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An accurate assessment of the stress concentration of the surrounding rock is crucial for ensuring safety in underground coal mines with a high potential for rockburst accidents. Traditional methods for measuring the stress of the surrounding rock use borehole stress-monitoring equipment and drilling and cutting methods. However, these methods do not always yield accurate and reliable data. In this study, we aim to explore the feasibility of a novel approach for characterizing stress concentration in surrounding rock using monitoring while drilling technology during large-diameter drilling. This study combined microseismic monitoring to collect vibration signals during the drilling process and collection of pulverized coal samples at each drilling stage. We analyzed the amplitude, number of coal vibration events and proportion of coarse pulverized coal. By integrating these indices, we characterized the stress concentration in the surrounding rock. Our approach was validated by comparing accurate, stable and representative data from deep- and shallow-hole stress gauges installed in similar locations and data from the conventional drilling cutting method. Our findings indicate that the proposed method provides a reliable and effective alternative to traditional techniques during large-diameter drilling. The proposed approach can significantly enhance safety management in underground coal mines prone to rockburst accidents. [ABSTRACT FROM AUTHOR]

Magnetotelluric (MT) inversion and forward modeling are closely linked. The optimization and iteration processes of the inverse algorithm require frequent calls to forward modeling. However, traditional numerical simulations for forward modeling are computationally expensive; here, deep learning (DL) networks can simulate forward modeling and significantly improve forward speed. Applying DL for forward modeling in inversion problems requires a high-precision network capable of responding to fine changes in the model to achieve high accuracy in inversion optimization. Most existing MT studies have used a convolutional neural network, but this method is limited by the receptive field and cannot extract global feature information. In contrast, the Mix Transformer has the ability to globally model and extract features. In this study, we used a Mix Transformer to hierarchically extract feature information, adopted a multiscale approach to restore feature information to the decoder, and eliminated the skip connection between the encoder and decoder. We designed a forward modeling network model (MT-MitNet) oriented toward inversion. A sample dataset required for DL forward was established using the forward data generated from the traditional inverse calculation iteration process. The trained network quickly and accurately calculates the forward response. The experimental results indicate a high agreement between the forward results of MT-MitNet and those obtained with traditional methods. When MT-MitNet replaces the forward computation in traditional inversion, the inversion results obtained with it are also highly in agreement with the traditional inversion results. Importantly, under the premise of ensuring high accuracy, the forward speed of MT-MitNet is hundreds of times faster than that of traditional inversion methods in the same process. [ABSTRACT FROM AUTHOR]

To better understand the geothermal system of the Gonghe Basin, we deployed 471 magnetotelluric survey points with an average distance of 2~3 km, covering the eastern and southern areas of the Basin. We used ModEM inversion software to carry out 3D inversion of 431 survey points and established a 3D-electrical model at a depth of 50 km in the area. The resistivity model shows that the low resistivity in the shallow part of the basin is related to the Cenozoic loose sedimentary cover, while the resistivity values of the mountains around the basin and the magmatic rock uplift zone are higher. The electrical model also shows that the high-conductivity layer is widely distributed in the middle and lower crust (15~35 km) of the basin, and direction of the high-conductivity layer is consistent with that of NW–SE fault in the basin. These high-conductivity layers may be the principal reason for the high heat flow values in the Gonghe Basin. Our resistivity model also shows that there is an obvious discontinuity between high- and low-resistivity blocks at different depths in the middle and upper crust. These discontinuities are consistent with the faults observed on the surface, which are related to the strong topographic relief. Our electrical model shows that these faults in the middle and upper crust are connected with the high-conductivity layer as the channel of heat transfer to the shallow part. Finally, the heat energy is enriched in the Triassic granite to form dry hot rock (HDR). The 3D-magnetotelluric imaging results depict the 3D-distribution characteristics of the geothermal system in the eastern and southern parts of the Gonghe Basin. [ABSTRACT FROM AUTHOR]

The identification markers of paleokarst have been described in detail and include a combination of surface geology, drilling data and the isotopic characteristics of carbon, oxygen and strontium. The relationships between the vertical zoning, plane distribution and karstification degree of paleokarst and the reservoir development were further analyzed. The study shows that karstic breccias are the most direct sign of paleokarst features. Karsts of different genetic types and the intensity of dissolution have different stable isotopic characteristics from the surrounding cements, and their formation and evolution are significantly influenced by fluid sources, fluid nature and isotopic fractionation. The identification markers of small scale pinholes are quite different from that of large scale holes. The development and evolution of palaeokarsts are mainly controlled by structure, palaeoclimate and lithology. The best lithology for the development of reservoirs is dolomite in the C2hl2 formation. The best zones for the development of the reservoir is located in the upper and lower dissolution intervals of the C2hl2 formation and the best position for the development of reservoir is located in the karst highlands, remnant hills and sloping fields in the karst slope. Key words: eastern Sichuan, Huanglong Formation, palaeokarst reservoir, geochemistry, karst topography, karst cycle