Relationship between joint roughness coefficient and fractal dimension of rock fracture surfaces

AbstractNumerous empirical equations have been proposed to estimate the joint roughness coefficient (JRC) of a rock fracture based on its fractal dimension (D). A detailed review is made on these various methods, along with a discussion about their usability and limitations. It is found that great variation exists among the previously proposed equations. This is partially because of the limited number of data points used to derive these equations, and partially because of the inconsistency in the methods for determining D. The 10 standard profiles on which most previous equations are based are probably too few for deriving a reliable correlation. Different methods may give different values of D for a given profile. The h–L method is updated in this study to avoid subjectivity involved in identifying the high-order asperities. The compass-walking, box-counting and the updated h–L method are employed to examine a larger population of 112 rock joint profiles. Based on these results, a new set of empirical equations are proposed, which indicate that the fractal dimension estimated from compass-walking and the updated h–L method closely relate to JRC, whereas the values estimated from box-counting do not relate as closely

A coupled thermo-hydro-mechanical model of jointed hard rock for compressed air energy storage

Renewable energy resources such as wind and solar are intermittent, which causes instability when being connected to utility grid of electricity. Compressed air energy storage (CAES) provides an economic and technical viable solution to this problem by utilizing subsurface rock cavern to store the electricity generated by renewable energy in the form of compressed air. Though CAES has been used for over three decades, it is only restricted to salt rock or aquifers for air tightness reason. In this paper, the technical feasibility of utilizing hard rock for CAES is investigated by using a coupled thermo-hydro-mechanical (THM) modelling of nonisothermal gas flow. Governing equations are derived from the rules of energy balance, mass balance, and static equilibrium. Cyclic volumetric mass source and heat source models are applied to simulate the gas injection and production. Evaluation is carried out for intact rock and rock with discrete crack, respectively. In both cases, the heat and pressure losses using air mass control and supplementary air injection are compared

Acceleration Characteristics of a Rock Slide Using the Particle Image Velocimetry Technique

The Particle Image Velocimetry (PIV) technique with high precision and spatial resolution is a suitable sensor for flow field experiments. In this paper, the PIV technology was used to monitor the development of a displacement field, velocity field and acceleration field of a rock slide. It was found that the peak acceleration of the sliding surface appeared earlier than the peak acceleration of the sliding body. The characteristics of the rock slide including the short failure time, high velocities, and large accelerations indicate that the sliding forces and energy release rate of the slope are high. The deformation field showed that the sliding body was sliding outwards along the sliding surface while the sliding bed moved in an opposite direction. Moving upwards at the top of the sliding bed can be one of the warning signs for rock slide failure

A hybrid machine-learning model to estimate potential debris-flow volumes

Empirical-statistical models of debris-flow are challenging to implement in environments where sedimentary and hydrologic triggering processes change through time, such as after a large earthquake. The flexible and adaptive statistical methods provided by machine learning algorithms may improve the quality of debris flow predictions where triggering conditions and the nature of sediment that can bulk flows varies with time. We developed a hybrid machine-learning model of future debris-flow volumes using a dataset of measured debris-flow volumes from 60 catchments that generated post-Wenchuan Earthquake (Mw 7.9) debris flows. We input topographic variables (catchment area, topographic relief, channel length, distance from seismic fault, and average channel gradient) and the total volume of co-seismic landslide debris into the PSO-ELM_AdaBoost machine-learning model, created by combining Extreme learning machine (ELM), particle swarm optimization (PSO) and adaptive boosting machine learning algorithm (AdaBoost). The model was trained and tested using post-2008 Mw 7.9 Wenchuan Earthquake debris flows, then applied to understand potential volumes of post-earthquake debris flows associated with other regional earthquakes (2013 Mw 6.6 Lushan Earthquake, 2010 Mw 6.9 Yushu Earthquake). We compared the PSO-ELM_Adaboost method with different machine learning methods, including back-propagation neural network (BPNN), support vector machine (SVM), ELM, PSO-ELM. The Comparative analysis demonstrated that the PSO-ELM_Adaboost method has a higher statistical validity and prediction accuracy with a mean absolute percentage error (MAPE) less than 0.10. The prediction accuracy of debris-flow volumes trigged by other earthquakes decreases to 0.11–0.16 (absolute percentage error), suggesting that once calibrated for a region this method can be applied to other regional earthquakes. This model may be useful for engineering design to mitigate the risk of large post-earthquake debris flows

Modelling the role of material depletion, grain coarsening and revegetation in debris flow occurrences after the 2008 Wenchuan earthquake

A large amount of debris was generated by the co-seismic mass wasting associated with the 2008 Mw 7.9 Wenchuan earthquake. The abundance of this loose material along the slopes caused more frequent debris flows, triggered by less intense and/or shorter rainfalls. However, both the triggering rainfall and the debris flow frequency seem to have normalised progressively during the past decade. Although changes of rainfall thresholds for post-seismic debris flows were recorded after several major earthquakes, the factors controlling these changes remain poorly constrained. With the aid of a virtual experiment, we investigate the roles of material depletion, grain coarsening and revegetation of the co-seismic debris on the propagation and deposition of debris flows initiated by runoff, as well as their influence on the triggering rainfall thresholds. We employ a Geographic Information System (GIS)-based simulation of debris flow initiation by runoff erosion, which we first calibrate on the 14th August 2010 Hongchun gully event that occurred near the Wenchuan earthquake epicentre. We obtain, by investigating each of the aforementioned processes, changing critical rainfall intensity-duration thresholds for given debris flow runout distances. Grain coarsening appears to play a major role, which is consistent with published laboratory experiments, while material depletion and revegetation do not seem able to account alone for the actual quick decay of debris flow frequency. While the virtual experiment has proven useful in identifying the first-order controls on this decay, model improvements and verification over multiple catchments are needed to make the results useful in hazard assessments

Variable selection with FDR control for noisy data -- an application to screening metabolites that are associated with breast and colorectal cancer

The rapidly expanding field of metabolomics presents an invaluable resource for understanding the associations between metabolites and various diseases. However, the high dimensionality, presence of missing values, and measurement errors associated with metabolomics data can present challenges in developing reliable and reproducible methodologies for disease association studies. Therefore, there is a compelling need to develop robust statistical methods that can navigate these complexities to achieve reliable and reproducible disease association studies. In this paper, we focus on developing such a methodology with an emphasis on controlling the False Discovery Rate during the screening of mutual metabolomic signals for multiple disease outcomes. We illustrate the versatility and performance of this procedure in a variety of scenarios, dealing with missing data and measurement errors. As a specific application of this novel methodology, we target two of the most prevalent cancers among US women: breast cancer and colorectal cancer. By applying our method to the Wome's Health Initiative data, we successfully identify metabolites that are associated with either or both of these cancers, demonstrating the practical utility and potential of our method in identifying consistent risk factors and understanding shared mechanisms between diseases

The fate of sediment after a large earthquake

Large earthquakes rapidly denude hillslopes by triggering thousands of coseismic landslides. The sediment produced by these landslides is initially quickly mobilised from the landscape by an interconnected cascade of processes. This cascade can dramatically but briefly enhance local erosion rates. Hillslope and channel processes, such as landsliding and debris flows, interact to influence the total mass, caliber, and rate of sediment transport through catchments. Calculating the sediment budget of an earthquake lends insight into the nature of these interactions. Using satellite imagery derived landslide inventories, channel surveys and a literature review combined with a Monte Carlo simulation approach we present a constrained sediment budget of the first decade after the 2008 Mw7.9 Wenchuan earthquake. With this sediment budget we demonstrate that debris flows are dominant process for delivering sediment into channels and that large volumes of sediment remain in the landscape. In our study area over 88% (469.7 Mega tonnes) of the coseismically generated sediment remains on the hillslopes in 2018. Of the 12% of the sediment that was mobilised, 67% (45.2 ± 22 Mt) was mobilised by debris flows. Despite the large proportion of sediment remaining on the hillslope, the frequency of debris flows declined significantly over our observation period. The reduction in debris-flow frequency is not correlated to reductions in the frequency of triggering storms, suggesting changes in the mechanical properties of hillslope sediment may drive this observation. The stabilization of coseismically generated sediment greatly extends its residence time and may influence catchment sediment yields for centuries or millennia

The landslide story

The catastrophic Wenchuan earthquake induced an unprecedented number of geohazards. The risk of heightened landslide frequency after a quake, with potential secondary effects such as river damming and subsequent floods, needs more focused attention

The impact of earthquakes on orogen-scale exhumation

Individual, large thrusting earthquakes can cause hundreds to thousands of years of exhumation in a geologically instantaneous moment through landslide generation. The bedrock landslides generated are important weathering agents through the conversion of bedrock into mobile regolith. Despite this, orogen-scale records of surface uplift and exhumation, whether sedimentary or geochemical, contain little to no evidence of individual large earthquakes.We examine how earthquakes and landslides influence exhumation and surface uplift rates with a zero-dimensional numerical model, supported by observations from the 2008 Mw 7:9 Wenchuan earthquake. We also simulate the concentration of cosmogenic radionuclides within the model domain, so we can examine the timescales over which earthquake-driven changes in exhumation can be measured. Our model uses empirically constrained relationships between seismic energy release, weathering, and landsliding volumes to show that large earthquakes generate the most surface uplift, despite causing lowering of the bedrock surface. Our model suggests that when earthquakes are the dominant rock uplift process in an orogen, rapid surface uplift can occur when regolith, which limits bedrock weathering, is preserved on the mountain range. After a large earthquake, there is a lowering in concentrations of 10Be in regolith leaving the orogen, but the concentrations return to the long-term average within 103 years. The timescale of the seismically induced cosmogenic nuclide concentration signal is shorter than the averaging time of most thermochronometers (> 103 years). However, our model suggests that the short-term stochastic feedbacks between weathering and exhumation produce measurable increases in cosmogenically measured exhumation rates which can be linked to earthquakes