A review of open software resources in python for electrical resistivity modelling

Geophysical modelling performs to obtain subsurface structures in agreement with measured data. Freeware algorithms for geoelectrical data inversion have not been widely used in geophysical communities; however, different open-source modelling/inversion algorithms were developed in recent years. In this study, we review the structures and applications of openly Python-based inversion packages, such as pyGIMLi (Python Library for Inversion and Modelling in Geophysics), BERT (Boundless Electrical Resistivity Tomography), ResIPy (Resistivity and Induced Polarization with Python), pyres (Python wrapper for electrical resistivity modelling), and SimPEG (Simulation and Parameter Estimation in Geophysics). In addition, we examine the recovering ability of pyGIMLi, BERT, ResIPy, and SimPEG freeware through inversion of the same synthetic model forward responses. A versatile pyGIMLi freeware is highly suitable for various geophysical data inversion. The SimPEG framework is developed to allow the user to explore, experiment with, and iterate over multiple approaches to the inverse problem. In contrast, BERT, pyres, and ResIPy are exclusively designed for geoelectric data inversion. BERT and pyGIMLi codes can be easily modified for the intended applications. Both pyres and ResIPy use the same mesh designs and inversion algorithms, but pyres uses scripting language, while ResIPy uses a graphical user interface (GUI) that removes the need for text inputs. Our numerical modelling shows that all the tested inversion freeware could be effective for relatively larger targets. pyGIMLi and BERT could also obtain reasonable model resolutions and anomaly accuracies for small-sized subsurface structures. Based on the heterogeneous layered model and experimental target scenario results, the geoelectrical data inversion could be more effective in pyGIMLi, BERT, and SimPEG freeware packages. Moreover, this study can provide insight into implementing suitable inversion freeware for reproducible geophysical research, mainly for geoelectrical modelling.

Electromagnetic Induction Measurements for Investigating Soil Salinization Caused by Saline Reclaimed Water

This paper focused on the use of electromagnetic induction measurements in order to investigate soil salinization caused by irrigation with saline reclaimed water. An experimental activity was carried out during the growing season of tomato crop in order to evaluate expected soil salinization effects caused by different saline agro-industrial wastewaters used as irrigation sources. Soil electrical conductivity, strictly related to the soil salinity, has been monitored for three months by means of Electromagnetic Induction (EMI) measurements, and evident differences in the soil response have been observed. The study highlighted two aspects that can improve soil investigation due to the utilization of geophysical tools. First, EMI data can map large areas in a short period of time with an unprecedented level of detail by overcoming practical difficulties in order to massively sample soil. At the same time, repeated measurements over time allow updating real-time soil salinity maps by using accurate correlations with soil electrical conductivity. This application points out how integrated agro-geophysical research approaches can play a strategic role in agricultural saline water management in order to prevent soil salinization risks in medium to long-term periods.

Comment on: “Interseismic Strain Accumulation near Lisbon (Portugal) from Space Geodesy” by Fonseca et al. (2021)

The paper by Fonseca et al. (2021), hereafter referred as FON21, published in Geophysical Research Letters2 make several conclusions that are not convincingly supported by the evidence of the data that is made available. In this comment we will address the following statements: 1) FON21 “provides new evidence of sinistral simple shear driven by a NNE-SSW first-order tectonic lineament; 2) “PSInSAR vertical velocities corroborate qualitatively the GNSS strain-rate field, showing uplift/subsidence where the GNSS data indicate contraction/extension”; 3) FON21 proposes “the presence of a small block to the W of Lisbon moving independently toward the SW with a relative velocity of 0.96 ± 0.20 mm/yr”; 4) FON21 shows “that the contribution of intraplate faults to the seismic hazard in the LMA is more important than currently assumed”. We conclude that more evidence needs to be collected to confirm or infirm FON21 statements and conclusions. For the moment the proposal of an autonomous crustal block moving with significant velocity in relation to the neighboring domain should be considered speculative and unproved.

Two months of measurements with an autonomous thermodynamic Raman lidar in Lindenberg

<p>Between 15 July 2020 and 19 September 2021, the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) collected data at the Lindenberg Observatory of the Deutscher Wetterdienst (DWD), including temperature and water vapor mixing ratio with a high temporal and range resolution.</p> <p>During the operation period, very stable 24/7 operation was achieved, and ARTHUS demonstrated that is capable to observe the atmospheric boundary layer and lower free troposphere during both daytime and nighttime up to the turbulence scale, with high accuracy and precision, and very short latency. During nighttime, the measurement range increases even up to the tropopause and lower stratosphere.</p> <p>ARTHUS measurements resolve the strength of the inversion layer at the planetary boundary layer top, elevated lids in the free troposphere, and turbulent fluctuations in water vapor and temperature, simultaneously (Lange et al., 2019, Wulfmeyer et al., 2015). In addition to thermodynamic variables, ARTHUS provides also independent profiles of the particle backscatter coefficient and the particle extinction coefficient from the rotational Raman signals at 355 nm with much better resolution than a conventional vibrational Raman lidar.</p> <p>At the conference, highlights of the measurements will be presented. Furthermore, the statistics of more than 150 comparisons with local radiosondes will be presented which confirm the high accuracy of the temperature and moisture measurements of ARTHUS.</p> <p><strong><em>Acknowledgements</em></strong></p> <p>The development of ARTHUS was supported by the Helmholtz Association of German Research Centers within the project Modular Observation Solutions for Earth Systems (MOSES). The measurements in Lindenberg were funded by DWD.</p> <p><strong><em>References </em></strong></p> <p>Lange, D., Behrendt, A., and Wulfmeyer, V. (2019). Compact operational tropospheric water vapor and temperature Raman lidar with turbulence resolution. <em>Geophysical Research Letters</em>, 46. https://doi.org/10.1029/2019GL085774</p> <p>Wulfmeyer, V., R. M. Hardesty, D. D. Turner, A. Behrendt, M. P. Cadeddu, P. Di Girolamo, P. Schlüssel, J. Van Baelen, and F. Zus (2015), A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles, <em>Rev. Geophys.</em>, 53,819–895, doi:10.1002/2014RG000476</p>

Ozonsimulationen mit ICON für die Verbesserung von UV-Vorhersagen

<p class="Default">Ozon (O<sub>3</sub>) in der Stratosphäre absorbiert die biologisch schädliche ultraviolette Strahlung der Sonne (den größten Teil der UV-B-Strahlung) und verhindert, dass sie die Erdoberfläche erreicht. Die energiereiche UV-Strahlung kann das genetische Material in den Zellen von Pflanzen und Tieren, sowie von Menschen zerstören. Ohne die stratosphärische Ozonschicht wäre das Leben auf der Erde, wie wir es kennen, nicht möglich.</p> <p class="Default">Der Deutsche Wetterdienst (DWD) stellt UV-Indexkarten zur Verfügung, um die Bevölkerung bezgl. hoher UV-Belastungen zu informieren und zu warnen [1]. Dazu werden Daten aus dem golobalen Vorhersagemodell ICON (ICOsahedral Non-hydrostatic model) [2], externe Ozondaten und ein eigenes UV-Modell verwendet, um eine Vorhersage des UV-Index zu erstellen, der z.B. auf der DWD-Webseite als Vorhersage visualisiert wird.</p> <p class="Default">In diesem Projekt wird in Zusammenarbeit mit dem DWD ein selbstkonsistentes System entwickelt, um UV-Indexkarten vollständig mittels ICON zu generieren. Zu diesem Zweck wird ein linearisiertes Ozonschema (LINOZ) [3] für tägliche Ozonvorhersagen optimiert. Dies geschieht als Erweiterung der ICON-ART Struktur [4] [5] (ART: Aerosols and Reactive Trace gases). Für die Berechnung von UV-Strahlungsflüssen und -indizes wurde ein Strahlungstransportmodell für Sonnenstrahlung (Cloud-J) [6] implementiert und angepasst. Da das gesamte System als effiziente Lösung für UV-Indexvorhersagen dem DWD zur Verfügung gestellt werden soll, wird besonders Wert auf eine umfassende Funktionalität bei sehr geringem Rechenaufwand gelegt. Ein wichtiger Teil der Arbeit ist daher auch die Validierung und Optimierung der Verfahren und Abläufe, um zuverlässige und qualitativ hochwertige Vorhersagen zu erstellen.</p> <p class="Default">Wir präsentieren erste Ergebnisse des von ICON-ART modellierten UV-Strahlungsflusses durch die Atmosphäre auf globaler Skala und über ausgewählten Gebieten, dessen tageszeitliche Variation, sowie den Einfluss von Wolken auf die UV-Intensität.</p> <p><strong>Anmerkung:</strong></p> <p>Dieses Projekt wird durch den Deutschen Wetterdienst im Rahmen der Extramuralen Forschung mit folgender Nummer gefördert: 4819EMF03.</p> <p><strong>Referenzen:</strong></p> <p>[1]  https://kunden.dwd.de/uvi/index.jsp</p> <p>[2]   Zängl, G., et al., The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD MPI-M: Description of the non-hydrostatic dynamical core. Q.J.R. Meteorol. Soc., 141(687), 563-579 (2014)</p> <p>[3]   McLinden, C. A., et al., Stratospheric ozone in 3-D models: A simple chemistry and the cross-tropopause flux, Journal of Geophysical Research: Atmospheres, 105(D11), 14653-14665 (2000)</p> <p>[4]  Rieger, D., et al., ICON-ART - A new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8(6), 1659-1676 (2015)</p> <p>[5]  Schröter, et al., ICON-ART 2.1: a flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations. Geosci. Model Dev., 11(10), 4043-4068 (2018)</p> <p>[6]  Prather, M.J., Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c. Geosci. Model Dev., 8(8), 2587-2595 (2015)</p>

CHINGIZ EARTHQUAKE on January 20, 2015, KR=12.2, mb=5.6, I_0^P=5–6 NEAR the SEMIPALATINSK POLYGON (Eastern Kazakhstan)

The work presents the results of data analysis of the earthquake on January 20, 2015, mb=5.6 occurred at the low-active region of Eastern Kazakhstan near the border of the former Semipalatinsk Test Site. The work briefly describes the seismicity of the Semipalatinsk Test Site related to aseismic regions according to the acting map of general seismic zoning. The earthquake was recorded by permanent seismic stations of the Institute of Geophysical Research of the Ministry of Energy of the Republic of Kazakhstan and by two strong-motion accelerometers. The earthquake was felt at distances up to 300 km from the epicentre, its maximum intensity was noted at Medeu settlement where the shaking intensity was 56 by the MSK-64 scale. The earthquake focal mechanism is shown, the parameters of seismic effects and response spectra are calculated.

SEISMICITY of KAZAKHSTAN and ADJACENT TERRITORIES in 2015

In 2015, the seismic monitoring in Kazakhstan was conducted by two Organizations: Seismological Experience-Methodical Expedition of the Ministry of Education and Science of the Republic of Kazakhstan (SEME), and the Republican State Enterprise Institute of Geophysical Research of the Ministry of Energy of the Republic of Kazakhstan (IGR). The paper presents detailed information on seismic observation networks. The joint catalogue based on the data of two organizations was compiled. It includes 489 earthquakes having energy class KR=6.6–12.2. The largest earthquake occurred on January 20, 2015 near the Semipalatinsk Test Site. The earthquake had magnitude MPVA=5.3, energy class KR=12.2, and was felt in a range of Kazakhstan settlements.