Fault Detection in Coal Seam by the Channel Wave Method

Transcription

Fault Detection in Coal Seam by the Channel Wave Method
Scientific Journal of Earth Science
March 2014, Volume 4, Issue 1, PP.27-36
Fault Detection in Coal Seam by the Channel
Wave Method
Wei Wanga, Xing Gaoa#, Songying Lib, Yong Leb, Jiwen Tengc, Yuying Lia,d
a. The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy of Science, Beijing 100101, China
b. HenanYima Coal Group Co.,LTD, Yima 472300, China
c. Institute of Geology & Geophysics, CAS, Beijing 100029, China
d. Northeast Petroleum University, Daqing 163318, China
#Email:
[email protected]
Abstract
Secondary faults are typical structures in coal seams and commonly encountered during coalfield seismic exploration. Because of
the low accuracy of 3D seismic prospecting, secondary faults are partly omitted or positioned with large errors, which affects
safety of coal mining, sometimes even causes serious casualties. In order to improve the positioning accuracy of secondary faults
and to explore small abnormalities within the workface range, reflected channel wave observation was carried out in the 2505
working face of Yima mine, Henan province. There are 26 sources and 27 receivers in the profile with length of 390 m. The fault,
roadway and rock of coal seam were imaged by the Kirchhoff integral migration and common middle point post-stack migration,
and their reflection types were identified by the virtual source migration and attributes analysis. The structure and position of the
faults were revealed with a length of 300 m and a distance of 40 m or 70 m between survey line and the right beginning or left
end. Much rock mass in coal seam was delineated in right exploration area. The reflection channel wave observation is more
accurate to detect secondary faults than the 3D seismic prospecting, and so far is the most effective method for detecting small
abnormal body in coal face.
Keywords: Attributes Analysis; Channel Wave; Fault; Migration; Reflection
1 Introduction
Coal has long been taking dominant position in Chinese energy supply structure. However, safety production in coal
mining is always challenging. In China, most coal mines are located in areas where geological conditions are
complex. This includes areas of steeply dipping strata, coal seams which are deep, thin coal seams, coal seams of
variable thickness and so on, therefore it increases the difficulty of mining, and can easily cause serious disasters and
accidents.
FIG.1 STATISTICS OF COAL MINE ACCIDENTSWITHIN L NEARLY 11 YEARS.
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Nearly 11 years of coal mining accident statistics (Figure 1) shows that the number of deaths and accidents both have
declined year by year, but a high proportion of accidents still happens. In China, the mortality rate per million tons of
coal mining is 70 times higher than that in the US, 17 times than that in South Africa and 10 times compared to
Russia and India. During the 11 years, there have been totally 49574 deaths and 30128 accidents caused by coal
mine disasters.
The disasters in coal mines have a direct or indirect close relationship with structural fractures. These fracture
structures which mainly include joints and faults, are usually the areas where gas, stress and water accumulate, or are
where water is channeled simultaneously, and roof collapse. Therefore, structural fracture is the most critical factor
in the safe production of coal (Zhang, 2009).
In underground geophysical exploration methods, the detection range of direct current and its enhanced high
resolution direct current method is between 10 and100 m. The exploration distance of transient electromagnetic
exploration is generally not more than 150 m, however, the exploration width of a radio mine locator is less than 300
m. In addition, the exploration distance of Rayleigh waves and portable geological detectors is limited by energy
since hammering is normally used as a source. Nevertheless, the risk gradually increases with depth in coal mining
due to small faults, fracture zones and areas where water accumulates under pressure. With a focus on high yield and
more efficient production at working faces, the number of working faces with dip widths of more than 300 m is
likely to increase. Therefore, it is difficult to meet the demand both in production and advanced detection of danger
zones by virtue of current underground geophysical exploration methods.
In recent years, finding small geological structures in coal seams has been becoming possible due to the development
of channel wave detection technology. It is an effective seismic method to fix local and small structures and anomaly
in coal seam with high resolution and long distance exploration. Channel wave technology had successfully revealed
discontinuities within coal seams, such as thickness, small faults or fracture zones, dirt bed distribution, eroding belt,
under washing belt and rock wall ,etc.(Wilson, 1987)
The earliest application of channel wave exploration was carried out by Evison (1955) in a coal mine of New
Zealand. This research revealed the disperse characteristics of channel waves, and compared the field data dispersion
curve with the Love-wave theory. Evison also described the channel wave exploration technology from data
acquisition, data analysis and processing, to the final results explanation, and predicted the application prospects.
After comparing the properties of coal seams and the surrounding rock, Krey (1963) thought that coal seams can act
as a good waveguide for the propagation of seismic wave energy and he predicted the faults in coal seams that can be
detected by channel waves. And his further work set up the foundation for in-seam seismic exploration.
The channel wave is a guided wave propagated in a coal seam, similar to Rayleigh wave, or Love wave, or their
superposition. Ewing (1957), Brekhovskikh (1960), and Aki and Richards (1980) have demonstrated guided wave
theory in detail. In actual application, Rader et al. (1985) proposed using the matrix method, Haskell (1953)
preferred using the relationship with the Love-wave dispersion curve in the level and layer medium model, while
Kerner and Dresen (1985) calculated the dispersion curve in the presence of dirt beds and faults in a coal seam.
Korn and Stockl (1982), Edwards et al. (1985), and Asten et al. (1984) researched the reflection and scatter of
channel wave based on finite difference or finite element modeling, and concluded, in accordance with actual
observations, that high frequency SH wave has stronger reflection and scattering energy than the P-SV wave.
Channel wave dispersion is still a focus for many geophysicists (Booer et al., 1977 a,b, Beresford-Smith and Mason
(1980), Buchanan and Jackson (1983)), especially in frequency spectrum decomposition for recording signal, such as
multiple filtering, the purpose is to reduce the dispersion effect through the grouping of different signal frequencies,
therefore, most geophysicists used the delay summation method with narrow-bandwidth signals to image the faults
in coal seams (Buchanan et al., 1981; Buchanan, 1983, 1986; Krey, 1976; Mason et al., 1980; Mason, 1981).
In 1990s, in-seam seismic exploration technology was developed rapidly in China, especially in instrument research
and experimental works. Combined with their substantial field data, the researchers tried to classify the range of
applications of channel waves and the forming conditions in coal seam under different physical parameter of coal
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and surrounding rock.(Liu, 1997; Cheng, 1994;Pan and Liu, 1990; Yang, 2001; Liu et al., 1983; Song et al., 1994;
Song et al., 1996; Xu, et al., 1998; Feng et al., 1999; Yang et al., 2009; Yang et al., 2010; Cheng et al., 2009; Xu et
al., 2009).
Ge et al. (2007) adopted the simple elliptic mapping methods to locate the boundary of a void in a working face 46 m
away in the Harmony coal mine by the reflection channel wave method, and the error was just 4.6 ms. The success
factors were the exceptionally high quality data and wide frequency band range, which was mainly due to the stable
thickness of the coal seam presenting clear dispersion characteristics (the Airy phase was easily identified within a
frequency range mainly between 400 and 600 Hz and a stable average velocity of 975 m/s). The elliptical mapping
method regards the source and receiver points as focal points and calculates their distance by multiplying velocity
and travel time to obtain an ellipse, then the tangential line is the reflection boundary. The method is flexible enough
to accommodate irregular geometric survey system and is robust in data processing; however, it is only applicable to
a few abnormal bodies including stable thickness and the velocity of the coal seam. Green halgh et al. (2007), from
the point of view of numerical simulation, researched the amplitude variation with velocity ratio and density ratio of
surrounding rock and coal and S-wave incident angle at the location of a fault, and the forward scattering at the
‘washout’ zone or brecciated zone surrounding the fault.
Channel wave would be reflected when meets fault or other abnormal body, but further research is required to
distinguish reflection type of fault, roadway and rock mass. We modify the elliptical delay summation migration
through adding dipping and spreading factor, which can obtain more accurate imaging and meet with kirchh off
integral migration method. By using attribute analysis method which has been applied in oil seismic exploration, we
preliminarily distinguish the reflection type to improve the reliability of the explanation.
2 Theory and method
2.1 Theory
Similar to seismic exploration on the ground, the ahead of the channel wave detection is regarded as the underground
half infinite space Z in land seismic prospecting, and the roadway, in which sources and receivers are located, is
regarded as the free surface. According to the Huygens principle, every point of vibrating in a wave propagation
space can be regarded as a secondary original source, so the wave propagation space is made up by scattering points.
The Kirchhoff integral migration method based on the wave equation integral solution, makes use of Kirchhoff
diffraction integral formulas to converge diffraction energy from the same point and distribute it in different seismic
traces, and to map into the corresponding physics diffraction point. In Figure 2, the observation plane S1 receives the
diffracted wave field generated from point diffraction source S. Here, coordinate origin locates at R (0, 0, 0). The
following is Kirchhoff migration derived from the wave equation (Yilmaz, 2000):
P( x, y, z; t ) 
1
1 P cos
w cos
r

Pi
P)exp(i (t  ))ds1d 
  (
2
4  s1 r z
v
r
v
r
(1)
The first term of integral formulas is controlled by the vertical gradient of wave field; and the second is the near-field
source term; - these two terms are ignored in the seismic migration. Taking into account the time-variations and the
dispersion of channel, the channel wave migration formula is discretized into the following form:
cos 
U
Pn (tn ) exp(ic (1  )tn
c
n 1 rU
N
I  x, y   
2
(2)
Here, c is the center frequency of the channel wave; U is the group velocity and c is the phase velocity where
  c ; tn  (rn1  rn2 ) / U is the travel time of the No. n receiver trace and n  sm  rk ; Pn (t n ) is the amplitude of
the No. n trace and tn ; I  x, y  is the sum of the amplitude in the migration surface.
2.2 Exploration area introduction and working design
The in-seam seismic exploration working area is located in the 2505 working face of Yima, Henan province. In the
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working face, the coal seam thickness is between 0~8.54 m with average 3.0 m, and has a roof dip of 13°. The old
roof thickness is between 10~18 m and its lithology is feldspar quartz sandstone of medium grain size. The direct
bottom consists of carbonaceous mudstone and its thickness is between 1.0 and 3.6 m. The old bottom is fine grained
pink sandstone with thickness 20~30 m. The inferred fault DF8 is a normal fault, north-east strike, north-west
inclination, dip 70°and approximately 8 m offset. The new found fault of F1 is also a normal fault with a north-east
strike, north-west inclination, dip 75°, and offset is between approximately 20~28 m. By synthesizing a variety of
geological and geophysical data, the maximum water in flow was 10~20 m3 / h , while the normal inflow was 0~5
m3 / h ; the gas absolute emission rate was approximately 2.3 m3 / min ; the coal-dust explosion coefficient was
approximately 23.8%. In addition, it was revealed that the coal was not easily prone to spontaneous combustion and
classified as II conditions of combustion; the geothermal temperature was normal. The task was to locate the F1 fault
in the 2505 working face.
FIG.2 POINT DIFFRACTION GEOMETRIC SKETCH MAP FROM THE WAVE EQUATION BASED ON KIRCHHOFF INTEGRAL SOLUTIONS.
The working area map and channel wave survey system are shown in Figure 3. Because the up roadway is not
connected to the down roadway, the conditions were not suitable for transmission method observation and therefore,
the reflection method was used. Exploration holes are drilled in the down roadway of 2505 working face and the
exploration length is 390 m. The black points indicate the positions of the receivers, and there are a total of 27
receivers (G1-G27) spaced at 15 m intervals, 2 m depth, apertures of 55 mm (specially drilled), and parallel to the
seam and located centrally. The bottom of the hole was slightly higher than the top, and each hole was cleared with a
drill rod after drilling. The red points show the source positions, and there are a total of 26 sources (S1–S26) spaced
at15 m intervals, 2 m depth, with apertures matching the diameter of 200 g explosives, parallel to the seam and
located centrally. After loading the explosives, the holes were sealed with plaster in order to reduce energy leakage.
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FIG.3 THE RED LINES DESIGNATETHE FUTURE ADVANCEMENT OF THE TUNNEL, THE BLACK SOLID LINES INDICATE THEROADWAY, WITHIN
THEROADWAY THE GREEN COLORED AREA SHOWSTHE EXPOSED ROCK, THE NUMBERSOF THE ROADWAY INDICATETHE ELEVATION
COORDINATES, WITHIN THE ROADWAY THE RED POINTSSHOWTHE SOURCES, THE BLACK POINTS SHOWTHE RECEIVERS, THE RED DASHED
LINESINDICATE THE INFERRED FAULTS, ANDDOT-LINE-CIRCLESDENOTEPOSITIONS OF DRILLING HOLE (BOTTOM-DISTANCE-OPENING).
3 Results and discussion
3.1 Migration
The travel time from virtual source to all receivers replaces the time-termin migration formula (2), and virtual source
migration is implemented in single shot gather to obtain virtual source position. Finally, the strait fault and roadway
position were inferred from the perpendicular bisector of the virtual source and the receivers (in figure 4).
FIGURE 4 SHOWS THE VIRTUAL SOURCE RADIAL DELAY SUMMATION MIGRATION IMAGE FROM A SINGLE SHOT GATHER. THE BLACK LINES
SHOW THE CONNECTIONS FROM THE SOURCE TO THE VIRTUAL SOURCE, THE RED LINES SHOW THE RAYS FROM THE VIRTUAL SOURCE TO
RECEIVER POINTS, AND THE BLUE LINES SHOW THE REFLECTIVE SURFACE POSITION FROM THE ROADWAY, FAULT OR ROCK. (A) THE NO.5 SHOT
GATHER MIGRATION IMAGING, (B) THE NO.6 SHOT GATHER MIGRATION IMAGING, (C) THE NO.10 SHOT GATHER MIGRATION IMAGING, AND (D)
THE NO.11 SHOT GATHER MIGRATION IMAGING.
The virtual source of Kirchhoff’s integral migration method cannot be distinguished fromt he real source and is
applied for straight reflecting interfaces only, therefore it images poorly on curve reflection interfaces. The migration
results show that the errors of roadway, fault and rock are less 5 m (coordinate origin is in G01).
The migration formula (2) was used to image the migration for all effective shot gather data, and the result is shown
in Figure 5b, and the coordinate origin is the No.27 receiver (G27). From the figure, the rock and fault boundary are
clearly shown. Figure 5a is a common reflection point stack migration profile, and this method does not obtain the
correct image for the interface of flank rock.
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FIG.5 (A) CONSTANT VELOCITY SCAN STACK AND MIGRATION RESULT.(B) KIRCHHOFF MIGRATION RESULT.
3.2Attributes analysis
FIG. 6 THREE INSTANTANEOUS ATTRIBUTESANALYSIS FROMTHE NO.10 SOURCE AND NO.23 RECEIVER DATA; THE ABNORMAL ATTRIBUTE
NEAR 240MS INDICATES THE SMALL FAULT POSITION, AND BETWEEN 650 AND 660MSTHE ABNORMAL ATTRIBUTE INDICATES SURROUNDING
ROCK REFLECTION INFORMATION.
Three instantaneous attributes of seismic waves consist of Instantaneous Amplitude (IA), Instantaneous Frequency
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(IF) and Instantaneous Phase (IP), and these attributes mainly utilize the Hilbert transformation signal processing
method. IA measures the reflection intensity and mainly shows energy variation and highlights the special changes in
rock properties. The IP is a measurement of the phase continuation in aseismic section, no matter how strong or
weak in energy, it can clearly display the phase. The IF is the time gradient, and shows the variation in rock
composition. Figure 6 shows information on the three instantaneous attributes from the No.10 source and No. 23
receiver. Near 240 ms, the three instantaneous attributes undergo significant change, indicating the reflection
informationsent from fault. Between 650 and 660 ms, the IP greatly changes with significant discontinuities shown
in the reflection information brought from the flank rock interface. Figure 7 shows the three instantaneous attribute
information from No.5 source and No. 27 receiver. Near 540 ms and 660 ms, the variations of the three
instantaneous attributes IP and IF from the faultare similar to that from the roadway, but the IA from the roadway is
smaller than that from the fault because the reflection wave of the roadway travels longer and the energy is greatly
absorbed.
FIG. 7 THREE INSTANTANEOUS ATTRIBUTES ANALYSIS FROM THE NO.5 SOURCE AND NO.27 RECEIVER DATA, THE ABNORMAL ATTRIBUTES
NEAR 540 AND 660MS INDICATE ROADWAY REFLECTION INFORMATION.
3.3 Comprehensive explanation
The migration imaging, attribute analysis and geological information revealed in the roadway, have been used to
obtain a comprehensive explanation for exploration area, as shown in Figure 8. The blue area is referred to as the
safe mining range, the yellow area indicates rocks, and the red area indicates faults.
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FIG.8.THE RED LINE SHADOW DENOTES THE EFFECTIVE REFLECTION DETECTION AREA, THE BLACK LINE SHOWS THE REFLECTION INTERFACES
ON THE ROADWAY, THE YELLOW AREA INDICATEROCKS OR FAULTS, THE BLUE AREA INDICATES THE SAFE MINING RANGE.
4 Conclusions
1) Each imaging method has its respective advantages, disadvantages and applicability. For example, the virtual
source of Kirchhoff integral migration method cannot distinguish between the real source and the virtual source and
only works well for the straight reflecting interface; and the other has low resolution because it ignores the waveform
conversion information.
2) Three instantaneous attributes obviously change in the same position, which indicates variation in physical
properties; thereby we can infer the reflected type.
3) The migration error of the fault and rock is within ±5 m based on the mining checks.
4) There are three differences in data processing between in-seam seismic exploration and conventional ground
seismic prospecting: the first is that the angle of geological target body and survey line is highly arbitrary or random,
such as it is nearly 90° between side rock reflective surface and measuring line; the second is that signal-tonoiseratio (SNR) is not improved through the high folds stack methods because data acquisition in the complex
environment is poor, or due to limited space of few roadways; the third is that constant velocity scanning stack
causes low resolution following envelope calculation for the channel wave signal.
Because the reflective wave generated by a fault has weak energy and every imaging method has its pros and cons,
the fault is often marked at the wrong position. The reflection channel wave observation is more accurate to detect
secondary faults than the 3D seismic prospecting, and is the most effective method in detecting small abnormal body
in coal face.
Acknowledgement
The research was supported by the National Natural Science Foundation of China (Grant No. 41130419, 41240027,
41374061, 41204048) and China Postdoctoral Science Foundation (Grant No. 2012M510533).
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