Journal of Applied Geophysics最新文献

Radon, a naturally occurring radioactive gas, has become a subject of increasing interest and concern, particularly in the context of subterranean environments such as caves. This research investigates these dynamics in the Sterkfontein Cave in South Africa, which has formed in the karst geology of the Cradle of Humankind, Gauteng. Additionally, it set out to compile a radon map for the cave, identifying potential radon hotspots. Twenty-four electret ion chambers were placed in the tourist section of the cave and left for a period of 24 h. The radon concentrations were found to be between 53 Bq/m³ and 2770 Bq/m³. Three regions within the cave exhibited elevated radon concentrations, with these occurrences being linked to phosphatic deposits. A subterranean lake also concentrates radon gas in the lower areas of the cave. While the cave's average radon concentration of 427 Bq/m³ exceeds the World Health Organization's (WHO) hazardous level of 300 Bq/m³, occupational exposure remains minimal during a typical cave tour. Consequently, there is no discernible risk during an average tour through the cave.

Conventional seismic acquisition systems deal with, as a rule, regular topology of sources and receivers layout because it is oriented to a horizontal structure of media. However, the need to involve so-called unconventional hydrocarbon deposits in development requires the use of more complex media models that describe, for example, structures such as fractured-cavernous oil traps. As a result, a new seismic processing methods are emerging, which, in turn, require changes in the seismic recording systems themselves. It turns out that stochastic seismic acquisition systems, which differ from conventional regular systems by randomly placing both sources and receivers, are optimal from the point of view of estimating environmental parameters for new methods of vector seismic exploration on scattered waves. The purposes of paper to describe the features of the one of such approach called Reverse Time Holography and show the new possibilities of its acquisition systems. We demonstrate that substantial reductions in both the number of sources and receivers can be achieved without compromising the quality of seismic attributes using the new approach. Through empirical validation we illustrate that sources reduction can reach up to 8 times, while receivers reduction can reach up 3 times.

Canossa Castle is located in the municipality of Canossa 18 km South of Reggio Emilia (North Italy). It was constructed in 940 by Adalberto Atto, son of Sigifredo of Lucca. Lombard chieftains needed this strategic hill to defend their lands against intrusions of other barbarian tribes. Subsequent improvements made the stronghold one of the best-defended castles in the country. Canossa Castle became particularly famous as a site of reconciliation between King Henry IV and Roman Pope Gregory VII during the Investiture Controversy in 1077.

To redevelop the area and create an easy tourist route, the Superintendence of Archaeology, Fine Arts and Landscape for the Metropolitan City of Bologna and the Provinces of Modena, Reggio Emilia and Ferrara planned excavations in the area close to the Castle. To get precise information on where to carry out excavations geophysical surveys were undertaken in the spring of 2021. The castle stands on a rock with a steep slope and dense vegetation and this makes it very difficult to carry out geophysical prospecting. This guided the choice of geophysical methodologies to be used. For this reason, electrical resistivity tomography was used along the steep slope, while in the narrow flatter area, the ground penetrating radar methodology was used. The results demonstrate the effectiveness of the chosen geophysical methodologies.

High-order pseudo-random signal is gradually being applied in controlled-source electromagnetic (CSEM) exploration. In contrast to the conventional single-frequency sweep mode, the high-order pseudo-random signal enables simultaneous transmission of multiple frequencies. However, estimating a fixed acquisition time based on observed noise levels often results in poor adaptability for high-order pseudo-random signal, which only require reception of one set of waveform. In this study, we presented an estimation method for acquisition time for CSEM with high-order pseudo-random signal using an improved logistic function. The improved logistic function was proposed to introduce a time-decay factor into the governing equation for the first time. By considering the transformation rule of noise statistical characteristics with time, the specific parameters of the function have been determined to better describe the dynamic evolution process of the signal quality. The effective frequencies were extracted at various acquisition times based on the noise evaluation number, and the resulting quantity of effective frequencies was used as the fitting target. Guidance for the fieldwork was determined based on the average time of the saturation period, in accordance with the properties of the function. The reliability of the improved logistic function was validated through a transmission current data simulation. The proposed method was demonstrated through the measured data from both strong and weak interference areas.

Electromagnetic radiation (EMR) is a crucial tool for monitoring and early warning of underground engineering disasters. Investigating the inherent pore characteristics of rocks is essential for a comprehensive understanding of the EMR phenomenon. The EMR was monitored during various types of rock splitting failures. The pore structure and micromorphology of rocks are studied using quantitative methods such as mercury intrusion porosimetry, fractal analysis, and the Gray Level Co-occurrence Matrix (GLCM). Results indicate that the fractal dimension of red sandstone is significantly lower than the other three rocks. The fractal complexity increases sequentially from red sandstone to marble, granite, and limestone. As the fractal dimension decreases, the signal waveform characteristics of the four rocks become more complex before the main fracture, with a significant increase in signals during the compaction and elasticity stages. Higher fractal dimensions lead to a shift in energy and count from elasticity stage to the post-peak stage. The main fracture amplitudes of the four rocks generally exhibited a consistent pattern, following the sequence of granite > marble > limestone > red sandstone. The main fracture amplitude decreases with increasing complexity of the rock's pore micromorphology. Rock pore characteristics affect frequency domain characteristics by influencing rock strength and crack expansion. An increase in the average pore diameter tends to decrease both the main and center frequencies.

The Extreme gradient boosting algorithm XGBoost has been confirmed to be an accurate method for predicting rock stiffnesses and anisotropic parameters from basic input features such as rock porosity, density, vertical compression stress, pore pressure and burial depth (Nguyen-Sy, T., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T., 2020. Study the elastic properties and the anisotropy of rocks using different machine learning methods. Geophysical Prospecting, 68(8), 2557–2578). This study has the following contributions: reducing the R2-error score (that is, 1-R2) by 35 %, RMSE by 21 % and MAE by 16 % comparing to the previous study by considering an advanced CatXG hybrid boosting model in combination with the optimizer Optuna for predicting $C_{13}$ (the most difficult stiffness to accurately predict); 2-conduct a reliability analysis for the predicted stiffness $C_{13}$ with respect to the randomness of the input features. We also discuss the use of $C_{11}$ or $C_{33}$ as additional input features for accurately predicting $C_{13}$ as well as the prediction of the related anisotropic parameter $\delta$. This significant improvement of predicted stiffness $C_{13}$ is extremely important because it encourages petrophysical engineers to use machine learning for predicting the elastic stiffnesses of rocks.