Earthquake Report: Central China – Tibetan Plateau

Wow, we are lucky that a region of central China is sparsely populated because this M 7.3 earthquake could have caused many more to suffer.

Tectonic Background

Central China here, part of the Tibetan Plateau, is dominated by the plate tectonics and climate.

To the south is the India plate that is moving northwards, pummeling into the southern part of the Eurasia plate, at a rate of about 25 to 50 mm per year, depending upon the reference frame (Pusok and Stegman, 2020). The India plate began moving northward from Antarctica since before 80 million years ago.

The details of the story has changed as more geological information is interpreted. But the general story is that the India plate moved away from Antarctica as an oceanic spreading center formed between these plates. The India plate moved towards Asia.

Prior to about 45-50 Ma, there was oceanic crust between India and Eurasia. But at this time, the continental crust of the India and Eurasia plates collided. This collision would eventually cause uplift of the Himalaya mountain range and the Tibetan Plateau to the north of the Himalaya.

There are marine fossils on the top of the mountains in the Himalaya! (this is how we know there was ocean between these plates in the past)

Here is a time series showing the convergence of these two plates modified from Pusok & Stegman (2020) and the USGS.

A part of the tectonic story is told by one of the rock stars of plate tectonics, Dr. Paul Tapponier. Tapponier conducted experiments that showed how north-south convergence, like that of India and Asia, coupled with a backstop (something that is more difficult to move) from the west, would lead to some crust to squish out to the east.

This is called extrusion tectonics as the crust of eastern Asia is being extruded to the east, like a watermelon seed is extruded from between one’s fingers when they squeeze on the wet seed.

Below is a color version of the results from Tapponier’s experiment. Compare this with maps showing the GPS motion of the crust in this region.

Note that the plastic has numerous faults develop as part of the extrusion. We can see how the blue and yellow lines show lateral offset along these faults.

Many of these faults are left-lateral strike-slip faults. Strike-slip means that the curst moves side-by-side when looking down on the crust from outer space (or an airplane, or Google Earth).

Left-lateral means that, when standing on one side of the fault, looking across the fault at something, that thin one is looking at is moving to the left during an earthquake. More about tectonic fundamentals here.

In the map on the right ^^^ we can see that these left-lateral strike-slip faults that are mapped in the region are just like the faults in the blue-yellow plastic.

One of the major left-lateral strike-slip faults along the Tibetan Plateau is the Kunlun fault, which has been well studied in places. A tectonic history of the region, and how the Kunlun fault fits into this history, is presented by Staisch et al., 2020.


The earthquake yesterday was a magnitude M 7.3 earthquake that happened along the Kunlun fault system. The earthquake mechanism is that of an oblique strike-slip fault. Earthquake mechanisms (focal mechanisms or moment tensors) are derived from seismic wave data surrounding the earthquake.

These mechanisms are visual depictions of the orientation of the fault that slipped and there are always two possible fault planes shown on these mechanisms (also called beach balls).

Without additional information, we don’t know which potential fault plane is the correct one. Because the mapped faults in the region are left-lateral strike-slip faults, and the mechanism is oriented (relative to north) to these fault lines, one can use these faults as a basis to interpret which fault is the correct fault on the mechanism and the type of earthquake that happened. Which type of earthquake do you think slipped yesterday?

I interpret (as have many others) that this M 7.3 was a left-lateral strike-slip earthquake. There is a splay fault off the Kunlun fault system called the Maduo Grande fault (e.g. Chen Xia et al., 2011).

The aftershocks align with this mapped fault, supporting my interpretation. The fault is mapped for about 150 km and the aftershocks span about 160 km. Using Wells and Coppersmith empirical fault relations (i.e. using the relations between earthquake fault surface rupture length and earthquake magnitude), we can take the length of the fault that has cut through to the surface to estimate the earthquake magnitude.

Using these relations, taking 150-160 km would produce a M 7.5-7.6 earthquake. This is much larger than an M 7.3. However, the actual surface rupture length is probably less than the span of the aftershocks. regardless, this magnitude and mapped fault are compatible with each other (the length and the magnitude of the earthquake are a moderate match).

There are probably secondary effects from this M 7.3, like maybe landslides or liquefaction. The epicenter is in a part of the river valley where the river has a braided form. Basically, there is more sediment that the water available to move it. In places like this, there are lots of wetlands and swamps. The waterlogged subsurface can tend to promote liquefaction, so there is a high possibility that evidence of liquefaction will be found. Because this is an arid region, InSAR satellite imagery analysis may reveal evidence of liquefaction (in addition to revealing tectonic deformation).

There may have even been a seiche or tsunami in the nearby lake.

Of note is a M 6.1 earthquake that occurred hundreds of kilometers to the south before the M 7.3. While these two earthquakes are unlikely to be related (because of the distance), it is interesting because they are both evidence supporting the extrusion tectonics interpretation.

Below is my interpretive poster for this earthquake

  • I plot the seismicity from the past 3 months, with diameter representing magnitude (see legend). I include earthquake epicenters from 1921-2021 with magnitudes M ≥ 7.0.
  • I plot the USGS fault plane solutions (moment tensors in blue and focal mechanisms in orange), possibly in addition to some relevant historic earthquakes.
  • A review of the basic base map variations and data that I use for the interpretive posters can be found on the Earthquake Reports page. I have improved these posters over time and some of this background information applies to the older posters.
  • Some basic fundamentals of earthquake geology and plate tectonics can be found on the Earthquake Plate Tectonic Fundamentals page.

    I include some inset figures. Some of the same figures are located in different places on the larger scale map below.

  • In the upper left corner is a plate tectonic map that shows the major faults, plates, and plate boundaries. The Gray arrows show the ways the plates are moving (compare with the Extrusion Tectonics figure).
  • Below that tectonic map is the photo of the Tapponier et al. (1982) experiment on the left and a map showing the faults that really exist that match faults from the model.
  • On the right is a diagram that shows the evolution of faulting in the region that are the result of changes in driving forces (e.g. broad scale tectonic plate motions and how their relative motions cause different types of faults at different times; also that these older faults remain and may be reactivated as different types of faults later (Staisch et al., 2020).
  • In the lower left center is a map that has red and blue arrows (Tayler and Lin, 2009). These arrows represent places that are moving as measured by GPS observations. The orientation of the arrow shows the direction the plate is moving relative to “stable” Eurasia (data are from Zhang et al., 2004). I place a yellow star in the location of the M 7.5 epicenter.
  • In the upper left-center there is a map that shows some of the mapping of faults in the area (Chen Xia et al., 2011). The Kunlun fault is the main fault line with a thicker width. I place a yellow star in the location of the M 7.5 epicenter.
  • In the upper right corner are maps that display data from the USGS event page. On the left is the modeled landslide probability for this earthquake. On the right is a map that shows the susceptibility (chance of) liquefaction from this M 7.3 earthquake. The headwaters of the Yellow River feed the lakes to the west of the earthquake and this river valley is where the Maduo Grande fault is mapped.
  • In the lower right corner is a map that shows the earthquake shaking intensity modeled for this earthquake. The colors use the Modified Mercalli Intensity scale (MMI), which is listed in the upper right margin (compare the numbers on the map with the data in the table). I also plot the aftershocks here to show how they relate to the mapped fault.
  • Here is the map with 3 month’s seismicity plotted.

Other Report Pages

Some Relevant Discussion and Figures

Global and Regional Tectonic Faults

  • Here is an excellent overview of the faults in the region from Taylor and Yin (2009).
  • I especially like this figure as it helps us understand what the fault patterns mean by looking at the fault types. For example, I thought that the faults in the northwest Tarim Bain would have been strike-slip, but they appear to be predominantly thrust or reverse faults (e.g. the Southern Tian Shan thrust).

  • A color-shaded relief map with active to recently active faults related to the Indo-Asian collision zone and surrounding regions. (The paper lists the sources of their fault data, but are “augmented by our own kinematic interpretations.”)
    Thrust faults have barbs on the upper plate, normal faults have bar and ball on the hanging wall, arrows indicate direction of horizontal motion for strike-slip faults. Dashed white lines are Mesozoic suture zones: IYS—Indus Yalu suture zone; BNS—Bangong Nujiang suture zone; JS—Jinsha suture zone; SSZ—Shyok suture zone; TS—Tanymas suture zone; AMS—Anyimaqen-Kunlun-Muztagh suture zone.

  • Here is the map from ChenXia et al, (2011) that shows the faults in teh area of the M 7.3 earthquake (located near the MD-GD F fault label. See the interpretive poster.

  • Geometric distribution map of the mid-eastern part of the Kunlun Fault [5, 9, 12], Diebu-Wudu Fault and Awancang Fault [12]. Other faults are drawn according to ref. [21], the areal geological map with scale 1:200000 [20] and interpretation of ETM satellite imagery. Topography is generated from Shuttle Radar Topography Mission (SRTM) data ( listImages.asp). HDSW, Huaideshuiwai; ELS F, Elashan Fault; RYS F, Riyueshan Fault; LT-TC F, Lintan-Tanchang Fault; DB-WD F, Diebu-Wudu Fault; TZ F, Tazang Fault; MD-GDF, Maduo-Gande Fault; MDSF, the southern Maduo Fault; Dari F, Dari Fault; LRB F, Longriba Fault; MJ F, Minjiang Fault; XGZ, Xigongzhou; TZ, Tazang.

  • Here is a map that shows some earthquake mechanisms (centroid moment tensors) for earthquakes from 1977 to 2009 (Taylor and Yin, 2009).
  • Compare this map with the one above and the mechanisms match pretty well with the types of faults mapped in the above map.

  • Color-shaded relief map overlain with Harvard centroid moment tensor (CMT) earthquake focal mechanisms from 1 January 1977 to 1 January 2009 and background seismicity from Engdahl and Villasenor (2002) with events >M5.5 for both data sets. Green, purple, and light-blue earthquake focal mechanisms are locations of 2008 western Kunlun, Nima, and Wenchuan events, respectively.

  • Here is a map from Zhu et al. (2019) that shows the slip rates for some of the faults. Paleoseismologists (geologists who study the prehistoric record of earthquakes) dig holes into the Earth to excavate and expose earthquake faults. They are in search of evidence of past earthquakes, generally in the form of discrete offsets of geologic materials (like rocks, soils, and other stuff).
  • The timing of earthquakes can be interpreted and numerical ages (like radiocarbon ages) can be used as a basis for this timing. If there are ways to measure how much the fault is offset during these earthquakes, the distance and time information can be used to calculate a “slip rate” for the fault
  • The slip rate tells us how fast the fault slips over time. The map below shows the slip rates calculated from these studies. Also, geologists can take older geologic units, measure the offset of these units, and calculate a long term geologic slip rate. The map below presents long term geologic slip rates.
  • For example, The Kunlun fault (the thick red line) has a slip rate of 10+-1.05 mm per year near the 2001 M 8.1 earthquake (labeled M 7.8 on the interpretive poster).

  • Structural background of the East Kunlun Fault, seismic activity, and long-term geologic slip rate.

  • Dr. Lydia Staisch and her colleagues (Staisch et al., 2020) use geologic and fault mapping to interpret the tectonic geologic history of this region of central Asia. The next few figures walk us through this story, but read their paper for more information. I will try to update these figures with better quality versions when i get a good copy of the paper (this one is from sci-hub).
  • This first map shows their interpretation of when each fault system had initiated through time.

  • Map of major active strike-slip and normal faults in the Tibetan Plateau, adapted from Styron et al. (2010) and Fu et al. (2011). The estimated initiation age of each fault system is denoted, along with abbreviated names. Abbreviations for strike-slip faults are as follows; cKF: central Kunlun Fault, eKF: east Kunlun Fault , wKF: west Kunlun Fault, wHF: west Haiyuan Fault, EF: Elashan Fault, RF: Riyueshan Fault, ATF: Altyn Tagh Fault, JF: Jiali Fault, RRF: Red River Fault; KF: Karakoram Fault, XF: Xianshuihe Fault, RCF: Riganpei Co Fault, GCF: Gyaring Co Fault, BCFS: Bue Co Fault System. Abbreviations for normal faults are as follows; TG: Thakkola Graben, ADR: Ama Drime Rift, KCR: Kung Co Rift, GMR: Gurla Mandhata Rift, RG: Ringbung Graben, YR: Yadong Rift, LR: Lunggar Rift, NR: Nyainqentanghlah Rift, LKR: Lopukangri Rift, TYCR: Tangra Yum Co Rift, PXR: Pumqu-Xainza Rift, GR: Gulu Rift, SHG: Shuang Hu Graben.

  • Here is their low-angle oblique view of the cut-away view (cross-section) of the fault bound tectonic blocks in the region of the Dongdatan Valley (a valley formed by the Kunlun fault system).
  • In the Late Triassic, the dominant tectonic forcing was North-South shortening.
  • Over time, the tectonic forcing shifted to include lateral motion (i.e. strike-slip faulting) in the Miocene.

  • Schematic block diagrams showing the evolution of deformation within the Dongdatan Valley. (A) Field observations of Permian carbonates thrust over Triassic metapelites and regional dating of plutons and metamorphic cooling episodes (Mock et al., 1999; Liu et al., 2005; Wu et al., 2019) suggest that the East Kunlun Shan experienced late Triassic north-south oriented compression from the northward accretion of the Qiangtang block. (B) North-south compression may have been reactivated during Jurassic – Cretaceous accretion of the Lhasa block based on the timing of Yangqu Group deposition and a separate regional cooling event documented in the West and East Kunlun Shan (Liu et al., 2005; Li et al., 2019). Permian marbles and Triassic metapelites were exposed at the time of Yangqu deposition. (C) Late Cretaceous to Eocene shortening from collision between India and Eurasia resulted in thrust faulting along the Wenquan Hu thrust fault and burial of terrestrial strata in the footwall. Shortening and exhumation may have continued elsewhere in the East Kunlun Shan into late Eocene time, but ceased along the Wenquan Hu thrust fault by 43 Ma. (D) East-west oriented strike-slip faulting locally causes exhumation and erosion. Thermochronologic modeling suggest that strike-slip faulting initiated by ~20 Ma. Miocene – present strike-slip faulting results in basin subsidence in the East Wenquan basin, deposition of terrestrial strata, and juxtaposition of Jurassic – Cretaceous and Cenozoic strata.

  • Here they present a more detailed cross section of the region through time.

  • Schematic orogen-scale cross section of the geodynamic evolution of the Tibetan Plateau. (a) The onset of crustal shortening and thickening in northern Tibet soon after the Indo-Asian collision continued into late Oligocene time with moderate elevation gain. (b) Shortening in northern Tibet continues as a southward sweep of magmatism suggests the onset of slab rollback in southern Tibet. (c) Shortening ceases within the Hoh Xil Basin by 27 Ma and likely by 24 Ma in the East Kunlun Shan. Surface uplift may have continued due to crustal thickening via lower crustal flow in northern Tibet. Continued slab rollback may have destabilized the northern Tibetan mantle root by removing is southern buttress. (d) The onset of strike-slip faulting in the East Kunlun Shan between 23 and 20 Ma is coincident with proposed slab breakoff and elevation gain in southern Tibet, and proposed mantle root loss, surface uplift and increased magmatism within the northern Tibet. (e) After 20 Ma, strike-slip and normal faulting expanded throughout the Tibetan Plateau, coincident with the proposed onset of eastward-directed lower crustal flow.

  • This is an excellent figure that shows how Haibing et al. (2005) used fluvial geomorphologic features (shapes in the landscape formed by fluvial or other physical processes) to derive slip rates.
  • Basically, there are features in the landscape that are formed over time and their formation is abandoned at some time. Also, these “geomorphic” features (like river banks) can be offset by the earthquake fault during earthquakes.
  • If we know something about the age of a feature (when it was abandoned or when it was formed) and we know the amount that feature was offset during an earthquake, we can calculate the slip rate of that fault. The more slip rate calculations that can be made provides us with knowledge about how the slip rate can vary through time.
  • This figure shows a Satellite image of an area with the Kunlun fault curring through river banks. Can you see the fault? Hint, the river banks are on the left and are generally running north-south. The fault cuts across these river banks (and other features)
  • The lower part of the figure is their interpretation of these features and the ages of them. Harkins et al. (2010) has an excellent compilation of similar features for the eastern Kunlun fault.

  • (A) Enlarged Ikonos image of eastern part of Hongshui Gou confluence with (B) corresponding geomorphic field map, showing different terrace levels offset by the Kunlun fault. Black dots indicate locations of sampling pits for thermo-luminescence dating on T2 and T3, with corresponding ages

  • Here Haibing et al. (2005) present the results from their analysis (offsets at different times) and these data all support an interpretation of 10 mm per year of slip on the western Kunlun fault.

  • (A) Enlarged Ikonos image of eastern part of Hongshui Gou confluence with (B) corresponding geomorphic field map, showing different terrace levels offset by the Kunlun fault. Black dots indicate locations of sampling pits for thermo-luminescence dating on T2 and T3, with corresponding ages

Geodetic Analyses

  • Geodesy is the study of the motion of the Earth. The data used to measure how the Earth moves can be from GPS data, tide gage data, benchmark surveys, and satellite remote sensing data (e.g. InSAR, LiDAR, etc.). The motion can be partitioned into different directions (e.g. horizontal, vertical, and rotational).
  • The maps below are from Taylor and Yin (2009, lower map) and Zhang et al. (2004, upper map) shows the velocity (speed) of locations where GPS locations (positions) have been collected over a period of time (probably decades). The arrows called vectors represent the direction of motion and the rate of motion (speed or velocity). The GPS sites are where the dots are and the uncertainty (sometimes called error) of the velocity calculation is represented by the ellipse at the tip of the arrow.
  • These arrows represent motion relative to stable Eurasia. So, arrows that are pointing to the north tell us that that GPS site is moving north relative to Eurasia. Unfortunately there is no scale, but based on the Zhang et al. (2004) paper (also shown below), the most southwest GPS site (in India) has a velocity of about 25 mm per year (mm/yr)
  • We can make some simple observations and interpretations from these data. Look at the lower map with the red and blue arrows (vectors).
    1. GPS sites in northern India (in the lower left (southwest) part of the map) show that this region is moving north-northeast relative to Eurasia. This matches the long term motion of the India plate we discussed in the introduction to this report above.
    2. GPS sites in the Tian Basin (the low, green colored area in the central upper left (northwest) part of the map) are also moving north relative to Asia. However, they are moving to the north more slowly than the sites in India
    3. Because the GPS sites (and the crust in that location) are moving slower in the north, north of the Himalaya and Tibetan Plateau. This tells us that the crust is slowing down between India and the Tarim Basin. Why is this?
    4. The crust is slowing down because the crust is deforming, either elastically where the deformation of the crust buldges up or flexes sideways, or anelastically where the deformation is accommodated by fault slip on tectonic compressional faults (e.g. reverse or thrust faults).
    5. Note how these GPS plate motion vectors (the red and blue arrows) change whether they are slightly to the east or slightly to the west of North. In northeast India, the motion is slightly to the northeast and in the Tarim Basin some of them are moving slightly to the west. If this difference is larger than the error ellipses, it would tell us that the crust may also be experiencing changes in lateral motion through this region. This type of lateral motion may be accommodated by strike-slip faults (see the arrow shaped figure from Taylor and Yin (2009) below).

  • Caption from Zhang et al., 2004)
  • Global positioning system (GPS) velocities (mm/yr) in and around Tibetan Plateau with respect to stable Eurasia, plotted on shaded relief map using oblique Mercator projection. Ellipses denote 1s errors. Blue polygons show locations of GPS velocity profiles in Figures 3 and DR1 (see footnote 1). Dashed yellow polygons show regions that we used to calculate dilatational strain rates. Yellow numbers 1–7 represent regions of Himalaya, Altyn Tagh, Qilian Shan, Qaidam Basin, Longmen Shan, Tibet, and Sichuan and Yunnan, respectively.

  • Caption from Taylor and Yin (2009), Figure 4 is the arrow shaped figure below.
  • Color-shaded relief map of the Indo-Asian collision zone with global positioning system (GPS) velocities (arrows) from the Zhang et al. (2004) compilation. Blue arrows indicate data used in Figure 4A and line indicates data used in Figure 4B.

  • Here is the cross section of the India-Eurasia plate convergence through time from Pusok and Stegman (2020) shown above. They use numerical modeling of the mantle convection to try to interpret the data presented in this cross section.

  • Proposed convergence history between India and Eurasia. (A) Schematic diagram for the evolution of Neo-Tethys Ocean as a sequence of stages with approximate times for important events. (B) Hypothesized convergence rate versus age, with numbered segments corresponding to stages in (A), superimposed on the relative plate motions between India-Africa and India-Antarctica (dotted blue and green lines, respectively) (4).

  • Here Pusock and Stegman (2020) present the long term geodetic data (geologic rates) and compare the results of their modeling with these source data.

  • India-Eurasia convergence data and results of numerical models. (A) Observed spreading rates versus age for the CIR (black lines) and SEIR (gray lines). Solid lines use the geomagnetic polarity time scale GTS04 and a three-plate algorithm to calculate Euler rotations (2, 4), while dotted lines use the GTS12 time scale and a two-plate rotation algorithm (17). Blue line shows computed convergence rate between the left plate and the overriding plate in the reference model (ConvIndia35). Colored markers correspond to model snapshots in Fig. 3. (B) Speedup over time: Observational data are scaled to a velocity of 70 mm/year, representative of the present-day global subduction. Numerical data (blue line) are scaled to velocities in single subduction experiments (ConvIndia31). (C) Convergence velocity versus the DSF (see Materials and Methods and the Supplementary Materials) for all simulations. Time-averaged values given for single subduction (black), periods of plume push (white), and periods of free double subduction (blue) including reference model (red). Maximum/minimum values during model evolution shown with gray bars. (D) Speedup versus the DSF for all numerical simulations [colors same as (C)]. Least-squares fit (dashed lines) for periods of forced convergence (white markers) and periods of free double subduction (blue markers).

  • Here Pusok and Stegman (2020) present the details from their numerical modeling. What do you think about this?

  • Time evolution of the reference model ConvIndia35. The system is composed of three plates representing India (left plate), an intraoceanic plate (middle plate), and Eurasia (overriding plate). This model was performed with plume-like influx boundary conditions imposed for 9 Ma (Influx BC06) and a mantle density and viscosity profile (25, RLLB15-Mean_norm; see details in Materials and Methods and the Supplementary Materials). Frames correspond to colored markers in Fig. 2A, and full-time evolution is available in movie S1. The dynamic pressure (background field) indicates when each regime dominates. (A) During the plume push regime, the entire system is dominated by the influx velocity, seen by positive dynamic pressures throughout the domain. (B) During the double subduction regime, the mantle flow and plate convergence are dominated by interaction between the two slabs, as indicated by pressure increasing within the region confined between them. Changes in driving forces are also seen by the mantle velocity field (uniformly spaced arrows). The influx boundary conditions, which mimic the stages of a plume push, gradual arrival (1), peak activity (2), and decline (3), are sufficient to initiate a secondary subduction at a weak zone. As the plume push force wanes, the secondary subduction becomes self-sustaining, transitioning the system into a double subduction–driven system (4). The pull from both slabs drives fast convergence and then slows when the middle plate is consumed during arc-continent collision (5) and continental collision (6). Relative motion between the red markers is used to calculate convergence rates.

  • Here is a map that shows the Zhang et al. (2004) interpretation of how the GPS motion is accommodated in different areas.

  • Simplified tectonic map showing active faults and movements of Tibetan Plateau and its margins constrained by global positioning system measurements. Numbers are rates of movement (mm/yr). Map covers same region as Figure 1. Bold black lines show active faults. Bold purple arrows indicate N208E shortening across interior of plateau. Blue arrows indicate shortening perpendicular to margins of plateau. Green arrows indicate extension in western and central Tibet. Red arrows indicate strikeslip faulting. Open black arrows denote relative motion with respect to stable Eurasia. Colored numbers show respective amounts

  • Here Zhang et al (2004) show how they interpret these GPS data to be accommodated along strike-slip fault systems.

  • Components velocities of global positioning system (GPS) control points along profiles perpendicular to northeastern and eastern margins of Tibet with respect to stable Eurasia. A: Velocity components parallel to N208E vs. distance (in km) from south to north (left to right) along each of four profiles in Figure 1. Blue—profile A-A9, red— B-B9, green—C-C9, and orange—D-D9. Squares show GPS stations on Indian plate; triangles— within Himalaya; diamonds— in interior of Tibetan Plateau; stars— in Qaidam and Qilian Shan; dots—in Tarim or Gobi Alashan regions north of Tibetan Plateau. B: Velocity components parallel to N1108E at stations between 31.58N and 348N and between 798E and 1008E (profile E- 9). Yellow bars denote ranges of velocity measurements. C: Velocity components normal to N208E, inferred India-Eurasia relative motion. Symbols as in A. Curves of different colors sketch approximately eastward flow of each respective profile. D: N108E trending profile orthogonal to Kunlun fault showing components of east-west motion (profile FF9,
    Fig. 1). Yellow bars mark range of measurement (~1 standard deviation). Thick red line marks Kunlun fault. Red arrows denote locations of other active faults within this ~400 km zone.

  • This is the Taylor and Yin (2009) plot that shows their interpretation of the GPS data.
  • Note how they further note that the strike-slip relative motion can be seen as right-lateral in the south and left-lateral in the north.

  • Zhu et al. (2019) attempted to use GPS data to model the faults to see where they are accumulating tectonic strain. This map shows how they separated the Earth into different rigid tectonic blocks.

  • Observed crustal deformation and optimal inversion values. a The fit conditions of the 1999–2013 GPS data. The three inserts are the frequency distributions of north–south and east–west residuals, respectively. Black arrows are observed values, and blue arrows are fitted GPS values. b The location of leveling profile. EP, WP, and MP indicate the best fit of 1970–2013 long-term leveling data. Blue dots show the original observed leveling data, and red dots are fit data from west to east along the three leveling lines. c, d The best fit velocity of 2013–2015 and 2015–2017 GPS data, respectively

  • Here is a result from Zhu et al. (2019) that shows their estimate of the slip rate on the East Kunlun fault. This is based on an “inversion” of the GPS data along with their block model.
  • Their paper includes additional analyses to incorporate knowledge about earthquakes that happened during the time GPS data had been recorded.

  • Slip velocities along the East Kunlun Fault calculated using 1999–2013 GPS velocity data. The 3D drawings represent the results obtained using the inversion strategy proposed in this paper. P1–P4 are the long-term slip velocities and locking depths calculated using the arc tangent method (Savage and Burford 1973). In P1–P4, the blue curves are the fitted curves. The red dots are the observed slip rates parallel to the fault (including errors)

  • Kirby et al. (2007) use GPS velocities to also interpret strike-slip motion through the region.
  • The upper map shows the source data for cross sections of those data in the lower plots. Note that there is abundant evidence for strike-slip shear in the western cross sections, but this appears to diminish to the east. Do you agree with this interpretation?

  • Geodetic velocities across the Kunlun fault. (a) Map of velocity vectors relative to a stable Eurasian reference frame. Data compiled from Shen et al. [2005] and Zhang et al. [2004]. (b–d) Fault-parallel velocities (110 azimuth) in transects located across the central portion of the fault (Figure 6b), the eastern fault tip (Figure 6c), and the Tazang
    fault (Figure 6d). Solid lines represent velocities predicted from a dislocation model in an elastic half-space with a locking depth of 15 km and serve to illustrate the range of reasonable slip rates permitted by the data. Position of Kunlun fault shown as vertical line.

  • Here Kirby et al. (2007) compare their results with the slip-rate estimates from other studies.

  • Compilation of slip rate estimates and recent seismicity along the Kunlun fault. Open and shaded boxes represent slip rate estimates from previous studies, dark circles new slip rates presented in this work, and heavy vertical lines represent the allowable range of slip rates from geodetic velocities. The lateral extent of previous ruptures along the Kunlun fault are depicted for earthquakes in 1937 [Li et al., 2005; Tapponnier and Molnar, 1977] and 1963 [Tapponnier and Molnar, 1977] and for the 2001 Kokoxili event [Lin et al., 2002; Van der Woerd et al., 2002a]. Note that slip rates (gray shading) decrease markedly toward the eastern tip of the fault, coincident with an absence of historic seismicity and imply strain accumulation in the surrounding Tibetan Plateau.

InSAR Analyses (stay tuned for more)

Shaking Intensity and Potential for Ground Failure

  • Below are a series of maps that show the shaking intensity and potential for landslides and liquefaction. These are all USGS data products.
  • There are many different ways in which a landslide can be triggered. The first order relations behind slope failure (landslides) is that the “resisting” forces that are preventing slope failure (e.g. the strength of the bedrock or soil) are overcome by the “driving” forces that are pushing this land downwards (e.g. gravity). The ratio of resisting forces to driving forces is called the Factor of Safety (FOS). We can write this ratio like this:

    FOS = Resisting Force / Driving Force

    When FOS > 1, the slope is stable and when FOS < 1, the slope fails and we get a landslide. The illustration below shows these relations. Note how the slope angle α can take part in this ratio (the steeper the slope, the greater impact of the mass of the slope can contribute to driving forces). The real world is more complicated than the simplified illustration below.

    Landslide ground shaking can change the Factor of Safety in several ways that might increase the driving force or decrease the resisting force. Keefer (1984) studied a global data set of earthquake triggered landslides and found that larger earthquakes trigger larger and more numerous landslides across a larger area than do smaller earthquakes. Earthquakes can cause landslides because the seismic waves can cause the driving force to increase (the earthquake motions can “push” the land downwards), leading to a landslide. In addition, ground shaking can change the strength of these earth materials (a form of resisting force) with a process called liquefaction.

    Sediment or soil strength is based upon the ability for sediment particles to push against each other without moving. This is a combination of friction and the forces exerted between these particles. This is loosely what we call the “angle of internal friction.” Liquefaction is a process by which pore pressure increases cause water to push out against the sediment particles so that they are no longer touching.

    An analogy that some may be familiar with relates to a visit to the beach. When one is walking on the wet sand near the shoreline, the sand may hold the weight of our body generally pretty well. However, if we stop and vibrate our feet back and forth, this causes pore pressure to increase and we sink into the sand as the sand liquefies. Or, at least our feet sink into the sand.

    Below is a diagram showing how an increase in pore pressure can push against the sediment particles so that they are not touching any more. This allows the particles to move around and this is why our feet sink in the sand in the analogy above. This is also what changes the strength of earth materials such that a landslide can be triggered.

    Below is a diagram based upon a publication designed to educate the public about landslides and the processes that trigger them (USGS, 2004). Additional background information about landslide types can be found in Highland et al. (2008). There was a variety of landslide types that can be observed surrounding the earthquake region. So, this illustration can help people when they observing the landscape response to the earthquake whether they are using aerial imagery, photos in newspaper or website articles, or videos on social media. Will you be able to locate a landslide scarp or the toe of a landslide? This figure shows a rotational landslide, one where the land rotates along a curvilinear failure surface.

  • Below is the liquefaction susceptibility and landslide probability map (Jessee et al., 2017; Zhu et al., 2017). Please head over to that report for more information about the USGS Ground Failure products (landslides and liquefaction). Basically, earthquakes shake the ground and this ground shaking can cause landslides. We can see that there is a low probability for landslides. However, we have already seen photographic evidence for landslides and the lower limit for earthquake triggered landslides is magnitude M 5.5 (from Keefer 1984)
  • I use the same color scheme that the USGS uses on their website. Note how the areas that are more likely to have experienced earthquake induced liquefaction are in the valleys. Learn more about how the USGS prepares these model results here.


    Basic & General References

  • Frisch, W., Meschede, M., Blakey, R., 2011. Plate Tectonics, Springer-Verlag, London, 213 pp.
  • Hayes, G., 2018, Slab2 – A Comprehensive Subduction Zone Geometry Model: U.S. Geological Survey data release,
  • Holt, W. E., C. Kreemer, A. J. Haines, L. Estey, C. Meertens, G. Blewitt, and D. Lavallee (2005), Project helps constrain continental dynamics and seismic hazards, Eos Trans. AGU, 86(41), 383–387, , /li>
  • Jessee, M.A.N., Hamburger, M. W., Allstadt, K., Wald, D. J., Robeson, S. M., Tanyas, H., et al. (2018). A global empirical model for near-real-time assessment of seismically induced landslides. Journal of Geophysical Research: Earth Surface, 123, 1835–1859.
  • Kreemer, C., J. Haines, W. Holt, G. Blewitt, and D. Lavallee (2000), On the determination of a global strain rate model, Geophys. J. Int., 52(10), 765–770.
  • Kreemer, C., W. E. Holt, and A. J. Haines (2003), An integrated global model of present-day plate motions and plate boundary deformation, Geophys. J. Int., 154(1), 8–34, ,
  • Kreemer, C., G. Blewitt, E.C. Klein, 2014. A geodetic plate motion and Global Strain Rate Model in Geochemistry, Geophysics, Geosystems, v. 15, p. 3849-3889,
  • Meyer, B., Saltus, R., Chulliat, a., 2017. EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) Version 3. National Centers for Environmental Information, NOAA. Model.
  • Müller, R.D., Sdrolias, M., Gaina, C. and Roest, W.R., 2008, Age spreading rates and spreading asymmetry of the world’s ocean crust in Geochemistry, Geophysics, Geosystems, 9, Q04006,
  • Pagani,M. , J. Garcia-Pelaez, R. Gee, K. Johnson, V. Poggi, R. Styron, G. Weatherill, M. Simionato, D. Viganò, L. Danciu, D. Monelli (2018). Global Earthquake Model (GEM) Seismic Hazard Map (version 2018.1 – December 2018), DOI: 10.13117/GEM-GLOBAL-SEISMIC-HAZARD-MAP-2018.1
  • Silva, V ., D Amo-Oduro, A Calderon, J Dabbeek, V Despotaki, L Martins, A Rao, M Simionato, D Viganò, C Yepes, A Acevedo, N Horspool, H Crowley, K Jaiswal, M Journeay, M Pittore, 2018. Global Earthquake Model (GEM) Seismic Risk Map (version 2018.1).
  • Zhu, J., Baise, L. G., Thompson, E. M., 2017, An Updated Geospatial Liquefaction Model for Global Application, Bulletin of the Seismological Society of America, 107, p 1365-1385,
  • Specific References

  • Harkins, N., Kirby, E., Shi, X., Burbank, D., and Chun, F., 2010. Millennial slip rates along the eastern Kunlun fault: Implications for the dynamics of intracontinental deformation in Asia in Lithosphere, v. 2, no. 4, p. 247-266, doi: 10.1130/L85.1
  • Kirby, E., Harkings, N., Wang, E., Shi, X., Fan, C., and Burbank, D., 2007. Slip rate gradients along the eastern Kunlun fault in Tectonics, v. 26, TC2010, doi:10.1029/2006TC002033
  • Pusok, A.E. and Stegman, D.R., 2020. The convergence history of India-Eurasia records multiple subduction dynamics processes in Science Advances, v. 6 : eeaz8681, 8 p., DOI: 10.1126/sciadv.aaz8681
  • Staisch, L.M., Niemi, N.A., Clark, M.K., and Hong, C., 2020. The Cenozoic evolution of crustal shortening and left-lateral shear in the central East
    Kunlun Shan: implications for the uplift history of the Tibetan Plateau in Tectonics, v. 39, no. 9,
  • Tapponier, P., Peltzer, G., Le Dain, A.Y., Armijo, R., and Cobbold, P., 1982. Propagating extrusion tectonics in Asia: New insights from simple experiments with plasticine in Geology, v. 10, p. 611-616, doi: 10.1130/0091-7613(1982)10<611:PETIAN>2.0.CO;2
  • Taylor, M. and Yin, A., 2009. Active structures of the Himalayan-Tibetan orogen and their relationships to earthquake distribution, contemporary strain field, and Cenozoic volcanism in Geosphere, v. 5, no. 3, doi: 10.1130/GES00217.1
  • Zhang, P-Z., Shen, Z., Wang, M., Gan, W., Bürgmann, R., Molnar, P., Wang, Q., Niu, Z., Sun, J., Wu, J., Hanrong, S., Xinzhao, Y, 2004. Continuous deformation of the Tibetan Plateau from global positioning system data in Geology, v. 23, no. 9, p. 809-812,

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