Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation

Abstract

Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides.

Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements

“…As depicted in Figure 3b, a 1D SwinT block comprises two consecutive SwinT layers. The first one encompasses a standard 1D window multihead self-attention module (WMSA) and the latter one comprises a 1D shifted window MSA (SW-MSA), both followed by a set of LayerNorm (LN), two-layer multilayer perceptron (MLP) with Gaussian error linear unit nonlinearity [32,33], and residual connection [34,35] modules. With the shifted window partitioning approach, the two consecutive SwinT layers are computed as follows:…”

MV-GPRNet: Multi-View Subsurface Defect Detection Network for Airport Runway Inspection Based on GPR

“…However, it can only measure pre-defined sampling positions and cannot quantitatively detect minor defects. Other NDT technologies, such as impact-echo (IE) [12], electrical resistivity (ER) [13], and GPR, are often used in the applications of subsurface transportation infrastructure inspection. Among these technologies, GPR is currently the best choice for airport runway inspection since it can achieve fast and full coverage detection with high resolution and good coverage in range and depth.…”

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

“…Initiating an exploration into the deepening depths of coal mining, it's clear that mine safety production is navigating through formidable challenges. Notably, issues such as mine water inrush [ 1 , 2 ], rock burst [ 3 ], coal and gas outburst [ 4 ], and other dynamic disasters involving coal and rock have become more pronounced. The fundamental cause underlying these disasters lies in the heightened degree of internal damage within coal and rock formations as a consequence of mining activities.…”