On: “Effect of temporal and spatial variations of the primary signal on VLF total‐field surveys” by M. A. Vallee, M. Chouteau, and G. J. Palacky (January 1992 GEOPHYSICS, p. 97‐105)

Geophysics ◽  
1993 ◽  
Vol 58 (5) ◽  
pp. 756-756 ◽  
Author(s):  
Jean Roy
Keyword(s):  
Numerical Modeling ◽  
Field Amplitude ◽  
Primary Field ◽  
Dense Network ◽  
Total Field ◽  
Field Surveys ◽  
Attitude Stability ◽  
Amplitude Data ◽  
Primary Signal ◽  
Temporal And Spatial

Vallee et al. (1992) remark on the sensitivity of airborne ratio measuring VLF instruments to platform attitude stability. The authors also remind the users of VLF total field amplitude data, as produced by instruments such as the Herz TOTEM, of two problems associated with this type of data: spatial and temporal fluctuations of the VLF primary field. They recommend the use of a dense network of VLF monitoring stations and numerical modeling of field propagation to cope with these problems. These two recommendations are briefly discussed here and one alternative recommendation is made.

Effect of temporal and spatial variations of the primary signal on VLF total‐field surveys

Geophysics ◽  
1992 ◽  
Vol 57 (1) ◽  
pp. 97-105 ◽  
Author(s):  
Marc A. Vallée ◽  
Michel Chouteau ◽  
G. J. Palacky
Keyword(s):  
Survey Data ◽  
Solar Flares ◽  
Temporal Variations ◽  
Spatial Variations ◽  
Spatial And Temporal Variations ◽  
Significant Implication ◽  
Primary Field ◽  
Total Field ◽  
Field Surveys ◽  
Primary Signal

Most of the airborne and ground VLF instruments presently used measure the total‐field response in addition to field ratios. Results of surveys using these instruments are adversely affected by spatial and temporal variations in the VLF primary field. Until now, the nature of such variations has not been studied from the point of view of geophysical surveying practice. Spatial variations are analyzed using radio propagation models. The most important result is the identification of primary field minima where surveys would be unreliable. Their dependence on the transmitter location is rather complex, and modeling should be carried out before specifying VLF stations for a survey area. Spatial and temporal variations have been studied using field monitoring of the transmitted signal. The results of field experiments indicate that the nature of the received VLF fields changes significantly even over moderate distances (20–30 km) and that data cannot be reliably corrected over larger distances. This observation has a significant implication for VLF total‐field surveys, particularly airborne, in which base stations have been routinely used to monitor the primary field strength and to correct the survey data. The results of primary signal monitoring are also used to demonstrate the effect of solar flares on VLF surveys. Because of the large intensity and complex electromagnetic character of solar flares, survey data recorded during such events cannot be used for map compilation and interpretation.

Constrained 3D inversion of magnetic data with structural orientation and borehole lithology: A case study in the Macheng iron deposit, Hebei, China

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. B121-B133 ◽  
Author(s):  
Shida Sun ◽  
Chao Chen ◽  
Yiming Liu
Keyword(s):  
Remanent Magnetization ◽  
Magnetic Data ◽  
Iron Deposit ◽  
Reference Field ◽  
Total Field ◽  
Equivalent Source ◽  
Structural Orientation ◽  
Amplitude Data ◽  
Ore Bodies

We have developed a case study on the use of constrained inversion of magnetic data for recovering ore bodies quantitatively in the Macheng iron deposit, China. The inversion is constrained by the structural orientation and the borehole lithology in the presence of high magnetic susceptibility and strong remanent magnetization. Either the self-demagnetization effect caused by high susceptibility or strong remanent magnetization would lead to an unknown total magnetization direction. Here, we chose inversion of amplitude data that indicate low sensitivity to the direction of magnetization of the sources when constructing the underground model of effective susceptibility. To reduce the errors that arise when treating the total-field anomaly as the projection of an anomalous field vector in the direction of the geomagnetic reference field, we develop an equivalent source technique to calculate the amplitude data from the total-field anomaly. This equivalent source technique is based on the acquisition of the total-field anomaly, which uses the total-field intensity minus the magnitude of the reference field. We first design a synthetic model from a simplified real case to test the new approach, involving the amplitude data calculation and the constrained amplitude inversion. Then, we apply this approach to the real data. The results indicate that the structural orientation and borehole susceptibility bounds are compatible with each other and are able to improve the quality of the recovered model to obtain the distribution of ore bodies quantitatively and effectively.

scholarly journals Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data

2019 ◽  
Vol 23 (6) ◽  
pp. 2647-2663 ◽  
Author(s):  
Yingchun Huang ◽  
András Bárdossy ◽  
Ke Zhang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Model Performance ◽  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Dense Network ◽  
Spatially Distributed ◽  
Important Input ◽  
Temporal And Spatial ◽  
Improved Model

Abstract. Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

scholarly journals Field surveys and numerical modeling of the August 2016 Typhoon Lionrock along the northeastern coast of Japan: the first typhoon making landfall in Tohoku region

2020 ◽  
Vol 105 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Mohammad Heidarzadeh ◽  
Takumu Iwamoto ◽  
Tomohiro Takagawa ◽  
Hiroshi Takagi
Keyword(s):  
Numerical Modeling ◽  
Numerical Model ◽  
Tide Gauge ◽  
Wave Runup ◽  
Field Surveys ◽  
Death Toll ◽  
Hurricane Wind ◽  
Northeastern Japan ◽  
Strong Winds ◽  
Severe Flooding

AbstractTyphoon Lionrock, also known as the national number 1610 in Japan, caused severe flooding in east Japan in August 28–31, 2016, leaving a death toll of 22. With a maximum sustained wind speed of ~ 220 km/h from the Joint Typhoon Warning Center’s best track, Lionrock was classified as a category 4 hurricane in Saffir–Simpson Hurricane Wind Scale and as a typhoon in Japan Meteorological Agency’s scale. Lionrock was among unique typhoons as it started its landfall from north of Japan. Here, we studied the characteristics of this typhoon through tide gauge data analysis, field surveys and numerical modeling. Tide gauge analysis showed that the surges generated by Lionrock were in the ranges of 15–55 cm with surge duration of 0.8–3.1 days. Our field surveys revealed that the damage to coastal communities/structures was moderate although it caused severe flooding inland. We measured a maximum coastal wave runup of 4.3 m in Iwaisaki. Such a runup was smaller than that generated by other category 4 typhoons hitting Japan in the past. Our numerical model was able to reproduce the storm surge generated by the 2016 Typhoon Lionrock. This validated numerical model can be used in the future for typhoon-hazard studies along the coast of northeastern Japan. Despite relatively small surge/wave runups in coastal areas, Lionrock’s death toll was more than that of some other category 4 typhoons. We attribute this to various primary (e.g., flooding, surges, waves, strong winds) and secondary (e.g., landslides, coastal erosions, debris flows, wind-blown debris) mechanisms and their combinations and interactions that contribute to damage/death during a typhoon event.

Assessing and ameliorating edge effects in magnetic data transformations

2020 ◽  
Author(s):  
Peter Lelièvre ◽  
Dominique Fournier ◽  
Sean Walker ◽  
Nicholas Williams ◽  
Colin Farquharson
Keyword(s):  
Edge Effects ◽  
Mineral Exploration ◽  
Magnetization Vector ◽  
Magnetic Data ◽  
Separation Procedure ◽  
Magnetic Intensity ◽  
Source Inversion ◽  
Total Field ◽  
Equivalent Source ◽  
Amplitude Data

<p>Reduction to pole and other transformations of total field magnetic intensity data are often challenging to perform at low magnetic latitudes, when remanence exists, and when large topographic relief exists. Several studies have suggested use of inversion-based equivalent source methods for performing such transformations under those complicating factors. However, there has been little assessment of the importance of erroneous edge effects that occur when fundamental assumptions underlying the transformation procedures are broken. In this work we propose a transformation procedure that utilizes magnetization vector inversion, inversion-based regional field separation, and equivalent source inversion on unstructured meshes. We investigated whether edge effects in transformations could be reduced by performing a regional separation procedure prior to equivalent source inversion. We applied our proposed procedure to the transformation of total field magnetic intensity to magnetic amplitude data, using a complicated synthetic example based on a real geological scenario from mineral exploration. While the procedure performed acceptably on this test example, the results could be improved. We pose many questions regarding the various choices and control parameters used throughout the procedure, but we leave the investigation of those questions to future work.</p>

Quantifying the error level in computed magnetic amplitude data for 3D magnetization inversion

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. J75-J84 ◽  
Author(s):  
Camriel Coleman ◽  
Yaoguo Li
Keyword(s):  
Field Data ◽  
Regularization Parameter ◽  
Three Dimensional ◽  
Inverse Model ◽  
Magnetic Data ◽  
Total Field ◽  
Noise Estimate ◽  
Amplitude Inversion ◽  
Amplitude Data ◽  
Magnetic Amplitude

Three-dimensional inversion plays an important role in the quantitative interpretation of magnetic data in exploration problems, and magnetic amplitude data can be an effective tool in cases in which remanently magnetized materials are present. Because amplitude data are typically calculated from total-field anomaly data, the error levels must be characterized for inversions. Lack of knowledge of the error in amplitude data hinders the ability to properly estimate the data misfit associated with an inverse model and, therefore, the selection of the appropriate regularization parameter for a final model. To overcome these challenges, we have investigated the propagation of errors from total-field anomaly to amplitude data. Using parametric bootstrapping, we find that the standard deviation of the noise in amplitude data is approximately equal to that of the noise in total-field anomaly data when the amplitude data are derived from the conversion of total-field data to three orthogonal components. We then illustrate how the equivalent source method can be used to estimate the error in total-field anomaly data when needed. The obtained noise estimate can be applied to amplitude inversion to recover an optimal inverse model by applying the discrepancy principle. We test this method on synthetic and field data and determine its effectiveness.

Field surveys and numerical modeling of the December 2018 Anak Krakatau volcanic tsunami

2020 ◽  
Author(s):  
Mohammad Heidarzadeh ◽  
Purna Sulastya Putra ◽  
Abdul Muhari ◽  
Septriono Hari Nugroho
Keyword(s):  
Numerical Modeling ◽  
Source Model ◽  
Tsunami Source ◽  
Tsunami Height ◽  
Tsunami Inundation ◽  
Field Surveys ◽  
Source Models ◽  
Krakatau Volcano ◽  
Source Dimension ◽  
Maximum Tsunami Height

<p>We report results of field surveys and numerical modeling of the tsunami generated by the Anak Krakatau volcano eruption on 22 December 2018. We conducted two sets of field surveys of the coastal areas destroyed by the Anak Krakatau tsunami in 26-30 December 2018 and 4-10 January 2020. Field surveys provided information about the maximum tsunami height as well as the most damaged area. The maximum tsunami height was up to 13 m. Most locations registered a wave height of 3-4 m. Tsunami inundation was limited to approximately 100 m. For modeling, we considered 12 source models and conducted numerical modeling. The scenarios have source dimensions of 1.5–4 km and initial tsunami amplitudes of 10–200 m. By comparing observed and simulated waveforms, we constrained the tsunami source dimension and initial amplitude in the ranges of 1.5–2.5 km and 100–150 m, respectively. The best source model involves potential energy of 7.14 × 10<sup>13</sup>–1.05 × 10<sup>14</sup> J which is equivalent to an earthquake of magnitude 6.0–6.1.</p>

Temporal and spatial structure of a volcanic ash cloud: ground-based remote sensing and numerical modeling

2010 ◽  
Author(s):  
Klaus Schäfer ◽  
Wolfram Birmili ◽  
Josef Cyrys ◽  
Stefan Emeis ◽  
Renate Forkel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Numerical Modeling ◽  
Spatial Structure ◽  
Volcanic Ash ◽  
Volcanic Ash Cloud ◽  
Temporal And Spatial ◽  
Ash Cloud
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