A Multiparametric Approach to Study the Preparation Phase of the 2019 M7.1 Ridgecrest (California, United States) Earthquake

The 2019 M7.1 Ridgecrest earthquake was the strongest one in the last 20 years in California (United States). In a multiparametric fashion, we collected data from the lithosphere (seismicity), atmosphere (temperature, water vapor, aerosol, and methane), and ionosphere (ionospheric parameters from ionosonde, electron density, and magnetic field data from satellites). We analyzed the data in order to identify possible anomalies that cannot be explained by the typical physics of each domain of study and can be likely attributed to the lithosphere-atmosphere-ionosphere coupling (LAIC), due to the preparation phase of the Ridgecrest earthquake. The results are encouraging showing a chain of processes that connect the different geolayers before the earthquake, with the cumulative number of foreshocks and of all other (atmospheric and ionospheric) anomalies both accelerating in the same way as the mainshock is approaching.

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Swarm Satellite Magnetic Field Data Analysis Prior to 2019 Mw = 7.1 Ridgecrest (California, USA) Earthquake

Geosciences ◽

10.3390/geosciences10120502 ◽

2020 ◽

Vol 10 (12) ◽

pp. 502

Author(s):

Dedalo Marchetti ◽

Angelo De Santis ◽

Saioa A. Campuzano ◽

Maurizio Soldani ◽

Alessandro Piscini ◽

...

Keyword(s):

Magnetic Field ◽

Field Data ◽

Geomagnetic Latitude ◽

Earthquake Forecasting ◽

Magnetic Data ◽

Epicentral Area ◽

Magnetic Field Data ◽

Preparation Phase ◽

Swarm Satellite ◽

The Magnetic Field

This work presents an analysis of the ESA Swarm satellite magnetic data preceding the Mw = 7.1 California Ridgecrest earthquake that occurred on 6 July 2019. In detail, we show the main results of a procedure that investigates the track-by-track residual of the magnetic field data acquired by the Swarm constellation from 1000 days before the event and inside the Dobrovolsky’s area. To exclude global geomagnetic perturbations, we select the data considering only quiet geomagnetic field time, defined by thresholds on Dst and ap geomagnetic indices, and we repeat the same analysis in two comparison areas at the same geomagnetic latitude of the Ridgecrest earthquake epicentre not affected by significant seismicity and in the same period here investigated. As the main result, we find some increases of the anomalies in the Y (East) component of the magnetic field starting from about 500 days before the earthquake. Comparing such anomalies with those in the validation areas, it seems that the geomagnetic activity over California from 222 to 168 days before the mainshock could be produced by the preparation phase of the seismic event. This anticipation time is compatible with the Rikitake empirical law, recently confirmed from Swarm satellite data. Furthermore, the Swarm Bravo satellite, i.e., that one at highest orbit, passed above the epicentral area 15 min before the earthquake and detected an anomaly mainly in the Y component. These analyses applied to the Ridgecrest earthquake not only intend to better understand the physical processes behind the preparation phase of the medium-large earthquakes in the world, but also demonstrate the usefulness of a satellite constellation to monitor the ionospheric activity and, in the future, to possibly make reliable earthquake forecasting.

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Analysis of Swarm Satellite Magnetic Field Data Before the 2016 Ecuador (Mw = 7.8) Earthquake Based on Non-negative Matrix Factorization

Frontiers in Earth Science ◽

10.3389/feart.2021.621976 ◽

2021 ◽

Vol 9 ◽

Author(s):

Kaiguang Zhu ◽

Mengxuan Fan ◽

Xiaodan He ◽

Dedalo Marchetti ◽

Kaiyan Li ◽

...

Keyword(s):

Magnetic Field ◽

Matrix Factorization ◽

Field Data ◽

Cumulative Number ◽

Sensitive Area ◽

Plasma Bubble ◽

Magnetic Field Data ◽

Swarm Satellite ◽

Benioff Strain ◽

Non Negative Matrix Factorization

In this paper, based on non-negative matrix factorization (NMF), we analyzed the ionosphere magnetic field data of the Swarm Alpha satellite before the 2016 (Mw = 7. 8) Ecuador earthquake (April 16, 0.35°N, 79.93°W), including the whole data collected under quiet and disturbed geomagnetic conditions. The data from each track were decomposed into basis features and their corresponding weights. We found that the energy and entropy of one of the weight components were more concentrated inside the earthquake-sensitive area, which meant that this weight component was more likely to reflect the activity inside the earthquake-sensitive area. We focused on this weight component and used five times the root mean square (RMS) to extract the anomalies. We found that for this weight component, the cumulative number of tracks, which had anomalies inside the earthquake-sensitive area, showed accelerated growth before the Ecuador earthquake and recovered to linear growth after the earthquake. To verify that the accelerated cumulative anomaly was possibly associated with the earthquake, we excluded the influence of the geomagnetic activity and plasma bubble. Through the random earthquake study and low-seismicity period study, we found that the accelerated cumulative anomaly was not obtained by chance. Moreover, we observed that the cumulative Benioff strain S, which reflected the lithosphere activity, had acceleration behavior similar to the accelerated cumulative anomaly of the ionosphere magnetic field, which suggested that the anomaly that we obtained was possibly associated with the Ecuador earthquake and could be described by one of the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) models.

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An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

Journal of Geological Research ◽

10.30564/jgr.v1i3.1316 ◽

2020 ◽

Vol 1 (3) ◽

Author(s):

Maysam Abedi

Keyword(s):

Magnetic Field ◽

Magnetic Susceptibility ◽

Least Squares ◽

Minimization Problem ◽

Potential Field ◽

Field Data ◽

Least Squares Method ◽

Porphyry Copper ◽

Magnetic Data ◽

Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit. The real case study located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential of mineral occurrences, especially porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

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A reexamination of “interstellar ion waves” previously identified in Pioneer 10 magnetic field data

Geophysical Research Letters ◽

10.1029/98gl02802 ◽

1998 ◽

Vol 25 (19) ◽

pp. 3721-3724 ◽

Cited By ~ 3

Author(s):

Neil Murphy ◽

Edward J. Smith ◽

Joyce Wolf ◽

Devrie S. Intriligator

Keyword(s):

Magnetic Field ◽

Field Data ◽

Magnetic Field Data ◽

Ion Waves ◽

Pioneer 10

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Ogo 5 magnetic-field data near the Earth's bow shock: A correlation with theory

Journal of Geophysical Research Atmospheres ◽

10.1029/ja077i004p00604 ◽

1972 ◽

Vol 77 (4) ◽

pp. 604-610 ◽

Cited By ~ 19

Author(s):

J. K. Guha ◽

D. L. Judge ◽

J. H. Marburger

Keyword(s):

Magnetic Field ◽

Field Data ◽

Bow Shock ◽

Magnetic Field Data ◽

Earth’S Bow Shock

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Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽

10.1190/1.1444837 ◽

2000 ◽

Vol 65 (5) ◽

pp. 1489-1494 ◽

Cited By ~ 34

Author(s):

Richard S. Smith ◽

A. Peter Annan

Keyword(s):

Magnetic Field ◽

Field Data ◽

Indirect Measurement ◽

Rate Of Change ◽

Induction Coil ◽

Voltage Response ◽

Magnetic Field Data ◽

Transient Electromagnetic ◽

Different Characteristics ◽

The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

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