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During the last magmatic eruption period of Aso volcano (November 2014 to May 2015), a controlled-source electromagnetic volcano monitoring experiment (ACTIVE) was conducted. Here, we interpret the temporal variations in the electromagnetic responses. The ACTIVE system installed at the first Nakadake crater, the only active crater of Aso, consisted of a transmitter located northwest of the crater and four (before the eruptions) or three (after the eruptions) vertical induction coil receiver stations. The ACTIVE system succeeded in detecting temporal variations in the resistivity structure during the latest magmatic eruption period. The response amplitude started to increase in November 2014, peaked in February 2015, and decreased slightly in August 2015. An unstructured tetrahedral finite element three-dimensional inversion that accounted for topographic effects was used to interpret temporal variations in the ACTIVE response. The 3-D inversion results revealed that temporal variations in the ACTIVE response are attributed mainly to (1) a broad increase in resistivity at elevations from 750 to 850 m, not only directly beneath the crater bottom but also outside the crater, and (2) a thin layer of decrease in resistivity at the elevation of ~ 1000 m on the western side of the crater. The increase in resistivity can be ascribed to a decrease in the amount of conductive groundwater in the upper part of an aquifer located below the elevation of 800 m, while the decrease in resistivity implies that enhanced fluid temperature and pressure changed the subsurface hydrothermal system and formed a temporal fluid reservoir at the shallow level during the magmatic eruption period.

The electrical conductivity of the ocean is a fundamental parameter in the electrodynamics of the Earth System. This parameter is involved in a number of applications ranging from the calibration of in situ ocean flow meters, through extensions of traditional induction studies, and into quite new opportunities involving the remote sensing of ocean flow and properties from space-borne magnetometers such as carried aboard the three satellites of the Swarmmission launched in 2013. Here, the first ocean conductivity data set calculated directly from observed temperature and salinity measurements is provided. These data describe the globally gridded, three-dimensional mean conductivity as well as seasonal variations, and the statistics of spatial and seasonal variations are shown. This “climatology” data set of ocean conductivity is offered as a standard reference similar to the ocean temperature and salinity climatologies that have long been available.

We present a new finite element simulation approach in time domain for electromagnetic (EM) fields associated with motional induction by tsunamis. Our simulation method allows us to conduct three‐dimensional simulation with realistic smooth bathymetry and to readily obtain broad structures of tsunami‐generated EM fields and their time evolution, benefitting from time domain implementation with efficient unstructured mesh. Highly resolved mesh near observation sites enables us to compare simulation results with observed data and to investigate tsunami properties in terms of EM variations. Furthermore, it makes source separations available for EM data during tsunami events. We applied our simulation approach to the 2011 Tohoku tsunami event with seawater velocity from linear‐long and linear‐Boussinesq approximations. We revealed that inclusion of dispersion effect is necessary to explain magnetic variations at a northwest Pacific seafloor site, ~1,500 km away from the epicenter, while linear‐long approximation is enough at a seafloor site ~200 km east‐northeast of the epicenter. Our simulations provided, for the first time, comprehensive views of spatiotemporal structures of tsunami‐generated EM fields for the 2011 Tohoku tsunami, including large‐scale electric current circuits in the ocean. Finally, subtraction of the simulated magnetic fields from the observed data revealed symmetric magnetic variations on the western and eastern sides of the epicenter for 30 min since the earthquake origin time. These imply a pair of southward and northward electric currents in the ionosphere that exist on the western and eastern sides of the source region, respectively, which was likely to be caused by tsunami‐generated atmospheric acoustic/gravity waves reaching the ionosphere. We developed a new three‐dimensional time domain simulation code for tsunami‐generated electromagnetic signalsSeafloor tsunami magnetic signals were explained by our time domain simulationsSubtraction of simulated tsunami magnetic signals from observed data revealed tsunami‐generated electric current system in the ionosphere

This study presents tsunami inversion analyses that simultaneously use ocean‐bottom pressure and ocean‐bottom electromagnetic data along with an accurate tsunami propagation model to estimate the earthquake source model for the 2009 Samoa earthquake. This earthquake was a doublet event that occurred along a pair of normal and thrust faults. To the best of our knowledge, this joint inversion of pressure and electromagnetic data is unprecedented. The incorporation of electromagnetic data in the tsunami inversion analysis was relatively simple, and the inclusion of frequency dispersion, self‐attraction, and loading effects in the tsunami propagation calculations allowed the use of tsunami waveforms recorded at distant locations, which improved the reliability of the solutions obtained. Our best source model indicated that the east‐west extent of the plate‐boundary slip area was approximately 50 km, which is narrower than that of a previous fault model, and explained both electromagnetic and pressure data from the 2009 Samoa tsunami. On 29 September 2009, the 2009 Samoa earthquake occurred in the southern Pacific Ocean adjacent to the Tonga Trench subduction zone. This doublet earthquake was initiated as a normal‐faulting event seaward of the trench axis, followed by a thrust‐faulting event on the subduction zone interface close to the trench axis. An ocean bottom pressure gauge and magnetometers deployed in the South Pacific Ocean recorded the tsunami caused by the earthquake. Although tsunami inversion analysis generally uses ocean bottom pressure data alone, we employed ocean bottom electromagnetic data to estimate the slip area of this earthquake in this study. To the best of our knowledge, this is the first to join pressure and electromagnetic data were used jointly to yield an accurate slip model of the 2009 Samoa earthquake. We also considered wavelength dependence in the tsunami phase velocities to stabilize the inversion. Consequently, we found a slip area on the thrust fault that was shorter in the east‐west direction than that estimated from the pressure and tide gauge data in previous studies. Sea surface fluctuations converted from the electromagnetic data were as precise as ±5 mmTsunami magnetic field data were successfully incorporated into a conventional tsunami waveform inversion schemeThe inclusion of the effects of frequency dispersion, self‐attraction, and loading was important in finite‐fault inversion Sea surface fluctuations converted from the electromagnetic data were as precise as ±5 mm Tsunami magnetic field data were successfully incorporated into a conventional tsunami waveform inversion scheme The inclusion of the effects of frequency dispersion, self‐attraction, and loading was important in finite‐fault inversion

Tsunami flow coupled with the geomagnetic field generates electric currents and associated magnetic fields. Although electromagnetic (EM) tsunami signals can be used for analysis and even forecasting tsunami propagation, the dynamically self‐consistent effect of shoaling water depth on the fluid + electrodynamics has not been adequately clarified. In this study, we classify tsunami EM phenomena into three cases based on the ocean depth and find that the deeper ocean results in stronger self‐induction due to the increase in both tsunami phase velocity and ocean conductance. In this deep‐ocean case, the phase lead of the vertical magnetic variation relative to the sea surface elevation is smaller, while an initial rise in the horizontal magnetic component becomes observable prior to tsunami arrival. Furthermore, we confirm that the enhancement of tsunami height in shallower oceans shifts the ocean depth supplying maximum amplitudes of tsunami magnetic fields from approximately 2.0 km to 1.5 km. Tsunami‐generated magnetic fields are strongly controlled by the ocean depthMagnetic tsunami signals become largest around an ocean depth of 1.5 kmMagnetic fields observable prior to tsunami arrivals change with the ocean depth

Recently, electromagnetic observations have become common not only on land but also on the seafloor. In particular, thanks to the development of instruments for use in shallow seas, one can conduct observations along land-sea arrays. However, since there is a large contrast in conductivity between sea water and rocks in the crust and the mantle, we have to pay more attention to the accuracy of forward solvers used for electromagnetic induction problems, including bathymetry. In this paper, we develop a two-dimensional forward code using triangular finite elements, and confirm the accuracy of the new code by TM mode responses. The accuracy of the improved solver was tested by comparison with an analytical solution in a hemi-cylindrical geometry. We also show that triangular elements are more reliable than rectangular elements in determining conductivity structures beneath land-sea arrays. Our results indicate the importance of precisely discretized bathymetry and the accuracy of spatial derivatives of electromagnetic field components, especially in the vicinity of coastlines.

The motion of conductive seawater by tsunamis can generate magnetic fields in the presence of the background geomagnetic main field. Previous studies found that, using the tsunami‐generated seafloor magnetic field, it is possible to predict the propagation direction and wave height prior to the actual arrivals of tsunamis. This study correlates the tsunami magnetic field and the tsunami sea level change using observed data and three‐dimensional simulations of the 2009 Samoa and 2010 Chile tsunamis. Our direct comparison of the tsunami observed magnetic field and tsunami sea level change illustrate that the vertical tsunami magnetic component, bz, arrived earlier than the sea level change. The “initial rise” signal in the observed horizontal tsunami magnetic component, bh, which was arrived even earlier than bzalso is found by combing the observation with the three‐dimensional simulations. We further examine the precision of conversion of the tsunami magnetic field to the sea level change and find that the magnetic field derived tsunami sea levels are as precise as those obtained from differential pressure gauge data. However, our simulation shows that existing tsunami source models are incompatible with our tsunami magnetic data. Therefore, it is necessary to include magnetic field derived tsunami sea level changes to improve those source models. The tsunami‐generated magnetic field is a magnetic field that appears with movement of seawater by tsunamis. In the previous studies, researchers found that the tsunami‐generated magnetic field arrives earlier than the tsunami sea level change based on analytical solutions and numerical simulations. In this study, we used the world's first simultaneous data of sea level change and magnetic field in the 2009 Samoa and 2010 Chile tsunamis to study the relation between these two physical quantities. We found that the vertical component of tsunami magnetic field arrives earlier than the sea level change. Moreover, the horizontal component of tsunami magnetic field arrives even earlier than the vertical component. We also revealed that the tsunami magnetic field can be used to estimate the tsunami wave height very accurately. We investigated the observed tsunami magnetic field by the 3‐D time domain simulation. However, the currently available tsunami source models were unable to reproduce the observation in our research area. We confirmed that a better source model can improve the simulation. It follows that our high precision tsunami wave height data calculated from the magnetic field can improve the existing tsunami source models. Direct comparison of the observed tsunami magnetic field with tsunami sea level change for the 2009 Samoa and 2010 Chile tsunamisEstimation of the tsunami wave height by the tsunami‐generated magnetic field3‐D time domain simulation of both tsunami sea level change and magnetic field for the 2009 Samoa and 2010 Chile tsunamis Direct comparison of the observed tsunami magnetic field with tsunami sea level change for the 2009 Samoa and 2010 Chile tsunamis Estimation of the tsunami wave height by the tsunami‐generated magnetic field 3‐D time domain simulation of both tsunami sea level change and magnetic field for the 2009 Samoa and 2010 Chile tsunamis

We have submitted a secular variation (SV) candidate model for the thirteenth generation of International Geomagnetic Reference Field model (IGRF-13) using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code. This is the first contribution to the IGRF community from research groups in Japan. A geomagnetic field model derived from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data, has been used as input data to the assimilation scheme. We adopt an ensemble-based assimilation scheme, called four-dimensional ensemble-based variational method (4DEnVar), which linearizes outputs of MHD dynamo simulation with respect to the deviation from a dynamo state vector at an initial condition. The data vector for the assimilation consists of the poloidal scalar potential of the geomagnetic field at the core surface and flow velocity field slightly below the core surface. Dimensionless time of numerical geodynamo is adjusted to the actual time by comparison of secular variation time scales. For SV prediction, we first generate an ensemble of dynamo simulation results from a free dynamo run. We then assimilate the ensemble to the data with a 10-year assimilation window through iterations, and finally forecast future SV by the weighted sum of the future extension parts of the ensemble members. Hindcast of the method for the assimilation window from 2004.50 to 2014.25 confirms that the linear approximation holds for 10-year assimilation window with our iterative ensemble renewal method. We demonstrate that the forecast performance of our data assimilation and forecast scheme is comparable with that of IGRF-12 by comparing data misfits 4.5 years after the release epoch. For estimation of our IGRF-13SV candidate model, we set assimilation window from 2009.50 to 2019.50. We generate our final SV candidate model by linear fitting for the weighted sum of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derive errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members.