Outer trench slope extension to frontal wedge compression in a subducting plate

Faulting in subducting plates is a critical process that changes the mechanical properties the subducting lithosphere and serves as a carrier of surface materials into mantle wedges. Two intraplate earthquake sequences located in the northern Manila subduction system were investigated in this study, which revealed distinct fault planes but a contrasting seismogeny over the northern Manila Trench. The seismic sequences analyzed in this study were of small-to-moderate events. The events were separately acquired by two ocean-bottom seismometer networks deployed on the frontal accretionary wedge in 2005 and the outer trench slope in 2006. The retrieved seismicity in the frontal wedge (in 2005) mainly included the overpressured sequence, whereas that in the approaching plate (in 2006) was aftershocks of an extensional faulting sequence. The obtained seismic velocity models and Vp/Vs ratios revealed that the overpressure was likely caused by dehydration within the shallow subduction zone. By using the near-field waveform inversion algorithm, we determined focal mechanism solutions for a few relatively large earthquakes. Data from global seismic observations were also used to conclude that stress transfer may be responsible for the seismic activity in the study area in 2005–2006. In late 2005, the plate interface in the frontal wedge area was unlocked by overpressure effect with the thrusting-dominant sequence. This event changed the stress regime across the Manila Trench and triggered the normal fault extension at the outer trench slope in mid-2006. However, the hybrid focal solution indicating reverse and strike-slip mechanisms provided in this study revealed that the plate interface had become locked again in late 2006.

Massively Multidimensional Diffusion-Relaxation Correlation MRI

Diverse approaches such as oscillating gradients, tensor-valued encoding, and diffusion-relaxation correlation have been used to study microstructure and heterogeneity in healthy and pathological biological tissues. Recently, acquisition schemes with free gradient waveforms exploring both the frequency-dependent and tensorial aspects of the encoding spectrum b(ω) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These “D(ω)-distributions” allow investigation of restricted diffusion for each distinct component resolved in the diffusion tensor trace, anisotropy, and orientation dimensions. Likewise, multidimensional methods combining longitudinal and transverse relaxation rates, R1 and R2, with (ω-independent) D-distributions capitalize on the component resolution offered by the diffusion dimensions to investigate subtle differences in relaxation properties of sub-voxel water populations in the living human brain, for instance nerve fiber bundles with different orientations. By measurements on an ex vivo rat brain, we here demonstrate a “massively multidimensional” diffusion-relaxation correlation protocol joining all the approaches mentioned above. Images acquired as a function of the magnitude, normalized anisotropy, orientation, and frequency content of b(ω), as well as the repetition time and echo time, yield nonparametric D(ω)-R1-R2-distributions via a Monte Carlo data inversion algorithm. The obtained per-voxel distributions are converted to parameter maps commonly associated with conventional lower-dimensional methods as well as unique statistical descriptors reporting on the correlations between restriction, anisotropy, and relaxation.

Response Times Reconstructor Based on Mathematical Expectation Quotient for a High Priority Task over RT-Linux

Every computer task generates response times depending on the computer hardware and software. The response times of tasks executed in real-time operating systems such as RT-Linux can vary as their instances evolve even though they always execute the same algorithm. This variation decreases as the priority of the tasks increases; however, the minimum and maximum response times are still present in the same task, and this complicates its monitoring, decreasing its level of predictability in case of contingency or overload, as well as making resource sizing difficult. Therefore, the need arises to propose a model capable of reconstructing the dynamics of response times for the instances of a task with high priority in order to analyze their offline behavior under specific working conditions. For this purpose, we develop the necessary theory to build the response time reconstruction model. Then, to test the proposed model, we set up a workbench consisting of a single board computer, PREEMPT_RT, and a high priority task generated by the execution of a matrix inversion algorithm. This work demonstrates the application of the theory in an experimental process, presenting a way to model and reconstruct the dynamics of response times by a high-priority task on RT-Linux.

EXPRES. III. Revealing the Stellar Activity Radial Velocity Signature of ϵ Eridani with Photometry and Interferometry

The distortions of absorption line profiles caused by photospheric brightness variations on the surfaces of cool, main-sequence stars can mimic or overwhelm radial velocity (RV) shifts due to the presence of exoplanets. The latest generation of precision RV spectrographs aims to detect velocity amplitudes ≲ 10 cm s−1, but requires mitigation of stellar signals. Statistical techniques are being developed to differentiate between Keplerian and activity-related velocity perturbations. Two important challenges, however, are the interpretability of the stellar activity component as RV models become more sophisticated, and ensuring the lowest-amplitude Keplerian signatures are not inadvertently accounted for in flexible models of stellar activity. For the K2V exoplanet host ϵ Eridani, we separately used ground-based photometry to constrain Gaussian processes for modeling RVs and TESS photometry with a light-curve inversion algorithm to reconstruct the stellar surface. From the reconstructions of TESS photometry, we produced an activity model that reduced the rms scatter in RVs obtained with EXPRES from 4.72 to 1.98 m s−1. We present a pilot study using the CHARA Array and MIRC-X beam combiner to directly image the starspots seen in the TESS photometry. With the limited phase coverage, our spot detections are marginal with current data but a future dedicated observing campaign should allow for imaging, as well as allow the stellar inclination and orientation with respect to the debris disk to be definitively determined. This work shows that stellar surface maps obtained with high-cadence, time-series photometric and interferometric data can provide the constraints needed to accurately reduce RV scatter.

A Fast ANN Trained Solver Enables Real-Time Radial Inversion of Dielectric Dispersion Data & Accurate Estimate of Reserves in Challenging Environments

Dielectric dispersion measurements are increasingly used by petrophysicists to reduce uncertainty in their hydrocarbon saturation analysis, and subsequent reserves estimation, especially when encountered with challenging environments. Some of these challenges are related to variable or unknown formation water salinity and/or a changing rock texture which is a common attribute of carbonate reservoirs found in the Middle East. A new multi-frequency, multi-spacing dielectric logging service, utilizes a sensor array scheme which provides wave attenuation and phase difference measurements at multiple depths of investigation up to 8 inches inside the formation. The improvement in depth of investigation provides a better measurement of true formation properties, however, also provides a higher likelihood of measuring radial heterogeneity due to spatially variable shallow mud-filtrate invasion. Meaningful petrophysical interpretation requires an accurate electromagnetic (EM) inversion, which accommodates this heterogeneity, while converting raw tool measurements to true formation dielectric properties. Forward modeling solvers are typically beset with a slow processing speed precluding use of complex, albeit representative, formation petrophysical models. An artificial neural network (ANN) has been trained to significantly speed up the forward solver, thus leading to implementation and real-time execution of a complex multi-layer radial inversion algorithm. The paper describes, in detail, the development, training and validation of both the ANN network and the inversion algorithm. The presented algorithm and ANN inversion has shown ability to accurately resolve mud filtrate invasion profile as well as the true formation properties of individual layers. Examples are presented which demonstrate that comprehensive, multi-frequency, multi-array, EM data sets are inverted efficiently for dis-similar dielectric properties of both invaded and non-invaded formation layers around the wellbore. The results are further utilized for accurate hydrocarbon quantification otherwise not achieved by conventional resistivity based saturation techniques. This paper presents the development of a new EM inversion algorithm and an artificial neural network (ANN) trained to significantly speed up the solution of this algorithm. This approach leads to a fast turnaround for an accurate petrophysical analysis, reserves estimate and completion decisions.

Study of Homogeneous Reservoir Pressure Inversion Model Based on Permeability Mechanics and Interpretation Software Design

Well test analysis is required during the extraction of oil and gas wells. The information on formation parameters can be inverted by measuring the change in wellbore pressure at production start-up or after well shutdown. In order to calculate the characteristic parameters of the well, this paper creates a well test interpretation model for homogeneous reservoirs based on the theory of seepage mechanics, uses the Stehfest–Laplace inversion numerical inversion algorithm, and builds the Gringarten–Bourdet logarithmic curves model. The model can be used to evaluate the homogeneous reservoir. We use this model to design the pressure inversion interpretation software to implement a pressure inversion method based on permeability mechanics theory by using computer. The software can obtain the reservoir characteristic parameters such as permeability ( K ), skin coefficient ( S ), and wellbore storage coefficient ( C ). The homogeneous formation Gringarten–Bourdet curves data are available at https://github.com/JXLiaoHIT/Study-of-homogeneous-reservoir-pressure-inversion-model.

Semi-analytical model of multiple fractured horizontal well across a discontinuity in a linear composite reservoir

Many geologic settings can be treated as linear composite (LC) reservoirs, where linear discontinuities divide the formation into multiple zones with different properties. Although there have been many studies on pressure behavior of production wells in an LC reservoir, most of the studies focus on vertical wells. The modeling of multiple fractured horizontal (MFH) wells in an LC reservoir remains limited. The goal of the present work is to propose a general semi-analytical model of an MFH well situated anywhere in a two-zone LC reservoir. This model can take into account the situation where the horizontal well intersects with the discontinuity and hydraulic fractures are distributed in both the two zones. According to the point-source function method, the semi-analytical solution for an MFH well in LC reservoirs is derived by using superposition principle, fracture discrete scheme and numerical inversion algorithm of Laplace transformation. Type curves of MFH wells far away from a discontinuity and across a discontinuity in an LC reservoir are drawn and analysed, respectively. Furthermore, the effects of some parameters on pressure behavior and rate response of an MFH well across a discontinuity are studied. This research finds that the pressure behavior and rate response of an MFH well across a discontinuity are significantly affected by the well location, properties of hydraulic fractures and formation properties.

Robust Lp-Norm Inversion for High-Resolution Fluid Contents from Nuclear Magnetic Resonance Measurements

Fluid content computed from nuclear magnetic resonance (NMR) has proved to be an accurate and reliable tool for petrophysical property estimation. To overcome the limitations of conventional NMR measurements, high spatial resolution NMR (HSR-NMR) has been introduced to achieve the desired resolution for cores of any size. However, inversion of fluid contents from HSR-NMR data suffers from nonreliable measurements at the ends of the cores due to the heterogeneities of the magnetic fields caused by the relatively small size of the coil. A robust Lp-norm inversion algorithm, developed for geophysical inverse problems, has been implemented and applied on the inversion of NMR measurements. The estimated fluid content from Lp inversion matches well with the kerogen content in the cores both visually and quantitively. The resolution of the inverted fluid contents is as high as 1 inch. Further testing on the raw data with large derivations demonstrated that reliable results can only be achieved by using Lp inversion with low p’s values within the range of (1, 1.1].