Snorkeling adventures - accessible coastal gems

I take every opportunity I can to go snorkelling. Over the years I've explored Hikkaduwa Reef and Pigeon Island in Sri Lanka, the Great Barrier Reef in Cairns, Rottnest Island off the coast of Perth, Peanut Island and Biscayne Bay National Park in Miami, and Sharp Island in Hong Kong.

As spectacular as these destinations are, some of the most memorable snorkelling experiences I've had were much closer to home. They're the kinds of places that locals pass every day without a second thought, unaware of the underwater worlds hiding just beyond the shoreline.

Below are three of my favourites, along with a selection of photos I took during my visits. I'll let the pictures do most of the talking.

1. Jawbone marine sanctuary - Melbourne's hidden underwater wilderness.

Located in Williamstown in a quiet residential area, the beach features striking basalt rock formations and boulders which are part of the Newer Volcanics geology, creating an interesting underwater habitat for a variety of marine life. I was fortunate enough to spot a ray known as a banjo shark, pictured below.

2. Shelly Beach Sydney - where the city ends and reef begins 

Easily accessible from Circular Quay via the ferry to Manly, a short walk along the coastline brings you to the popular Shelly Beach. Situated within the Cabbage Tree Bay Aquatic Reserve, its calm, sheltered waters are teeming with colourful fish and marine life.

3. Ozone wreck - a shipwreck you can snorkel to

Located in Indented Head, about 1.5 hours drive from Melbourne, the Ozone wreck is a historic shipwreck that can be reached directly from the shore. The remains of the former paddle steamer lie in shallow water, making it one of Victoria's most accessible shipwreck snorkelling sites. Over time, the wreck has become an artificial reef, attracting fish and other marine life. 

Phone astrophotography

 Phone cameras have come a long way and are inching closer to the capabilities of DSLR and mirrorless cameras when it comes to astrophotography. Improvements in sensor technology and built in post processing features such as HDR, stacking and star alignment being have made them into a useful backup or even a viable replacement for traditional cameras, especially when used with a tripod. These days, I find myself using my phone more than my DSLR for milky way photography.

The photos that follow are some of my favorite shots of the milky way and landscapes taken using an iPhone 14 pro, on tripod with the maximum 30s exposure in the built-in camera app, and later edited in Lightroom mobile. 

The above was taken at Lerderberg State Park Victoria aiming directly at the zenith around the end of winter. 

These two photos were taken in Point Lonsdale lighthouse. The red light from the lighthouse lit up the surrounding rock formations in red as seen in the top image. What is also interesting is that the second image would not have been possible on a single exposure on a DSLR, as the light from the lighthouse would have washed out the image. It appears that the phone has automatically stacked images of various exposures to create a composite image.

These images were taken at the Gravity Observatory in Perth. While a lot of stars are visible the image contains a lot of noise highlighted during the edits, possibly showing the limits of phone night photography.

The first two phots above were taken at Lake Tyrell, and third at the town of Sea Lake. In the second image, the moon illuminated the surroundings and overpowered the stars, but I was able to capture the reflections of a few stars on lake's calm surface. 

 

These two images are the latest ones taken at Aireys Inlet beach. The last one is my favorite so far since it highlights the details of the milky way as well as the rock formation. I used the flashlight from another phone to briefly light up the rocks while the image was being exposed for 30s, and in my opinion it resulted in perfect exposure of both landscape and the stars. 

While phone cameras have made impressive improvements and deliver impressive results, DSLR and mirrorless systems continue to advance in their own right. Still, smartphones offer unmatched accessibility and portability - and as the saying goes, "the best camera is the one you have with you".

Southern lights in Victoria

It's usually not easy to catch a glimpse of Aurora Australis, the southern lights, from Victoria. Tasmania and New Zealand are better places to be. The occasions when it is visible from in Victoria, it's best seen from the southern coasts of the state and usually appears as a faint glow to the naked eye. My last post on astrophotography had a picture of the southern lights taken from Flinders. But with the intense geomagnetic storm yesterday auroras were expected to be visible from many parts of the world. 

As soon as I got to know this I looked for the closest southernmost point, and being in the west of Melbourne, this was Kirk Point in Point Wilson. While driving there I was amazed to see the green and pink shifting columns of light to the naked eye while still on the highway, and stopped to snap a few pictures as soon as I took the exit. Afterwards we went to my intended location and was treated to the spectacular southern lights show. It waned off around 11pm but was still visible even from my backyard after reaching home around midnight. 

Some pictures are below:

More blue water - why is the Nil Diya Pokuna blue?

On my last visit to Sri Lanka, I was keen on exploring some lesser-known attractions and decided to visit Nil Diya Pokuna (නිල් දිය පොකුණ) located close to Ella in the Uva Province. I was impressed and fascinated by the massive underground cave complex and the blue water pond at the end of the 850m hike through the cave. This was the second time I saw clear blue water in Sri Lanka, the first being in a limestone quarry.  

The usual reason for ponded water to appear bright blue or turquoise in colour is the fine particulates that selectively scatter light through water (the same reason why the sky is blue). In the case of the limestone quarry the fine particulates are minute calcite crystals and in the case of glacial lakes they are finely ground rock particles known as glacial flour. 

Nil Diya Pokuna has a very interesting geology, with several different rock types present around and within the caves, and I wanted to understand what gives the water its blue colour. Caves of this scale are usually formed by the action of weathering and erosion of sedimentary rocks such as limestone. However, this region of Sri Lanka consists of primary of metamorphic rocks. This blog post by Dr Jayasingha describes the geological origins of the cave complex containing Nil Diya Pokuna. According to it, the caves have been formed by the initial dissolution of Marble, which leads to weakening of rock joints and bedding planes and subsequent collapses of the other rock masses creating the large underground caverns. 

Marble is formed by the metamorphosis of limestone, and its dissolution would lead to the release of calcite crystals. There are stalactites formed at several places within the cave, as seen in the photos below, that confirm the occurrence of marble or limestone dissolution. Therefore, it is reasonable to conclude that the reason for the blue coloured water in Nil Diya Pokuna is the calcite crystals that are accumulated in the water as it flows through the joints and fissures in rock containing marble or limestone before making its way into the pond. Below are some photos from my visit:

Stalactites in the cave indicating marble or limestone dissolution
 

Evidence of weathering and staining in the rock

Visible bedding planes and smooth joint surface of a possible collapse leading to cave formation

Blue water and more stalactites

High water levels were blocking off some more expansive areas of the cave

The water was a little murky due to recent rains

Estimating surface settlement induced by underbore or tunnel construction

It is often necessary to estimate the potential ground surface settlement caused by underground infrastructure projects involving tunnels and underbores. Such settlement assessments are used to determine if any additional protection works are necessary particularly if underbores are tunnels are to be constructed underneath roads, railways or buildings. Finite element analysis programs such as Plaxis 2D/3D, Optum G2/G3 or FLAC are typically used to model settlements in such instances. Accurate information on ground properties, tunnel parameters and loading conditions is required to provide accurate settlement assessments. 

In situations where a quick estimate with minimum data inputs is required, a common semi-empirical method developed by Peck (1969) is also commonly used. This method is based on field observations made by Peck, and the ground settlement trough profile is approximated by a Gaussian distribution curve. The volume loss in the tunnel (overbreak or annular collapse) is equated to the area under the Gaussian curve from which a settlement profile is generated. The width of the settlement trough varies between soil types and is controlled by a parameter (Kg) that is specified for different soil types and strengths. I developed a web application (https://underbore-settlement.anvil.app/), also embedded below, to estimate settlements based on the Gaussian curve method developed by Peck.

 

It should be noted that since this method does not consider any volume change in soils (consolidation or dilation), it is valid only as an initial estimate under short-term conditions. 

The figure below shows results from the above method compared to the results from a simple Plaxis 2D model. A tunnel with 1m diameter and 2m of cover subject to a volume loss of 10% bored through undrained soft clay and loose sand was modelled separately in Plaxis 2D to compare against the results using Peck's method with recommended numbers for Kg, for clay (0.5) and sand (0.3) respectively.

Comparison between Plaxis output and Gaussian curve method by Peck (1969)

It can be seen that the results from Plaxis and Gaussian curve method are similar for sand, but varies slightly for clay. Using a Kg value of 0.7 for clay leads to a curve very similar to the Plaxis 2D output. The choice of Kg for various soil types with different strengths is a subject of research, and available literature suggests that a Kg value of 0.4-0.7 is appropriate for soft clays. However, with an understanding of the limitations of the Gaussian curve method, it can be used as a rough initial estimate of settlements before embarking on detailed finite element analysis. The web application linked above will be useful for such quick assessments. 

More pictures looking to the heavens

Back in 2017, I wrote a post on beginner astrophotography as I was just getting into the hobby. It was mostly a collection of my very first Milky Way pictures that I took at the time. Since then I have been taking more pictures of the Milky Way, and some occasional pictures of the moon, planets and the aurora. So here's an update of my latest selected pictures in the same format as last time, with details in the caption.

This picture was taken at Cape Schanck, one of my favorite places to capture the stars. It's a single exposure with light painting, so the staircase is out of focus. 

One of may favorite pictures taken under perfect conditions. Moon was out on the opposite side of the Milky Way to illuminate the landscape perfectly. Location: Flinders

This is the first picture in which I was able to capture the reflection of the stars in water. Location: Lake Eildon. 

The daytime moon captured through my telescope. I attached the camera into the eyepiece of the telescope with a lens adapter. 

Two photos of Jupiter and Saturn also taken from my telescope. I recorded a short video and processed it using PIPP and AutoStakkert to create these images. Haven't done planetary imaging since, but I do hope to try it out again sometime soon. 

A photo captured from my balcony one evening showing earthshine on the moon. 

The rising moon captured using a telephoto lens without a tripod. 

My first attempt at capturing star trails. This is a single exposure taken over approximately 12 minutes. Photo taken at Lake Eildon. 

This is my first attempt to capture the Aurora. The pink glow is the Aurora Australis seen from the Southern Coast of Flinders. 

This picture shows the recent lunar eclipse- the blood moon, as seen from Flinders shortly after sunset on 8 November 2022. 

Spatial variability of soil and localised underground corrosion

Underground corrosion is a problem that often goes unnoticed, but has a significant economic impact. Corrosion of buried metallic infrastructure such as pipelines is highly variable, and can be influenced by many complex factors. In addition to phenomena such as stray currents and differential aeration that lead to rapid levels of corrosion, we have shown that the spatial variability, or heterogeneity of soil itself can lead to differential conditions that evolve into rapid localised corrosion.  

In a previous blog post I described how to create random field realisations using Python for various soil parameters with given statistical properties. The idea was to export these realisations as point clouds into numerical modelling software to simulate various processes under heterogenous soil conditions. When we simulated corrosion of buried metal in heterogenous soil using this method, we saw that the spatial variations in soil resulted in localised corrosion defects termed "corrosion patches" that evolve with time until failure occurs. In this context, failure is defined as the point when when the corrosion patch depth exceeds the pipe wall thickness. 

The emergence of localised corrosion from numerical modelling of spatially variable soil, similar to actual corrosion patches observed in the field 

However, running such simulations is computationally intensive, especially if multiple realisations over longer pipe lengths are needed to be simulated. To overcome this problem, we used Artificial Neural Networks (ANN). ANNs are a machine learning method that use a system of simulated neurons to identify and infer patterns from data without being given explicit instructions. It is similar to how neurons in our brains work, with a neuron having several inputs and is activated (similar to a neuron firing in the brain) when the weighted sum of the inputs exceed a certain threshold. An organised set of such neurons can "learn" to map input data to outputs by adjusting the weights and biases used for activation.   

We identified two of the most influential soil properties that influence corrosion: degree of saturation and saturated electrical resistivity. We then trained an ANN using input data from random field realisations of these two variables and numerical model outputs of corrosion parameters for the same realisations. Through this method we developed an ANN that was capable of predicting the level of corrosion over time for given values of these input variables. We were now able to use this trained neural network to simulate corrosion over much longer pipe segments for multiple realisations with lower computational effort and time. We outlined this method and simulation results in a recently published paper: 

https://link.springer.com/article/10.1007%2Fs11440-021-01385-5

In this paper we show how the soil degree of saturation and resistivity influence the corrosion patch configurations. We also describe a linear approximation that can be adopted for assessing information along buried pipelines, and note that the workflow and methods used in this work can be adopted together with underground sensing methods for non-destructive pipeline condition assessment. 

Using Artificial Neural Networks together with numerical modelling to predict pipeline corrosion in spatially variable soil