] Y0 and J0 can be transformed by Hilbert transformation, so under the condition of equal energy distribution, we have

[0035]

[0036] Since the seismic background noise is not white noise, the spectral coherence of the seismic background waveform needs to be normalized, that is,

[0037]

[0038] Re[γ AB ]=J0(kr) (5)

[0039] If the seismic background noise source is uniform, then γ AB The imaginary part is equal to 0. This condition can be met by spatial and temporal averaging of stations in different azimuths during micromotion array observations. In summary, the autocorrelation calculation of continuous waveforms between micromotion arrays is performed in the frequency domain, and the waveform of the real part obtained corresponds to the zero-order Bessel function. After obtaining the autocorrelation function between stations, the dispersion information of Rayleigh surface wave phase velocity at different frequencies can be obtained according to the following formula:

[0040]

[0041] f is the frequency, r is the distance between stations, C is the Rayleigh surface wave phase velocity, and k is the wave number. By fitting the zero-order Bessel function or determining the zero poles, the Rayleigh wave phase velocity at different periods can be determined. By averaging the space and time between different arrays, a reliable Rayleigh surface wave phase velocity dispersion curve under the array can be obtained. Then a genetic algorithm is used to complete the inversion of the dispersion curve.

[0042] The fifth step is to collect logging data of the work area. The point diagram is as follows: Figure 2 As shown in the black circle in the middle. The logging curves are extracted, mainly including lateral resistivity, density, natural gamma, short source distance gamma, natural potential and well diameter, etc., to complete the digitization of the logging curves. Then the Geolog software is imported to perform statistical analysis of the logging curves. According to previous mining experience, the logging curve characteristics of normal coal and wind-oxidized coal are statistically analyzed. Finally, the characteristic extraction results of coal seam wind oxidation are obtained, which are used to calibrate the coal seam wind oxidation range on the velocity structure. The schematic diagram of the logging curve is shown in Figure 4 shown.

[0043] The sixth step is to integrate the dense array background noise imaging results and the logging curve calibration results, including the S-wave velocity structure in the fourth step and the characteristic extraction results of coal seam wind oxidation in the fifth step, and jointly delineate the lower limit of the coal seam wind oxidation zone (such as Figure 2 and Figure 5 (shown by the black dashed line).

[0044] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for detecting wind-oxidized zones in coal seams based on dense array background noise imaging, characterized in that: The following steps are involved: Step 1: Determine the detection area and design a dense array observation system based on the geological information and mining plan of the mining area; Step 2: Conduct field construction, deploy short-period three-component seismic detectors, form a dense array observation system, and record background noise data for one week; Step 3, preprocessing the background noise data; Step 4: reconstruct the surface wave data from the preprocessed background noise data based on the cross-correlation algorithm, perform surface wave dispersion inversion, and obtain the underground S-wave velocity structure; Step 5: Collect the existing logging data in the mining area, and statistically analyze the logging curve characteristics of the wind-oxidized coal seams and normal coal seams according to the mining history to obtain the logging curve calibration results; Step 6, based on the underground S-wave velocity structure obtained in step 4 and combined with the calibration result of the well logging curve obtained in step 5, the lower limit of the wind oxidation zone of the target coal seam is given.

2. The method for detecting coal seam wind oxidation zones based on dense array background noise imaging according to claim 1 is characterized in that: The preprocessing described in step 3 includes: removing instrument response, removing mean, removing trend, bandpass filtering, data truncation, time domain regularization and spectrum equalization.

3. The method for detecting coal seam wind oxidation zones based on dense array background noise imaging according to claim 1 is characterized in that: The surface wave dispersion inversion in step 4 includes the steps of surface wave dispersion curve extraction and dispersion curve inversion.

4. The method for detecting coal seam wind oxidation zones based on dense array background noise imaging according to claim 1 is characterized in that: The logging curves in step 5 include density, lateral resistivity, natural gamma, short-distance gamma, natural potential and well diameter.

Images