Reviewerly/exHarmony huggingface dataset:
Predicting fault stress level and stress drop using seismo-mechanical and statistical features derived from acoustic signals in laboratory stick-slip friction experiments and assesing feature importance via the derived models.

Earthquake prediction relies on identification of distinctive patterns of precursory parameters that might precede a large earthquake. However, these patterns are typically not reliably observed in the field. Laboratory stick-slip experiments provide an analog of seismic cycle observed in nature in fully controlled conditions with the associated Acoustic Emission (AE) activity reproducing basic characteristics of seismicity preceding and following the large lab earthquake. Recent laboratory studies showed that the deployment of Machine Learning/Artificial Intelligence techniques has lead to new state of the art results in lab earthquake prediction on smooth faults while using simple statistical features derived from raw AE signals and AE-derived catalogs. However, not enough work has been done on explainability of earthquakes preparatory process on rough faults by leveraging deep learning techniques. In this work we attempt to mitigate this gap and analyze/grade a pool of &amp;#160;explainable seismo-mechanical features through the eyes of neural networks.We used&amp;#160;AE data from three laboratory&amp;#160;stick-slip experiments performed in triaxial pressure vessel on Westerly Granite samples. Samples were first fractured at 75MPa confining pressure creating rough fault surfaces. The following stick-slip experiments were performed at constant displacement rate. The experimental procedure led to an extremely complex slip pattern composed of large and small slips of the whole surface, as well as the confined slips highlighted only with AE data and no externally measured slip. The AE catalog was used to extract temporal evolution of 16 seismo-mechanical and statistical features characterizing evolution of stress and damage in response to the axial stress change. The feature pool included clearly physically interpretable parameters such as AE rates, b-value, fractal dimension, AE localization, clustering and triggering properties, and features characterizing the variability of local stress field.&amp;#160;We apply explainable AI techniques to identify what features are more important to forecast &amp;#160;axial stress and stress drop.&amp;#160; Our feature ranking and importance evaluation with the help of neural networks can serve as an indicator as to what research directions are more promising to take for further feature engineering efforts with an emphasis on explainability of earthquake phenomena.
Early warning method of power supply enterprise service network public opinion based on fuzzy reasoning

To improve the accuracy of the power supply enterprise service network public opinion crisis early warning, the fuzzy reasoning theory is introduced to carry out the design research of the power supply enterprise service network public opinion early warning method. Based on public opinion topic intensity, development heat and public attitude, the power supply enterprise service network public opinion early warning index system is constructed. Combined with fuzzy reasoning theory, the index membership degree and early warning level membership degree are calculated. Through the learning method, the public opinion early warning level judgment rule is learned, and the public opinion early warning level judgment and early warning display are completed. The experiment proves that the new public opinion early warning method can accurately judge the degree of public opinion crisis, and give a reasonable and intuitive early warning display result.
3D alteration mapping and remote-sensing applications for porphyry -copper deposits (PCD) exploration, in Panagyurishte ore district, Bulgaria

The Panagyurishte ore district, Bulgaria is situated in the Srednogorie zone situated in the western part of the global Cu-Au dominant Tethyan-Eurasian Cu-Au metallogenic belt. Significant examples of Late Cretaceous volcano-plutonic structures with porphyry-copper deposits (Elatsite, Medet, Asarel, Tsar Assen, Vlaykov Vruh), connected to subvolcanic granodiorite porphyry intrusions occur in the Panagyurishte ore region, Bulgaria. The PSD are closely associated with high-sulphidation type Cu-Au epithermal deposits (Chelopech, Krasen, Radka, Elshitsa) that are related to andesite - dacite magmatic activity. 3D UAV-supported alteration mapping of Vlaykov Vruh and Tsar Assen PCD have been performed to identify and prove the geometry of the alteration mineral assemblages and ore controlling structures. Domains consisting of phyllic, argillic, propylitic and K-silicate alteration zones associated with and porphyry-copper style of mineralization in Vlaykov Vruh and Tsar Assen deposits were outlined. 3D modelling of Popovo Dere PCD by means of Leapfrog Geo and mineral alteration mapping study outlined fault controlled proximal K-silicate domain and more distal propylitic domain as a potential Cu-porphyry deposit target for further mineral exploration and evaluationTwo types of K-silicate alterations, one with magnetite and another without magnetite that hosted Cu-porphyry mineralization have been distinguished within the proximal Cu-rich zone. More distal propylitic domain has also been outlined by 3D modelling. Strike-slip fault control within the K-silicate alteration domain outlined the cone shaped Cu-porphyry ore body. The UAV-Multispectral and TIR mapping in addition to XRD study confirmed the geometry of phyllic alteration domain hosted in andesitic and dacitic volcanic rocks. The propylitic alteration zone is developed in the granodiorite porphyry intrusion in Vlaykov Vruh PCD and with K-silicate domains hosts Cu-Mo mineralization. Fe-oxide and malachite rich domains have been traced in Tsar Assen PCD in addition to Cu-rich zone in the western part of the open pit.The recent study demonstrates that combined UAV-supported remote sensing and mineral alteration field and XRD mapping could provide an effective vectoring and exploration targeting tool toward PCD mineralizationThis study is supported by the Horizon 2020 co-funded GOLDENEYE project through the Grant Agreement 869398.
A Study on Earthquake Detection Performance of MEMS Sensors According to Seismic Observation Network Density

Earthquake early warning (EEW) is a technology that aims to reduce damage by notifying an alarm message before a large shaking due to an earthquake. The EEW currently adopted by most national or local governments is a network-based method. Network-based EEW produces seismic source information based on seismic wave detection from at least three observation stations, so the density of the observation network is a very important factor in shortening the warning time. However, a huge budget and space are required to construct a dense seismic observation network. In order to compensate for such limitations, all sensors capable of detecting vibration are being expanded and applied to seismometers. The Korea Meteorological Administration (KMA) signed an MOU with a private telecommunication service provider and installed about 6,700 MEMS sensors across the country. This is about 22 times more sensors than the existing KMA seismic observation network. In this study, the seismic detection performance of MEMS sensors and the KMA seismometers installed across the country was analyzed from the perspective of time. Since it is difficult to apply the existing seismic wave detection technology to the MEMS sensor as it is, an artificial intelligence-based seismic detection technology was applied. We compared the analysis results of the KMA observation network and the MEMS observation network in real-time for earthquakes of M 3.5 or greater. As a result of real-time detection, it was found that the high-density of observation network was more effective in detecting earthquakes than the performance of the sensor.
Solar energy potential assessment on urban rooftops using digital surface models

Rooftop solar photovoltaic (PV) systems have the advantage of producing electricity without air pollution and greenhouse gas emissions, at the same time reducing carbon footprint as well as urban heat island effect. This study aims to assess physical, geographical and economic levels of solar energy potentials in actual atmospheric conditions on urban rooftops, using two municipalities in Athens, Greece as an example.&amp;#160;We utilize very high-resolution digital surface models for the computation of clear-sky solar irradiance considering surrounding shadows. For all-sky conditions, cloud and aerosol data from 2012 to 2021 are obtained from the Copernicus Atmosphere Monitoring Service radiation service and the ECMWF Atmospheric Composition Reanalysis 4 product, respectively. The goals are to quantify the effect of solar elevation, the shadowing effect from adjacent buildings and constructions, and the effects of clouds and aerosols on the solar radiation availability on the rooftops and to investigate their interconnections. The spatio-temporal resolution of the analyses ranges from individual rooftop to neighborhood scale (approximately 3000 buildings) and from hourly intervals to ten years periods.The results of the solar potential assessment are made available as a web GIS map for potential public access, intended to aid urban planning and encourage widespread adoption of solar energy in the public and private sectors.
More is different: On the emergence of collective phenomena in fractured geological media

Fractures such as joints and faults are widely present in crustal rocks. These discontinuity structures often form complex networks and dominate the bulk behaviour of geological media. Thus, understanding how fracture networks affect multiphysical processes and phenomena in subsurface rock formations is highly relevant to many geoenergy and geoengineering applications. However, the large-scale behaviour of fractured rocks consisting of many fractures cannot be derived by simple applications of the knowledge of single fractures, due to the hierarchy of scales, heterogeneities, and physical mechanisms as well as the possible emergence of qualitatively different macroscopic properties. In other words, macroscopic phenomena in fractured media arise from the many-body effects of numerous interacting fractures, such that the emergent properties at the fracture network scale are much richer and often surprising compared to the behaviour of each individual fracture. So, more is different!To study this problem, I have developed a novel physics-based discrete fracture network modelling framework to simulate seismo-thermo-hydro-mechanical-chemical processes in fractured rocks. This modelling approach faithfully honours the discontinuous nature of geological media via explicit representations of fracture populations in rock and numerically computes multiphysics processes by solving the governing equations of fundamental mechanics. No a priori assumption about the representative elementary volume is needed, rendering this approach as an appropriate tool to study hierarchical crustal rocks that may have no characteristic length scale. Using this modelling paradigm, diverse macroscopic phenomena are spontaneously captured as emergent properties physically arising from the collective behaviour of a large population of existing/growing fractures in rock.In this presentation, I will illustrate the richness of collective phenomena in fractured media and elucidate the underlying multiscale, multiphysical mechanisms that drive their emergence. I will also show some application examples of using this fractured media simulation framework to address subsurface engineering problems such as underground excavation, injection-induced seismicity, and nuclear waste disposal. The modelling framework established and research findings obtained have important implications for safe and sustainable development of geoenergy and geoengineering.
An Introduction to NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI)

NASA&amp;#8217;s Airborne Data Management Group (ADMG) works to promote and ensure the discoverability and accessibility of over 50 years of non-satellite Earth science observations. This includes leading the development of the Catalog of Archived Suborbital Earth Science Investigations (CASEI), which provides a single entry point to efficiently search across all of NASA&amp;#8217;s airborne and field data holdings.&amp;#160; CASEI supports NASA&amp;#8217;s Open Source Science Initiative vision by providing holistic, descriptive contextual metadata and links to streamline access to suborbital data products, regardless of which repository is responsible for their stewardship.Metadata in CASEI includes descriptive contextual details that are typically arduous to locate amid a synthesis of scattered publications, project and program websites, and disparate data discovery tools. These metadata include motivating science objectives, key events/time periods in observational records, complementary simultaneous observations, and programmatic details, among others. Diversity of data formats and science disciplines served by CASEI necessitate a common data model to organize suborbital observation metadata and appropriately represent the relationships among campaigns, platforms, and instruments.This presentation will describe the development of the CASEI system: well-defined data models to drive a cloud-based user data access portal, simultaneously provisioned interfaces enabling synchronous metadata updates, the curation process required to sustain this unique inventory of airborne and field metadata, management of CASEI information content, and connecting end users to data products relevant for their interests - regardless of which NASA distributed archive center holds the data.Particular attention will be granted to how CASEI facilitates discovery and (re)use of these lesser-known NASA data, supporting FAIR principles and Open Science to enhance the return on investments made in these unique and varied observations. An up-to-date summary of CASEI inventory content, avenues for CASEI enhancements, and potential improvements in suborbital data stewardship at various stages of the data life cycle will also be discussed.
A Programming Model for Geospatial Machine-Learning with Scalability in Hybrid Multiclouds

While deep machine learning approaches are getting pervasively used in remote sensing and modeling the earth, difficulties due to the size of satellite data are always pains for scientists in implementing such experiential software. We present a programming model for geospatial machine learning based on TorchGeo and PyTorch, which are getting the de fact standards in programming with PyTorch/Python.&amp;#160; TorchGeo is open-sourced and designed to make it simple for remote sensing experts to explore machine learning solutions. Our objective is to allow machine-learning programs using TorchGeo to scale leveraging proprietary high-performance computing (HPC) and multicloud HPC resources, from ones notebook. One of key technologies specifically needed in geospatial machine learning is the smart integration of peta-scale data services and data-distributed parallel frameworks.&amp;#160; We implement such a platform as a part of IBM Research Geospatial Discovery Network (GDN) and experiment segmentation tasks such as flood detection from satellite data to show its scalability.
Detection and characterization of the onset and cessation dates of the dry season in Western Equatorial Africa based on solar radiation

Western Equatorial Africa (WEA) is characterized by a long and cloudy dry season extending from June to September. So far, no study has investigated the intra-seasonal characteristics of this dry season especially its onset and cessation dates. In our study, the onset and cessation dates are determined over the 38-year period 1983&amp;#8211;2020, using daily surface solar radiation (SSR) data from CMSAF SARAH-2. The maximum and minimum values of the cumulative anomalies of a regional index, for each year, are used to extract the onset and cessation dates. The mean onset date of the dry season in the region is May 17, the mean cessation date is October 3. We obtain very distinct anomaly patterns of SSR but also of low-level clouds and precipitation before/after the onset/cessation dates. The onset and cessation dates show strong year-to-year variability but no significant trend is detected over the 4 decades studied. Lastly, the cumulative anomalies for each year are also used to classify the dry seasons according to the SSR intra-seasonal evolution. Three types of years are obtained which are associated to different patterns of SST anomalies in the tropics.
Arrival Picking for Distributed Acoustic Sensing seismic based on fractional lower order statistics

In recent years, fiber-optic distributed acoustic sensing (DAS) has been gradually applied to seismology because of its long-distance and dense observation capability. It is a great challenge to effectively process the massive seismic data recorded by DAS. At present, the seismic data processing methods based on deep learning have achieved great success, especially in the tasks of seismic detection and arrival-time picking. However, due to the differences between DAS and geophone, such as sensing principles, spatial and temporal sampling rates, and noise intensity. The seismic arrival time picking model based on deep learning, which is trained by geophone seismic data with low spatial and temporal sampling rates and low noise intensity, severely degrades in performance on DAS seismic data with high spatial and temporal sampling rates and high noise intensity. In addition, a new seismic arrival time picking model is trained by fully supervised learning, which usually requires a large number of seismic data with accurate labels.&amp;#160;However, the huge cost of manual picking and the lack of effective automatic picking models make it very difficult to build large-scale DAS seismic data sets with accurate labels. Therefore, it is very difficult to build an arrival time picking model based on fully supervised learning for DAS seismic data.In this study, we propose a DAS seismic arrival time picking method based on fractional lower order statistics. Based on the difference of probability density function between noise and seismic signal, the proposed method uses alpha-stable distribution modeling noise (generally follow a Gaussian distribution) and seismic signal (generally follow a non-Gaussian distribution), and uses fractional lower order statistics under the assumption of alpha-stable distribution as the characteristic function to pick the arrival time.Synthetic and actual DAS data tests show that the proposed method has better performance and robustness to random noise than other methods based on characteristic functions, such as STA/LTA, AR-AIC and kurtosis. Since the actual DAS seismic data has no ground truth of arrival time, we have further the performance of the proposed method on the geophone seismic data set. The proposed method provides better results on geophone seismic data and the data after up-sampling them to the typical time sampling rate of DAS.
Going FAIR by the book: Accelerating the adoption of PID-enabled good practices in software communities through reference publication.

This is a report from the chapter editor's perspective of a high visibility publication effort to foster the adoption of the FAIR principles (Findable, Accessible, Interoperable, Reusable) by encouraging the adoption of Persistent Identifiers (PID) and repository-based workflows in geospatial open source software communities as good practices. Lessons learned are detailed about how to communicate the benefits of PID adoption to software project communities focussing on professional software-development and meritocracy. Also encountered communication bottleneck patterns, the significance of cross-project&amp;#160; multiplicators, remaining challenges and emerging opportunities for publishers and repository infrastructures are reported. For the second Edition of the Springer Handbook of Geographic Information, a team of scientific domain experts from several software communities was tasked to rewrite a chapter about Open Source Geographic Information Systems (DOI: 10.1007/978-3-030-53125-6_30). For this, a sample of representative geospatial open source projects was selected, based on the range of projects integrated in the OSGeo live umbrella project (DOI: 10.5281/zenodo.5884859). The chapters authors worked in close contact with the respective Open Source software project communities. Since the editing and production process for the Handbook of Geographic Information was delayed due to the pandemic, this provided the opportunity to explore, improve and implement good practices for state of the art PID-based citation of software projects and versions, but also project communities, data and related scientific video ressources. This was a learning process for all stakeholders involved in the publication project. At the completion of the project, the majority of the involved software projects had minted Digital Object Identifiers (DOI) for their codebases. While the adoption level of software versioning with automated PID-generation and metadata quality remains heterogeneous, the insights gained from this process can simplify and accelerate the adoption of PID-based best software community practices for other open geospatial projects according to the FAIR principles.