Hypocenter and Magnitude Analysis of Aftershocks of the 2018 Lombok, Indonesia, Earthquakes Using Local Seismographic Networks

2020 ◽  
Vol 91 (4) ◽  
pp. 2152-2162 ◽  
Author(s):  
Annisa Trisnia Sasmi ◽  
Andri Dian Nugraha ◽  
Muzli Muzli ◽  
Sri Widiyantoro ◽  
Zulfakriza Zulfakriza ◽  
...  
Keyword(s):  
Elevated Temperatures ◽  
Detection Threshold ◽  
Double Difference ◽  
Type Model ◽  
Northwestern Part ◽  
S Wave ◽  
Waveform Data ◽  
Arrival Times ◽  
Seismicity Pattern ◽  
Rupture Area

Abstract The island of Lombok in Indonesia is located between the Indo-Australian and Eurasian subduction trenches and the Flores back-arc thrust, making it vulnerable to earthquakes. On 29 July 2018, a significant earthquake Mw 6.4 shook this region and was followed by series of major earthquakes (Mw>5.8) on 5, 9, and 19 August, which led to severe damage in the northern Lombok area. In this study, we attempt to reveal the possible cause of the sequences of the 2018 Lombok earthquakes based on aftershock monitoring data. Twenty stations were deployed to record earthquake waveform data from 4 August to 9 September 2018. In total, 3259 events were identified using 28,728 P- and 20,713 S-wave arrival times during the monitoring. The aftershock hypocenters were determined using a nonlinear approach and relocated using double-difference method. The moment magnitude (Mw) of each event was determined by fitting the displacement spectrum amplitude using a Brune-type model. The magnitudes of the aftershocks range from Mw 1.7 to 6.7. The seismicity pattern reveals three clusters located in the Flores oceanic crust, which fit well with the occurrences of the four events with Mw>6. We interpret these events as the main rupture area of the 2018 Lombok earthquake sequence. Furthermore, an aseismic zone in the vicinity of Rinjani extending toward the northwestern part of Lombok was observed. We propose that the crust in this area has elevated temperatures and is highly fractured thus inhibiting the generation of large earthquakes. The aseismic nature is therefore an artifact of the detection threshold of our network (Mw 4.6).

scholarly journals A CROSS-CORRELATION TECHNIQUE FOR RELOCATION OF SEISMICITY IN THE WESTERN CORINTH RIFT

2017 ◽  
Vol 43 (4) ◽  
pp. 2015
Author(s):  
V. Kapetanidis ◽  
P. Papadimitriou ◽  
K. Makropoulos
Keyword(s):  
Cross Correlation ◽  
Time Lag ◽  
P Wave ◽  
Double Difference ◽  
Correlation Technique ◽  
S Wave ◽  
Waveform Data ◽  
Arrival Times ◽  
Linkage Algorithm ◽  
Seismogenic Structures

Local seismological networks provide data that allow the location of microearthquakes which otherwise would be dismissed due to low magnitudes and low signal-to-noise ratios of their seismic signals. The Corinth Rift Laboratory (CRL) network, installed in the western Corinth rift, has been providing digital waveform data since 2000. In this work, a semi-automatic picking technique has been applied which exploits the similarity between waveforms of events that have occurred in approximately the same area of an active fault. Similarity is measured by the crosscorrelation maxi-mum of full signals. Events with similar waveforms are grouped in multiplet clusters using the nearest-neighbour linkage algorithm. Manually located events act as masters, while automatically located events of each multiplet cluster act as slaves. By cross-correlating the P-wave or S-wave segments of a master event with the corresponding segments of each of its slave events, after appropriately aligning their offsets, the measured time-lag at the cross-correlation maximum can be subtracted from the arrival-time of the slave event. After the correction of the arrival-times, a double-difference technique is applied to the modified catalogue to further improve the locations of clusters and distinguish the active seismogenic structures in the tectonically complex Western Corinth rift.

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Arrival Time ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Arrival Times ◽  
Monitoring Well ◽  
The Cross ◽  
Microseismic Event ◽  
Wave Arrival

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

scholarly journals Preliminary Results of Regional P and S Wave Tomography at Seram, Indonesia

2021 ◽  
Vol 873 (1) ◽  
pp. 012066
Author(s):  
P A Subakti ◽  
M I Sulaiman ◽  
D Y Faimah ◽  
I Madrinovella ◽  
I Herawati ◽  
...  
Keyword(s):  
Tectonic Setting ◽  
Shallow Depth ◽  
Double Difference ◽  
S Wave ◽  
Low Velocity ◽  
Seismicity Pattern ◽  
Velocity Models ◽  
Event Distribution ◽  
Wave Tomography ◽  
Subduction System

Abstract The Seram Trough is located in the northern part of Indonesia and has a complex tectonic setting. The uniqueness of these regions lies in the U-shape subduction system. Several models have been proposed in this region, such as one subduction system that has been rotated 90° or 180°, two subduction systems, and one subduction that having a slab roll-back that causes extension systems. In this study, we try to invert velocity and seismicity using double-difference tomography with the target of better imaging the sub-surface structure in the region. We use data catalogue collection from the Indonesian Agency of Meteorology, Climatology, and Geophysics. The length of data is 4 years from January 2015 to December 2019 from 16 permanent stations. Earthquake relocations show a focused hypocenter distribution at shallow depth, and we interpreted some of these shallow depth events are related to the magmatic activity. Event distribution also displays a steep angle of seismicity pattern that represents the dipping subduction slab. Inverted Tomography models show a band of faster velocity models that dip from North to South, suggesting a subductions slab. We also observe a possibility of a tear in the slab from the seismicity pattern and tomogram model. The slower velocity perturbation is seen at shallow depth that may associate with magmatic and frequent shallow seismicity. A possibility of partial melting is also seen with low-velocity zone at a depth of 70 km next to the fast dipping velocity.

scholarly journals The 30 June 2017 North Sea Earthquake: Location, Characteristics, and Context

2020 ◽  
Vol 110 (2) ◽  
pp. 937-952
Author(s):  
Annie E. Jerkins ◽  
Hasbi Ash Shiddiqi ◽  
Tormod Kværna ◽  
Steven J. Gibbons ◽  
Johannes Schweitzer ◽  
...  
Keyword(s):  
North Sea ◽  
Moment Tensor ◽  
P Wave ◽  
Double Difference ◽  
High Signal ◽  
Seismic Events ◽  
S Wave ◽  
Surface Reflection ◽  
Moment Tensor Solution ◽  
Seismicity Pattern

ABSTRACT The Mw 4.5 southern Viking graben earthquake on 30 June 2017 was one of the largest seismic events in the Norwegian part of the North Sea during the last century. It was well recorded on surrounding broadband seismic stations at regional distances, and it generated high signal-to-noise ratio teleseismic P arrivals at up to 90° with good azimuthal coverage. Here, the teleseismic signals provide a unique opportunity to constrain the event hypocenter. Depth phases are visible globally and indicate a surface reflection in the P-wave coda some 4 s after the initial P arrival, giving a much better depth constraint than regional S-P time differences provide. Moment tensor inversion results in a reverse thrust faulting mechanism. The fit between synthetic and observed surface waves at regional distances is improved by including a sedimentary layer. Synthetic teleseismic waveforms generated based on the moment tensor solution, and a near-source 1D velocity model indicates a depth of 7 km. Correlation detectors using the S-wave coda from the main event were run on almost 30 yr of continuous multichannel seismic data searching for repeating signals. In addition to a magnitude 1.9 aftershock 33 min later, and a few magnitude ∼1 events in the following days, a magnitude 2.5 earthquake on 13 November 2016 was the only event found to match the 30 June 2017 event well. Using double-difference techniques, we find that the two largest events are located within 1 km of the main event. We present a Bayesloc probabilistic multiple event location including the 30 June event and all additional seismic events in the region well recorded on the regional networks. The Bayesloc relocation gave a more consistent seismicity pattern and moved several of the events more toward the west. The results of this study are also discussed within the regional seismotectonic frame of reference.

scholarly journals Data-oriented constraint on the interpretation of S receiver function and its application to observations of seismic discontinuities in the lithosphere–asthenosphere system

2019 ◽  
Vol 219 (1) ◽  
pp. 496-513
Author(s):  
Xuzhang Shen ◽  
YoungHee Kim ◽  
Teh-Ru Alex Song ◽  
Hobin Lim
Keyword(s):  
Amplitude Ratio ◽  
Receiver Function ◽  
Source Parameters ◽  
Detection Threshold ◽  
Coda Waves ◽  
S Wave ◽  
Waveform Data ◽  
Seismic Discontinuities ◽  
S Receiver Function ◽  
Converted Waves

SUMMARY This paper aims to improve the robustness of interpretation in the S receiver function (SRF), a technique commonly used to retrieve forward scattering of S-to-P converted waves (Sdp) originated from the lithosphere–asthenosphere system (LAS) beneath the stations. Although the SRF does not suffer interferences from backward scattering waves such as the first multiples from the Moho, one major drawback in the method is that Sdp phases can interfere with P coda waves and it is conceivable that these signal-generated noise may be misinterpreted as Sdp phase from the LAS beneath seismic stations. Through systematic analysis of full-waveform synthetics and SRFs from catalogued source parameters, we find that the strong P coda waves before the S wave in the longitudinal-component waveforms result in unwanted signal-generated noise before the S wave in the synthetic SRFs. If the mean amplitude of SRFs after the S wave is large, dubious signal-generated noise before the S arrival are strong as well. In this study, we honor the level of these unwanted signal-generated noise and devise data-oriented screening criteria to minimize the interference between P coda waves and genuine S-to-P converted waves. The first criterion is LQR, a direct measure of the amplitude ratio between longitudinal P coda waves and radial S wave in the waveform data. The second criterion is AMP, the amplitude of SRFs after the S arrival. We illustrate that these criteria effectively measure the energy level of mantle waves such as the SP wave. With synthetics and real data, we demonstrate the effectiveness of LQR and AMP criteria in minimizing these unwanted signal-generated noise in the stacked SRFs down to 1–2 per cent, improving detection threshold and interpretation of Sdp phases from seismic discontinuities in the LAS.

Microseismic and Induced Seismicity Monitoring and Tomography of the Changning-Zhaotong shale gas play in China using dense nodal arrays

2020 ◽  
Author(s):  
Wen Yang ◽  
Junlun Li ◽  
Yuyang Tan ◽  
Yaxing Li ◽  
Jiawei Qian ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Shale Gas ◽  
Velocity Structure ◽  
Double Difference ◽  
Steady Increase ◽  
S Wave ◽  
Two Phase ◽  
Arrival Times ◽  
Seismicity Monitoring ◽  
Tomography Method

<p>With the development of shale gas in the Changning-Zhaotong play in the southern Sichuan basin of China, which is the largest shale gas prospect in China, the frequency and magnitude of earthquakes in this region have increased significantly in recent years. For example, a M5.7 earthquake occurred on December 16, 2018, and a M5.3 earthquake on January 6, 2019 in addition to many M4.0+ earthquakes in this area. Some studies argue the large magnitude earthquakes are triggered by hydraulic fracturing in for the local shale gas development, which commenced in 2011. The frequency of the earthquake occurrence has been on steady increase in the past few years that local residents often reported felt quakes. To further understand the correlation between the shale gas development and local seismic activity, we conducted a two-phase dense array seismic monitoring with about 200 Zland 3C and SmartSolo 3C 5 Hz seismic nodes, from late February to early May, 2019 for a period of 70 days. The survey consists of roughly 340 deployments at 240 sites, with an average interstation distance of 1.5 km, covering 500 km<sup>2</sup> in total. We have processed seismic records from late February to early April, 2019 (phase I), and picked some 600,000 P- and S-wave arrival times from 4385 detected local earthquakes. The earthquake hypocenters and the subsurface velocity structure of the Changning-Zhaotong area are inverted for using the double-difference tomography method. The relocation results show that the majority of hypocenters were located at depths ranging from 1.0km to 4.0km, in the proximity of the horizontal hydraulic fracturing wells. The tomographic results (< 3 km) correlate well with the known surface geological units, and most earthquakes occurred along the velocity discontinuities, likely characterizing a large hidden fault which, interestingly, is where the January 2019 M5.3 occurred. Our study is very important for understanding the seismic potentials in this area, and should provide useful information for the shale gas development in this region and other areas in China with similar geological, tectonic and stress conditions.</p>

scholarly journals An automatically generated high-resolution earthquake catalogue for the 2016–2017 Central Italy seismic sequence, including P and S phase arrival times

2020 ◽  
Author(s):  
D Spallarossa ◽  
M Cattaneo ◽  
D Scafidi ◽  
M Michele ◽  
L Chiaraluce ◽  
...  
Keyword(s):  
Real Time ◽  
Central Italy ◽  
Earthquake Catalogue ◽  
Hazard Evaluation ◽  
Depth Range ◽  
Data Set ◽  
Epicentral Area ◽  
Earthquake Parameters ◽  
Waveform Data ◽  
Arrival Times

Summary The 2016–17 central Italy earthquake sequence began with the first mainshock near the town of Amatrice on August 24 (MW 6.0), and was followed by two subsequent large events near Visso on October 26 (MW 5.9) and Norcia on October 30 (MW 6.5), plus a cluster of 4 events with MW > 5.0 within few hours on January 18, 2017. The affected area had been monitored before the sequence started by the permanent Italian National Seismic Network (RSNC), and was enhanced during the sequence by temporary stations deployed by the National Institute of Geophysics and Volcanology and the British Geological Survey. By the middle of September, there was a dense network of 155 stations, with a mean separation in the epicentral area of 6–10 km, comparable to the most likely earthquake depth range in the region. This network configuration was kept stable for an entire year, producing 2.5 TB of continuous waveform recordings. Here we describe how this data was used to develop a large and comprehensive earthquake catalogue using the Complete Automatic Seismic Processor (CASP) procedure. This procedure detected more than 450,000 events in the year following the first mainshock, and determined their phase arrival times through an advanced picker engine (RSNI-Picker2), producing a set of about 7 million P- and 10 million S-wave arrival times. These were then used to locate the events using a non-linear location (NLL) algorithm, a 1D velocity model calibrated for the area, and station corrections and then to compute their local magnitudes (ML). The procedure was validated by comparison of the derived data for phase picks and earthquake parameters with a handpicked reference catalogue (hereinafter referred to as ‘RefCat’). The automated procedure takes less than 12 hours on an Intel Core-i7 workstation to analyse the primary waveform data and to detect and locate 3000 events on the most seismically active day of the sequence. This proves the concept that the CASP algorithm can provide effectively real-time data for input into daily operational earthquake forecasts, The results show that there have been significant improvements compared to RefCat obtained in the same period using manual phase picks. The number of detected and located events is higher (from 84,401 to 450,000), the magnitude of completeness is lower (from ML 1.4 to 0.6), and also the number of phase picks is greater with an average number of 72 picked arrival for a ML = 1.4 compared with 30 phases for RefCat using manual phase picking. These propagate into formal uncertainties of ± 0.9km in epicentral location and ± 1.5km in depth for the enhanced catalogue for the vast majority of the events. Together, these provide a significant improvement in the resolution of fine structures such as local planar structures and clusters, in particular the identification of shallow events occurring in parts of the crust previously thought to be inactive. The lower completeness magnitude provides a rich data set for development and testing of analysis techniques of seismic sequences evolution, including real-time, operational monitoring of b-value, time-dependent hazard evaluation and aftershock forecasting.

Passive seismic tomography using recorded microseismicity: Application to mining-induced seismicity

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. B41-B57 ◽  
Author(s):  
Himanshu Barthwal ◽  
Mirko van der Baan
Keyword(s):  
Seismic Tomography ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
Lateral Velocity ◽  
Study Region ◽  
Arrival Times ◽  
3D Velocity Model ◽  
Passive Seismic ◽  
Dip Angle

Microseismicity is recorded during an underground mine development by a network of seven boreholes. After an initial preprocessing, 488 events are identified with a minimum of 12 P-wave arrival-time picks per event. We have developed a three-step approach for P-wave passive seismic tomography: (1) a probabilistic grid search algorithm for locating the events, (2) joint inversion for a 1D velocity model and event locations using absolute arrival times, and (3) double-difference tomography using reliable differential arrival times obtained from waveform crosscorrelation. The originally diffusive microseismic-event cloud tightens after tomography between depths of 0.45 and 0.5 km toward the center of the tunnel network. The geometry of the event clusters suggests occurrence on a planar geologic fault. The best-fitting plane has a strike of 164.7° north and dip angle of 55.0° toward the west. The study region has known faults striking in the north-northwest–south-southeast direction with a dip angle of 60°, but the relocated event clusters do not fall along any mapped fault. Based on the cluster geometry and the waveform similarity, we hypothesize that the microseismic events occur due to slips along an unmapped fault facilitated by the mining activity. The 3D velocity model we obtained from double-difference tomography indicates lateral velocity contrasts between depths of 0.4 and 0.5 km. We interpret the lateral velocity contrasts in terms of the altered rock types due to ore deposition. The known geotechnical zones in the mine indicate a good correlation with the inverted velocities. Thus, we conclude that passive seismic tomography using microseismic data could provide information beyond the excavation damaged zones and can act as an effective tool to complement geotechnical evaluations.

Tuning S-Wave Velocity Structure of Deep Sedimentary Layers in the Shimousa Region of the Kanto Basin, Japan, Using Autocorrelation of Strong-Motion Records

2020 ◽  
Vol 110 (6) ◽  
pp. 2882-2891
Author(s):  
Kosuke Chimoto ◽  
Hiroaki Yamanaka
Keyword(s):  
Wave Velocity ◽  
Velocity Structure ◽  
Strong Motion ◽  
S Wave ◽  
Reflected Waves ◽  
Arrival Times ◽  
Strong Motion Records ◽  
Kanto Basin ◽  
Determination Of Parameters

ABSTRACT The autocorrelation of ambient noise is used to capture reflected waves for crustal and sedimentary structures. We applied autocorrelation to strong-motion records to capture the reflected waves from sedimentary layers and used them for tuning the S-wave velocity structure of these layers. Because a sedimentary-layered structure is complicated and generates many reflected waves, it is important to identify the boundary layer from which the waves reflected. We used spectral whitening during autocorrelation analysis to capture the reflected waves from the seismic bedrock with an appropriate smoothing band, which controls the wave arrival from the desired layer boundary. The effect of whitening was confirmed by the undulation frequency observed in the transfer function of the sedimentary layers. After careful determination of parameters for spectral whitening, we applied data processing to the strong-motion records observed at the stations in the Shimousa region of the Kanto Basin, Japan, to estimate the arrival times of the reflected waves. The arrival times of the reflected waves were found to be fast in the northern part of the Shimousa region and slow in the western and southern parts. These arrival times are consistent with those obtained using existing models. Because we observed a slight difference in the arrival times, the autocorrelation function at each station was used for tuning the S-wave velocity structure model of the sedimentary layers using the inversion technique. The tuned models perfectly match the autocorrelation functions in terms of the arrival time of the reflected waves from the seismic bedrock.

scholarly journals P-wave first-motion polarity determination of waveform data in western Japan using deep learning

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Shota Hara ◽  
Yukitoshi Fukahata ◽  
Yoshihisa Iio
Keyword(s):  
Training Data ◽  
P Wave ◽  
Data Sets ◽  
Waveform Data ◽  
Arrival Times ◽  
Distribution Maps ◽  
Seismic Waveforms ◽  
Polarity Determination ◽  
Western Japan ◽  
First Motion

AbstractP-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high ($${ \gtrsim }$$≳ 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.

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