The inversion of data from very large three-dimensional electrical resistivity tomography mobile surveys

New developments in mobile resistivity meter instrumentation have made it possible to survey large areas with dense data coverage. The mobile system usually has a limited number of electrodes attached to a cable that is pulled along behind an operator so that a large area can be covered within a short time. Such surveys can produce three-dimensional datasets with hundreds of thousands of electrodes positions and data points. Similarly, the inverse model used to interpret the data can have several hundred thousand cells. It is impractical to model such large datasets within a reasonable time on microcomputers used by many small companies employing standard inversion techniques. We describe a model segmentation technique that subdivides the finite-element mesh used to calculate the apparent resistivity and Jacobian matrix values into a number of smaller meshes. A fast technique that optimizes the calculation of the Jacobian matrix values for multi-channel systems was also developed. A one-dimensional wavelet transform method was then used to compress the storage of the Jacobian matrix, in turn reducing the computer time and memory required to solve the least-squares optimization equation to determine the inverse model resistivity values. The new techniques reduce the calculation time and memory required by more than 80% while producing models that differ by less than 1% from that obtained using the standard inversion technique with a single mesh. We present results using a synthetic model and a field dataset that illustrates the effectiveness of the proposed techniques.     

Chaos in a quasar model with a disk and a massive nucleus

We present a quasar model with a rotating disk and a massive nucleus. We use this model in order to characterize the motion in the model (regular or chaotic) and to connect the extent of the chaotic regions to the physical parameters of the model. Numerical experiments suggest that, there are connections between the extent of the chaotic areas and the parameters of the system. Furthermore, it is shown that the form of numerically found relationships can be expressed analytically. Comparison to previous work is also made. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Investigating sea-state effects on flash flood hydrograph and inundation forecasting

A common source of uncertainty in flood inundation forecasting is the hydrograph used. Given the role of sea-air-hydro-land chain processes on the water cycle, flood hydrographs in coastal areas can be indirectly affected by sea state. This study investigates sea-state effects on precipitation, discharge, and flood inundation forecasting implementing atmospheric, ocean wave, hydrological, and hydraulic-hydrodynamic coupled models. The Chemical Hydrological Atmospheric Ocean wave System (CHAOS) was used for coupled hydro-meteorological-wave simulations ‘accounting’ or ‘not accounting’ the impact of sea state on precipitation and, subsequently, on flood hydrograph. CHAOS includes the WRF-Hydro hydrological model and the WRF-ARW meteorological model two-way coupled with the WAM wave model through the OASIS3-MCT coupler. Subsequently, the 2D HEC-RAS hydraulic-hydrodynamic model was forced by the flood hydrographs and map the inundated areas. A flash flood event occurred on 15 November 2017 in Mandra, Attica, Greece, causing 24 fatalities, and damages was selected as case study. The calibration of models was performed exploiting historical flood records and previous studies. Human interventions such as hydraulic works and the urban areas were included in the hydraulic modelling geometry domain. The representation of the resistance caused by buildings was based on Unmanned Aerial System (UAS) data while the local elevation rise method was used in the urban-flood simulation. The flood extent results were assessed using the Critical Success Index (CSI), and CSI penalize. Integrating sea-state affected the forecast of precipitation and discharge peaks, causing up to +24% and from −8% to +36% differences, respectively, improving inundation forecast by 4.5% and flooding additional approximately 70 building blocks. The precipitation forcing time step was also highlighted as significant factor in such a small-scale flash flood. The integrated multidisciplinary methodological approach could be adopted in operational forecasting for civil protection applications facilitating the protection of socio-economic activities and human lives during similar future events.     

A hybrid optimization scheme for self-potential measurements due to multiple sheet-like bodies in arbitrary 2D resistivity distributions

Self-potential is a passive geophysical method that can be applied in a straightforward manner with minimum requirements in the field. Nonetheless, interpretation of self-potential data is particularly challenging due to the inherited non-uniqueness present in all potential methods. Incorporating information regarding the target of interest can facilitate interpretation and increase the reliability of the final output. In the current paper, a novel method for detecting multiple sheet-like targets is presented. A numerical framework is initially described that simulates sheet-like bodies in an arbitrary 2D resistivity distribution. A scattered field formulation based on finite differences is employed that allows the edges of the sheet to be independent of the grid geometry. A novel analytical solution for two-layered models is derived and subsequently used to validate the accuracy of the proposed numerical scheme. Lastly, a hybrid optimization is proposed that couples linear least-squares with particle-swarm optimization in order to effectively locate the edges of multiple sheet-like bodies. Through numerical and real data, it is proven that the hybrid optimization overcomes local minimal that occurs in complex resistivity distributions and converges substantially faster compared to traditional particle-swarm optimization.     

Finite element analysis of the seismic response of ancient columns

This paper presents a numerical study of the response to earthquake actions of eight ancient Greek multidrum columns, which were chosen as representative of a broad spectrum of the ancient columns preserved to date. The study was conducted using the FE software Abaqus, in three steps. At first, the software efficiency to predict the rocking response of dry-stone structures was verified. Afterwards, various numerical models of one typical ancient multidrum column were seismically excited. Records of four real earthquakes differing in frequency content were used for the excitation of the models. Each column model was different from the others at least in one geometric or modeling parameter. Although the examined parameters affected the numerical results, their variation did not alter significantly the overall behavior of the column. In the last step of the study, numerical models of eight columns were seismically excited using four seismic records. In these analyses, among the simulation approaches tested in the second step, the simpler one was adopted. On the basis of the numerical results, conclusions were drawn regarding the seismic resistance of the columns and its correlation with the dynamic characteristics of the columns (size, slenderness, and frequency parameter) and with the basic intensity measures and frequency content indicator of the seismic motions (PHA, PHV, PHD, and T_g). Thus, criteria of seismic collapse for the multidrum columns are proposed, which can be used for the approximate assessment of the seismic vulnerability of free-standing ancient columns, provided that the columns are in good preservation state.     

Field Survey of the 27 February 2010 Chile Tsunami

On 27 February 2010, a magnitude M _w?=?8.8 earthquake occurred off the coast of Chile??s Maule region causing substantial damage and loss of life. Ancestral tsunami knowledge from the 1960 event combined with education and evacuation exercises prompted most coastal residents to spontaneously evacuate after the earthquake. Many of the tsunami victims were tourists in coastal campgrounds. The international tsunami survey team (ITST) was deployed within days of the event and surveyed 800?km of coastline from Quintero to Mehuín and the Pacific Islands of Santa María, Mocha, Juan Fernández Archipelago, and Rapa Nui (Easter). The collected survey data include more than 400 tsunami flow depth, runup and coastal uplift measurements. The tsunami peaked with a localized runup of 29?m on a coastal bluff at Constitución. The observed runup distributions exhibit significant variations on local and regional scales. Observations from the 2010 and 1960 Chile tsunamis are compared.     

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

One of the main challenges that deep mining faces is the occurrence of rockburst phenomena. Rockburst prediction with the use of machine learning (ML) is currently gaining attention, as its prognosis capability in many cases outperforms widely used empirical approaches. However, the required data for conducting any analysis are limited, while also having imbalances in their recorded instances associated with rockburst intensities. These, combined with the multiparametric nature of the phenomenon, can deteriorate the performance of the ML algorithms. This study focuses on the enhancement of the prediction performance of ML algorithms by utilizing the oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE). Five ML algorithms, namely Decision Trees, Naïve Bayes, K-Nearest Neighbor, Random Forest and Logistic Regression, were used in a series of parametric analyses considering different combinations of input parameters, such as the maximum tangential stress, the uniaxial compressive and tensile strength, the stress coefficient, two brittleness coefficients and the elastic energy index. All models kept their hyperparameters fixed, and were trained with the initial dataset, in which synthetic instances were added gradually aiming in the attenuation of a balanced dataset and its further expansion, until the number of synthetic instances reached the number of real data. The assessment of the SMOTE technique is given and its performance is evaluated though the different strategies adopted. The results indicate that SMOTE has a considerable positive effect in the accuracy of the overall classification and especially in the improvement of the within-class classification accuracy, even after the balancing of the dataset.

     

Shear Modulus and Damping Ratio of Organic Soils

The paper presents results from a laboratory investigation into the dynamic properties of natural intact and model organic soils by means of resonant-column tests. The natural intact organic soils were sands, cohesive soils and peats with varying content of calcium carbonate. The model organic soils were formed in laboratory by mixing kaolinite and paper pulp. The influence of various soil parameters, such as strain level, confining stress, void ratio, plasticity index, organic content and secondary consolidation time on shear modulus, G, and damping ratio, DT, is presented and discussed. The test results on natural organic soils show that only high organic contents (OC ≥ 25%) have significant influence on G and DT at both small and high shear strains. For the model organic soils, however, a significant influence of even lower values of organic content (5% ≤ OC ≤ 20%) on G at small strains and DT at both small and high strains is observed.     

On the theory of the type III burst exciter

In situ satellite observations of type III burst exciters at 1 AU show that the beam does not evolve into a plateau in velocity space, contrary to the prediction quasilinear theory. The observations can be explained by a theory that includes mode coupling effects due to excitation of the parametric oscillating two-stream instability and its saturation by anomalous resistivity. The time evolution of the beam velocity distribution is included in the analysis.NAS/NRC Postdoctoral Resident Research Associate.