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Collapse columns are complex geological anomalies that present safety hazards in coal mines. In practice, the boundaries and internal features are difficult to finely characterize. In this study, we use the Jeffrey divergence to reveal the spectral disparity when the channel wave seismic ray path encounters a collapse column. We use the forward mode and applications for verification. We find that the centroid peak frequency shifts toward lower frequencies, the frequency band narrows and the rays through a collapse column cause attenuation of energy. When a seismic ray path encounters a large collapse column, no channel wave appears in the seismic record, the dispersion curve velocity is greater than the velocity of a normal coal seam, the centroid peak frequency drops and the frequency band shrinks. When a seismic ray passes through the boundary of a collapse column, the dispersion curve shows two parallel velocity phases. When a seismic ray path passes through a small collapse column, the decrease in the centroid frequency becomes the only identifying feature. The proposed channel wave spectral disparity characteristic and velocity tomography imaging method can effectively identify small collapse columns and improve the accuracy and precision of the location of collapse column boundaries, with a position deviation of less than 10 m.

The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.

The quantitative detection of faults using the channel wave seismic method has been a major but challenging area of interest. In this study, we adopted an effective technical process to evaluate fault attribution. First, we use integrated transmission and reflection channel wave information to improve the accuracy of extraction velocity. Then, the location of the fault is determined by the elliptical tangent offset method, and feature extraction and fault location extension determination are achieved through logistic regression and a neural network. This is combined with the prior geological information, the fractional dimension D to the quantitative analysis of the fault throw. Data regarding the 4203 working face of a mine in Shanxi, China, are considered as an example. Two groups of faults were predicted, with the location error in the f30 fault position as 6.7 m. In addition, the f29 fault throw first increased, and then gradually decreased from the return airway to the haulage gateway. These predicted results have been drill-verified and were used to modify the original design. The proposed method has good stability and promising application prospects for fault evaluation.

The computed tomography (CT) reconstruction algorithm is one of the crucial components of the CT system. To date, total variation (TV) has been widely used in CT reconstruction algorithms. Although TV utilizes the a priori information of the longitudinal and lateral gradient sparsity of an image, it introduces some staircase artifacts. To overcome the current limitations of TV and improve imaging quality, we propose a multidirectional anisotropic total variation (MATV) that uses multidirectional gradient information. The surrounding rock of coal mining faces uses principles of tomography similar to those of medical X-rays. The velocity distribution for the surrounding rock can be obtained by the first-arrival traveltime tomography of the transmitted waves in the coal mining face. Combined with the geological data, we can interpret the geological hazards in the coal mining face. To perform traveltime tomography, we first established the objective function of the first-arrival traveltime tomography of the transmitted waves based on the MATV regularization and then used the split Bregman method to solve the objective function. The simulated data and real data show that the MATV regularization method proposed in this paper can better maintain the boundaries of geological anomalies and reduce the artifacts compared with the isotropic total variation regularization method and the anisotropic total variation regularization method. Furthermore, this approach describes the distribution of geological anomalies more accurately and effectively and improves imaging accuracy.

As the only project for CO2 storage in deep saline aquifers in China, the Shenhua CCS demonstration project is of great importance in the geological storage of CO2 in continental sedimentary strata. Despite achieving the designed injection goal of 302,000 tons, the demonstration project currently does not have a widely accepted storage site geological model because of the large overall thickness of the injection layers, low porosity and permeability, and high heterogeneity. Based on geological study of the storage site system, combined with vertical seismic profiling (VSP) seismic monitoring data, we used the well log-constrained seismic inversion method to predict the lithology, porosity and permeability of the storage site. We constructed a new 5 km × 5 km × 1200 m site geological model centered on the injection well. Our new geological modeling method and the related reservoir parameters are innovative compared with previous studies and enhance geological understanding of the site, providing a reference for CO2 migration reservoir simulation and deep geophysical monitoring in the future stages of the project.

One of the great challenges in carbon capture and storage (CCS) development is the monitoring and determination of the underground CO2 plume migration. We show that time-lapse 2D vertical seismic profiling (VSP) technology can support the monitoring and numerical modeling of CO2 plume migration at a storage site. Using the Shenhua CCS demonstration project in China as a case study, we predicted the physical parameters of the main reservoir, including the distribution of bodies of sandstone and their porosity and permeability, required to upgrade the geological model. We also interpreted the characteristics of historical CO2 plume migration in one seismic profile of the second and third periods of VSP monitoring. Based on a new upgraded geological model, we simulated the historical CO2 plume migration characteristics under the actual injection scenario of the demonstration project. The results of the simulation exhibited good consistency with the time-lapse VSP monitored in the profile, thus indicating that both the geological and numerical models have a certain degree of credibility. We further simulated the characteristics of CO2 plume migration after the injection well shutoff before 2035.

In China, south of the Yangtze River, there are a large number of carbon sources, while the Sichuan Basin is the largest sedimentary basin; it makes sense to select the targets for CO2 geological storage (CGUS) early demonstration. For CO2 enhanced oil and gas, coal bed methane recovery (CO2-EOR, EGR, and ECBM), or storage in these depleted fields, the existing oil, gas fields, or coal seams could be the target areas in the mesoscale. This paper proposed a methodology of GIS superimposed multisource information assessment of geological suitability for CO2 enhanced water recovery (CO2-EWR) or only storage in deep saline aquifers. The potential per unit area of deep saline aquifers CO2 storage in Central Sichuan is generally greater than 50 × 104 t/km2 at P50 probability level, with Xujiahe group being the main reservoir. CO2 storage potential of depleted gas fields is 53.73 × 108 t, while it is 33.85 × 108 t by using CO2-EGR technology. This paper recommended that early implementation of CGUS could be carried out in the deep saline aquifers and depleted gas fields in the Sichuan Basin, especially that of the latter because of excellent traps, rich geological data, and well-run infrastructures.

Seismic physical modeling technique is a kind of experimental methods, the seismic physical modeling experiments are mainly used for the exploration and development of oil and gas, this technique in the coal fields is rarely used to solve practical problems at present. In order to achieve the fine exploration of the geological condition of coal seam, aimed at the geological conditions of the coalfield,the distribution of coal strata and lithologic features,completes the production seismic geology model of coal measure strata. The results show seismic physical modeling can simulate the typical geologic structure of coal mine, simulate complex geological phenomenon of coal mine, validate mathematical calculation method, validate related theory, reduce costs, also introduce the prospect of coal seismic physical modeling technology.

To study the impact of modern coal mining on overlying strata and its water bearing conditions, integrated time-lapse geophysical prospecting integrating 3D seismic, electrical and ground penetrating radar method were used. Through observing and analyzing the geophysical data variations of all stages of pre-mining, mining and post-mining as well as post-mining deposition stable period, impacts of coal mining on stratigraphic structure and its water bearing were studied and modern coal mining induced stratigraphic change pattern was summarized. The research result shows that the stratigraphic structure and the water bearing of surface layer during modern coal mining have self-healing pattern with mining time; the self-healing capability of near-surface strata is relatively strong while the roof weak; water bearing self-healing of near-surface strata is relatively high while the roof strata adjacent to mined coal beds low. Due to integrated time-lapse geophysical prospecting technology has extra time dimension which makes up the deficiency of static analysis of conventional geophysical methods, it can better highlight the dynamic changes of modern coal mining induced overburden strata and its water bearing conditions.