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

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.

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

Geophysics ◽

10.1190/geo2019-0325.1 ◽

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.

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Hypocenter Analysis of Aftershocks Data of the Mw 6.3, 27 May 2006 Yogyakarta Earthquake Using Oct-Tree Importance Sampling Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.881.89 ◽

2018 ◽

Vol 881 ◽

pp. 89-97 ◽

Cited By ~ 1

Author(s):

Asri Wulandari ◽

Ade Anggraini ◽

Wiwit Suryanto

Keyword(s):

Importance Sampling ◽

Sampling Method ◽

Velocity Model ◽

Fault Scarp ◽

P Wave ◽

Double Difference ◽

S Wave ◽

Maximum Probability ◽

Importance Sampling Method ◽

Dip Angle

Yogyakarta earthquake, Mw 6.3, 27 May 2006 had killed 5,571 victims and destroyed more than 1 million buildings. This incident became the most destructive earthquake disaster over the last 11 years in Indonesia. Earthquake mitigation plan in the area has been carried out by understands the location of the fault. The location of the fault is still unclear among geoscientists until now. In this case, analysis of the aftershocks using oct-tree importance sampling method was applied to support the location of the fault that responsible for the 2006 Yogyakarta earthquake. Oct-tree importance sampling is a method that is recursively subdividing the solution domain into exactly eight children for estimating properties of a particular distribution. The final result of the subdividing process is a cell that has a maximum Probability Density Function (PDF) and identified as the location of the hypocenter. Input data consists of the arrival time of the P wave and S wave of the aftershocks catalog from 3-7 June 2006 and the coordinate of the 12 seismometers, and 1D velocity model of the study area. Based on the hypocenter distribution of the aftershocks data with the proposed method show a clearer trend of the fault compared with the aftershocks distribution calculated with theHypo71program. The fault trend has a strike orientation of N 42° E with a dip angle of 80° parallel with the fault scarp along the Opak River at the distance of about 15 km to the east. This fault trend is similar with the fault orientation obtained using the Double Difference Algorithm.

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Passive seismic tomography using induced seismicity at a petroleum field in Oman

Geophysics ◽

10.1190/1.3253059 ◽

2009 ◽

Vol 74 (6) ◽

pp. WCB57-WCB69 ◽

Cited By ~ 60

Author(s):

Haijiang Zhang ◽

Sudipta Sarkar ◽

M. Nafi Toksöz ◽

H. Sadi Kuleli ◽

Fahad Al-Kindy

Keyword(s):

Seismic Tomography ◽

Induced Seismicity ◽

Gas Content ◽

Double Difference ◽

Depth Range ◽

Arrival Times ◽

Monitoring Wells ◽

Passive Seismic ◽

Petroleum Field ◽

Tomography Method

A borehole network consisting of five monitoring wells monitored the induced seismicity at a producing petroleum field for about [Formula: see text]. Nearly 5400 microseismic events were analyzed and used to image the reservoir based on a new double-difference (DD) seismic tomography. The DD tomography method simultaneously solved for event locations and [Formula: see text], [Formula: see text], and [Formula: see text] models using absolute and differential P, S, and S-P arrival times. Microseismicity in the field was caused primarily by compaction of the reservoir in and above the gas-bearing formation and was distributed along the two major northeast-southwest faults in the field. The model resolution analysis based on the checkerboard test and the resolution matrix showed that the central part of the model was resolved relatively well for the depth range of [Formula: see text]. Clear velocity contrasts were imaged across most parts of the two northeast-southwest faults. The [Formula: see text] ratio estimates from the tomographic inversion were low [Formula: see text] in the shallow depth range, likely caused by rock type and gas content; but they were large [Formula: see text] in the deeper part of the model, likely because of fluid-saturated formation. Thus, seismic tomography shows great potential for reservoir imaging and property estimation using induced seismicity.

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Structure-controlled asperities of the 1920 Haiyuan M8.5 and 1927 Gulang M8 earthquakes, NE Tibet, China, revealed by high-resolution seismic tomography

Scientific Reports ◽

10.1038/s41598-021-84642-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Quan Sun ◽

Shunping Pei ◽

Zhongxiong Cui ◽

Yongshun John Chen ◽

Yanbing Liu ◽

...

Keyword(s):

High Resolution ◽

Seismic Tomography ◽

High Velocity ◽

Three Dimensional ◽

Large Earthquake ◽

Velocity Model ◽

Tectonic Stress ◽

Double Difference ◽

Source Regions ◽

Large Earthquakes

AbstractDetailed crustal structure of large earthquake source regions is of great significance for understanding the earthquake generation mechanism. Numerous large earthquakes have occurred in the NE Tibetan Plateau, including the 1920 Haiyuan M8.5 and 1927 Gulang M8 earthquakes. In this paper, we obtained a high-resolution three-dimensional crustal velocity model around the source regions of these two large earthquakes using an improved double-difference seismic tomography method. High-velocity anomalies encompassing the seismogenic faults are observed to extend to depths of 15 km, suggesting the asperity (high-velocity area) plays an important role in the preparation process of large earthquakes. Asperities are strong in mechanical strength and could accumulate tectonic stress more easily in long frictional locking periods, large earthquakes are therefore prone to generate in these areas. If the close relationship between the aperity and high-velocity bodies is valid for most of the large earthquakes, it can be used to predict potential large earthquakes and estimate the seismogenic capability of faults in light of structure studies.

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Constraints of Crustal Heterogeneity and Q(f) from Regional (<4 Hz) Wave Propagation for the 2009 North Korea Nuclear Test

Bulletin of the Seismological Society of America ◽

10.1785/0120170195 ◽

2018 ◽

Vol 108 (3A) ◽

pp. 1369-1383 ◽

Cited By ~ 6

Author(s):

Kim B. Olsen ◽

Michael Begnaud ◽

Scott Phillips ◽

Bo Holm Jacobsen

Keyword(s):

Wave Propagation ◽

North Korea ◽

National Laboratory ◽

Source Area ◽

Velocity Model ◽

P Wave ◽

Small Scale ◽

Los Alamos ◽

Crust And Upper Mantle ◽

3D Velocity Model

Abstract We carried out 3D finite‐difference (FD) simulations (<4 Hz) of regional wave propagation for the 2009 North Korea nuclear explosion and compared the synthetics with instrument‐corrected records at stations INCN and TJN in South Korea. The source is an isotropic explosion with a moment magnitude of 4.1. Synthetics computed in the relatively smooth Sandia/Los Alamos National Laboratory SALSA3D (SAndia LoS Alamos 3D) velocity model significantly overpredict Rayleigh‐wave amplitudes by more than an order of magnitude while underpredicting coda amplitudes. The addition to SALSA3D of a von Karman distribution of small‐scale heterogeneities with correlation lengths of ∼1000 m, a Hurst number of 0.1, and a horizontal‐to‐vertical anisotropy of ∼5 produces synthetics in general agreement with the data. The best fits are obtained from models with a gradient in the strength of the velocity and density perturbations and strong scattering (10%) limited to the top 7.5–10 km of the crust. Deeper scattering tends to decrease the initial P‐wave amplitudes to levels much below those for the data, a critical result for methods discriminating between explosive and earthquake sources. In particular, the amplitude at the onset of Pn can be affected by as little as 2% small‐scale heterogeneity in the lower crust and upper mantle. Simulations including a constant Q of 200 (INCN) to 350 (TJN) below 1 Hz and a power‐law Q(f) formulation at higher frequencies, with an exponent of 0.3, generate synthetics in best agreement with the data. In our simulations, very limited scattering contribution from the near‐source area accumulates along the regional path.

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Local high-resolution passive seismic tomography and Kohonen neural networks — Application at the Rio-Antirio Strait, central Greece

Geophysics ◽

10.1190/1.2729473 ◽

2007 ◽

Vol 72 (4) ◽

pp. B93-B106 ◽

Cited By ~ 18

Author(s):

G-Akis Tselentis ◽

Anna Serpetsidaki ◽

Nikolaos Martakis ◽

Efthimios Sokos ◽

Paraskevas Paraskevopoulos ◽

...

Keyword(s):

Neural Networks ◽

High Resolution ◽

Seismic Tomography ◽

Least Squares Method ◽

Velocity Model ◽

Nonlinear Inversion ◽

Self Organizing Maps ◽

Central Greece ◽

Passive Seismic ◽

First Time

A high-resolution passive seismic investigation was performed in a [Formula: see text] area around the Rio-Antirio Strait in central Greece using natural microearthquakes recorded during three months by a dense, temporary seismic network consisting of 70 three-component surface stations. This work was part of the investigation for a planned underwater rail tunnel, and it gives us the opportunity to investigate the potential of this methodology. First, 150 well-located earthquake events were selected to compute a minimum (1D) velocity model for the region. Next, the 1D model served as the initial model for nonlinear inversion for a 3D P- and S- velocity crustal structure by iteratively solving the coupled hypocenter-velocity problem using a least-squares method. The retrieved [Formula: see text] and [Formula: see text] images were used as an input to Kohonen self-organizing maps (SOMs) to identify, systematically and objectively, the prominent lithologies in the region. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. This analysis revealed the existence of five major clusters, one of which may be related to the existence of an evaporite body not shown in the conventional seismic tomography velocity volumes. The survey results provide, for the first time, a 3D model of the subsurface in and around the Rio-Antirio Strait. It is the first time that passive seismic tomography is used together with SOM methodologies at this scale, thus revealing the method’s potential.

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Earthquake relocation for North-Western Greece using 3D crustal model; method comparison and seismotectonic interpretation.

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.10901 ◽

2016 ◽

Vol 47 (3) ◽

pp. 1269 ◽

Cited By ~ 1

Author(s):

O. Stavroulopoulou ◽

E. Sokos ◽

N. Martakis ◽

G. A. Tselentis

Keyword(s):

Standard Procedure ◽

Method Comparison ◽

Velocity Model ◽

Focal Mechanisms ◽

Double Difference ◽

Location Method ◽

Hypocenter Distribution ◽

3D Velocity Model ◽

1D Model ◽

Northwestern Greece

A dense microseismic network was installed in Northwestern Greece for a period of eleven months. A total of 1368 events were recorded and located using a 1D model. These events were also used to derive a 3D velocity model for the area. This work presents results from further processing of the data using (a) simple location method of events in a 1D medium through Hypo71 standard procedure; (b) location via the probabilistic, non-linear earthquake location method in 3D medium; (c) relocation of the events using the Double - Difference method in 1D medium; and (d) the same relocation procedure invoking 3D medium. The application of different location methodologies results in slightly different locations, which are evaluated using as criterion the compactness of hypocenter distribution. The three point method was used in order to derive linear characteristics from the hypocenter distribution and the final results were compared against the focal mechanisms of the events as computed using the polarity method and the 3D velocity model. The combination of accurately computed hypocenters and focal mechanisms provides important information for the seismotectonics of Epirus

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An integrated 3D velocity inversion—joint hypocentral determination relocation analysis of events in the Northridge area

Bulletin of the Seismological Society of America ◽

10.1785/bssa08601bs138 ◽

1996 ◽

Vol 86 (1B) ◽

pp. S138-S155

Author(s):

Jose Pujol

Keyword(s):

Weighted Average ◽

Velocity Model ◽

Actual Data ◽

Low Velocity ◽

Lateral Velocity ◽

Epicentral Area ◽

Velocity Inversion ◽

Station Corrections ◽

3D Velocity Model ◽

Velocity Zone

Abstract A subset of 3371 events recorded in the Northridge area by the Southern California Seismic Network during January to April 1994 was relocated with the joint hypocentral determination (JHD) technique. This analysis showed two unexpected results: (a) the JHD locations are shifted about 3.9 km on average in a northwest direction with respect to the locations determined using a single-event location (SEL) program, and (b) the station corrections vary between −0.55 and 1.26 sec, a rather large range. In addition, the JHD locations are less scattered than the SEL locations. For each station, the weighted average of the arrival time residuals obtained when the events are located with the SEL program (which does not apply distance or error weighting) are generally smaller than the corresponding JHD corrections. The locations determined with SEL and using the weighted average residuals as station corrections do not differ much from the SEL locations, but on average the RMS residuals become as small as those corresponding to the JHD locations. As the magnitude of the station corrections indicates the presence of large lateral velocity variations, a 3D velocity model for the area was determined using the arrival times of 1012 events recorded by at least 17 stations. The initial velocity model was that used routinely by the Southern California Earthquake Center. The first two layers (5.5- and 10.5-km thick) were subdivided into 100 blocks each (12 × 12 km). These layers show a pronounced low-velocity anomaly (24% and 16%, respectively) immediately to the northwest of the epicentral area. This low-velocity zone coincides with the west Ventura Basin. Another pronounced low-velocity zone to the southeast of the epicentral area reflects the presence of the Los Angeles Basin. The locations obtained with the 3D velocity model are consistently to the southeast of the JHD locations, 2.4 km on average. To establish the effect of these pronounced lateral velocity variations on the SEL and JHD locations, synthetic travel times were analyzed. The synthetic times were generated for event locations determined by JHD (shifted by various amounts) and the 3D velocity model and were subsequently treated as the actual data. The most important result of this analysis is that the JHD locations are affected by a quasi-systematic shift in a northwest direction (up to about 2.7 km on average, depending on the initial shift) but that the relative locations are well preserved. Therefore, both the velocity inversion of the actual data and the analysis of the synthetic data indicate that the JHD locations determined for the actual data are quasi-systematically mislocated. To account for this mislocation, an overall shift of 2.5 km to the southeast was applied to all the JHD locations. One of the most important implications of the shifted locations is the possibility that the northeasterly dipping Santa Susana fault, to the northwest of the epicentral area, was seismically active during the aftershock sequence. This feature is more diffuse in other published locations.

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Passive seismic imaging using microearthquakes

Geophysics ◽

10.1190/1.1443846 ◽

1995 ◽

Vol 60 (4) ◽

pp. 1178-1186 ◽

Cited By ~ 37

Author(s):

M. Reza Daneshvar ◽

Clarence S. Clay ◽

Martha K. Savage

Keyword(s):

Wave Velocity ◽

Signal To Noise Ratio ◽

Velocity Model ◽

P Wave ◽

Seismic Signals ◽

Subsurface Structure ◽

Near Surface ◽

P Wave Velocity ◽

Recording Station ◽

Passive Seismic

We have developed a method of processing seismic signals generated by microearthquakes to image local subsurface structure beneath a recording station. This technique uses the autocorrelation of the vertically traveling earthquake signals to generate pseudoreflection seismograms that can be interpreted for subsurface structure. Processed pseudoreflection data, from microearthquakes recorded in the island of Hawaii, show consistent reflectivity patterns that are interpreted as near‐surface horizontal features. Forward modeling of the pseudoreflection data results in a P‐wave velocity model that shows reasonable agreement with the velocity model derived from a refraction study in the region. Usable signal‐to‐noise ratio is obtained down to 2 s. A shear‐wave velocity model was also generated by applying this technique to horizontal component data.

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An analysis of the first-arrival times picked on the DSS and wide-angle seismic section recorded in Italy since 1968

Annals of Geophysics ◽

10.4401/ag-3369 ◽

2009 ◽

Vol 47 (6) ◽

Author(s):

L. De Luca ◽

R. De Franco ◽

G. Biella ◽

A. Corsi ◽

R. Tondi

Keyword(s):

Travel Time ◽

Velocity Model ◽

P Wave ◽

Wide Angle ◽

Seismic Section ◽

Arrival Times ◽

First Arrival ◽

Time Offset ◽

Refraction Data ◽

1D Velocity Model

We performed an analysis of refraction data recorded in Italy since 1968 in the frame of the numerous deep seismic sounding and wide-angle reflection/refraction projects. The aims of this study are to construct a parametric database including the recording geometric information relative to each profile, the phase pickings and the results of some kinematic analyses performed on the data, and to define a reference 1D velocity model for the Italian territory from all the available refraction data. As concerns the first goal, for each seismic section we picked the P-wave first-arrival-times, evaluated the uncertainties of the arrival-times pickings and determined from each travel time-offset curve the 1D velocity model. The study was performed on 419 seismic sections. Picking was carried out manually by an algorithm which includes the computation of three picking functions and the picking- error estimation. For each of the travel time-offset curves a 1D velocity model has been calculated. Actually, the 1D velocity-depth functions were estimated in three different ways which assume: a constant velocitygradient model, a varying velocity-gradient model and a layered model. As regards the second objective of this work, a mean 1D velocity model for the Italian crust was defined and compared with those used for earthquake hypocentre locations and seismic tomographic studies by different institutions operating in the Italian area, to assess the significance of the model obtained. This model can be used in future works as input for a next joint tomographic inversion of active and passive seismic data.

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