Application of Integrated Geophysical Methods for Site Suitability of Research Infrastructures (RIs) in China

Abstract

Research Infrastructures (RIs) are essential to achieve excellence in innovative scientific research. However, because of limited land availability and specific geological requirements, evaluating the viability of a site for a new RI can be a challenging task. Stringent safety construction requirements include developing site-specific architectural and geoengineering solutions, minimizing construction disturbances, and reinforcing rock and soil in a timely fashion. For successful development of the RIs in China, such as the Daya Bay Neutrino Laboratory (DBNL) and the China Spallation Neutron Source (CSNS), an integrated approach of joint geophysical methods including the electrical resistivity tomography (ERT), controlled-source audio-frequency magneto telluric (CSAMT)), gravity and seismic refraction methods, and geological mapping and surveys were carried out. Geophysical parameters, such as electrical resistivity, density, and seismic velocity, show inverse proportion to the degree of rock fracturing or weathering. The results show that the low values of geophysical parameters suggest the weathered/fractured rock, while high values reveal the fresh bedrock. The Engineering Geological Suitability Index (EGSI) value can represent the individual EGSI values at a constant and summed over varying depths. EGSI methodology is an improvement on the existing siting process and has been applied to CSNS. Our integrated approach provides clearer insight into the subsurface for site suitability of RIs in challenging geological engineering conditions and removes any ambiguity caused by a single geophysical parameter. The obtained geological knowledge of the area not only provides engineers with much-needed information about the construction conditions of a potential site but also gives scientists the opportunity to explore the local geology. In this study, we demonstrate our innovative approach for siting RIs, as demonstrated by the synthetic evaluation of the site location and utilization for two established RIs (DBNL and CSNS).

Guided wave signal‐based sensing and classification for small geological structure

“…Chang et al [11] combined multi-domain energy distribution with radial neural network to achieve feature extraction and classification and identification of seismic signals, while ensuring strong robustness, the classification accuracy is improved. However, the above research only considers two-dimensional data information, and is only based on the vector level, ignoring the important correlation in the spatial domain, and cannot meet the high-resolution and high-accuracy requirements of the complex guided wave signal [12]. Koushkaki et al [13] analysed many databases with various qualities, utilised tensor analysis to extract colour information and minimise the dimensionality of the data, and incorporated tensor analysis into machine learning and pattern recognition.…”

Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach

“…It should be noted that this is a problem for most geophysical methods. One way to overcome this problem, or at least to reduce uncertainty about the results, is to apply different filters and integrate methods [47,48]. However, this is not always successful either.…”

Evaluation of rock mass units using a non-invasive geophysical approach

“…However, it is always a difficult task to obtain RQD and RCI from the numerous drilling tests, since it is never easy to accurately locate place of collection samples (even up to 200 m) which in addition are so diverse in terms of the degree of fracture. Such techniques offer only point-scale vertical measurements, are difficult to carry out in steep topographic terrains and need more equipment 3 , 10 , 17 . Moreover, geotechnical tests are high-priced and time-consuming 16 , 21 .…”