Reviewerly/exHarmony huggingface dataset:
A study on input ground motion processing platform for evaluating seismic fragilities using Deep Learning Phase Determination Model

The 9.12 Gyeongju earthquake(Sep. 12, 2016, Ml 5.8) and the Pohang earthquake(Nov. 15, 2017, Ml 5.4) have occurred in the Korean Penisula, resulting in emphasizing the stability of nuclear power plants. For safety evaluation, it is necessary to study the earthquake vulnerability caused by input ground motion. The input ground motion can be obtained from the earthquakes, and it is essential to acquire good quality and many samples input ground motion database for accurate evaluation. In this study, we tried to develop a platform that can automatically generate a ground motion database from past or real-time waveforms. To determine the detailed time window for data processing, deep learning-based earthquake detection, and phase-picking models were used. A voting method was conducted on these models to increase reliability in various environments. The platform produces a RotD50 5% damped pseudo-spectral acceleration, peak ground acceleration, and meta information related to site, hypocenter, and path. It also provides a web service to confirm generated data and meta information. The database generated by the platform could be used as input ground motion data to evaluate the safety of operating power plants and could be applied as fundamental data for the seismic design of planned nuclear power plants.
Integration of deep-time databases: towards building a deep-time digital Earth

The integration of deep-time databases is a key aspect in the creation of a comprehensive digital model of Earth's history, known as the "deep-time digital Earth." This model would enable scientists to better comprehend the intricate processes that have shaped our planet and its life forms over time. Currently, the data are dispersed across various databases and research institutions, making it challenging for scientists to fully utilize the information contained in the data.We present a virtual integration of deep-time databases, where the data remain in the sources and are accessed as needed at query time. We will demonstrate this integration using the Macrostrat, PBDB, GBDB, GeoLexicon, and the Paleogeographic Atlas Project databases. The first step involves identifying the available information from each data source and deciding on the relevant data attributes, such as lithology, age, and formation name. The next step is aligning the schemata of different data sources to a common mediated schema, allowing attribute names with the same semantics to be merged. For example, Palaeo-block in GBDB is equivalent to geoplate in PBDB. The third step is to create a virtual integration layer that allows users to access and query data from various sources as if they were stored in a single database. This virtual integration layer uses the mediated schema to translate queries and data among different sources and provides tools and interfaces for data visualization and analysis. Our goal is to make deep-time data more easily accessible and usable for a better understanding of Earth's history.
Stochastic models for earthquake occurrence

The complexity of seismogenesis requires the development of stochastic models, the application of which aims to improve our understanding on the seismic process and the associated underlying process. Semi Markov models are introduced for estimating the expected number of earthquake occurrences with the classification of the model states based on earthquake magnitudes. The instantaneous earthquake occurrence rate between the model states as well as the total earthquake occurrence rate can be calculated. Seismotectonic characteristic of the study area, incorporated in the model as important component of the model, increase the consistency between the model and the process of earthquake generation and support a classification that integrates magnitudes and style of faulting, thus being more effective for the seismic hazard assessment. For revealing the stress field underlying the earthquake generation, which is not accessible to direct observation, the hidden Markov models (HMMs) are engaged. The HMMs consider that the states correspond to levels of the stress field and its application aim to reveal these states. Different number of states may be examined, dependent upon the organization of observations, and the HMMs are capable to reveal the number of stress levels as well as the way in which these levels are associated with the occurrence of certain earthquakes. Even better results are obtained via the application of hidden semi&amp;#8211;Markov models (HSMMs) considering that the stress field constitutes the hidden process and which, compared with HMMs, allow any arbitrary distribution for the sojourn times. The investigation of the interactions between adjacent areas is accomplished by means of the linked stress release model (LSRM), based upon the consideration that spatio&amp;#8211;temporal stress changes and interactions between adjacent fault segments constitute the most important component in seismic hazard assessment, as they can alter the occurrence probability of strong earthquakes onto these segments. The LSRM comprises the gradual increase of the strain energy due to continuous tectonic loading and its sudden release during the earthquake occurrence. The modeling is based on the theory of stochastic point process, and it is determined by the conditional intensity function. In an attempt to identify the most appropriate parameterization that better fits the data and describes the earthquake generation process, a constrained &amp;#8220;m&amp;#8211;memory&amp;#8221; point process is introduced, the Constrained&amp;#8211;Memory Stress Release Model (CM&amp;#8211;SRM) implying that only the m most recent arrival times are taken into account in the conditional intensity function, for some suitable m&amp;#206;N, instead of the entire history of the process. The performance of the above mentioned models application are evaluated and compared in terms of information criteria and residual analysis.
EarthNets: An Open Deep Learning Platform for Earth Observation

Earth observation (EO) data are critical for monitoring the state of planet Earth and can be helpful for various real-world applications [1]. Although numerous benchmark datasets have been released, there is no unified platform for developing and fairly comparing deep learning models on EO data [2]. For deep learning methods, the backbone networks, hyper-parameters, and training details are influential factors while comparing the performances.. However, existing works usually neglect these details and even evaluate the performance with different training/validation/test dataset splits. This makes it difficult to fairly and reliably compare different algorithms. In this study, we introduce the EarthNets platform, an open deep-learning platform for remote sensing and Earth observation. The platform is based on PyTorch [3] and TorchData. There are about ten different libraries, covering different tasks in remote sensing. Among them, Dataset4EO is designed as a standard and easy-to-use data-loading library, which can be used alone or together with other high-level libraries like RSI-Classification (for image classification), RSI-Detection (for object detection), RSI-Segmentation (for semantic segmentation), and so on. Two factors are considered for the design of the EarthNets platform: the first one is the decoupling between dataset loading and high-level EO tasks. As there are more than 400 RS datasets with different data modalities, research domains, and download links, efficient preparation of analysis-ready data can largely accelerate the research for the whole community. The other factor is to bring advances in machine learning to EO by providing new deep-learning models. The EarthNets platform provides a fair and consistent evaluation of deep learning methods on remote sensing and Earth observation data [4]. It also helps bring together the remote sensing and a larger machine-learning community. The platform, dataset collections are publicly available at https://earthnets.github.io.[1] Zhu, Xiao Xiang, et al. "Deep learning in remote sensing: A comprehensive review and list of resources." IEEE Geoscience and Remote Sensing Magazine 5.4 (2017): 8-36.[2]Long, Yang, et al. "On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid." IEEE Journal of selected topics in applied earth observations and remote sensing 14 (2021): 4205-4230.[3] Paszke, Adam, et al. "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019).[4] Xiong, Zhitong, et al. "EarthNets: Empowering AI in Earth observation." arXiv preprint arXiv:2210.04936 (2022).
Geology, geochemical typification and petrogenic model of formation of Middle Paleozoic metabasites of the Khrami crystalline massif (Georgia)

The Khrami crystalline massif is located in the northern part of the Lesser Caucasus, in the Black Sea - Transcaucasian terrane. The massif outcrops Middle Paleozoic metabasites, which crosscut the Precambrian gneiss-migmatite complex and, in turn, are cut by Late Variscan granites. These metabasites have not experienced Precambrian prograde HT/LP (720-770&amp;#176;C, P&lt;1.5 kbar) regional metamorphism, although retrograde LT/LP (T&amp;#8776;430-5100C, P&amp;#8776;0.6-1 kbar) metamorphism, associated with the Sudetian orogeny has been recorded. According to the presented geological data, the age of metabasites is within the Cambrian and Upper Paleozoic. Considering the analogy between the metabasites spread in the Dzirula crystalline massif, which is exposed in the same terrane, and the metabasites of the Khrami massif, the age of the latter is most likely Middle Paleozoic (Shengelia et al., 2022). The metabasites of the Khrami massif are represented by veins (1-60 m) and stock-shaped bodies (80-800 m) of fine-grained ophitic gabbro, gabbro-diabases and diabases of various thicknesses. They are cut by numerous granite veins and penetrated by thin quartz-feldspar injections. The paragenesis of the high-temperature magmatic stage - Cpx+Pl78-84 has been preserved in metabasites in some places; Further, under the conditions of greenschist facies, the paragenesis Ab+Act(Tr)+Chl+Ep&amp;#177;Qz develops. According to the petrogenic diagrams Na2O+K2O &amp;#8211; SiO2, the metabasites of the Khrami massif belong to the formations of the subalkaline series (Irvine and Baragar, 1971), correspond to basalts and andesite-basalts (Le bas et al., 1986) and basalts and picrites (Cox et al., 1979). This is confirmed by the data of diagrams Zr/Ti-Nb/Y (Pearce, 1996) and Zr/TiO2 &amp;#8211; Yb/Y (Winchester, Floyd, 1977). According to the Na2O+K2O&amp;#8211;FeO*-MgO (Irvine and Baragar, 1971), a great part of the metabasites is of tholeiitic composition, and only a small part is of calc-alkaline composition. On the diagram Fe*-SiO2 (Frost et al., 2008) the dots denoting metabasites are completely disposed in the magnesian field. According to the TiO2 - Zr/(P2O5*104) diagram (Winchester, Floyd, 1976), the metabasites correspond to tholeiite basalts. According to the diagram V-Ti/1000 (Shervias, 1982), the metabasites belong to the MORB genetic formation, and according to the diagram Cr-Y (Pearce, 1982), they belong mainly to the VAB, and also to the MORB. According to the ratio MnO-TiO2/10-P2O5 (Mullen, 1983), dots of mafic rocks are located in the island-arc tholeiitic field. Thus, the Middle Paleozoic metabasites of the Khrami crystalline massif are represented by shallow subvolcanic magmatites predominantly of andesite-basalt and tholeiite-basalt groups of the tholeiitic series. They correspond to the MORB and VAB genetic groups.
Remote-sensing applications for Au-epithermal deposits mapping and exploration targeting in Panagyurishte ore district, Bulgaria

Remote sensing UAV based technology combined with modern alteration mapping tools (SWIR, XRD Raman and XRF) for mineral detection has experienced great advances in the Cu-Au epithermal exploration targeting. We demonstrate quick and cost-efficient approach for epithermal gold exploration mapping and evaluation on examples of high-sulphidation epithermal Cu-Au deposits and prospects in the Panagyurishte ore district, Bulgaria.The Panagyurishte ore district is part of the global Tethyan-Eurasian Cu-Au belt that developed during the Mesozoic as a copper-rich, andesite-dominated magmatic arc system characterized by obvious affiliation of porphyry-copper (PCD) and epithermal Cu-Au ore deposits with granodiorite and andesite dominated magmatic complexes. The target mapping has lacked high-resolution data to identify and prove the geometry of the alteration mineral assemblages and ore controlling fault structures. When distal sensing is combined with field mapping and proximal modern mineral detection methods such as SWIR (1300-2500nm) and Raman spectroscopy, XRD and ore petrography is more efficient tool for detection of hydrothermally altered minerals and zones by their diagnostic spectral signatures.Drone based photogrammetry approach was applied for hydrothermal alterations mapping and targeting for Cu-Au epithermal deposits exploration in the Panagyurishte ore district, Bulgaria. Mineral alterations maps for Pesovets, Petelovo and Krassen Au-epithermal deposits was assembled using orthophoto model and TIR- 3D mapping to utilize the time and cost efficiency of the subsequent geological exploration field work. For classification and verification of drone orthophoto mosaic geological mapping and rock sampling was carried out in addition to XRF and XRD mapping and stream sediments sampling. UAV- based mapping with selected light bands was used to recognize different hydrothermal alterations styles such as advanced argillic (AAA), argillic (AA) propylitic (Prop) and phyllic (Phy) alteration styles that are overprinting andesitic volcanic sequences in the central part of Panagyurishte ore district. Radial and concentric fault structures and regional strike-slip fault zones have also been proved by UAV-based mapping. Domains of proximal hypogene AAA and AA and more distal propylitic halo as possible host of HS gold mineralization were clearly outlined. XRF mapping of the Pesovets lithocap indicate increasing of As (20-50ppm) and Ti (570-2400ppm) concentrations when approaching AA and AAA alteration domains and could also provide effective vectoring tool for targeting of epithermal Cu-Au mineralization.&amp;#160;The recent study demonstrates UAV-based mineral mapping approach that will help to improve the exploration targeting and decision making in and eestimation of the Cu-Au mineral potential in cost-efficient manner.The study is supported by the Horizon 2020 co-funded GOLDENEYE project, which has received funds through the Grant Agreement 869398.
Improving STEM Education Across European Schools

My name is Nadia Grozavescu and I am a teacher at Gheorghe Magheru Secondary School in Caracal, Romania. I would like to present you the following project, entitled &amp;#8220;Improving STEM Education Across European Schools&amp;#8221; (Erasmus+ KA201). The project took place in the school I teach between 2020 and 2022.The overall goal of the project was developing a methodology for teachers to deliver innovative teaching styles to make the STEM evaluation more efficient and its implementation more attractive for students.Over the course of the project we obtained the following results:10 STEM pedagogical scenarios of which two were created by the teachers in our school. These were also translated in Romanian and listed on the project website (https://improving-stem-education.eu/teaching-materials/). I have personally contributed to the one about the active engagement in respecting the habitat. 25 online courses, grouped on nine STEM themes, of which five were created by our school&amp;#8217;s coordinator. Again, all courses were translated and can be found on the project&amp;#8217;s webpage (https://e-learning.improving-stem-education.eu/) 30 students' mentoring sessions, of which six were hosted by Romanian mentors who answered our call. The sessions were recorded and can be found here https://improving-stem-education.eu/mentoring-scheme/ Three learning and teaching training mobilities which ten of our school&amp;#8217;s teachers benefited of. These took place in Greece (Nov 2021), Belgium (Apr 2022) and Ireland (Jun 2022) and had the following main themes: Class Management, Students Motivation, Robotics and Programming, 3D printing and Laser cutting. I was one of the teachers who took part in the workshop in Belgium The project proved to be complex and challenging, even more given the fact that we were the only institution dealing with younger students. Over the project our school partnered with a university, a Science House Centre, a centre for teachers&amp;#8217; professional development, an IT company, and an NGO dedicated to improving teaching.
Toward a renewed data processing of the Engineering Strong Motion (ESM) database

Strong-motion records and open access to strong-motion data repositories are fundamental to seismology, earthquake engineering and practice. The main archive to disseminate high quality processed waveforms for the European-Mediterranean region is the Engineering Strong-Motion Database (ESM, https://esm-db.eu). ESM is developed under the general coordination of the ORFEUS Strong-Motion Management Committee (Observatories and Research facilities for European Seismology; http://orfeus-eu.org/), with the aim to provide users daily access to updated strong-motion waveforms of earthquakes with magnitude greater than 4, mainly recorded in the Pan-European regions. ESM is fully compatible with the European Integrated Data Archive (EIDA; http://orfeus-eu.org/data/eida/) and disseminates waveforms and related metadata according to the Federation of Digital Seismograph Networks (FDSN, https://www.fdsn.org/networks/).The strategy of ESM is to disseminate only manually processed data to ensure the highest quality. However, the rapid increase in the number of waveforms, due to the increment of seismic stations, leads to the need of automatic procedures for data processing and data quality control.In this work, we present ESMpro, a modular Python software for a renewed processing framework of ESM. The ESM data processing&amp;#160; is improved with:(1) automated data quality-check that speeds up the processing time through the rejection of poor-quality records;(2)&amp;#160; advancement&amp;#160; of the automatic settings for waveform trimming and filtering;(3) introduction of different algorithms for data processing (Paolucci et al., 2011; Schiappapietra et al., 2021);(4) modular and flexible software structure that allows the addition of new algorithms and custom workflows.The accuracy of the updated automatic processing is evaluated by comparison with the waveforms processed by expert analysts, used as benchmarks (Mascandola et al., 2022).ESMpro is distributed in a stand-alone Beta version available on GitLab (D&amp;#8217;Amico et al., 2022; https://shake.mi.ingv.it/esmpro/), following the Open Science principles to promote collaborations and contributions from the scientific community. In the next future, a renewed ESM web-processing frontend will be developed to include the ESMpro improvements, as well as new functionalities to process stand-alone data (i.e., not stored in the ESM database) and to allow different input seismic data formats.
Metallogenic processes that formed the Cu-Au-Mo porphyry and epithermal Au-Ag-Te deposits in the Almalyk ore district

The geologic setting of the Kurama mining district is controlled by its assignment to the structures of the eastern part of the extended Beltau&amp;#8211;Kurama volcanoplutonic belt in the Middle Tien Shan. This district of Paleozoic age exemplify unique deposits with perfectly preserved characteristic features of Cu-Au-Mo porphyry, epithermal Au&amp;#8211;Ag&amp;#8211;Te, Ag&amp;#8211;Pb&amp;#8211;Zn, as well as numerous occurrences of Au, Ag, Cu, Pb, Zn, Bi, and Sn mineralization. Intense magmatic processes occurred in this region throughout the whole Paleozoic, reaching a maximum at its end. The very high concentration of ore occurrences is related to the confinement of the region to the intersection of deep tectonic structures. Another unique feature of this region is that it is a rare example of paleovolcanic areas with well preserved epithermal deposits of Late Paleozoic age. Here we compile published and some new geodynamic, mineralogical and geochemical data, including regional geological features and ore characteristics, sequence of the mineral formation, main mineral assemblages and discuss factors controlling the enrichment mechanism of huge amounts of Cu, Au, Mo, Ag, Re, Pd, Pb, Zn and other elements in the Almalyk ore-field through field and laboratory investigation. We will present summary of the geological, geochronologic and geochemical characteristics of porphyry, scarn and epithermal gold-silver deposits to establish a series of discriminant indicators, which can provide reference for future research on the genetic connection of different deposits in the porphyry copper system. The occurrence of mafic dikes, post-dating the porphyry mineralization and high Re concentrations in the molybdenite may be attributed to a direct involvement of mantle in an arc-subduction environment. But it is becoming more obvious that the problem of sources of ore and rare elements forming cannot be solved only by their introduction with magma. In many specific examples of the formation of the largest and unique concentrations, their confinement to the underlying and enclosing sedimentary rocks, sharply enriched in valuable impurities, is observed. These rocks include organogenic dolomites or dolomitic limestones. During intrusion of igneous bodies, the presence of reactive carbonate rocks (limestone, dolomite, marl) led to the formation of gold-bearing Au-Cu- and Pb-Zn-skarn deposits. The integrated results of this study are used to discuss the metallogenic processes that formed the unique Cu-Au-Mo porphyry, epithermal Au&amp;#8211;Ag&amp;#8211;Te, Ag&amp;#8211;Pb&amp;#8211;Zn deposits in the largest porphyry copper-polimetallic ore Almalyk district in the Central Asian Orogenic Belt.
Physics Informed Deep Learning for Modeling Coseismic Crustal Deformation

Crustal deformation, which can be modeled by dislocation models, provides critical insights into the evolution of earthquake processes and future earthquake potentials. In this presentation, we introduce our recent work on a novel physics-informed deep learning approach for modeling coseismic crustal deformation (Okazaki et al. 2022). Physics-informed neural networks were proposed to solve both the forward and inverse problems by incorporating partial differential equations into loss functions (Raissi et al. 2019). The use of neural networks enables to represent continuous displacement fields in arbitrary geometrical structures and mechanical properties of rocks without discretization. To accurately model the displacement discontinuity on a fault, which cannot be directly approximated by neural networks composed of continuous functions, the polar coordinate system is introduced. We illustrate the validity and usefulness of the proposed approach through forward modeling of antiplane dislocations, which are used to model strike-slip faults. This approach would have considerable potential for extension to high-dimensional, anelastic, nonlinear, and inverse problems in a straightforward way.ReferenceOkazaki T, Ito T, Hirahara K, Ueda N, Physics-informed deep learning approach for modeling crustal deformation. Nature Communications, 13, 7092 (2022). https://doi.org/10.1038/s41467-022-34922-