Extraction of diffractions from seismic data using convolutional U-net and transfer learning

Diffraction images can be used for modeling reservoir heterogeneities at or below the seismic wavelength scale. However, the extraction of diffractions is challenging because their amplitude is weaker than that of overlapping reflections. Recently, deep learning (DL) approaches have been used as a powerful tool for diffraction extraction. Most DL approaches use a classification algorithm that classifies pixels in the seismic data as diffraction, reflection, noise, or diffraction with reflection, and takes whole values for the classified diffraction pixels. Thus, these DL methods cannot extract diffraction energy from pixels for which diffractions are masked by reflections. We proposed a DL-based diffraction extraction method that preserves the amplitude and phase characteristics of diffractions. Through the systematic generation of a training dataset using synthetic modeling based on t-distributed stochastic neighbor embedding (t-SNE) analysis, this technique extracts not only faint diffractions, but also diffraction tails overlapped by strong reflection events. We also demonstrated that the DL model pre-trained with basic synthetic dataset can be applied to seismic field data through transfer learning. Because the diffractions extracted by our method preserve the amplitude and phase, they can be used for velocity model building and high-resolution diffraction imaging.

Overdense Threads in the Solar Corona Induced by Torsional Alfvén Waves

High-resolution and high-cadence observations have shown that Alfvén waves are ubiquitous in the solar atmosphere. Theoretical works suggest their ability to transfer large energy fluxes from the photosphere to the corona and solar wind. In this proof-of-concept Letter we show that torsional Alfvén waves can induce the formation of filamentary plasma structures in the solar corona. We perform high-resolution 3D ideal MHD simulations in an initially uniform coronal plasma permeated by a line-tied twisted magnetic field. We find that torsional Alfvén waves develop Kelvin–Helmholtz instabilities as a result of the phase mixing process. The Kelvin–Helmholtz instability drives plasma compression that breaks the uniformity of density, creating elongated overdense threads aligned with the direction of the magnetic field. With synthetic modeling of SDO/AIA imaging we show that the overdense filaments could be seen in observations as fine strands that illuminate the underlying magnetic structure.

Internal multiple prediction using inverse-scattering series with sparsity promotion — Part 1: Background, analysis, and synthetic modeling

The presence of internal multiples in seismic data can lead to artefacts in subsurface images ob-tained by conventional migration algorithms. This problem can be ameliorated by removing themultiples prior to migration, if they can be reliably estimated. Recent developments have renewedinterest in the plane wave domain formulations of the inverse scattering series (ISS) internal multipleprediction algorithms. We build on this by considering sparsity promoting plane wave transformsto minimize artefacts and in general improve the prediction output. Furthermore, we argue forthe usage of demigration procedures to enable multidimensional internal multiple prediction withmigrated images, which also facilitate compliance with the strict data completeness requirementsof the ISS algorithm. We believe that a combination of these two techniques, sparsity promotingtransforms and demigration, pave the way for a wider application to new and legacy datasets.

The Effects of Exchange Flow on the Karst Spring Hydrograph under the Different Flow Regimes: A Synthetic Modeling Approach

In this study, a synthetic modeling approach is proposed to quantify the effect of the amount and direction of the exchange flow on the karstic spring discharge fluctuations under different hydrologic conditions corresponding to high and low flow conditions. We hypothesis that the spring discharge fluctuations constitute a valuable proxy to understand the internal processes of the karst system. An ensemble of spring hydrographs was synthetically produced to highlight the effect of exchange flow by exploring the plausible range of variability of coefficients of exchange flow, conduit diameter, and matrix hydraulic conductivity. Moreover, the change of the rate of point recharge through the karst conduit allows for the quantifying of the sensibility of the spring hydrograph to the directions of exchange flow. We show that increasing the point recharge lies to a remarkable linear recession coefficient (β) as an indication of the conduit flow regime. However, a reduction in and/or lack of the point recharge caused the recession coefficient to change to exponential (α) due to the dominant effect of the matrix restrained flow regime and/or conduit-influenced flow regime. The simulations highlight that the exchange flow process from the conduit to the matrix occurred in a short period and over a restricted part of the conduit flow regime (CFR). Conversely, the exchange flow dumped from the matrix to the conduit occurs as a long-term process. A conceptual model is introduced to compare spring hydrographs’ characteristics (i.e., the peak discharge, the volume of baseflow, and the slope of the recession curve) under the various flow conditions with the directions of the exchange flow between the conduit and the matrix.

EMI characterization in mountain catchments: multi-frequency versus multi-coil inversion using EMagPy

<p>Advanced modeling of hydrological processes in mountain catchments requires accurate characterization of the shallow subsurface, and in particular the depth to the soil/bedrock interface. Frequency domain electromagnetic induction (EMI) methods are well suited to this challenge as they have short acquisition times and do not require direct coupling with the ground; consequently they can be highly productive. Moreover, although traditionally used for revealing lateral electrical conductivity changes, EMI inversion is increasingly used to quantitatively resolve both lateral and vertical changes. These quantitative models can then be used to inform several properties relevant for hydrological modelling (e.g. water content, permeability).</p><p>In this work the open-source software EMagPy is used to compare between EMI data collected with a multi-coil device (i.e. a single frequency device with multiple receiver coils) and a multi-frequency device (i.e. a single inter-coil distance and multiple frequencies). The latter instrument is easier to handle because of its shorter length and lower weight, and thus it is potentially more suitable for the rugged topography of mountain slopes. However it is important to compare the value of information (e.g. sensitivity patterns and data quality) obtained from both instruments.</p><p>To begin with, the performance of both devices is assessed using synthetic modeling. Following from this the analysis is focused on two mountainous catchments: one located in the Alpine region above 2000 m a.s.l., the other in a Mediterranean catchment in Southern Italy. Both sites have differing geological and hydrological conditions and provide a useful comparison to determine the suitability of multi-frequency and multi-coil devices, and highlight necessary considerations of EMI acquisition.</p>

Utility of forward and inverse modeling in 2D electric tomography for Hydrogeologic studies

The choice of adequate techniques and arrays in electric tomography prospection is a difficult task; it depends generally on subsurface geology and the referred objective, this study is conducted in southern Algerian Sahara aquifer using 2D electric tomography techniques, the goal is testing different arrays configuration, using 2D model simulating the subsurface geology, the validated parameters from the theoretical study are applied to the real data, the results attest the usefulness of synthetic modeling choosing the correct parameters in geophysical prospection.