High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China

Gong, Meng; Lv, Jian; Zhang, **ngmian; Zheng, Yong; Chen, Hao; Dong, Jun; Zha, **aohui; Li, Zheng; Sheng, Shuzhong; Wang, Tongli

doi:10.1186/s40623-023-01882-9

High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China

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Published: 28 August 2023

Volume 75, article number 129, (2023)
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High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China

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Meng Gong^1,2,4,
Jian Lv ORCID: orcid.org/0000-0003-0961-946X³,
**ngmian Zhang⁴,
Yong Zheng⁵,
Hao Chen³,
Jun Dong³,
**aohui Zha³,
Zheng Li³,
Shuzhong Sheng⁴ &
…
Tongli Wang⁶

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Abstract

High-resolution seismic image is critically important for mining minerals. In this study, we collected seismic data from a local dense seismic array consisting of 154 stations around the Anyuan mining area and its adjacent region of **xiang City, Jiangxi Province in South China, and applied the ambient noise tomography (ANT) method to image the shear wave velocity structure in the study area. Shallow crustal velocities at depths less than 3.3 km were determined by direct inversion of Rayleigh wave group velocity dispersion curves at the period range of 0.5–5.0 s. Overall, the S-wave velocity structure has a tight correlation with surface geological and tectonic features in the study area. The shear wave velocity structure in the shallow crust of the Anyuan Mine and its adjacent areas displayed distinct low-velocity anomalies, which can be attributed to the depression of sedimentary structures and coal mining activities in the **xiang-Le** region. The zones surrounding the Anyuan fault (AYF) and Wangkeng fault (WKF) zones exhibited low-velocity anomalies from the ground surface to ~ 3.3 km underground. And the low-velocity anomalies at depths less than 1.2 km could be related to the sedimentary environment of coal mine and the coal mining activities, while the low-velocity anomalies at depths below 1.2 km are caused by the presence of fracture medium, oil and gas in the fault zone. The shear wave velocity changes sharply across the AYF, and the characteristics of the velocity change interface indicate that the AYF is inclined toward the northwest, with its extension reaching depths of approximately 3 km underground.

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Introduction

In recent decades, the ambient noise tomography (ANT) method develops quickly to image the crustal velocity structure. The basic idea of this method is to extract Empirical Green Functions (EGFs) from cross-correlations of ambient noise and coda waves between station pairs with sufficiently long records (Campillo and Paul 2003; Shapiro and Campillo 2004; Sabra et al. 2005). Based on the time reverse theory, each station can be set as a virtual source or a signal-receiving station (Cassereau et al. 1992). Therefore, this method is no longer limited by the temporal and spatial distribution of seismic events, and it is very suitable for deciphering precise crust and mantle velocity structures in low seismicity regions (Li et al. 2016a; Zhang et al. 2016; Liu et al. 2018). Benefiting from this advantage, the ANT has been widely used to image crustal and upper-mantle velocity structures at various scales (Sabra et al. 2005; Yao et al. 2006, 2008; Yang et al. 2007; Lin et al. 2007, 2008; Bensen et al. 2007, 2008; Zheng et al. 1), which is a key coal production base of Jiangxi Province with a mining history of over a century. The **xiang-Le** depression is a large depression oriented in the NEE direction, bounded by deep faults on the north and south sides, and the late Paleozoic and Mesozoic strata are widely exposed. The depression exhibits a variety of facies, including marine, continental, and transitional, from the Devonian to the Paleogene. Several sets of rock source strata, primarily composed of black shale, have formed in the depression, such as Middle Permian (** Formation), and Upper Triassic (Anyuan Formation) (Li et al. 2013). The red rectangle in the bottom map corresponding the study area as shown in Fig. 2a and the black lines denote the faults

Full size image

Currently, there are no dangerous rock, collapse, landslide, and other adverse geological phenomena in the mining area (Liu et al. 2021). However, due to the problems of resource exhaustion, gas leakage, ground fissures, ground collapse, and mine earthquake in century-old mine, it is particularly urgent to understand the 3-D structure of the shallow crust of the Anyuan Mining area and its adjacent region. Although Li et al. (2015) have studied the 3-D crustal velocity structure of Jiangxi Province, the detailed velocity structure of the shallow crust around the Anyuan mining area needs further investigation.

Therefore, it is essential to develop a high-resolution image of the shallow structure in the Anyuan mine region is of critical importance. In this study, we apply the direct surface-wave tomography method (Fang et al. 2015) to image the 3-D shear wave velocity structure of the shallow crust in Anyuan mining area and its adjacent region based on a local dense array, and our results could provide a significant scientific basis for coal mine resources exploring and seismic structure modeling.

Data and method

Data collection and processing procedure

The vertical component continuous data from 154 seismic stations around the Anyuan mining area (Fig. 2a) deployed by East China University of Technology and the Jiangxi earthquake Agency between 27 July 2020 and 31 August 2020 are used in this study. This temporary dense seismic array consists of 13 broadband seismographs of GL-PCS60 type (the blue triangles in Fig. 2a), 98 short-period seismographs of EPS type (the red triangles in Fig. 2a), and 43 short-period seismographs of QS-05A type (the yellow triangles in Fig. 2a). The seismic array spans an area of about 18 km × 18 km with an average station spacing of about 1 km in the center area and the minimum station spacing is ~ 0.5 km. The collected data sets are recorded with a simple rate of 100 Hz.

The data processing procedure follows the methodology proposed by Bensen et al. (2007). Seismograms are first cut into daily segments. After the removal of the mean, trend, and instrument response, a 0.5–10 s band-pass filter is applied. To reduce the effect of earthquakes and instrumental irregularities on cross-correlations, the seismograms are normalized using a time–frequency normalization method (Ekström et al. 2009; Shen et al. 2012). Then daily cross-correlations for each station pair are calculated and these are stacked using the normalized linear stacking method to obtain cross-correlation functions (CCFs). Finally, the empirical green functions (EGFs) are derived from the time derivative of the CCFs (Sabra et al. 2005; Yao et al. 2006).

Theoretically, surface wave signals should appear symmetrically on both the causal and acausal time lags of the EGFs. However, the uneven and varied spatial distribution of noise sources can affect the EGFs and make the signal emerge on the positive and negative time lags asymmetric (Stehly et al. 2007; Zhan and Clayton 2010). Therefore, to enhance the signal-to-noise ratio (SNR), the EGFs are reversed and stacked to create a symmetric component before the dispersion measurement extraction (Yang et al. 2007; Yao et al. 2011). Figure 3a illustrates the path distribution of the station pairs between the L124 station and other stations. Figure 3b displays the CCFs of station pairs with 0.5–5 s band-pass filtered and aligned by the station-pair distance. The fundamental order signals of the Rayleigh wave were clearly observed, and the surface wave group velocity ranges from approximately 1.7 to 3.6 km/s (Fig. 3b).

Surface wave dispersion curve extraction

In general, when using the ANT method to image the surface wave velocity structure over a long time period, the inter-station distance of at least three wavelengths is required to obtain reliable frequency dispersion curves (Yao et al. 2006; Zheng et al. 2008). Yao et al. (2011) and Luo et al. (2015) demonstrated that dense seismic arrays can yield accurate dispersion curves in short-period surface wave inversion, even when the inter-station distance is only one times the wavelengths. So, we use multiple filtering techniques (Dziewonski et al. 1969; Yao et al. 2011) to extract the high-quality dispersion data from the EGFs with inter-station distances greater than one wavelength and SNR > 5. The SNR was defined as the ratio of the maximum amplitude of the signal window of EGFs (− 15 to ~ 15 s) and the average absolute amplitude of the noise window (− 30 to ~ − 15 s and 15 to ~ 30 s).

Furthermore, we removed the measurements that exceed the average value of group velocity at each period plus or minus 0.5 km/s and eliminated the data points with too high or negative slopes. Ultimately, we retained 4538 high-quality group velocity dispersion curves out of the 11,781 theoretically available dispersion curves from 154 seismic stations (Fig. 4). Figure 4 illustrates the variation of surface wave group velocity ranging from approximately 1.8 to 3 km/s, with the majority of periods distributed between 0.6 and 3.5 s.

Shear wave velocity structure inversion

We use the direct surface wave tomography method (Fang et al. 2015) to directly invert the mixed path Rayleigh wave group velocity dispersion data into a 3-D velocity structure. This method first assumes an initial 3-D velocity model and then performs surface wave ray tracing at each period using a fast marching method (Rawlinson and Sambridge 2004) and iteratively update the sensitivity kernels of period-dependent dispersion measurements to updates of the 3-D velocity model. To mitigate the impact of the uneven path coverage, a wavelet-based sparsity-constrained seismic tomography method (Simons et al. 2011; Fang and Zhang 2014) is employed in the 3-D wave velocity inversion.

As is well-known, the essence of inversion is to find a model m that minimizes the differences $\updelta {\mathrm{t}}_{\mathrm{i}}(\upomega )$ between the observed arrival times ${t}_{i}^{obs}(\omega )$ and theoretical arrival time ${t}_{i}(\omega )$ for all frequencies $\omega$. The travel-time perturbation for the path i at frequency $\omega$ is expressed by Fang et al. (2015) as Eq. (1):

$$\delta t_{i} (\omega ) = t_{i}^{{{\text{obs}}}} (\omega ) - t_{i} (\omega ) = - \sum\limits_{{k = {1}}}^{K} {v_{ik} \frac{{\delta C_{k} (\omega )}}{{C_{k}^{2} (\omega )}}} ,$$

(1)

where ${t}_{i}(\omega )$ denote the calculated travel-time using a reference model that can be updated in the inversion, and ${C}_{k}$($\upomega$) and $\delta {C}_{k}(\omega )$ present the phase velocity and its perturbation of the kth grid node at the angular frequency $\omega$, respectively. $v_{ik}$ is the bilinear interpolation coefficients along the ray-path associated with the ith travel-time data.

Generally speaking, surface wave dispersion is most sensitive to shear wave velocity ($\beta$), but short-period Rayleigh wave dispersion is also very sensitive to compressional wave velocity ($\alpha$) in shallow crust. Therefore, when we use the empirical formula (Brocher 2005) to relate the compressional wave velocity and mass density ($\rho$) to the shear wave velocity, Eq. (1) can be rewritten to Eq. (2) as Fang et al. (2015) described:

$$\delta t_{i} (w) = \sum\limits_{k = 1}^{K} {( - \frac{{v_{ik} }}{{C_{k}^{2} (\omega )}})} \sum\limits_{j = 1}^{J} {[R_{\alpha } (Z_{j} )\frac{{\partial C_{k} (\omega )}}{{\partial \alpha_{k} (z_{j} )}} + R_{\rho } (z_{j} )\frac{{\partial C_{k} (\omega )}}{{\partial \rho_{k} (z_{j} )}} + \frac{{\partial C_{k} (\omega )}}{{\partial \beta_{k} (z_{j} )}}]} \left| {_{{\Theta_{k} }} \delta \beta_{k} } \right.(z_{j} ) = \sum\limits_{l = 1}^{M} {G_{il} m_{l} } ,$$

(2)

where $\Theta_{k}$ present the 1-D initial model at the kth surface grid point on the surface and ${\alpha }_{k}({z}_{j})$, ${\beta }_{k}({z}_{j})$, and ${\rho }_{k}({z}_{j})$ are the compression wave velocity, shear wave velocity, and mass density at the jth depth grid node, respectively. ${\mathrm{R}}_{\alpha }$ and ${\mathrm{R}}_{\beta }$ present the scaling factor, and J is the number of grid points in the depth direction. The total grid points number of the 3-D model is $M = KJ$. Thus, Eq. (2) can be written in the matrix form as Eq. (3):

$${\text{d = }}\,{\text{Gm,}}$$

(3)

where d is the travel-time residual, G is the data sensitivity matrix, and M is the model parameter. Equation (3) can be solved by minimizing the following loss function:

$$\Phi (m) = \left\| {d - Gm} \right\|_{2}^{2} + \lambda \left\| {Lm} \right\|_{2}^{2} ,$$

(4)

where the first term on the right-hand side gives the ${l}_{2}$-norm data misfit and the second term denotes the ${l}_{2}$-norm model regularization term. L is the model smoothing factor and λ is the weight factor of data fitting and model regularization (Simons et al. 2002; Aster et al. 2013). Thus, the solution m can be deduced from following equation (Paige and Saunders 1982a, 1982b):

$$\tilde{m} = (G^{T} G + \lambda L^{T} L)^{ - 1} G^{T} d.$$

(5)

To obtain a robust initial model, two methods are used to determine the 1-D mean velocity model. The first one is based on empirical formulas (Shearer et al. 2009; Fang et al. 2015), which involved using 1.1 times the average measured group velocities at a depth of 1/3 times the wavelength as the velocity model (Fig. 4a, hereafter, called AVG model). Subsequently, the AVG model is used as the input model and the iteratively damped least square method developed by Herrmann and Ammon (2002) in the CPS330 package (Herrmann 2013) is applied to obtain the other 1-D velocity model (Fig. 4b, hereafter, called CPS model). After that, the AVG and CPS models are used as the initial models for the direct 3-D surface wave inversion program to calculate the 1-D mean shear wave velocity model, respectively (Fig. 5).

Despite the significant differences between the two input models (AVG and GPS models), the inversion results were found to be essentially the same (Fig. 5a, b). This suggests that the two velocity models have minimal impact on the inversion results in this study. Previous studies by Yu et al. (2020) and ** Depression (advised from Li et al. 2003; Qiu and Li 2019). Therefore, the low-velocity anomalies observed at depths below 2 km around the AYF and WKF regions may indicate the presence of oil and gas between the fault zones.

Figure 12 shows the positions of the four sections and their corresponding shear wave velocity profiles. The AA’ profile shows that the basin areas to the west of the AYF exhibit low-velocity anomalies, and the depths of these anomalies are negatively correlated with the surface elevation. Along the profiles AA' and CC', there is a sharp change in the shear wave velocity structure across the AYF zone, with the velocity transition interface inclined towards the northwest, consistent with the trend of the AYF (Tan et al. 2001; Liu et al. 2004). The velocity transition interface reaches depths of approximately 3 km underground, which may indicate the extension depth of the AYF. The BB' and CC' profiles show a noticeable low-velocity anomaly on the northwest side of the AYF, possibly caused by the presence of sediment and debris in the piedmont floodplain (Tan et al. 2001; Li et al. 2016c), which significantly reduces the propagation speed of shear waves.

Along the AA’, BB’, CC' and DD’ profiles, it can be seen that the shear wave velocity structure in the shallow surface layer (< 1.2 km) presents low-velocity anomalies, which could be related to the sedimentary environment of the coal seam. ** et al. (1999) and Li et al. (2003) have suggested that the Anyuan mining area and its adjacent region are primarily composed of sea–land interaction deposition, with a maximum deposition thickness of up to 1.3 km. We observed that the low-velocity anomalies in the shallow surface layer (< 1.2 km) in the Baiyuan, Anyuan, and Gaokeng mine areas are particularly significant, which may be caused by coal mining activities. Because the presence of coalmine gob area and coalbed methane production during coal mining can slow down the shear wave velocity. Additionally, the low-velocity anomalies observed around the AYF and WKF zones extend to depths of approximately 3 km (Figs. 11 and 12), which could be associated with fracture media and the presence of oil and gas in the fault zone. The top coal mining area is often composed of swamp facies or tidal flat facies mudstone and silty mudstone, which has a strong sealing ability for coal reservoirs (Qiu and Li 2019). Consequently, the oil and gas produced during coal mining could migrate into the faults and gaps between the fault zones, resulting in low-velocity anomalies in the shear wave.

Conclusion

We collected about 1 month of seismic data recorded by a local dense array and applied direct surface-wave tomography to image the high-resolution shallow crustal shear wave structure around the Anyuan mining area and its adjacent region of **xiang City, Jiangxi Province in South China. The results show that the basins and piedmont basins areas presented low-velocity anomalies, while the uplifted mountainous areas without coal mining exhibited high-velocity anomalies. Influenced by the sedimentary structural depressions and coal mining activities, the mining areas exhibited significant low-velocity anomalies in the shallow surface (< 1.2 km). Specifically, the AYF and WKF zones exhibited low-velocity anomalies from the ground surface to a depth of ~ 3 km underground. The low-velocity anomalies at depths less than 1.2 km could be related to the sedimentary environment of coal mines and the coal mining activities, while the low-velocity anomalies at depths below 1.2 km were caused by the presence of fracture medium, oil and gas in the faults zone. Furthermore, the result also revealed the AYF inclined towards the northwest and extended to a depth of ~ 3 km underground. In summary, our results agreed well with the topography and regional geological structure of the study areas, which could reveal information on the coal seam distribution, coal mining activity, and fault geometry to a certain extent.

Availability of data and materials

The datasets and code used during the current study are available from the corresponding authors on reasonable request.

Abbreviations

ANT:: Ambient noise tomography
AYF:: Anyuan fault
WKF:: Wangkeng fault
LJCF:: Luojiachong fault
CCFs:: Cross-correlation functions
EGFs:: Empirical green functions
SNR:: Signal-to-noise
STD:: Standard deviation of residual error

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Acknowledgements

Most figures are made with Generic Map** Tools (GMT) (Wessel and Smith 1995). The authors would like to thank Yao Huajian Scientific Research Group for providing the direct surface inversion program (Fang et al. 2015).

Funding

This research is supported by open Research Project from State Key Laboratory of Nuclear Resources and Environment, East China University of Technology (2022NRE17), the Spark Program of Earthquake Technology of CEA, China (XH20032), open Research Project from the State Key Laboratory of Geological Processes, Mineral Resources, China University of Geosciences (GPMR202114), Jiangxi Provincial Natural Science Foundation (20202BABL203035; 20212BAB203004) and Bei**g Natural Science Foundation (8212041).

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Authors and Affiliations

State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang, 330013, China
Meng Gong
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Bei**g, 10008, China
Meng Gong
Jiangxi Earthquake Agency, Nanchang, 330026, China
Jian Lv, Hao Chen, Jun Dong, **aohui Zha & Zheng Li
East China University of Technology, Nanchang, 330013, China
Meng Gong, **ngmian Zhang & Shuzhong Sheng
School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China
Yong Zheng
Bei**g Earthquake Agency, Bei**g, 10008, China
Tongli Wang

Authors

Meng Gong
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Jian Lv
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**ngmian Zhang
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Yong Zheng
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Hao Chen
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Jun Dong
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**aohui Zha
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Zheng Li
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Shuzhong Sheng
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Tongli Wang
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Contributions

MG wrote most of the manuscript. LJ and Professor ZY provided technical guidance. ZXM assisted in processing data, and writing part of the manuscript. SSZ and WTL revised the manuscript. CH, DJ and ZXH assisted in collecting and analyzing data. All authors read and approved the final manuscript.

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Correspondence to Jian Lv.

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Gong, M., Lv, J., Zhang, X. et al. High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China. Earth Planets Space 75, 129 (2023). https://doi.org/10.1186/s40623-023-01882-9

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Received: 11 January 2023
Accepted: 14 August 2023
Published: 28 August 2023
DOI: https://doi.org/10.1186/s40623-023-01882-9

High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China