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

doi:10.21203/rs.3.rs-2469252/v1

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

https://doi.org/10.21203/rs.3.rs-2469252/v1

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published 28 Aug, 2023

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High-resolution seismic image is critically important for mining minerals. In this work, we collected seismic data from a local dense seismic array of 154 stations around the Anyuan mining area and its adjacent region of Pingxiang City, Jiangxi Province in South China, and applied the ambient noise tomography (ANT) method to image the shear wave velocity structure in the mining 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. Influenced by the depression of sedimentary structure and the coal mining activities in the Pingxiang-Leping region, the shear wave velocity structure in the shallow crust of the Anyuan Mine and its adjacent areas presents obvious low-velocity anomalies. The shear wave velocity structures around the Anyuan fault (AYF) and Wangkeng fault (WKF) zones exhibit low-velocity anomalies from the ground surface to ~ 3.3km 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 show the AYF is inclined toward the northwest and the depth of its extension reach to ~ 3km underground. Based on the depth of the obvious low-velocity intrusive anomalies beneath the Anyuan mining area, we deduce the mining depth of the Anyuan coal mine area exceeded 1 km underground.

Anyuan mining

Ambient noise tomography

Directly surface wave tomography

Shallow crustal velocity structure

Local dense array

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 (Casscreau et al., 1992). So this method is no longer limited by the temporal and spatial distribution of seismic events, thus is very suitable for deciphering precise crust and mantle velocity structures in low seismicity regions (Li et al. 2016; 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 (e.g., 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., 2011; Li et al. 2009, 2010, 2012).

In recent years, with the development of dense seismic array technology, the high-resolution structure of the shallow crust inversion at a small regional scale by high frequency surface wave in ambient noise has developed significantly (Huang et al., 2010; Lin et al., 2013; Fang et al.,2015; Li et al., 2016). In these inversion methods, Fang et al. (2015) developed a direct surface wave imaging technology based on the typical ANT method. This technology can directly image the 3-D shear wave velocity from the surface wave dispersion between station pairs, so that free from developing surface wave velocity images.. In addition, the direct surface-wave tomography method takes influences of surface wave ray bending in complex medium, thus can provide a more accurate velocity structure of the shallow crust at a small regional scale (Fang et al., 2015; Li et al., 2016), which has been verified by a number of tomographic studies (e.g., Wang et al., 2018; Gu et al., 2018; Xie et al., 2021; Huang et al., 2021). For example, Wang et al. (2018) used this method to obtain the shallow 3-D S-wave velocity structure of the Hutubi gas storage area in the southern margin of Junggar Basin and revealed the near-surface structure and structural characteristics of the gas storage. Gu et al. (2018) used 53 stations with average spacing close to 5 km and imaged the detailed shallow earth structure around the Tanlu fault zone. Xie et al. (2021) used this method to obtain a 3-D crustal velocity structure within 5.0 km beneath the surface of the Qingchengzi mining area in eastern Liaoning and provided an effective basis for exploring deep-buried gold deposits. Based on this method, Huang et al. (2021) obtained a 3-D S-wave velocity structure within 1 km underground of the well field area in the Sijiazhuang Coal mine of Yangquan in Shanxi Provinces of China and predicted the distribution characteristics of gas content in the mining area by combining petrologic experiment.

The shallow crustal structure provides fundamental clues for analyzing metallogenic mechanisms and estimating the ore reserve thereafter. Anyuan mining area is located in the western section of the Pingxiang-Leping depression of Jiangxi Province in South China (Fig. 1), which is the main coal production base of Jiangxi Province and its mining history has been over one hundred years. The Pingxiang-Leping depression is a NEE-oriented giant depression, bounded by deep faults on the north and south sides, and the late Paleozoic and Mesozoic strata are widely exposed. Marine, continental and transitional facies from Devonian to Paleogene were widely developed, and several sets of rock source strata with black shale as the main component were formed in the depression. Such as Middle Permian (Xiaojiangbian Formations-Maokou Formation Nangang section), Upper Permian (Leping Formation), and Upper Triassic (Anyuan Formation) (Li et al., 2003; Deng et al., 2005). Our study region belongs to Anyuan Formation, including three mining areas, named Baiyuan, Anyuan and Gaokeng minings (Fig. 1), among which only the Anyuan mining area is operating(Xiao et al., 2020). There are three NE-trending reverse faults distributed in the study region, namely Luojiachong fault (LJCF), Anyuan fault (AYF), and Wangkeng fault (WKF) (Tan et al., 2001).

At present, there are no dangerous rock, collapse, landslide, and other adverse geological phenomena in the mining area (Liu et al., 2021). However, because of 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) study 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, developing a high-resolution shallow structure image for 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 Anyuan mining area and its adjacent region in the shallow crust based on a local dense array, and our results could provide a significant scientific basis on coal mine resources exploring and seismic structure modeling.

2.1 Data collection and processing procedure

We collected continued ambient noise data between 27 July 2020 and 31 August 2020 from a local dense seismic array containing 154 seismic stations around the Anyuan mining area of Pingxiang City, Jiangxi Province in South China. The dense seismic array consists of 13 broadband seismographs of GL-PCS60 type (The blue triangles in Fig. 1), 98 short-period seismographs of EPS type (The red triangles in Fig. 1), and 43 short-period seismographs of QS-05A type (The yellow triangles in Fig. 1). The seismic array spans an area of about 18 km×18 km with average station spacing of about 1 km in the center area and the minimum station spacing is about 0.5 km. The collected data sets are recorded with a simple rate of 100 Hz.

The procedure of data processing is similar to the procedures proposed by Bensen (2007). Seismograms are firstly cut into daily segments and decimated at a sampling rate of 20 Hz, followed by the removal of mean values, trend, and instrument responses, filtering the data with a bandpass filter of 0.5 ~ 10 s to reduce the effect of earthquakes and instrumental irregularities on cross-correlations, and normalization the seismograms with a time-frequency normalization method (Ekström et al., 2009; Shen et al., 2012). Then daily cross-correlations for each station pair are calculated and stacked by using the normalized linear stacking method to obtain high signal-to-noise 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 emerge on both the causal and acausal time lags of the EGFs and should be symmetric. However, the uneven and varied spatial distribution of noise sources may 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 improve the Signal to Noise ratio (SNR), the EGFs were reversed and stacked to form a symmetric component before the dispersion measurement (Yang et al., 2007; Yao et al., 2011). Figure 2a shows the path distribution of the station pairs between the L124 station and the other stations. Figure 2b shows the station pairs CCFs with 0.5 ~ 5s band 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 is arranged from about 1.7 to 3.6 km/s (Fig. 2b ).

2.2 Surface wave dispersion curve extraction

In general, when using the ambient noise tomography method to image the surface wave velocity structure over a long period, the interstation distance of at least three wavelengths is required to obtain reliable frequency dispersion curves (Yao et al., 2006; Zheng et al.,2008). But Yao et al. (2011) and Luo et al. (2015) proved that high-quality dispersion curves could be obtained even the interstation distance only one times the wavelengths in short-period surface wave inversion based on the dense seismic array. So to get more available dispersion curves, we use multiple filtering techniques (Dziewonski et al., 1969, Yao et al., 2011) to extract the high-quality dispersion data from the EGFs with an interstation distance of at least one wavelength and with an SNR > 5 to obtain high-quality dispersion data. 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).

In addition, we removed the measurements that exceed the average value of group velocity at each period plus or minus 0.5 km/s and deleted the points with too high or negative slopes. Finally, 4538 high-quality group velocity dispersion curves were reserved from the 11,781 theoretically available dispersion curves from 154 seismic stations (Fig. 3). Figure 3 shows the group velocity of the surface wave variate from ~ 1.8 to 3 km/s, and the domination period range is mainly distributed from 0.6 to 3.5s.

2.3 Shear wave velocity structure inversion

We use the ray tracing-based inversion method ( Fang et al., 2015) to directly invert the mixed path Rayleigh wave group velocity dispersion data to obtain the 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 & Sambridge 2004) and iteratively update the sensitivity kernels of period-dependent dispersion measurements to updates of the 3-D velocity model. To eliminate the impact of the uneven path coverage, a wavelet-based sparsity-constrained seismic tomography method (e.g. Simons et al., 2011; Fang & Zhang, 2014) is used in the 3-D wave velocity inversion.

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), 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). Then use the AVG model as the input model and apply the iteratively damped least square method developed by Hermann and Ammon (2002) in the CPS330 package (Hermann, 2013) 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 of the direct 3D surface wave inversion program to calculate the 1-D mean shear wave velocity model, respectively (Fig. 4).

Figure 4a & b shows that although the two input models (AVG and GPS models) differ greatly, the inversion results are basically the same, which indicate that the two velocity models have little effect on the inversion results in this study. Yu et al(2020) and Xie et al (2022) have proved that short-period surface wave inversion has relatively little dependence on the initial model. Thus, in view of the smaller difference between the CPS model and the inverse model, the CPS velocity model is chosen as the initial model for the direct 3D surface wave velocity structure inversion. Figure 4c shows the fitting of the dispersion curve (solid black line) and the original dispersion curve (black circle).

The depth sensitivity kernel can be used to evaluate the sensitivity of surface wave group velocity on shear wave velocity in the depth direction (Yao et al.,2008; Zheng et al., 2008). Figure 5 shows the depth sensitivity kernel functions at periods of 0.6, 1.0, 2.0, 3.0, 4.0, and 4.5s for the fundamental Rayleigh wave group velocity calculated from the CPS model in Fig. 4b. It can be seen that the surface wave dispersion data between 0.6 and 4.5 s have the ability to resolve at depth down to ~ 5 km underground.

In the direct inversion method, the weighting factor λ is particularly important measuring the data residual term and the model regularization term (Paige and Saunders, 1982a, 1982b). If the λ is too small, the result may be over-fitted and the error will increase. On contrary, if the λ is too large, the inversion model will be overly smooth, making the inversion result extremely reliant on the initial model (Luo et al., 2019). Hence, the L-curve analysis method (Hansen, 2007) is used to determine the optimal λ and the result shows the optimal weighting factor λ is 3(Fig. 6).

To enhance the robustness of the 3D shear wave inversion results, the checkerboard resolution tests are performed to assess the lateral resolution and the procedure is similar to what Fang et al (2015) described. The 3D reference velocity model is constructed based on the CPS model in Fig. 4b. After several comparative tests, the study region is meshed with 25 by 25 grid points with an interval of 0.01°in latitude and longitude. And 18 grid nodes are set along the depth direction from the ground surface to 13 km underground, according to the interface depth of the velocity structure.

Theoretically, the recovery ability of checkerboards depends primarily on the surface-wave ray-path coverage. Thus, we first check the surface-wave ray-path coverage at different periods and then conduct the checkerboard resolution test. Figure 7 shows the path coverage of group velocity measurements at the 6 selected periods. The result shows that the surface-wave path coverage density of the study region is relatively high, indicating the data set can well resolve the structure of the study region. The ray-path number decreases with the period's increase, such as the ray-path number at 4 s period is only about 27% of that at 1.5 s period.

Figure 8 shows the recovery results of the lateral checkerboard at different depths. The recovery results of the lateral checkerboard show that the recovery of the detection board is affected by the ray-path coverage density(Fig. 8). The checkerboard test is well recovered in the area with dense ray-path coverage and the recovery is poorly in the area with poor ray-path coverage. Overall, the Anyuan mining area and its adjacent region are well recovered with an anomaly size is approximately 0.02°×0.02°.

During the inversion, we used the same velocity model and the parameters as that in the checkerboard resolution test. The inversion process is relatively stable and the root-mean-square (RMS) value of surface-wave travel-time residuals decreases with the increase of iterations times as Fig. 9 shows. The RMS value of travel-time residuals decreases quickly at the first three iterations, and then it decreases slowly and finally converges. After 10 times iterations, the average travel time residuals decreased from 0.454s to 0.354s, which presents that the final velocity model fits the observed data better.

The coal-bearing strata of the study region belong to the Anyuan Formation of the Upper Triassic, which can be divided into Zijiacheng member, Sanjiacheng member, and Sanqiutian member from bottom to top (Fig. 10). The lithofacies assemblages of the Anyuan Formation are mainly gray to light gray, light purple sandstone, siltstone, sandy shale, and carbonaceous shale (Li et al., 2016). And the sedimentary are mainly Marine-continental transitional facies and the coal seams are characterized by multiple layers, complex structures, and uneven distribution (Liu et al., 2021).

Figure 11 shows the shear wave velocity structure maps at different depths from the direct surface inversion of group velocity dispersion data. The patterns show that the shear wave velocity structure varies greatly at the same depth, which reveals that the shallow crustal structure of the study region has strong lateral inhomogeneous. At the depth of 0.4 km, underground, the shear wave velocity structure in the basin areas and coal mining areas shows low-velocity anomalies, while the uplifted mountain areas without coal mine distribution exhibit high-velocity anomalies. Generally speaking, the low-velocity anomalies of shear waves in the basin areas could be attributed to the sedimentary layer in the ground surface and the high-velocity anomalies of shear waves in the uplifted mountain areas could be interpreted as sedimentary layers missing or bedrock exposing. However, the Anyuan mining area is located in the uplifted mountainous, but its shear wave velocity structure exhibits low-velocity anomalies. This abnormal phenomenon could be related to the sedimentary environment of coal seam and coal mining activities. The Sedimentary environment of the Anyuan mining area and its adjacent regions are Marine and continental facies interaction with sedimentary basins, containing large amounts of sand and silt (Xi et al.,1999; Li et al., 2003).In addition, the mining history of the Anyuan coal mine have over one hundred years, so a lot of coalmine gob area and coalbed methane would be produced in the mining process (Li et al.,2003; Qiu et al.,2019; Xi et al., 2020). All of these factors could cause the shear wave velocity structure of the Anyuan mining area present low-velocity anomalies.

The distribution of shear wave velocity structure in the depth ranges of 1.2–2.2 km exhibits similar characteristics. The Anyuan and Gaokeng mining areas still show low-velocity anomalies, but the range of the anomalies decreases with the increase of depth, and the distribution of anomalies gradually approaches the WKF and AYF zone. We also find that the shear wave velocity around the AYF zone and the piedmont basin form an obvious "Y-shaped" low-velocity anomalies zone. Overall, in the depth ranges of 0.4–1.2 km underground, the shear wave velocity of the whole Anyuan mining area presents obvious low-velocity anomalies. However, at the depth of 2.2 km underground, the shear wave velocity structure of the Anyuan mining area is bounded by the WKF zone. The shear wave velocity structure in the northern part of the mining area still presents low-velocity anomalies, while its changes to high-velocity anomalies gradually in the southern part, which is consistent with the distribution characteristic of shear wave velocity structure in high mountain areas. These phenomena may indicate the mining depth of the Anyuan mining area has reached ~ 1.2 km underground.

At the depth of 3.3 km underground, the shear wave velocity structure is well consistent with the surface and topographic features. The shear wave velocity structure in the mountainous area with higher terrain exhibit high-velocity anomalies, such as the area between LJCF and AYF zone, and the area in the south of the WKF zone, while its present low-velocity anomalies in the western basin and piedmont basin areas. It is worth noting that, although the AYF and WKF zone are located on the high mountain, the shear wave velocity structure between the faults zone still shows low-velocity anomalies. The exploration of oil and gas resources works(Li et al., 2003; Qiu et al., 2019)in the Leping depression of Jiangxi Province has proved that there are rich Coalbed methane resources beneath Anyuan mining and its adjacent region. Thus, the low-velocity anomalies of the shear wave at the depths below 2 km underground around the AYF and WKF areas may be a reflection of the presence of oil and gas between the faults zone.

Figure 12 shows the positions of the four sections and their corresponding shear wave velocity profile. AA’ profile shows the shear wave velocity of the basin area in the west of the AYF is characterized by low-velocity anomalies, and the depths of the low-velocity anomalies are negatively correlated with the surface elevation. Along the profiles, AA'and CC'the shear wave velocity structure change sharply across the AYF, and the velocity transition interface is inclined toward the northwest, which is consistent with the tendency of AYF (Tan et al., 2001; Liu et al., 2004). We also find the depths of the velocity transition interface reach ~ 3 km underground, and this may indicate the extension depth of AYF. The profiles BB'and CC' show that there is an obvious low-velocity anomaly in the northwest side of the AYF, which may be caused by the piedmont flood plain. Because the piedmont flood plain contains a large amount of sediment and detrital materials (Tan et al., 2001; Li et al., 2016), which could greatly reduce the propagation speed of the shear wave.

Along the profiles AA’, BB’, CC', and DD’, it can be seen that the shear wave velocity structure in the shallow surface layer (< 1.2km) presents low-velocity abnormal, which could relate to the sedimentary environment of the coal seam. Xi et al. (1999) and Li et al (2003) suggested that the Anyuan mining area and its adjacent region are the main composed sea-land interaction deposition and the maximum deposition thickness is up to 1.3 km. Meanwhile, the low-velocity anomalies of the shallow surface layer(<1.2km)in the Baiyuan, Anyuan, and Gaokeng mine areas are particularly significant and relate to coal mining activities. Such as coalmine gob area and coalbed methane would be produced in the process of coal mining and both of them can slow down the speed of the shear wave. It also can be seen that the low-velocity anomalies around the AYF and WKF zone are down to ~ 3 km in depth (Fig. 11 & Fig. 12) and these could be related to the fracture media, oil, and gas existing in the fault zone. The top coal mining area is often swamp facies or tidal flat facies mudstone and silty mudstone, which has a strong sealing ability for coal reservoirs (Qiu et al., 2019). Thus, the oil and gas produced during Coal mining could penetrate the faults and the gaps between the faults zone, resulting in a low-velocity anomaly.

We used 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 Pingxiang City, Jiangxi Province in South China. The result has shown shear wave velocity structures in the basin area presented low-velocity anomalies and exhibited high-velocity anomalies in the uplifted mountainous areas without coal mining. Influenced by the depression of the sedimentary structure and the coal mining activities, the shear wave velocity structures in the shallow surface layer (0-1.2 km) of the mining area presented a significant low-velocity anomaly. The shear wave velocity structures around AYF and WKF zones show low-velocity anomalies from the ground surface to ~ 3 km underground, and 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 fault zone. Meanwhile, the relative velocity difference between the upper and lower at the depth of ~ 1.2 km beneath the Anyuan mining area indicated the mining depth had reached ~ 1 km underground. The result also revealed the AYF incline to the northwest and extends to ~ 3 km underground. In summary, our results agreed well consistent 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.

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

Availability of data and material

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

Competing interests

The authors declare that they have no competing interests.

Funding

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

Authors’ contributions

Meng G wrote most of the manuscript. Lv J and Professor Zheng Y provided technical guidance. Zhang XM assisted in processing data, and writing part of the manuscript. Sheng SZ and Wang TL revised the manuscript. Chen H, Dong J and Zha Xh assisted in collecting and analyzing data. All authors read and approved the final manuscript.

Acknowledgements

Most figures are made with Generic Mapping 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).

Author information

Meng Gong, State Key Laboratory of Nuclear Resources and Environment，East China University of Technology, Nanchang, 330013, China. Also work at State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, 10008, China. Email:[email protected]

Jian Lv, Jiangxi Earthquake Agency, Nanchang, 330026, China. Email: [email protected]

Xingmian Zhang, East China University of Technology, Nanchang, 330013, China. Email: [email protected]

Yong Zheng, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China. [email protected]

Hao Chen, Jiangxi Earthquake Agency, Nanchang, 330026, China. Email: [email protected]

Jun Dong, Jiangxi Earthquake Agency, Nanchang, 330026, China. Email: [email protected]

Xiaohui Zha,Jiangxi Earthquake Agency, Nanchang, 330026, China. Email: [email protected]

Shuzhong Sheng, East China University of Technology, Nanchang, 330013, China Email: [email protected]

Tongli Wang,Beijing Earthquake Agency, Beijing, 10008, China. Email: [email protected]

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

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published 28 Aug, 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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1. Introduction

2. Data And Method

2.1 Data collection and processing procedure

2.2 Surface wave dispersion curve extraction

2.3 Shear wave velocity structure inversion

3. Result And Discussion

4. Conclusion

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