Hi, everyone. Today I’m going to show you
how you can use the laser scanner in a newer iOS device to easily create useful
3D models for paleoseismology and other geoscience applications.
If you were at the SCEC meeting two weeks ago,
then some of these slides will be familiar.
Unfortunately, being in the U.K., there aren’t any nearby active faults
to create a demo for you, but that also means that I live down the street
from this Egyptian tomb. Here, I’m using the 3D Scanner app.
When scanning, it is important to move slowly to avoid motion blur and make
sure the geometry is scanned correctly. This app creates a point cloud using the
Lidar scanner and simultaneously creates a texture using pictures from the camera.
It then puts these together to produce a photo-textured
3D model in centimeter-accurate real-world units.
Please check out my website below if you’d like to explore
this and some other models yourself.
The real-world scanning and processing time here was exactly four minutes,
and this video was sped up 4X.
Let’s compare this process to our photogrammetry workflow.
To produce a scaled SfM model, a control survey must first be established,
then photos taken, uploaded to a powerful computer with expensive
photogrammetry software and processed before the model can be viewed.
The fastest I have ever been able to produce an SfM model
was about 30 minutes. In my experience, it frequently
takes multiple iterations of both methods to produce a quality model.
IOS Lidar has a clear advantage in time, effort, and accessibility.
As I’ll show you today, the final image resolution is acceptable
but a bit less than that from a high-quality SfM survey.
This study by Luetzenburg et al. was published last month
in Scientific Reports. They compared scans to measurements
of real-world objects into an SfM model of a seaside cliff.
They find that the accuracy is about plus or minus a centimeter for small objects,
like a chair or box, and for bigger landscape-size objects, like a large fault
trench or an outcrop, accuracy is on the order of 10 centimeters.
The maximum range of the scanner is 5 meters.
The models are only roughly georeferenced using the GPS
of your device, but in the 3D Scanner app, this function
is off by default, so make sure that you turn it on.
The total file size of a project is less than 1 gigabyte, but the shareable
textured model outputs are generally less than 100 megabytes.
A single scan is usually limited to an area of several tens of square meters,
but multiple scans can be combined using software like CloudCompare.
The sensor performs badly with reflective surfaces, in dense vegetation,
or if the complexity of the object creates a lot of laser shadows.
Comparing SfM to Lidar, SfM is clearly the winner in image resolution,
sharpness, and clarity, but for many uses, the iDar scan quality will be
good enough. It also suffers from the same issues as SfM,
where the time of day, shadows, and lighting have a big impact
on the quality of your trench model.
My own experience learning how to use this tool has been a series of trials
and errors, so I’m going to show you several different examples
of applications before getting to our 3D trenching work.
The first example is from our field work in Kyrgyzstan this summer.
Here in the Kazarman Basin, a left-lateral strike-slip fault forms
a prominent surface expression across the landscape.
This fault forms a series of grabens, and in this area, we excavated two
trenches across uphill-facing scarps. The trench reveals coarse alluvial
stratigraphy that was readily imaged with the iOS scanner.
The final image resolution is quite good, and you can see the fine details of many
gravels and cobbles. There are some issues with duplicated cobbles,
one of them circled here in red, but the most noticeable issue
is the string jumping around. As the string is hanging in front of the
trench wall, the scanner has difficulty placing it in the correct location.
This is also a frequent problem with SfM, and I don’t recommend hanging
a full string grid prior to photographing with either of these methods.
I can make measurements and interpretations of the resulting
3D trench, and here I used Blender to make a 3D trench log.
The cobbly fan layer is dropped down by the fault, forming a graben,
and has been infilled with finer sediments.
One of the places where this tool really shines is when you are
in a situation with limited time or equipment.
During an initial project kickoff visit to Azerbaijan this fall, we were only
allotted two days of field time to do some rapid reconnaissance.
Here the Iranian Plate is crushing into the Caucasus Mountains at a rate of
8 to 12 millimeters per year, and we think most of this compression
is accommodated by this massive thrust fault. At the site I’m going to show you,
a fault scarp at the front of this folded terrace intersects a natural river bed.
Due to the time constraints, we only had about 45 minutes to spend at this site,
similar to many earthquake response situations.
When we arrived, this crew was in the process
of destroying this natural fault exposure to build a new irrigation canal,
so scanning the site quickly was our only option.
Here is a preview of the model. This model is also available
on my website. The outcrop is convex, and due to the height, scanning was
difficult. Using Blender, I can create a 3D trench log.
I can also make measurements in the model. I can then export the model.
It’s important to note the difference in 3D viewer terminology between a
prospective view, where dimensions are scaled based on distance from the
camera, and an orthographic view, where dimensions are true to reality.
To make a proper trench log, you need an orthographic view.
Unfortunately, there isn’t a built-in way in the 3D scanner app to export
the 3D models into a full-resolution 2D orthoimage for making your log,
but I published a tutorial on how to do this on my website with Blender.
Here’s an exported orthoimage.
On the right, sub-horizontal cobble and silt layers rest on top of the dipping,
and apparently folded, cobble layers to the left.
At the lower right of the outcrop,
the cobble layers appear to steeply work down.
I interpret this folding to be a result of coseismic deformation but that the
main fault is still buried. The horizontal layers post-date an earthquake.
The radiocarbon age of charcoals sampled from the folded layers
demonstrate that this earthquake happened within the last 2,000 years.
This is a completely usable quality image for a publication figure.
Now we’ll get to the proper 3D trenching that I promised.
North of Lake Tahoe in Truckee, California, in the northern Walker Lane
is the northeast-striking left-lateral Dog Valley Fault.
This fault runs directly through the Stampede Reservoir Dam.
It produced a magnitude 6 earthquake in 1966,
but no surface rupture was observed along the fault.
We excavated a trench across this 2-meter-high fault scarp
and then expanded that trench one slice at a time to cover
approximately 4 meters along the fault.
In total, we recorded 14 across-fault exposures
and two fault-parallel exposures.
The across-fault exposures are separated by about 20 to 80 centimeters.
Now I will give you a tour of the 3D trench using the software program
Georeka. First is the initial fault-perpendicular cut across
the scarp showing the initial two trench walls.
Then I added two fault-parallel cuts on either side of the fault zone
and two more across-fault cuts.
Next, I zoom in, and we’ll fly through the fault zone one cut at a time.
There is variability in the expression of the fault in stratigraphy exposed in each
cut, with sometimes multiple fault strands and sometimes a single strand.
I interpret that this is the result of echelon shearing.
This variability in exposure really highlights the utility
of making multiple cuts.
Alone, some of these slices would not give us much useful information.
[silence]
In each cut, I can draw the fault as a line.
I can then interpolate these fault lines into fault surfaces
through multiple cuts, here shown in translucent red.
I can do the same with the upper and lower contacts of this
15-centimeter-thick channel margin to build a 3D model of this bed.
The margin of this channel sequence makes a piercing line that I will trace
to the fault zone and use to measure the offset.
For the non-paleoseismologists here, it is often quite difficult to get offset
measurements of a strike-slip fault from trenches.
Finally, I’m able to measure 115-centimeter lateral offset
of the margin of the channel sequence where it intersects the fault.
[silence]
I can check whether this offset is reasonable by trying to match offset
stratigraphy across sections, effectively back-slipping the trench walls.
On the left, I’ve matched two sides of the fault that are offset about
90 centimeters. The layers match nearly perfectly across the fault.
On the right I’m showing an oblique view with
the 90-centimeter measurement between the matching layers.
Again, doing the same, but this time the fit is at 140 centimeters.
Again, but this time 125 centimeters matches.
Finally, another match at 140 centimeters.
So all together, there are offsets ranging from 90 to 140 centimeters,
or 115 plus or minus 25 centimeters. This uncertainty is reasonable as it is
about the same as the thickness of most of these slices, and this result
is the same as what I reconstructed using the other 3D method.
The rupture extends nearly to the surface, so we don’t have
any radiocarbon ages that post-date the earthquake.
Our age data and the clearly offset black peat layers all fall between
8,100 and 8,600 years before present. So ultimately, we have one event
with 115 centimeters of displacement in the last 8,100 years.
We received our iPad in the mail the day before we started this trenching project,
and this was before the 3D Scanner app was released, so unfortunately,
the best laser scans for this project are these colored point clouds that
we collected using the SiteScape app, which aren’t quite as nice as
the textured models that 3D Scanner produces.
So, for this project, I used CloudCompare to combine textured
model using camera-based SfM with the spatial information from the Lidar scans.
I first had to align all of the different Lidar scans
into a common spatial reference.
Then I fit each textured SfM model to the respective scans.
I used the trench flags as my reference points when aligning
the different models. In this video, I’m showing how that
fitting works, and while there is some error, the end result is quite good.
This was an incredibly time-consuming process, and it’s difficult to convey
how much information is really contained in this 3D trench.
This is a really powerful tool for accurately scaling your SfM model.
You can also directly import your 3D texture into Agisoft
and use it to provide control points.
I recommend placing high-contrast colored flags throughout your model
to use as reference points when you’re processing
the SfM and Lidar models together.
Flags that are in stable areas also serve to tie models together
when you’re progressively excavating a 3D trench.
Before I finish today, I want to give an overview
of some of the different software packages I’ve tried.
How many of you have worked with an OBJ or other 3D textured file before?
It requires a different set of tools than you’re probably used to using.
I’ve shown video so far of the 3D scanner app Georeka
and CloudCompare. Blender is a free 3D model software
and quite powerful, though the learning curve is steep.
LIME is much more user-friendly than Blender and has functions
for drawing points, lines, and planes, as well as making measurements,
but it requires an annual subscription.
Sketchfab is a browser-based viewer that is very easy to use,
and the free version is useful. You can upload directly to
Sketchfab from within the 3D Scanner app
and add a few interpretive points without paying.
I uploaded this model from the field
less than 10 minutes after starting the scanning.
V3Geo is another browser-based model viewer, similar to Sketchfab,
but with a geologic focus. It has more advanced measurement tools
than Sketchfab, and they plan to add interpretation functions in the future.
In sum, iDar is a powerful and accessible field tool that allows for
collecting accurate spatial data.
For most results, it should still be combined with GPS and SfM surveys.
Aside from laser scanning and mapping tools, you can also draw directly on
your iPad using a pen in a variety of different 2D drawing softwares.
This is really useful creating a trench log in the field.
I recommend using the 3D Scanner app, Sketchfab,
CloudCompare, and LIME or Blender.
MATLAB is also a useful free software but redundant
functionality with CloudCompare.
I’ve also tried Autodesk and Adobe’s 3D modeling softwares and
cannot recommend them for this use. I didn’t find Golden Software’s Surfer
or Voxler modeling programs useful for this, either.
Unfortunately, standard seismic interpretation softwares
aren’t able to use these types of 3D models.
I’m still waiting to try Moog, but it is very expensive,
so cheaper or free options are preferable.
Learning to work with 3D data is an important new skill for geoscientists.
We’re truly in the Wild West of the 3D data era.
With these – with these tools, we’ve effectively able to digitally
recreate the Earth at 1-to-1 scale. There are many emerging technologies
and applications right now, from scanning and imaging to 3D printing,
virtual reality, and augmented reality, and it’s really exciting to see
what the future might bring.