Backward erosion piping (BEP), a form of internal soil erosion, often threatens the safety of dykes built on alluvial deposits. To reduce the risk of dyke failure due to piping, reliable and cost-effective mitigation measures are essential. For the first time, this paper proposes the use of nature-inspired low-permeability barriers to mitigate BEP. The potential of this novel solution is demonstrated in a series of laboratory physical tests. Low-permeability barriers are created by mixing sand either with aluminium-organic matter flocs, or clay. The results show that both kinds of barriers can significantly inhibit pipe progression and intercept the erosion channels. The hydraulic gradients required for pipes to reach the barrier are significantly higher than the critical gradient measured in the absence of barriers, ranging from 2·2 to 7·4 times greater than those in sand alone. The associated mitigating mechanisms include the dissipation of flow energy, resistance to internal erosion due to pore space clogging and prevention of sand fluidisation. The mitigating effect is affected by the reduction of hydraulic conductivity, the depths and the heterogeneity of barriers. The findings of this experimental work provide guidance for the design of low-permeability barriers in practice and contribute to the development of numerical models for BEP.

Downward continuation is a critical task in potential field processing, including gravity and magnetic fields, which aims to transfer data from one observation surface to another that is closer to the source of the field. Its effectiveness directly impacts the success of detecting and highlighting subsurface anomalous sources. We treat downward continuation as an inverse problem that relies on solving a forward problem defined by the formula for upward continuation, and we propose a new physics-trained deep neural network (DNN)-based solution for this task. We hard-code the upward continuation process into the DNN’s learning framework, where the DNN itself learns to act as the inverse problem solver and can perform downward continuation without ever being shown any ground truth data. We test the proposed method on both synthetic magnetic data and real-world magnetic data from West Antarctica. The preliminary results demonstrate its effectiveness through comparison with selected benchmarks, opening future avenues for the combined use of DNNs and established geophysical theories to address broader potential field inverse problems, such as density and geometry modelling.