Determination of coordinates of the earthquake hypocenter by the method of circles

Objective. The aim of the study is to develop a method for determining the coordinates of the earthquake hypocenter using various combinations of second and fourth order figures as a geo-locus of the hypocenter position points.Method. It is known that the line of intersection of figures of the second and fourth orders, in the case of coincidence of focuses, is a circle. To determine the coordinates of the earthquake source, data from seismographs are used, which are used to construct figures of the second and fourth order, the intersection point of which is the hypocenter. When using data from two seismic sensors, there are two figures, the intersection line of which is a circle. A sphere with a radius equal to the radius of the circle is constructed through the center of this circle. For the other two pairs of seismic sensors, two more spheres are also formed, The intersection point of the three spheres obtained is the sought-for hypocenter of the earthquake.Result. A method has been developed for determining the coordinates of an earthquake source using different shapes of the second and fourth orders for different pairs of seismic sensors.Conclusion. The method allows one to select one of the second or fourth order figures for different pairs of seismic sensors, which makes it possible to reduce the error in determining the source coordinates.

Pre-Earthquake Ionospheric Perturbation Identification Using CSES Data via Transfer Learning

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data.

Determination of Earthquake Hypocenter Distribution using the Modified Joint Hypocenter Determination (MJHD) Method throughout the Palu-Koro Fault (Case Study: August-October, 2018)

The area of Sulawesi, especially along the Palu Koro Fault, is an area that is largely influenced by the confluence and movement of plates as well as regional fault activity pathways with high levels of seismicity. Determining the location of the hypocenter accurately through relocation is required in identifying the detailed tectonic structures in the area. Relocation of the hypocenter using the Modified Joint Hypocenter Determination (MJHD) method using the IASP91 velocity model in the period August to October 2018 with the arrival time data from BMKG catalog. The results of hypocenter relocation using the MJHD method show that from 132 earthquake distribution points to 63 earthquake hypocenter points after the relocation. The change in the location of the hypocenter was much denser along the Palu Koro Fault route than before the relocation as evidenced by the mean value of rms (root mean square) before relocation was 1.31 and after relocation it became smaller (0.61). Changes in parameter values after relocation using the MJHD method caused the distribution of the earthquake hypocenter to be tighter towards the Palu Koro fault than before the relocation, where the distribution had a random and scattered pattern.

Determination of Hypocenter Using Geiger Method in Sinabung Volcano, April-July 2016 Period

Sinabung is a volcano located in the Karo Highlands, Karo District, North Sumatra, Indonesia, with the highest peak of 2460 meters mean sea level. Volcanic earthquake is an earthquake that occurs due to volcanic activity. This is caused by the movement of magma upwards in the volcano. This study aims to determine the type of earthquake, hypocenter position and epicenter of volcanic earthquakes in Sinabung volcano in April-July 2016. The principle of this study was carried out by analyzing volcanic earthquake data in Sinabung volcano in April-July 2016. The data is recorded data (seismogram) or in other words is secondary data from Sinabung volcano on 7 seismometer stations namely Sukanalu, Lau Kawar, Sigarang-Garang, Mardinding, Gamber, Sibayak, and Kebayaken stations. Earthquake data in April-July 2016 revealed that there were 24 earthquake events in a period of 3 months which were the results of picking up the P and S waves, where volcanic earthquakes were obtained only in the form of volcanic earthquake type A and type B volcanic earthquake. Sinabung volcano has an earthquake activity that high enough so that the status of Sinabung volcano is still at level III (standby) status. Based on the hypocenter of several VA and VB earthquakes that occurred in April-July 2016, it can be concluded that the distribution of the hypocenter of the volcanic earthquake shows that the maximum depth of the volcanic earthquake is 10.000 meters and the position of the earthquake is spread at the point between Sinabung volcano and Mount Sibayak.

Average error determination in the calculation of earthquake epicenter coordinates

Objective. The study is aimed at determining the dependence of the average error in calculating the epicenter coordinates of an earthquake on errors in measuring the velocities of seismic waves for various methods of seismic event localization. Error distribution investigation for the method for determining the earthquake hypocenter coordinates using the Cassinian oval. Methods. The problem was solved using statistical methods: methods of frequency and regression analyzes, means comparison method, and uniform search method. Results. A relationship between the accuracy of measuring the velocities of seismic waves when determining the coordinates of an earthquake epicenter were established for four different earthquake hypocenter coordinates calculation methods. A method for determining the earthquake hypocenter coordinates using the fourth-order figure of the Cassinian oval was proposed. The error distribution density of the Cassinian oval method was compared with the ones of other methods. Conclusion. The results obtained make it possible to choose one or another method for calculating the hypocenter coordinates depending on the specific area in which a seismic event occurred and the locations of seismic sensors.

Numerical Simulation Study on the Influence of Mine Earthquake on the Bolt Stress

Mine earthquake, as an underground disaster that occurs frequently, has a great impact on coal mine roadway and support. The stability analysis of the bolt support in roadway under different mine earthquake magnitudes is a key issue to be solved urgently in mining fields. This paper attempted to simulate the occurrence state of mine earthquake with explosive blasting process and verified it with actual coal mine microseismic monitoring data. ANSYS/LSDYNA software was used to analyze the impact of magnitude and location of mine earthquake hypocenter on the stability of bolt support and the dynamic stress characteristics of bolt. The results showed that with the increase in source energy of mine earthquake, the damage location of bolt mainly appears in the front of bolt and the loading position has no obvious change, but there is stress wave superposition effect, which deepens the damage of bolt. The bolts in the middle of the lane and the middle of the roof are greatly affected, so the support strength should be strengthened in these places. In addition, this paper compared the safety factor of bolt and the supporting effect of different schemes from three aspects such as roof subsidence, axial stress of bolt, and safety factor of bolt and then put forward a more economical and effective supporting scheme.

Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor

Anomalous ultra-low frequency electromagnetic (ULFEM) pulses occurring before the M5.4 2007 and M4.0 2010 Alum Rock earthquakes have been claimed to increase in number days to weeks prior to each earthquake. We re-examine the previously reported ultra-low frequency (ULF: 0.01-10 Hz) magnetic data recorded at a QuakeFinder site located 9 km from the earthquake hypocenter, as well as data from a nearby Stanford-USGS site located 42 km from the hypocenter, to analyze the characteristics of the pulses and assess their origin. Using pulse definitions and pulse-counting algorithms analogous to those previously reported, we corroborate the increase in pulse counts before the 2007 Alum Rock earthquake at the QuakeFinder station, but we note that the number of pulses depends greatly on chosen temporal and amplitude detection thresholds. These thresholds are necessarily arbitrary because we lack a clear physical model or basis for their selection. We do not see the same increase in pulse counts before the 2010 Alum Rock earthquake at the QuakeFinder or Stanford-USGS station. In addition, when comparing specific pulses in the QuakeFinder data and Stanford-USGS data, we find that the majority of pulses do not match temporally, indicating the pulses are not from solar-driven ionospheric/magnetospheric disturbances or from atmospheric lightning, and lack a common origin. Notably, however, our assessment of the temporal distribution of pulse counts throughout the day shows pulse counts increase during peak human activity hours, strongly suggesting these pulses result from local cultural noise and are not tectonic in origin. The many unknowns about the character and even existence of precursory earthquake pulses means that otherwise standard numerical and statistical test cannot be applied. Yet here we show that exhaustive investigation of many different aspects of ULFEM signals can be used to properly characterize their origin.

Visualizing heterogeneities of earthquake hypocenter catalogs: modeling, analysis, and compensation

As basic data for seismic activity analysis, hypocenter catalogs need to be accurate, complete, homogeneous, and consistent. Therefore, clarifying systematic errors in catalogs is an important discipline of seismicity research. This review presents a systematic model-based methodology to reveal various biases and the results of the following analyses. (1) It is critical to examine whether there is a non-stationary systematic estimation bias in earthquake magnitudes in a hypocenter catalog. (2) Most earthquake catalogs covering long periods are not homogeneous in space, time, and magnitude ranges. Earthquake network structures and seismometers change over time, and therefore, earthquake detection rates change over time and space. Even in the short term, many aftershocks immediately following large earthquakes are often undetected, and the detection rate varies, depending on the elapsed time and location. By establishing a detection rate function, the actual seismic activity and the spatiotemporal heterogeneity of catalogs can be discriminated. (3) Near real-time correction of source locations, far from the seismic observation network, can be implemented based on better determined source location comparisons of other catalogs using the same identified earthquakes. The bias functions were estimated using an empirical Bayes method. I provide examples showing different conclusions about the changes in seismicity from different earthquake catalogs. Through these analyses, I also present actual examples of successful modifications as well as various misleading conclusions about changes in seismic activity. In particular, there is a human made magnitude shift problem included in the global catalog of large earthquakes in the late nineteenth and early twentieth centuries.

THE EARTHQUAKE IN DZHEYRAKH REGION OF THE REPUBLIC OF INGUSHETIA ON MAY 24, 2020.

В настоящей статье приводится краткий анализ землетрясения с КР = 10.6, произо- шедшего 24 мая 2020 года на территории Джейрахского района Республики Ингушетия в 12h33m по Гринвичу с интенсивностью сотрясений в эпицентре 4 балла. Представлены инструментальные данные об очаге и макросейсмические проявления события. Описаны волновая картина сейсмиче- ского сигнала и историческая сейсмичность, рассчитан механизм очага, рассмотрена тектониче- ская позиция эпицентральной зоны. .This article provides a brief analysis of an earthquake with KP = 10.6 that occurred on May 24, 2020 in Dzheyrakh region of the Republic of Ingushetia at 12h33m GMT with 4 points of earthquake intensity at the epicenter. Instrumental data on the earthquake hypocenter and macroseismic manifestations are presented. The wave pattern of the seismic signal and the historical seismicity are described, the mechanism of the hypocenter is calculated, and the tectonic position of the epicentral zone is considered.

Potential megathrust earthquakes and tsunamis off the southern coast of West Java, Indonesia

High seismicity rates in and around West Java occur as a result of the Indo-Australian plate converging with and subducting beneath the Sunda plate. Large megathrust events associated with this process likely pose a major earthquake and tsunami hazard to the surrounding community, but further effort is required to help understand both the likelihood and frequency of such events. With this in mind, we exploit catalog and phase data sourced from the Agency for Meteorology, Climatology, and Geophysics (BMKG) of Indonesia and the International Seismological Centre (ISC) for the period of April 2009 through July 2020, in order to conduct earthquake hypocenter relocation using a teleseismic double-difference method. Our results reveal a seismic gap to the south of West Java, which is in agreement with a previous GPS study that finds the same region to be a potential future source of megathrust earthquakes. Tsunami modeling was conducted in the region based on two scenarios using sources from the Indonesian National Center for Earthquake Studies, and show that the maximum tsunami height may be up to ~ 8 m. This estimate is approximately half the maximum tsunami height predicted by a previous study in which earthquake sources were derived from GPS data inversion.