Microseismic network sensitivity in case of no seismic activity

Underground human activities, such as mining, shale gas and oil exploitation, waste-water disposal or geo-thermal plants, can cause earthquakes. These industry projects need to be monitored by local seismic networks in order to contain the risk. An ideal seismic network should have a triangulated grid, with spacing equal to the depth of the industrial activity with no associated industry noise. In many cases, stations are placed near noisy roads, factories or in a private garden, none of which are located at optimal nodes and which thus introduce great variations in the nose level. In this article, we present a work-flow to determine the sensitivity of any local network, even if there is no local event recorded. In other words: how small are the earthquakes that such seismic networks detect? This knowledge can be used as an argument for claiming an area to be seismically silent-inactive down to a certain magnitude or for evaluating the effect of an additional seismic station.A brief theory and work-flow description is followed by two real-case demonstrations from Czech Republic, Europe: first, a proof-test on a well- studied seismically active area of West Bohemia / Vogtland and second, an application to an uprising geothermal project in Litoměřice, where no seismic activity was detected in years of monitoring.

Network Replicability & Generalizability: Exploring the Effects of Sampling Variability, Scale Variability, and Node Reliability

Work surrounding the replicability and generalizability of network models has increased in recent years, prompting debate as to whether network properties can be expected to be consistent across samples. To date, certain methodological practices may have contributed to observed inconsistencies, including the common use of single-item indicators to estimate nodes, and use of non-identical measurement tools. The current study used a resampling approach to systematically disentangle the effects of sampling variability from scale variability when assessing network replicability. Additionally, we explored the extent to which consistencies in network characteristics were improved when precision in node estimation was increased. Overall, scale variability produced less stability in network properties than sampling variability, however under more optimal measurement conditions (i.e. larger sample, greater node precision), discrepancies were markedly reduced. Findings also importantly underscored the value of improving node reliability: Use of multi-item indicators led to denser networks, higher network sensitivity, greater estimates of global strength, and greater levels of consistency in network properties (e.g., edge weights, centrality scores). Taken together, variability in network properties across samples may be less indicative of a lack of replicability, but may arise from poor measurement precision, and/or may reflect properties of the underlying true network model or scale-specific properties. All data and syntax are openly available online (https://osf.io/m37q2/).

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

Mathematical tools and software for the distributed geophone network of Prognoz-ADS geomechanical monitoring system

Introduction. The article considers the problems of the present-day algorithm of acoustic emission events location in Prognoz-ADS geomechanical monitoring system. The ways of solving the flaws in the applied algorithm have been proposed with the account of the specified requirements. Research aim is to propose certain solutions aimed at achieving higher accuracy of seismoacoustic events coordinates calculation; to solve the flat antenna problem and get the opportunity of assessing the efficiency of separate observing network elements; develop the structure of a new location algorithm and calculate its preliminary complexity. Methodology. It is proposed to correct the problems of the existing algorithm by constructing the velocity map of the controlled object and assessing the efficiency of seismoacoustic receivers and data on the observing network sensitivity in various sections of the rock mass. Results. The article provides logical structure of the developed algorithm with complexity O(n). It is proposed to solve the flat antenna problem by using data on seismic receivers complex sensitivity. Certain media of collecting information on the observing network state and signal propagation velocities at the controlled object have been introduced. Summary. The designed algorithm provides for multiple parameters variation, many of them are not taken into account in the existing location method of Prognoz-ADS system. The indicated characteristics selection and the efficient use of the calculating tool’s hardware resources will make it possible to obtain a more accurate and universal location algorithm.

Development of an interactive Cloud-based seismic network modelling application on a common Geophysical Processing Toolkit platform

<p>Detecting and locating earthquakes relies on seismic events being recorded by a number of deployed seismometers. To detect earthquakes effectively and accurately, seismologists must design and install a network of seismometers that can capture small seismic events in the sub-surface.</p><p>A major challenge when deploying an array of seismometers (seismic array) is predicting the smallest earthquake that could be detected and located by that network. Varying the spacing and number of seismometers dramatically affects network sensitivity and location precision and is very important when researchers are investigating small-magnitude local earthquakes. For cost reasons, it is important to optimise network design before deploying seismometers in the field. In doing so, seismologists must accurately account for parameters such as station locations, site-specific noise levels, earthquake source parameters, seismic velocity and attenuation in the wave propagation medium, signal-to-noise ratios, and the minimum number of stations required to compute high-quality locations.</p><p>AuScope AVRE Engage Program team has worked with researchers from the seismology team at the University of Melbourne to better understand their solution for optimising seismic array design to date: an analytical method called SENSI that has been developed by Tramelli et al. (2013) to design seismic networks, including the GipNet array deployed to monitor seismicity in the Gippsland region in Victoria, Australia. The underlying physics and mechanics of the method are straightforward, and when applied sensibly, can be used as a basis for the design of seismic networks anywhere in the world. Our engineers have built an application leveraging a previously developed Geophysical Processing Toolkit (GPT) as an application platform and harnessed the scalability of a Cloud environment provided by the EASI Hub, which minimised the overall development time. The GPT application platform provided the groundwork for a web-based application interface and enabled interactive visualisations to facilitate human-computer interaction and experimentation.</p>

Data Processing of a Local Seismological Network for West Texas Seismicity Characterization

We propose a template-matching workflow capable of improving detection sensitivity of a seismic network and demonstrate its performance on the local seismic network comprising Texas Seismological Network installations in West Texas. We use three earthquakes from three clusters as our templates. Template matching is applied to each station independently. Then, SeisComP3 scanloc associator groups the obtained picks into seismic events following moveouts between stations consistent with a velocity model. In comparison to short-term over long-term average detection workflow, the number of “new,” previously undetected events more than doubles. The events detected by the template-matching workflow are registered on a set of stations, thus allowing for their absolute location. Template matching improves local network sensitivity. Among network parameters, station noise conditions appear to have the highest influence on the effectiveness of the workflow.

Artificial Intelligence Medical Ultrasound Equipment: Application of Breast Lesions Detection

Breast cancer ranks first among cancers affecting women’s health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation. And we use the technique of knowledge distillation to train the low-precision network helped with the high-precision network; (2) asynchronous calculations: consider four frames of ultrasound images as a group; the image of the first frame of each group is used as the input of the network, and the result is respectively fused with the images of the fourth to seventh frames. An amount of computation of 30 GFLO/frame is required for the proposed lightweight neural network, about 1/6 of that of the large high-precision network. After trained from scratch using the knowledge distillation technique, the detection performance of the lightweight neural network (sensitivity = 89.25%, specificity = 96.33%, the average precision [AP] = 0.85) is close to that of the high-precision network (sensitivity = 98.3%, specificity = 88.33%, AP = 0.91). By asynchronous calculation, we achieve real-time automatic detection of 24 fps (frames per second) on the ultrasound equipment. Our work proposes a method to realize the intelligence of the low-computation-power ultrasonic equipment, and successfully achieves the real-time assistant detection of breast lesions. The significance of the study is as follows: (1) The proposed method is of practical significance in assisting doctors to detect breast lesions; (2) our method provides some practical and theoretical support for the development and engineering of intelligent equipment based on artificial intelligence algorithms.