A Novel Shortwave Infrared Proximal Sensing Approach to Quantify the Water Stability of Soil Aggregates

Soil structure and aggregate stability (AS) are critical soil properties affecting water infiltration, root growth, and resistance to soil and wind erosion. Changes in AS may be early indicators of soil degradation, pointing to low organic matter (OM) content, reduced biological activity, or poor nutrient cycling. Hence, efficient and reliable AS measurement techniques are essential for detection, management, and remediation of degraded soil resources. Here we quantify soil AS by developing a novel proximal sensing technique based on shortwave infrared (SWIR) reflectance measurements. The novel approach is similar to the well-documented high energy moisture characteristic (HEMC) method, which yields a stability ratio (SR) derived from comparison of hydraulic and structural characteristics of slowly- and rapidly-wetted soil samples near-saturation. We rapidly wetted aggregated soil samples (i.e., high energy input) and hypothesized that an AS index can be derived from SWIR surface reflectance spectra due to differences in post-wetting surface roughness that is intimately linked to AS. To test this hypothesis, surface reflectance spectra from a wide range of structured soil textures under both slowly- and rapidly-wetted samples, were measured with a SWIR spectroradiometer (350–2500 nm). The ratio between pre- and post-wetting spectra was determined and compared with the HEMC method’s volume of drainable pore ratio (VDPR). We found a strong correlation (R2 = 0.87) between the VDPR and the SWIR-derived reflectance index (RI) and also between the SR (R2 = 0.90) and the RI for all soils. These results point to the feasibility and appeal of quantifying AS using the newly proposed and more time-efficient proximal sensing method

geoelectrical characterization and monitoring of slopes on a rainfall triggered landslide simulator

Abstract In this paper, we present the results of time-lapse electrical resistivity tomography (ERT) monitoring of rainfall-triggered shallow landslides reproduced on a laboratory-scale physical model. The main objective of our experiments was to monitor rainwater infiltration through landslide body in order to improve our understanding of the precursors of failure. Time-domain reflectometry (TDR) data were also acquired to obtain the volumetric water content. Knowing the porosity, water saturation was calculated from the volumetric water content and we could calibrate Archie's equation to calculate water saturation maps from inverted resistivity values. Time-lapse ERT images proved to be effective in monitoring the hydrogeological conditions of the slope as well as in detecting the development of fracture zones before collapse. We performed eight laboratory tests and the results show that the landslide body becomes unstable at zones where the water saturation exceeds 45%. It was also observed that instability could occur at the boundaries between areas with different water saturations. Our study shows that time-lapse ERT technique can be employed to monitor the hydrogeological conditions of landslide bodies and the monitoring strategy could be extended to field-scale applications in areas prone to the development of shallow landslides

Sperimentazione alla scala di laboratorio per il monitoraggio di frane indotte da precipitazioni con misure geoelettriche time-lapse

Secondo l’Inventario dei Fenomeni Franosi d’Italia (IFFI), al 2016 le frane censite in Italia sono 614.799, interessano un’area pari al 7.5% del territorio nazionale e il 70.5% dei Comuni italiani. Le frane indotte da precipitazioni, inoltre, per la loro evoluzione veloce, sono una grave minaccia per l’incolumità della popolazione, dei beni e delle infrastrutture del territorio che, in assenza di un sistema di allerta adeguato non possono essere salvaguardati. Lo studio e il monitoraggio di fenomeni franosi può essere realizzato a diverse scale e con diverse tecnologie, ma negli ultimi decenni le metodologie geofisiche sono state largamente utilizzate per questo scopo, grazie alla peculiarità di essere non invasive e di poter rilevare la variazione di parametri fisici in un volume di terreno. Per quanto riguarda le frane superficiali, analizzate in questo studio, uno dei fattori predisponenti per l’attivazione è l’apporto precipitativo, che va a determinare variazioni nel contenuto d’acqua del suolo e nella pressione interstiziale. Diversi ricercatori hanno constatato l’utilità di misure geoelettriche per la valutazione del contenuto idrico nel corpo di frane superficiali (Perrone et al., 2008; De Bari et al., 2011; Ravindran e Prabhu., 2012) e in alcuni casi è stato predisposto un sistema di monitoraggio in continuo (Supper et al., 2008; Kuras et al., 2009; Hilbich et al., 2011). L’obiettivo di questo studio è quello di valutare, partendo dalla sperimentazione di laboratorio, l’applicabilità di un monitoraggio geoelettrico nel riconoscimento di un livello soglia di contenuto d’acqua per l’instaurarsi dell’instabilità di una frana superficiale

Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders

To reduce both the computational cost of probabilistic inversions and the ill-posedness of geophysical problems, model and data spaces can be re-parameterized into low-dimensional domains where the inverse solution can be computed more efficiently. Among the many compression methods, deep learning algorithms based on deep generative models provide an efficient approach for model and data space reduction. We present a probabilistic electrical resistivity tomography inversion in which the data and model spaces are compressed through deep convolutional variational autoencoders, while the optimization procedure is driven by the ensemble smoother with multiple data assimilation, an iterative ensemble-based algorithm. This method iteratively updates an initial ensemble of models that are generated according to a previously defined prior model. The inversion outcome consists of the most likely solution and a set of realizations of the variables of interest from which the posterior uncertainties can be numerically evaluated. We test the method on synthetic data computed over a schematic subsurface model, and then we apply the inversion to field measurements. The model predictions and the uncertainty assessments provided by the presented approach are also compared with the results of an MCMC sampling working in the compressed domains, a gradient-based algorithm, and with the outcomes of an ensemble-based inversion running in the uncompressed spaces. A finite-element code constitutes the forward operator. Our experiments show that the implemented inversion provides most likely solutions and uncertainty quantifications comparable to those yielded by the ensemble-based inversion running in the full model and data spaces, and the MCMC sampling, but with a significant reduction of the computational cost

Discrete cosine transform for parameter space reduction in Bayesian ERT inversion

Markov Chain Monte Carlo (MCMC) algorithms are employed for accurate uncertainty assessments in non-linear geophysical inverse problems. However, one of their main drawbacks is the considerable number of sampled models needed to attain stable posterior estimations, especially in high-dimensional parameter spaces. We use the Discrete Cosine Transform (DCT) to reparametrize a Bayesian Electrical Resistivity Tomography (ERT) inversion solved through an MCMC sampling. In this framework, the unknown parameters become the series of coefficients associated with the retained DCT base functions. We employ the Differential Evolution Markov Chain (DEMC) algorithm that guarantees a more accurate and rapid sampling of the posterior density than more standard MCMC algorithms (such as the random walk Metropolis). To draw essential conclusions about the reliability of the implemented algorithm, we focus on inversions of a synthetic subsurface block model. We assess the benefits provided by the DCT compression of the model space by comparing the outcomes of the implemented inversion approach with those provided by a DEMC algorithm running in the full, un-reduced model space. Although preliminary, our results are promising and prove that the implemented inversion approach guarantees rapid convergence toward the stationary regime, thereby preserving an accurate sampling of the posterior model

Discrete Cosine Transform Reparameterization for Bayesian Time-Lapse ERT Inversion

Time-Lapse electrical resistivity tomography (TL-ERT) is used to monitor dynamic processes through mapping the resistivity variations in the subsurface. Inversion of TL-ERT data is a highly non-linear and ill-conditioned problem characterized by non-unique solutions. For this reason, an accurate uncertainty appraisal is essential to quantify the ambiguity affecting the estimated resistivity model. We present a probabilistic TL-ERT inversion in which the Differential Evolution Markov Chain (DEMC) algorithm samples the posterior probability density function, while the Discrete Cosine Transform (DCT) is used to compress the model space. The model compression aims at mitigating both the ill-conditioned nature of the inversion problem and the curse of dimensionality issue. On the other hand, the DEMC combines principles coming from metaheuristic optimisation methods and Markov Chain Monte Carlo algorithms to speed up the probabilistic sampling. To draw essential conclusions about the reliability and applicability of the implemented algorithm, we focus on synthetic inversion experiments in which we simulate two data acquisitions at different time instants (t0 and t1) and we jointly estimate the resistivity model at t0 along with the resistivity changes at t1. The results demonstrate that the implemented method provides accurate model predictions and uncertainty estimations with an affordable computational cost

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Electrical resistivity tomography is a non-linear and ill-posed geophysical inverse problem that is usually solved through gradient-descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two-dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high-dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non-linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high-frequency components of the resistivity modelwhich are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un-reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient-based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification