Technical Tuesday: Linking Lithology and Geochemistry with ioGAS and Leapfrog Geo workflows

This video covers:

• Sending Leapfrog Geo interval and point data to ioGAS
• Analysing data and assigning attributes in ioGAS
• Advanced Geochemistry Workflows
• Visualising geochemical data in Leapfrog Geo’s 3D scene over the live link
• Importing ioGAS data into Leapfrog Geo as points or intervals
• Building geological models and interpolants from geochemical data

Overview

Video Transcript

[00:00:00.065](gentle music)

[00:00:06.440]<v Allan>I am Allan Ignacio senior project geologist here</v>

[00:00:09.140]at Seequent based in Perth,

[00:00:11.270]and I’ve over 25 years experience

[00:00:13.470]with the exploration and resource evaluation.

[00:00:16.640]My co-presenter is Putra Sadikin,

[00:00:19.760]an experienced geochemist and senior software analyst

[00:00:22.510]from Imdex Limited.

[00:00:24.130]Putra, can you tell more about yourself?

[00:00:26.650]<v Putra>Thanks, Allan.</v>

[00:00:28.010]It’s a pleasure to be here to be presenting this webinar

[00:00:31.140]with you guys today.

[00:00:32.440]So I work in a product management team

[00:00:34.650]for the ioGAS software.

[00:00:36.500]I look after product support training,

[00:00:38.760]and a bit of sales and research

[00:00:40.430]and development work for the software.

[00:00:42.970]My background is in geology.

[00:00:44.790]Today, we’ll be presenting a bit of a workflow

[00:00:48.140]between ioGAS and Leapfrog Geo and I’m really excited

[00:00:50.940]to be here today.

[00:00:52.080]Thanks Allen.

[00:00:53.530]<v Allan>Thank you Putra.</v>

[00:00:54.890]So today’s presentation is about linking lithology

[00:00:58.340]and Geochemistry through the use of Leapfrog Geo and ioGAS.

[00:01:02.670]The purpose of this presentation

[00:01:04.250]is for us to appreciate the live link

[00:01:05.910]between Leapfrog Geo and ioGAS,

[00:01:08.780]as well as have the opportunity

[00:01:10.180]to analyze various multi element data,

[00:01:13.360]which could provide hints

[00:01:14.530]to determine different rock types, alteration,

[00:01:17.470]and mineralization patterns

[00:01:19.320]that normally our naked eye couldn’t immediately see.

[00:01:23.420]So for today’s webinar,

[00:01:24.910]we’ll first give you an overview about our company,

[00:01:27.606]then I’ll be going through a series of PowerPoint slides

[00:01:30.670]on sequence solutions, including Leapfrog Geo

[00:01:34.820]and ioGAS link, we’ll give you a bit

[00:01:37.030]of background about the dataset that we used

[00:01:39.490]for multi-element analysis

[00:01:41.640]before moving into the Leapfrog Geo

[00:01:44.665]and ioGAS workflows, which I’m pretty sure all

[00:01:46.920]of you will find interesting and useful.

[00:01:49.045]And then we’ll jump into the live demo

[00:01:51.620]on Leapfrog Geo and ioGAS workflows,

[00:01:54.580]and then end it with a summary.

[00:01:58.070]So about Seequent.

[00:01:59.070]Seequent is a global leader

[00:02:01.420]in the development of visual data science software

[00:02:05.670]and collaborative technologies.

[00:02:08.310]We turn complex data into geological understanding,

[00:02:11.770]provide timely insights,

[00:02:13.437]and gave decision-makers confidence that they need.

[00:02:17.480]Our Seequent solutions harness information,

[00:02:20.460]extract value, bring meaning, and reduce risk.

[00:02:25.200]Our 3D modeling tools

[00:02:26.550]and technology are widely applied across industries

[00:02:30.755]and projects, including road and tunnel construction,

[00:02:34.620]groundwater detection and management,

[00:02:37.540]geothermal exploration, we also have something

[00:02:41.247]in resource evaluation and estimation, subterranean storage

[00:02:47.220]of spent nuclear fuel and a whole lot more.

[00:02:50.410]So the sequence solution encompasses a range

[00:02:52.670]of software products applicable for use

[00:02:55.740]across the volume mining chain.

[00:02:59.100]We have the Geosoft suite of products

[00:03:02.480]and the Leapfrog suite of products, both for GIS mapping,

[00:03:07.040]geophysical, geochemical, geological, and resource modeling,

[00:03:12.450]and we also have the GEOSLOPE products

[00:03:14.630]that are designed for geotechnical engineering problems.

[00:03:18.930]I’m sure many of you are already familiar

[00:03:20.770]with some of these solutions.

[00:03:23.000]Most can be integrated using Seequent Central

[00:03:26.220]for model management and collaboration.

[00:03:29.780]Today, we will be focusing on Leapfrog Geo, ioGAS,

[00:03:33.940]and index product, and the Leapfrog Geo-ioGAS link

[00:03:37.790]so that we can visualize geochemical data in real-time.

[00:03:42.550]So what is Leapfrog Geo?

[00:03:45.257]Leapfrog Geo is workflow based 3D

[00:03:48.220]implicit geological modeling tool,

[00:03:50.680]which allows you to quickly construct models

[00:03:53.480]directly from various sources,

[00:03:56.110]including drill holes, points, and surfaces.

[00:04:00.640]Leapfrog uses volumetric algorithm called Fast RBF

[00:04:05.230]to construct fast pass surfaces quickly.

[00:04:08.520]The RBF or radial basis function

[00:04:11.510]is a mathematical backbone of Leapfrog.

[00:04:14.490]The surfaces such as in here in the 3D scene.

[00:04:18.820]Created, can be modified as needed

[00:04:21.130]to fit your geological interpretations.

[00:04:24.280]So due to its implicit nature,

[00:04:26.680]your models can be dynamically updated

[00:04:28.810]to honor new input data at any time.

[00:04:32.490]This new data can be new drilling information,

[00:04:35.650]new surface information, or any useful inputs.

[00:04:40.240]Under the geochemistry folder is the ioGAS link.

[00:04:44.680]We’re going to use this tool to bring assay data

[00:04:47.690]from Leapfrog Geo as points or interval file

[00:04:51.300]to ioGAS, where you can interrogate, visualize,

[00:04:57.103]and classify data, then back to Leapfrog Geo,

[00:05:00.030]where you can visualize and model the data.

[00:05:04.370]I’m now handing over to Putra,

[00:05:06.290]who will explain more about ioGAS, Putra?

[00:05:10.950]<v Putra>Thank you, Allan, and thanks for the introduction.</v>

[00:05:13.690]So for those of you who have not heard

[00:05:16.170]or used ioGAS software before,

[00:05:18.640]I just want to give a brief introduction

[00:05:21.333]of what ioGAS is and why we do what we do here.

[00:05:25.130]So ioGAS is developed by Imdex, a mining tech company,

[00:05:29.210]specializing in development of cloud connected devices

[00:05:33.790]and drilling optimization products

[00:05:35.540]to improve the process of identifying

[00:05:38.360]and extracting mineral for resource companies.

[00:05:41.040]And as a program,

[00:05:42.270]ioGAS has been around for more than 10 years now.

[00:05:45.440]It’s a software that lets you

[00:05:47.160]interpret your geoscientific data.

[00:05:50.040]We have years of experience supplying high-level advanced

[00:05:53.170]data analysis techniques to your practical applications

[00:05:56.670]in interpreting your geochemical and other geological data

[00:06:00.860]such as downhole gamma measurements and structural logging.

[00:06:04.980]So what we do best in ioGAS

[00:06:06.900]is in applying exploratory data analysis techniques

[00:06:10.830]to your geochemical data.

[00:06:12.560]And that is our focus through our very visual workflow.

[00:06:16.980]And that workflow in itself is suitable

[00:06:19.360]for looking at our data types as well.

[00:06:21.370]So it’s not just for your geochemistry data,

[00:06:24.410]so you can use the workload that we have

[00:06:26.340]in the program to interrogate spectral data

[00:06:29.110]such as those from Osiris and structural data,

[00:06:32.020]such as the ones that we get from our Iq other tool,

[00:06:34.980]also our downhole gamma data

[00:06:36.960]from our easy gamma tool as well.

[00:06:39.330]So that’s ioGAS.

[00:06:41.790]So this is an example

[00:06:43.210]of a typical workflow window for ioGAS.

[00:06:47.050]So this is an example of someone who wants to know more

[00:06:50.120]about the geological context behind his geochemical data.

[00:06:55.140]So say, if you got your assay data

[00:06:57.630]from the lab that corresponds to a draft that you upload

[00:07:01.590]from the field, and you just want to know more

[00:07:04.075]about them, about the context, about their background,

[00:07:09.430]and what processes can be inferred from this.

[00:07:12.090]So you can see on the right-hand side

[00:07:14.050]of this window is a couple of classification diagrams.

[00:07:17.730]So this is what we call X, Y

[00:07:20.480]or ternary scatterplots with overlays of shape and lines.

[00:07:24.340]So polygons and polylines

[00:07:26.340]that allows someone to classify their data points.

[00:07:30.410]And it’s a way for geologists

[00:07:33.230]to link back their geochem data

[00:07:36.200]to certain geological classification

[00:07:39.450]based on known composition

[00:07:41.470]and also infer certain geological processes.

[00:07:45.530]So it’s a process reduction technique.

[00:07:48.750]So it’s a way for someone

[00:07:50.000]to visualize their geochemical data

[00:07:53.881]in the 2D or 3D space

[00:07:56.865]and allowing them to link it back to what they know

[00:07:59.010]about the rocks, either from visual logging

[00:08:03.145]or from other ways that they have interpreted their data.

[00:08:06.000]And next to it in the middle, we have our decision tree.

[00:08:09.420]So that’s what we call our classification

[00:08:11.650]and regression tree algorithm.

[00:08:14.030]So this is a way for someone

[00:08:15.550]to figure out which elements contribute the most

[00:08:18.380]to classifying the data points based on

[00:08:21.374]their geology and what they have locked

[00:08:23.930]in the field and things like that.

[00:08:25.720]And next to it, just on the left is a wavelet plot.

[00:08:30.260]And that is a way for someone to define their boundaries

[00:08:35.448]using downhole data from either their instrumentation

[00:08:39.900]from gamma data or their geochemistry data

[00:08:43.800]that they plotted up on a downhole plot, such as this one.

[00:08:46.180]So it’s allowing them to automatically pick up boundaries

[00:08:49.264]and enabling them to make better decisions

[00:08:53.280]on where your boundaries are sitting

[00:08:55.290]with respect to alteration or lithological processes.

[00:08:58.890]And just on the very left

[00:08:59.980]is a couple of principal components analysis plots.

[00:09:04.160]So in the bottom is an example of a biplot.

[00:09:07.020]So you’re plotting up in the result

[00:09:09.280]of principal components analysis on your data points,

[00:09:13.360]and you’re plotting up the principal components access

[00:09:16.130]on top of it.

[00:09:17.180]And you’re just basically seeing which elements contribute

[00:09:19.590]the most to the variation that’s represented

[00:09:22.990]by the data points.

[00:09:23.990]And you’re linking, basically linking all these processes

[00:09:27.710]that you’re able to identify using classification diagrams

[00:09:31.470]and with some mathematical analysis as well to back that up.

[00:09:35.620]So we will go through some

[00:09:37.800]of these in more detail, especially on the right-hand side

[00:09:40.600]of the screen with the classification diagrams.

[00:09:43.180]We’ll go through this workflow

[00:09:44.340]in more detail within this webinar.

[00:09:46.300]So thanks, Allan.

[00:09:49.040]<v Putra>Thank you Putra for a brief introduction on ioGAS.</v>

[00:09:53.750]I’m now going to show you some information

[00:09:55.920]about the dataset that we’re going to use.

[00:09:58.780]So there are 11 drill holes in this project.

[00:10:02.320]So displayed on the 3D scene are the different rock types.

[00:10:05.730]We’ve got two felsic rocks, A and B,

[00:10:08.465]and we’ve got a mafic rock,

[00:10:11.230]which is the dark blue in color.

[00:10:13.700]We have a total of 1084 data points

[00:10:18.228]with 59 assays per suite per interval.

[00:10:22.620]Displayed on the right side of the screen are tables

[00:10:25.160]for your color for assay, and also for your geology.

[00:10:30.730]Assay data is composed

[00:10:32.060]of major, immobile, rare earth elements,

[00:10:35.910]precious and base metals.

[00:10:37.750]This assay groups can be viewed in ioGAS.

[00:10:41.190]Once again, and I’ll hand over to Putra

[00:10:42.940]who’ll briefly discuss the Leapfrog Geo and ioGAS workflows.

[00:10:47.750]Off to you, Putra.

[00:10:50.090]<v Putra>Thanks, Allan.</v>

[00:10:51.150]So just jumping off on this slide a bit.

[00:10:53.850]So this is a display

[00:10:55.040]of how you would select your elements in ioGAS.

[00:10:58.810]And what we provide to our users

[00:11:00.880]is a way of easily selecting

[00:11:03.970]from a list of provided elements based on common groupings.

[00:11:09.900]So major elements, immobiles, and rare elements,

[00:11:13.225]they’re grouped in a certain way

[00:11:15.933]that we can pick them easily from ioGAS

[00:11:18.660]and they also provide a list of pathfinder elements

[00:11:21.480]for common deposit types and that sort of thing.

[00:11:24.000]So this is just a way for you to group your elements

[00:11:27.070]together before you visualize them within ioGAS.

[00:11:31.630]So I’m just going to go through the workflow

[00:11:34.770]that we’re going to be presenting to you today.

[00:11:37.700]So the first workflow is igneous rock classification.

[00:11:41.690]So this is what we will do

[00:11:44.320]to look at your geochem data in ioGAS.

[00:11:50.150]So we’re going to bring intervals data containing assay

[00:11:54.540]information from Leapfrog Geo into ioGAS.

[00:11:58.260]And we are going to use a combination

[00:12:01.810]of classification diagrams

[00:12:04.120]in ioGAS to classify the rocks based on certain processes

[00:12:08.520]and what we think are actually happening behind the data

[00:12:14.430]on its own.

[00:12:15.360]And what we ended up actually discovering from this

[00:12:18.320]is we were able to subdivide our log classification.

[00:12:23.280]So not just felsic A, felsic B, and mafic

[00:12:25.930]we were able to actually split them up into sub groups,

[00:12:29.270]just using a combination of relatively simple

[00:12:32.900]geochemical classification diagrams.

[00:12:35.180]And what we’re able to do in ioGAS

[00:12:36.970]is we’re able to save them as a new column,

[00:12:42.960]and we’re able to push that across back to Leapfrog Geo,

[00:12:46.170]where Allan is able to turn them into

[00:12:50.170]a refined geological model

[00:12:52.940]based on our geochemical classification.

[00:12:55.730]That’s what we call the back flagging process.

[00:12:58.530]And moving on to the second workflow.

[00:13:01.620]So we were able to take this one step further

[00:13:05.090]and we will again use a combination

[00:13:07.210]of simple classification diagrams,

[00:13:09.840]and in this case, a combination of the alteration box plot,

[00:13:13.400]and a couple of molar element ratio diagrams

[00:13:17.986]to identify the processes really

[00:13:21.980]that we can observe here in this rock.

[00:13:23.710]So it’s important for us to

[00:13:26.860]go through the process of first identifying

[00:13:29.450]the lithology before we delve into the alteration.

[00:13:32.850]But the way that we’ve used this alteration modeling

[00:13:36.020]is we were able to identify the data points

[00:13:39.410]that corresponds to the processes that we were able

[00:13:41.930]to see using these diagrams.

[00:13:43.990]And then through the creation of new color groups,

[00:13:47.410]based on this information, that’s in a new column

[00:13:50.490]in ioGAS that we are able to backtrack into the Leapfrog Geo

[00:13:55.020]as a way for someone like Allan

[00:13:57.420]to create the new alteration model

[00:14:00.320]based on the alteration modeling

[00:14:02.290]that we’ve come up within ioGAS.

[00:14:05.290]So that’s the essence of today’s webinar.

[00:14:08.160]And I’ll hand over the control

[00:14:09.520]to Allan who will go through the process,

[00:14:11.700]starting from the Leapfrog Geo side of it.

[00:14:13.680]Thanks, Allan.

[00:14:15.518]<v Allan>Thank you, Putra.</v>

[00:14:17.180]So let’s now jump into the live demo

[00:14:19.200]on Leapfrog Geo and ioGAS.

[00:14:21.670]Have now opened Leapfrog Geo together

[00:14:24.260]with a lithological model that we have previously created,

[00:14:28.500]it’s a very simple model.

[00:14:30.370]It contains three lithologies, felsic A, felsic B,

[00:14:34.190]and the mafic rock.

[00:14:36.380]Again, for those who are not too familiar with Leapfrog,

[00:14:39.850]just as a brief rundown on the user interface

[00:14:43.080]we have in here, the project tree on the left side

[00:14:47.400]of the screen, which is designed with a top-down approach.

[00:14:51.600]The first half of the folders,

[00:14:53.300]so from topography down to around like meshes folder.

[00:14:58.560]So this is where data is imported or created.

[00:15:02.210]And the second half is where all the various models

[00:15:05.100]are going to be constructed.

[00:15:06.710]So from the geological models folder

[00:15:09.790]down to the block models folder.

[00:15:12.490]And of course, you’ve got in here to save scenes and movies,

[00:15:16.620]sections and contours for presentation purposes,

[00:15:20.960]and lastly, we have in here a folder

[00:15:23.520]for a geochemical analysis, of course.

[00:15:25.550]And you also have in here

[00:15:27.000]an option to import color gradients.

[00:15:30.680]We also have in here the 3D scene

[00:15:32.480]in the middle where you can interact objects,

[00:15:35.080]where as you can drag and drop things from the project tree

[00:15:39.280]to the 3D scene and everything

[00:15:41.840]that is displayed on the 3D scene is actually listed down

[00:15:46.010]in here under the shape place at the bottom of the 3D scene.

[00:15:50.770]We can choose the different visualization settings

[00:15:53.650]such as turning on and off layers,

[00:15:57.740]turning on and off objects,

[00:16:01.010]or determining what you want to visualize in the 3D scene.

[00:16:06.640]So underneath the save scenes folder,

[00:16:08.760]I’m just going to expand this particular folder over here.

[00:16:12.300]I’ve created a few save scenes, which act as bookmarks.

[00:16:16.710]I’ll be running through this

[00:16:17.860]for the purposes of this presentation.

[00:16:21.170]This is a great way of preserving perspectives

[00:16:23.640]of different objects or to tell a story

[00:16:25.631]without bringing different objects into the scene.

[00:16:29.840]So one of the more important features mentioned earlier

[00:16:33.180]is that Leapfrog Geo is dynamic in nature.

[00:16:36.840]So any changes that you make

[00:16:38.880]to the data will automatically be reflected throughout.

[00:16:43.480]So I’m just going to go through

[00:16:44.490]with the following save scenes now.

[00:16:48.250]So these are the colors of the drill holes,

[00:16:52.570]and I’m going to just going to display the assay now

[00:16:55.060]with scandium and also titanium.

[00:17:00.230]So I’ve just set in here two examples

[00:17:01.533]by displaying scandium and titanium.

[00:17:04.330]However, under the drop down in here under the assay table,

[00:17:07.890]feel free to, you can choose whatever elements you want

[00:17:11.410]to display, for example, nickel.

[00:17:15.720]Just going to go through with the geology again.

[00:17:19.320]So these are displaying the three major deformities,

[00:17:23.370]felsic A, felsic B, and the mafic rock.

[00:17:26.880]The first thing that we’re going to do

[00:17:28.530]is we’re going to construct a surface

[00:17:30.420]in between felsic A and B.

[00:17:33.990]So I’ve created an intrusion surface in here

[00:17:37.255]to model between the two lithologies.

[00:17:41.300]And after that, I was able to model the mafic rock

[00:17:45.280]as a vein.

[00:17:47.680]And then from there,

[00:17:49.173]I was able to create the simple lithogical model.

[00:17:52.500]So everything happens under the geological models folder.

[00:17:56.120]I’m just going to expand this a bit more

[00:17:58.310]and all the surfaces

[00:17:59.650]are created under the surface chronology

[00:18:01.940]by right clicking on it.

[00:18:03.860]And then by creating the output volumes of course,

[00:18:05.785]for those who of familiar,

[00:18:07.910]just double click on the surface cohomology

[00:18:10.170]we activate the surfaces, click okay,

[00:18:12.980]and we create the output volumes down in here.

[00:18:17.910]Okay, let’s move on.

[00:18:18.777]And I’m just going to kind of slice through

[00:18:21.290]this simple geological model here.

[00:18:23.940]So just to give us an idea on how this

[00:18:27.765]lithology looks like at various sections.

[00:18:33.810]Okay, so the next step for us to do is

[00:18:35.990]I’m just going to connect now to ioGAS.

[00:18:39.730]So more importantly

[00:18:41.460]is you need to have a licensed ioGAS software.

[00:18:44.720]So I’m just going to make this,

[00:18:48.451]I’m just going to collapse those folders.

[00:18:51.620]And first thing is okay,

[00:18:55.409]if we start in here, if you say,

[00:18:57.560]if you see this ioGAS not connected,

[00:19:00.390]what do you need to do now is to connect.

[00:19:03.250]All right, and make sure again,

[00:19:04.610]make sure that you have ioGAS software turned on as well.

[00:19:09.260]And the next step that you need to do

[00:19:10.780]is to right click and then click on the new ioGAS data.

[00:19:14.850]So I’m now going to click on this option.

[00:19:18.810]And of course you need to select the base table.

[00:19:21.770]In this case, I want it to select the assay base table.

[00:19:25.440]And under the available column

[00:19:29.995]on here, I could choose all my multi-element data in one go

[00:19:35.860]and put them onto the other side under the selected columns.

[00:19:39.840]And then click okay.

[00:19:42.648]I’m just going to click cancel now

[00:19:43.970]because I’ve already created one in here.

[00:19:46.040]I’m just going to expand the ioGAS link.

[00:19:50.890]And this is now the ioGAS data.

[00:19:55.110]So I’m now going to hand over to Putra

[00:19:58.390]to open the link in ioGAS

[00:20:01.870]in order for him to interrogate

[00:20:04.430]the various multi-element assays that’s available.

[00:20:08.420]<v Putra>Thank you, Allan.</v>

[00:20:09.260]Just jumping off from where we left off with Allan.

[00:20:12.980]So we already brought the data into Leapfrog Geo,

[00:20:17.240]and then we’ve already made the geology model.

[00:20:19.700]And now what we want to do

[00:20:21.050]is we want to see if we can use ioGAS

[00:20:24.010]to define your geology model,

[00:20:26.260]or even come up with a brand new geology model

[00:20:28.650]based on any classification

[00:20:30.060]that you made based on your assay data.

[00:20:32.240]So the next thing that we’re going to do is on my end,

[00:20:35.360]I’m just going to make sure that the link is turned on.

[00:20:39.030]And once that’s turned on the Leapfrog Geo in ioGAS,

[00:20:43.430]it’s always a good idea to make sure

[00:20:45.085]that there’s nothing open within the current workspace

[00:20:49.460]within ioGAS, just leave it as empty

[00:20:52.950]as when you first start the program.

[00:20:55.390]So the link is already active

[00:20:57.760]within both programs at this stage.

[00:21:00.100]And before we go forward,

[00:21:02.890]I’m just going to show you a few things about the interface.

[00:21:06.470]So what we have at the top is a collection of ribbon tabs.

[00:21:11.010]So this corresponds to pretty basic functionalities split

[00:21:15.470]into each of these steps.

[00:21:17.190]And most people will just live within the home tab

[00:21:21.770]for their basic graphing needs.

[00:21:24.880]Where are we going to go through now is the process

[00:21:27.670]of actually bringing in this new ioGAS data

[00:21:31.010]that you generated in Leapfrog Geo into ioGAS itself.

[00:21:35.230]So if we go into the file tab

[00:21:37.550]and if we click on the open link menu.

[00:21:40.370]And before we do that, I’m just going

[00:21:41.650]to just kind of try and make this a bit smaller

[00:21:44.220]so we can see both programs at the same time.

[00:21:46.820]So clicking on this icon,

[00:21:48.850]this will allow me to essentially bring in the assay data

[00:21:53.710]from Leapfrog Geo to ioGAS.

[00:21:57.300]On the view here, we see the geology model.

[00:22:00.330]So we just like to turn all of this off for now.

[00:22:04.914]We just want to check that we are looking

[00:22:07.550]at the same thing within ioGAS.

[00:22:09.560]So notice that everything is black here

[00:22:11.600]as I drag this new ioGAS down into the Leapfrog view.

[00:22:14.780]And that’s because in ioGAS when you first open a file,

[00:22:18.310]your colors, shape, and size properties,

[00:22:21.110]so that’s how your data points are displayed

[00:22:23.120]within any plot in ioGAS,

[00:22:25.185]they’re displayed as black circles as default.

[00:22:28.690]So that’s an important note,

[00:22:31.140]important thing to know at the start.

[00:22:33.490]And what we can do now

[00:22:35.300]is we just want to make sure that our data has been aliased.

[00:22:39.765]So aliasing is a very important concept in ioGAS.

[00:22:45.080]So aliasing is a way for ioGAS to know

[00:22:49.220]you’re dealing with geochemical data

[00:22:51.770]in your file as opposed to purely numerical information.

[00:22:57.200]So it’s a way for ioGAS to know that you’re bringing

[00:23:00.240]in SIO2 in percent as an assay data,

[00:23:05.448]as opposed to SIO2_% just as any numeric information.

[00:23:09.650]While it’s simple enough to describe it like that,

[00:23:12.070]but it’s important in a way that it allows people

[00:23:15.370]to plot their data in classification diagrams

[00:23:18.610]and do automatic conversion in the background.

[00:23:21.240]So say if they want to create a molar element ratio diagram,

[00:23:25.450]if you have tried doing that in a place like Excel

[00:23:29.840]or things like that, it’s got a cumbersome process,

[00:23:32.880]especially if your data is all in silica

[00:23:36.210]in weight percent, for example, not in molar equivalent.

[00:23:40.810]It’s just one of those things that we’ll need to check

[00:23:42.870]to make sure that all of your assay data has been aliased.

[00:23:46.010]So we’re pretty happy with this

[00:23:47.467]’cause we’ve already done this step before.

[00:23:50.230]And what we want to do now

[00:23:52.630]is we want to select a few elements.

[00:23:56.430]So say if we start looking at your immobile elements,

[00:23:59.670]because we know that we’re dealing

[00:24:02.090]with a number of volcanic rocks in this area.

[00:24:05.850]So what we want to do

[00:24:07.035]is we want to use a combination of these elements

[00:24:09.890]to see if there’s any pattern in them that can be used

[00:24:12.640]to further differentiate them apart.

[00:24:15.170]So the best way to do that is to select them

[00:24:18.330]as I did just then.

[00:24:20.770]And open up a scatterplot matrix.

[00:24:23.630]And that allows you to see

[00:24:24.730]that there might be some subpopulation

[00:24:29.145]within this data that we can cluster up

[00:24:31.410]and easily identify using our attribute manager

[00:24:34.850]and things like that.

[00:24:36.100]The most powerful thing

[00:24:37.260]about ioGAS that I find really useful and the visual aspect

[00:24:42.610]of it is it’s absolutely essential

[00:24:45.520]for someone examining their data visually.

[00:24:48.470]And it’s the way that we can assign a color group

[00:24:52.570]to a selection of data points

[00:24:55.360]and have that reflected in every other open plot in ioGAS.

[00:24:59.610]And that allows you to see that this cluster right here,

[00:25:03.330]then if I create a new one,

[00:25:06.860]if I use that to attribute just this cluster, just manually

[00:25:11.875]by I, and you can see how it corresponds

[00:25:13.890]to clusters in every other related elements.

[00:25:17.440]And it’s one thing that sort of got you thinking,

[00:25:20.980]what processes am I actually looking at here?

[00:25:24.230]So this is an important step.

[00:25:26.610]The first time that you’re trying to do this sort

[00:25:29.320]of thing in ioGAS is that we provide a number

[00:25:32.260]of advanced analytics tools

[00:25:34.955]and diagrams and geochemical classification

[00:25:37.370]within ioGAS that is important to know

[00:25:39.480]that if you know what elements are useful

[00:25:41.590]in splitting out between certain volcanic rock types,

[00:25:45.300]it’s a pretty good idea to just plot them up

[00:25:47.310]in a simple scatter plot matrix like this

[00:25:50.380]and attribute them and see

[00:25:52.835]where they plot in 3D space, because what you can see here,

[00:25:55.410]once you get them plotted up in a Leapfrog Geo,

[00:25:57.980]you can see that there’s actually two layerings here.

[00:26:00.150]So based on the two clusters that I’ve identified just

[00:26:02.930]by a visual identification,

[00:26:04.980]you see that they correspond to stratigraphic level.

[00:26:08.420]So this is a concept that we will explore

[00:26:10.550]in a bit more detail now.

[00:26:12.017]I’m just going to clear up, I leave the color groups here

[00:26:14.710]because we’re going to do something else.

[00:26:16.600]So just going to minimize this

[00:26:19.210]and I’m going to open a classification diagram.

[00:26:24.560]So as I was saying earlier on,

[00:26:27.545]in the presentation, a classification diagram is a way

[00:26:31.040]for someone to link their geochemical data back

[00:26:35.690]to a known geological process

[00:26:37.690]or geological classification based on a similar rock type.

[00:26:42.390]So most of these diagrams are based on the literature.

[00:26:45.570]So if you’re looking at sort of the same rock

[00:26:47.890]that the author or the maker

[00:26:49.990]of the paper, or the source of this diagram

[00:26:52.790]that they used to make those diagrams in the first place,

[00:26:56.750]if you’re looking at the same set

[00:26:58.170]of rocks, then it’s a good idea to start

[00:27:00.430]using this diagrams, but it’s absolutely important

[00:27:03.293]to know that when you’re looking

[00:27:05.410]at these diagrams to know exactly what you’re looking for.

[00:27:09.720]What’s important to look at from this one, for example?

[00:27:12.190]This is a task classification diagram.

[00:27:14.990]So it’s using a ratio

[00:27:16.070]of your sodium plus potassium over silica.

[00:27:18.960]And you can see that the rock types

[00:27:20.630]are actually just pretty uniform all along the Y axis,

[00:27:23.550]but the SIO2 is the one that’s splitting up the felsic

[00:27:26.233]and the mafic in a way.

[00:27:27.870]And that’s how you would use this sort

[00:27:30.270]of diagram to classify your data points.

[00:27:33.870]And the cool thing

[00:27:34.703]about this sort of a diagram classification diagram in ioGAS

[00:27:38.630]is that it’s divided into sub classification fields.

[00:27:42.610]And classification fields,

[00:27:44.480]the fact that you have data points right

[00:27:47.155]on top of them means that the obvious next thing to do

[00:27:49.905]is see where a data point is plotting

[00:27:52.550]and you color them based on where they plot in this diagram.

[00:27:55.450]So if I click on a small button here,

[00:27:57.590]color rows by polygon, you can watch how it auto attributes

[00:28:01.690]based on where they plot within this classification fields.

[00:28:05.600]And you just see it ticking away in the background

[00:28:08.370]or on the Leapfrog side on the right-hand side

[00:28:10.160]of the screen.

[00:28:10.993]And you can see how it updates automatically.

[00:28:13.510]I see this layering right here, under the Leapfrog Geo side.

[00:28:16.840]And that is actually what we expect to see.

[00:28:19.460]What we’ve managed to find

[00:28:21.070]is that we’re able to split the rock type space

[00:28:23.850]on where thay plot within this diagram,

[00:28:26.000]and more specifically based on the SIO2 value.

[00:28:29.840]But we can go one step further.

[00:28:32.350]And if we think of these rocks as being slightly altered,

[00:28:37.620]so we don’t know much about these rocks, right,

[00:28:40.030]and this is the sort of thing that you would get

[00:28:43.683]if you have limited logging and you don’t know much

[00:28:46.520]about the rocks at first place,

[00:28:48.280]or you’re uncertain about what they are,

[00:28:50.210]but you know for sure that there’s a bit of alteration here,

[00:28:53.470]which might move some of this numbers around,

[00:28:55.480]especially in the, Y axis not so much on the X axis,

[00:28:59.510]but with your sodium and potassium,

[00:29:02.470]if you expose them to a bit of alteration and weathering,

[00:29:05.230]you would move some of these numbers around.

[00:29:06.830]So you would want something that’s a bit more robust

[00:29:09.430]in that sense.

[00:29:10.670]So we provide a diagram

[00:29:13.913]for looking at immobile elements in this instance.

[00:29:17.590]So a particularly useful one that you would use

[00:29:20.320]in that scenario is plotting niobium ithrium

[00:29:23.340]versus uranium over titanium in the PSI 96 diagram.

[00:29:27.750]So this is absolutely useful,

[00:29:30.940]because you’re looking

[00:29:32.650]at elements that are known to be relatively immobile

[00:29:35.590]in weathered or hydrothermally altered settings.

[00:29:39.920]So what we can do now in here.

[00:29:42.520]So we’ve identified our rock type space in a task diagram,

[00:29:45.510]and we’d like to subdivide the dacite field even further,

[00:29:50.150]because as we can see here, the green rocks

[00:29:54.430]that I labeled as dacite from the first diagram,

[00:29:58.200]you see that they are present

[00:30:00.140]in two fields within a rhyolite based side,

[00:30:03.140]and the basaltic andesite.

[00:30:05.590]What we can see is actually

[00:30:08.580]some of the dacite, were probably miss logged

[00:30:13.256]as dacite in this classification.

[00:30:14.640]So they’re probably andesite, basaltic andesite.

[00:30:17.800]So what we can do is we can turn everything off,

[00:30:21.150]except for the dacite.

[00:30:23.420]And then we’ll just create a new classification

[00:30:27.610]for basaltic andesite, and I’ll just call them ABA.

[00:30:32.350]And what I’ll do now is I will reclassify the samples

[00:30:36.340]that plots here into a new group.

[00:30:38.960]So I got them active in the attribute manager in ioGAS.

[00:30:43.060]Before I move on to attribute managers, basically,

[00:30:45.310]as you’ve seen in the last five minutes or so,

[00:30:48.310]attribute manager is where the visual heart of ioGAS is.

[00:30:52.050]So everything you do here is reflected

[00:30:54.540]in every visual aspect

[00:30:56.800]of ioGAS, so every other open plot

[00:30:58.640]and every other diagram and things like that.

[00:31:01.500]So I’m going to make sure that ABA is active

[00:31:05.530]and I am going to attribute this into its new group.

[00:31:13.440]And this is what we call the new ABA group.

[00:31:16.330]And we just see where everything sort of sits.

[00:31:18.970]So what we came up here is just a way

[00:31:22.520]for pre-logging or classification based on

[00:31:25.270]where they plot in this two relatively simple diagrams.

[00:31:28.760]And it’s important to know the processes that’s tied

[00:31:31.950]to this diagrams and what works

[00:31:34.240]with your data, first of all, what minerals gets resolved

[00:31:39.070]within certain analytical method

[00:31:41.950]and whether it’s appropriate to use this diagram

[00:31:44.050]or another diagram.

[00:31:45.700]But so far what we see in here sort of works

[00:31:47.740]in a way that it subdivides them into a stratigraphy

[00:31:51.401]that we can easily model in Leapfrog Geo.

[00:31:54.660]And before we move on, let’s just very quickly see

[00:31:58.870]if we can change the shape of this to the major lithology,

[00:32:02.993]so felsic A, felsic B, and mafic.

[00:32:06.450]And we’ll just see what happens if I turn things on and off.

[00:32:09.730]So if I turn everything off

[00:32:11.230]and we’ll just see where the felsic A rock

[00:32:13.700]sort of sits, and you can see

[00:32:16.355]that I’ve divided felsic A into several groups,

[00:32:18.630]as it turns out these guys right here,

[00:32:20.480]they’re not felsic A at all,

[00:32:22.360]they’re actually are correctly classified

[00:32:24.800]as basaltic in composition,

[00:32:27.210]and it’s the same thing with the other ones.

[00:32:29.060]So it’s a way, it’s not asking you

[00:32:32.893]to replace what you know, it’s just a way

[00:32:35.520]for you to sort of think back about your rocks a bit more

[00:32:39.500]and trying to see if you can improve them further

[00:32:42.320]by combining your inspection

[00:32:46.547]of your rocks with your geochemical data.

[00:32:49.280]And as such, it’s always a good idea

[00:32:51.120]to get really good whole rock geochem data,

[00:32:53.968]because that allows you to do this sort of thing.

[00:32:56.820]So the next thing that I’m going

[00:32:58.863]to do is, and I’ve already done this step before,

[00:33:01.370]but basically what you can do now

[00:33:03.080]is if you go to the data tab

[00:33:05.300]in ioGAS, you’re able to create a new variable from color.

[00:33:10.560]So you’re basically creating a new column

[00:33:13.780]based on this color attribute.

[00:33:17.320]And it will be show,

[00:33:19.240]so I’m just going to create new column colors.

[00:33:21.163]I’m just going to click cancel

[00:33:22.170]’cause we’ve already done this.

[00:33:24.180]And so if I just pretend that I’ve already done

[00:33:26.650]that and if you go to the right,

[00:33:28.400]click under task classification, so right at the end.

[00:33:32.040]So I’ve already reclassified them

[00:33:37.159]into this new column based on a classification

[00:33:40.500]that I did just then using the color groups.

[00:33:43.290]And basically that process

[00:33:44.930]in itself, the newly created column is actually pushed back

[00:33:49.480]to Leapfrog Geo as new back flag column

[00:33:54.695]under assay, and it’s called color TAS class.

[00:33:57.490]And this is what Allan

[00:33:58.780]is going to use to refine his geological model,

[00:34:01.260]to make a new geological model based on this classification.

[00:34:04.710]So over to you now, Allan, thank you.

[00:34:08.170]<v Allan>Thank you, Putra.</v>

[00:34:09.320]Right, so as Putra discussed,

[00:34:11.980]this is now the back flagged alteration assemblage

[00:34:17.080]using the task specification diagram

[00:34:21.090]and everything is back flag from the drill hole data

[00:34:25.880]down to the assay table.

[00:34:27.880]If you’re going to look in here, that’s now

[00:34:33.350]the column that was added in Leapfrog Geo from ioGAS, okay?

[00:34:38.910]I’m going to go through the save scenes

[00:34:41.080]and movies again, just to continue on with the discussion.

[00:34:44.780]And also Putra earlier on

[00:34:48.730]was able to classify the dacite bodies into two.

[00:34:53.590]So we’ve got in here he has classify the dacite.

[00:34:57.250]This one here, the dark blue one remains

[00:34:59.530]as dacite, while the other one has been classified as ABI

[00:35:03.760]or understated basaltic andesite specie, rock specie.

[00:35:09.280]All right, so moving on to the task classification again,

[00:35:15.290]and then we’re now able to model this as intrusion surfaces.

[00:35:21.515]So I’ve got the basaltic andesite

[00:35:23.525]and dacite modeled as intrusion services.

[00:35:26.350]And next is I was able

[00:35:28.250]to model my basaltic contacts, rhyolite,

[00:35:34.238]and andesite as deposit surfaces in Leapfrog Geo.

[00:35:39.290]So everything in here, if I go up,

[00:35:43.060]go down to a geological models folder,

[00:35:46.520]everything will be constructed again

[00:35:50.050]under the surface chronology.

[00:35:51.260]So these are the surfaces that are actually being shown

[00:35:54.070]in the 3D scene now, all right?

[00:35:56.540]And then from there I’ve generated my output volumes.

[00:36:04.180]And this is now the output volume that was created

[00:36:07.370]and based from the predictions that was shown

[00:36:11.740]in ioGAS earlier on clearly reflects the layered nature

[00:36:17.030]of the different rock types

[00:36:18.330]that was modeled into Leapfrog Geo.

[00:36:21.405]So if you’re going to compare this with the simple lithology

[00:36:25.630]that was modeled earlier, which you can see is just

[00:36:29.480]three lithologies to start off with.

[00:36:31.970]And through ioGAS,

[00:36:33.310]we’re able to classify this further into various rock types.

[00:36:38.230]All right, I’m just going to change over the screen now back

[00:36:41.100]to Putra in order to explain more about alteration modeling.

[00:36:46.270]<v Putra>Thanks, Allan.</v>

[00:36:47.900]So we’re just going to touch on a few things

[00:36:50.470]that we did just before.

[00:36:53.390]So we’ve successfully created a new geology model

[00:36:56.720]based on your combination

[00:36:58.470]of tasks and their standard 96 classification in ioGAS,

[00:37:02.600]and we’ve allowed ourself to sub classify

[00:37:06.320]our known geology into new subgroups.

[00:37:09.960]And so this is a way for you to refine your lithology model.

[00:37:13.870]But what another thing that you can do

[00:37:15.808]with the data that you have here,

[00:37:18.200]and this is also dependent on how comfortable you are

[00:37:20.870]with the alteration that you’re looking at

[00:37:23.627]and the processes that you would sort of expect.

[00:37:26.330]But what we are able to do now

[00:37:28.010]is we’re going to do some alteration modeling.

[00:37:31.090]That’s a good place to start for this.

[00:37:32.530]So I’m just going to maybe turn everything off

[00:37:37.225]as I start in Leapfrog Geo,

[00:37:39.050]and I’m just going to look at the assay data

[00:37:42.295]on its own, and I’m going to reset the shapes to default,

[00:37:47.360]and I’m going to turn off the color groups as well.

[00:37:51.060]So we’re just basically going to start from scratch.

[00:37:53.850]We have already backed like this as a new information

[00:37:56.810]column in ioGAS, or you can always go back to it

[00:37:59.300]without any fear of having to repeat the process again.

[00:38:02.650]So it’s already safe as a categorical column

[00:38:05.180]in ioGAS so you can always go back to it.

[00:38:08.540]So we already have the information present somewhere.

[00:38:11.160]So what we can do now

[00:38:12.740]is I’m going to open an alteration boxplot.

[00:38:18.450]So this sort of diagram, as you can see here,

[00:38:22.170]it consists of two axis

[00:38:24.180]the alteration index, Ishikawa Alteration Index,

[00:38:27.000]versus the chloride carbonate pyrite index or CCPI.

[00:38:31.180]So the alteration box plot was developed specifically

[00:38:34.650]for the study of alteration associated with VHMS systems.

[00:38:39.250]And even though it’s used for that purpose,

[00:38:40.940]and while they’re not certain that we are looking

[00:38:44.220]at a VMS deposit here,

[00:38:46.490]we can use the processes that are presented

[00:38:49.470]in this sort of diagram as a starting point.

[00:38:53.050]So by no means we should take the information that’s present

[00:38:56.470]in this as the gospel,

[00:39:00.825]or as a silver bullet, to let you know

[00:39:03.650]if you’re looking at this sort

[00:39:04.690]of alteration type, but it’s a good place

[00:39:07.400]to start, and why is it a good place to start?

[00:39:09.770]And it’s because in this diagram,

[00:39:12.700]which consists of the AI and CCPI axes.

[00:39:17.390]So the X axis represents a loss of sodium and calcium

[00:39:22.760]and gain of potassium and magnesium within this index.

[00:39:26.750]So that’s represented by the development of muscovite

[00:39:29.927]and chloride from alteration of sodic plagioclase.

[00:39:33.540]And Y axis CCPI is constructed

[00:39:36.262]in a way that it represents an increase

[00:39:39.080]of magnesium and FEO related to core development.

[00:39:42.960]So it allows you to separate out

[00:39:44.900]between muscovite and chloride development.

[00:39:47.360]And if you’re aware of that sort

[00:39:48.660]of processes, and the fact that we’ve labeled the nodes

[00:39:52.560]that corresponds to development of this mineral.

[00:39:54.710]So you can see on the right here,

[00:39:56.520]you have IL for elite, and you have here AB for albite

[00:40:00.520]and CHL at a top for chloride.

[00:40:03.110]So as long as you’re aware that this diagram represents

[00:40:06.320]the development of that assemblage of minerals,

[00:40:09.720]and you’re familiar and comfortable with that process,

[00:40:14.370]you’re able to use this sort of diagram to start thinking

[00:40:18.330]about how you can model alteration in your data.

[00:40:22.020]And the reason why I do this is because it’s a quick

[00:40:25.820]and easy way of coloring your data points automatically

[00:40:29.499]based on where they plot here.

[00:40:31.370]So if we find, we managed to find great success in coloring

[00:40:35.330]where the data points plot within the polygons here,

[00:40:38.090]because the data points that it plots

[00:40:39.730]within three boxes in the middle,

[00:40:42.380]they represent more fresh sort of composition.

[00:40:45.320]So and by fresh, I mean, less altered

[00:40:47.460]than the ones that’s outside of the box.

[00:40:49.930]And we can go one step further.

[00:40:52.150]And if we can assume that there’s little albite development

[00:40:56.120]or epidote sort of mineralization in this rocks.

[00:40:59.160]And basically what we can do is we can expand the field

[00:41:03.330]for the least altered rocks a bit more

[00:41:05.930]to the left hand side,

[00:41:09.550]and then we’re going to do the same thing

[00:41:13.210]with the middle box as well.

[00:41:14.390]So we just colored them up as dacite.

[00:41:19.619]So basically what we’re doing is we’re just expanding

[00:41:23.000]on the least altered classified blocks

[00:41:25.970]into a bigger classification,

[00:41:27.970]because we’re comfortable

[00:41:28.870]in knowing that the rocks had plots here,

[00:41:30.860]that they don’t represent any sort of alteration,

[00:41:32.990]even though they plot outside the least altered boxes.

[00:41:36.360]But we can call these guys

[00:41:39.950]some from hydrothermal alteration.

[00:41:41.950]And one thing to be really careful

[00:41:44.900]about in this is that there’s a certain risk

[00:41:48.820]in looking at this diagram on its own,

[00:41:51.220]because samples that plot higher up on the CCPI access,

[00:41:54.370]for example, because you’re looking

[00:41:56.330]at a ratio of magnesium plus iron over this elements.

[00:42:03.990]And because you’re looking at some mafic rocks in here.

[00:42:06.940]So if you remember just in the first workflow,

[00:42:09.770]we’ve actually identified

[00:42:11.170]that some of these rocks actually correspond

[00:42:12.890]to the basaltic composition.

[00:42:15.570]And what it means is that while it’s plotting up

[00:42:18.860]in a hydrothermally altered field,

[00:42:20.770]they may not represent hydrothermal alteration,

[00:42:24.990]they just represent a more mafic variant in your data point.

[00:42:29.330]So samples that actually represent more mafic composition

[00:42:33.610]with the higher FEO and magnesium amount.

[00:42:38.922]So what’s the takeaway here?

[00:42:41.000]The takeaway is that we recommend using this sort

[00:42:43.290]of diagram in conjunction with molar element ratio diagrams

[00:42:47.700]to identify processes more closely.

[00:42:50.370]What I’m going to do now is I’m going

[00:42:53.110]to just keep this open here

[00:42:55.520]and I’m going to open a general element ratio diagram.

[00:42:59.660]So I’m going to start with this ratio

[00:43:02.290]of sodium over aluminum versus potassium over aluminum.

[00:43:07.780]So if you’re not familiar with molar element ratio diagrams,

[00:43:12.380]basically if you remember stoichiometric composition

[00:43:17.490]of muscovite and all your feldspar minerals,

[00:43:21.980]you know that this sort

[00:43:23.560]of diagram represents that ratio of say

[00:43:29.935]sodium over aluminum and albite,

[00:43:32.260]and then ratio of say potassium

[00:43:34.170]to aluminum and muscovite which is 0.33.

[00:43:37.440]So you’re using the stoichiometric composition

[00:43:40.330]to define where the nodes,

[00:43:43.090]these dots sort of sit in this sort of diagram.

[00:43:45.613]And you’re able to use this

[00:43:48.050]in conjunction with our known composition in ioGAS.

[00:43:53.110]So what we provide within ioGAS

[00:43:55.380]is a way to plot say known composition

[00:43:58.740]of sheet silica stoichiometric composition.

[00:44:02.100]So you have an option of selecting from chloride

[00:44:04.900]and muscovite and all of your clay minerals composition here

[00:44:09.910]that would be familiar to you

[00:44:12.060]in this sort of alteration setting.

[00:44:14.980]So if we just turn everything on here

[00:44:19.955]and then seeing where they plot in this of diagram,

[00:44:23.595]and you can see that the muscovite node

[00:44:27.950]sort of sits here and here.

[00:44:29.800]So this is a way for you to basically see the processes

[00:44:33.440]that you would expect to see in a sort

[00:44:34.980]of rock soil, it’s overlapping here, but you basically,

[00:44:37.260]you have a bunch of chloride composition that sits here.

[00:44:40.280]So what can we tell basically from this diagram?

[00:44:44.250]Basically, we’re seeing

[00:44:46.910]how we can further subdivide the rocks

[00:44:50.423]that were logged as hydrothermal into a way

[00:44:58.915]that’s more easier to understand and more useful to us.

[00:45:03.270]And the way we’ve done that is we’ve created

[00:45:05.370]new color groups based on where they plot.

[00:45:07.540]So I’m just going to show you a very quick example

[00:45:10.260]before we move on, so I’m going to basically set everything

[00:45:16.048]to invisible except for the hydrothermal altered rocks.

[00:45:21.360]And then I’m going to create a new color group.

[00:45:31.400]Now I’m just going to create a new color group

[00:45:32.840]for intense muscovite alteration.

[00:45:35.100]And I’m going to label the rocks

[00:45:38.730]that are here as a new color group,

[00:45:41.820]and I’m going to create a new color group

[00:45:46.968]for chloride alteration.

[00:45:53.060]So this is a way for you to basically sub-divide

[00:45:58.040]your classification a bit more, right?

[00:46:01.256]And so I’ve done this process a bit manually before,

[00:46:04.760]so I’m just going to jump through,

[00:46:06.260]I’m just going to cheat a bit

[00:46:07.980]and I’m going to author attribute by what I’ve done.

[00:46:11.780]And so basically,

[00:46:14.051]I’ve done this process over the entire dataset

[00:46:18.410]just before this webinar,

[00:46:20.150]but basically this is the shortcut that I did

[00:46:22.110]’cause I’ve already done this.

[00:46:23.177]So the sort of process involved was knowing

[00:46:26.470]that you’re looking at a range

[00:46:29.090]of alteration from sodium plagioclase to a more,

[00:46:33.295]your sodium plagioclase composition

[00:46:36.210]would plot somewhere here.

[00:46:37.660]And then you would see a pattern

[00:46:38.820]that leads them to be more altered towards here.

[00:46:41.860]And you see a development of chloride here.

[00:46:43.970]And you see sort of like the in-between zone

[00:46:46.550]so with the slight muscovite alteration,

[00:46:49.030]as opposed to intense muscovite alteration

[00:46:51.720]and slight colorization, sort of like that halfway point

[00:46:55.530]between more fresh composition and this one.

[00:46:59.050]And it’s important to know exactly

[00:47:01.880]what processes that you’re looking at

[00:47:04.400]because as I’ve tried to do here

[00:47:06.560]is I have managed to redefine the alteration process

[00:47:11.300]for the least altered ones.

[00:47:13.120]And you can see that the least altered rocks

[00:47:15.470]that are identified in the alteration boxplot,

[00:47:19.350]that when you bring them up,

[00:47:22.390]that they actually belong in three boxes,

[00:47:25.760]and that’s why they’re resulting in three color groups

[00:47:29.790]and because of that, they were presented

[00:47:31.910]in our fairing CCPI amount

[00:47:33.740]because of that mafic to felsic gradient.

[00:47:36.900]And it’s also represented to some extent in the X axis here.

[00:47:40.640]So it’s important to know the processes

[00:47:42.403]that you’re looking at and by applying large scale

[00:47:46.210]sort of alteration modeling to this

[00:47:48.890]based on the mineralogy of what you’re expecting to see.

[00:47:52.600]And I’m plotting them up as color groups

[00:47:55.595]within ioGAS, and then sub-dividing it as such based

[00:47:59.105]on your known, the minerals that you would expect to see,

[00:48:01.565]and then just doing the same as before.

[00:48:03.630]So just creating a new color group based on this.

[00:48:08.850]And I didn’t show this in the first workflow,

[00:48:09.947]but I’m just going to do it now.

[00:48:12.360]So I’m going to create a new color group

[00:48:14.000]and that’s going to be created

[00:48:15.910]as a new text column within ioGAS

[00:48:17.790]and within Leapfrog Geo, it’s going

[00:48:19.710]to be reflected under the assay tab once that’s done.

[00:48:23.430]And basically that allows you to create alteration model

[00:48:28.800]within Leapfrog Geo.

[00:48:30.420]And just one thing before we proceed.

[00:48:35.180]So just while on that subject,

[00:48:38.440]the alteration boxplot, the total FE alteration boxplot.

[00:48:42.640]You see that your at least altered rocks are plotting

[00:48:44.890]in the three boxes in the middle.

[00:48:46.700]And this is why it’s important

[00:48:49.205]to know that you will need

[00:48:50.440]to always look at this, not just on their own.

[00:48:54.940]So you’ll need to know why they’re different on the Y axis.

[00:48:59.910]And if you plot up your CCPI versus your SIO2 value,

[00:49:05.100]what’s actually interesting

[00:49:08.950]is that it varies with the silica

[00:49:12.310]that you’re looking at here.

[00:49:14.890]So it’s important to know

[00:49:16.900]that the variation in your CCPI is actually controlled,

[00:49:20.180]not just from the value

[00:49:22.260]of the numerator and denominator of the index,

[00:49:26.470]but also by other elements that might affect that as well.

[00:49:29.900]You’re looking at this

[00:49:30.840]in conjunction with your local lithology,

[00:49:36.150]or for example, by the task classification,

[00:49:40.860]you’re able to sort of see what’s actually happening here,

[00:49:44.320]so you can change the shape to the task classification

[00:49:48.530]that we did in workflow one,

[00:49:50.750]and you plot them with alteration modeling

[00:49:52.950]and you basically allow yourself

[00:49:55.310]to infer certain processes

[00:49:57.624]based on what you’ve already done so far.

[00:49:59.530]So it’s important not

[00:50:00.620]to just look at these diagrams on their own

[00:50:03.700]or these plots on their own, but using the various tools

[00:50:07.895]that we have in ioGAS, looking at them in conjunction

[00:50:10.110]with one another, to reach a certain conclusion,

[00:50:13.030]and that is the alteration modeling that we came up with.

[00:50:16.570]As that’s already being like back flagged into Leapfrog Geo

[00:50:20.270]as a new column under the assay table here,

[00:50:25.190]Allan can create a new geology model

[00:50:27.910]based on this new back flag information.

[00:50:30.310]So over to you Allan, thank you.

[00:50:32.653]<v Allan>Thank you Putra.</v>

[00:50:33.610]Back to Leapfrog Geo.

[00:50:35.990]This is where you can find the back flag alteration modeling

[00:50:40.870]in Leapfrog Geo, so I’m just going to highlight,

[00:50:43.320]just click on the edit colors menu in here.

[00:50:46.710]This is where you can see the chlorination occurs,

[00:50:51.900]intense muscovite alteration,

[00:50:54.930]and this are your less altered alteration.

[00:50:59.810]You’ve got light chloride alteration

[00:51:01.490]in here, and you also have the muscovite alteration.

[00:51:04.510]Just going to click okay.

[00:51:06.434]I got to go through back to my save scenes.

[00:51:10.220]First is in order to model this because the nature

[00:51:13.550]of the alteration is a bit layered complex as well.

[00:51:17.020]And what we need to do

[00:51:18.400]is a bit of a simplification to the alterations.

[00:51:22.900]So what I’ve done is I’ve grouped those lists out,

[00:51:27.730]it became fresh,

[00:51:29.390]and then from fresh to intense muscovite alteration

[00:51:33.190]as well is one group, the chloride alteration is one group,

[00:51:36.630]as well as the moderate muscovite alteration

[00:51:39.100]is also one group.

[00:51:40.870]So it’s going to show you this.

[00:51:44.710]This is now the model,

[00:51:46.850]the fresh to intense muscovite alteration.

[00:51:50.270]Secondly, I have the moderately muscovite alteration

[00:51:55.560]at the base of the fresh to intense muscovite alteration.

[00:52:00.220]And lastly, what you have

[00:52:01.750]in here is your chloride alteration.

[00:52:05.270]Generating the output volumes.

[00:52:08.080]We now have the fresh in the background as well.

[00:52:10.640]So again, due to the layering nature

[00:52:14.330]of both of the rocks and alteration, you can see

[00:52:17.200]in here, the alteration output volumes

[00:52:20.450]that was produced is also layered.

[00:52:24.470]Want to slice through on the alteration model now,

[00:52:30.130]just to gain a bit of

[00:52:32.050]an appreciation on how this looks like at various sections.

[00:52:44.080]So again, just to summarize this,

[00:52:46.970]we’ve generated this simple lithology first,

[00:52:50.370]which is displayed on the 3D scene and then

[00:52:57.350]we made a detailed rock classification

[00:52:59.750]on the simple lithology, and then lastly,

[00:53:03.145]we generated an alteration model out of that.

[00:53:07.860]So Leapfrog Geo is a workflow based

[00:53:10.010]3D implicit geological modeling tool,

[00:53:12.190]which allows you to quickly construct models directly

[00:53:15.750]from various sources, including drill holes,

[00:53:18.880]points, and surfaces.

[00:53:21.960]So ioGAS is an exploratory data analysis software

[00:53:25.210]for the detection of patterns, anomalies,

[00:53:28.550]and relationships in your geochemical data.

[00:53:33.150]We can bring data from Leapfrog Geo

[00:53:36.295]to ioGAS, and then back to Leapfrog Geo

[00:53:38.310]through the ioGAS link in the form of points

[00:53:41.260]or interval data.

[00:53:44.118]And then lastly,

[00:53:45.620]rock alteration or mineralization classification

[00:53:48.880]can be established in ioGAS

[00:53:51.160]and viewed and modeled in the Leapfrog Geo.

[00:53:56.620]Thank you so much for your time.

[00:53:58.390]Do you have any questions at this stage?

[00:54:01.874]<v Host>Hi, Allan and Putra, great presentation,</v>

[00:54:05.140]that was fantastic to see the workflows

[00:54:07.340]from Leapfrog to ioGAS and back to Leapfrog again.

[00:54:11.550]We do have a couple of questions from attendees online.

[00:54:16.590]The first question is for Putra,

[00:54:19.335]how can I just predict the type

[00:54:21.690]of mineralization within the dataset?

[00:54:25.000]<v Putra>So ultimately that depends on what you’re trying</v>

[00:54:29.220]to achieve within ioGAS

[00:54:32.607]and it also depends on what data that you have.

[00:54:36.170]So if you’re looking at purely at geochemical data

[00:54:40.710]with some information about

[00:54:42.890]what interval corresponds to what sort of mineralization,

[00:54:46.100]you’re sort of halfway there in terms of determining

[00:54:50.235]what mineralization is present within your data.

[00:54:53.340]Generally, it’s a very GIS type approach

[00:54:56.110]in that the more, like the more you can be certain

[00:54:59.290]about the interpretation that you come up with.

[00:55:02.260]But also in terms of what we can do

[00:55:05.455]in ioGAS, our classification diagrams will allow you

[00:55:08.940]to see if you’re within the ballpark

[00:55:11.600]of a certain prospective rock type.

[00:55:14.610]So one of the diagrams that we came up with

[00:55:18.080]quite recently is a Scott Haley diagram,

[00:55:21.850]just using a ratio of a couple of immobile elements

[00:55:25.570]in porphyry exploration.

[00:55:27.720]So that’s looking at a ratio

[00:55:29.995]of vanadium over scandium versus scandium.

[00:55:33.890]And that’s a really good way of determining if your data

[00:55:37.730]is situated within perspective porphyry copper field.

[00:55:42.200]So that’s just one example.

[00:55:44.470]And as I was showing in the presentation,

[00:55:46.160]we have a library of mineral composition

[00:55:50.710]and composition of known deposits, famous deposits.

[00:55:56.720]So those are the ones that Carl Brohart collated

[00:56:00.500]in his Austin ACA dataset,

[00:56:03.610]which we have implemented into ioGAS as a library.

[00:56:07.760]So it’s a library of known composition of type deposits,

[00:56:11.090]so famous deposits within certain deposit types.

[00:56:14.310]So say if you, for example,

[00:56:15.410]you’re exploring for IOCG type deposit and you have a sample

[00:56:20.700]that you think would be within the ballpark

[00:56:23.370]of let’s say an Olympic type deposit,

[00:56:27.680]and you can check if your data

[00:56:30.180]is plotting within that ballpark, within that range.

[00:56:33.790]So that’s one way of looking at it.

[00:56:35.510]But it’s always a good idea to have more info,

[00:56:39.620]so you plot your geochemical data

[00:56:43.830]in diagrams in ioGAS and you display out information,

[00:56:47.830]such as rock type and that sort of thing.

[00:56:50.250]If we have time, I might be able to share my screen

[00:56:54.440]and just show a few other things,

[00:56:56.970]but we’ll leave it up to another couple

[00:57:00.340]of questions before doing that, if that all right.

[00:57:02.930]<v Host>Thanks Putra, great answer.</v>

[00:57:05.170]This next question is for Allan.

[00:57:07.970]So can I use point data such as my surface samples

[00:57:11.960]in the same workflow that you just presented today?

[00:57:15.750]<v Allan>Thank you so much</v>

[00:57:16.988]for the question, very interesting question.

[00:57:18.990]So yes, you can.

[00:57:20.360]So if you have surface samples

[00:57:22.120]with multi-element geochemistry,

[00:57:24.510]similar to what we have in the drill holes.

[00:57:26.800]So you can use this.

[00:57:28.630]Actually, I’ve dealt with a previous project

[00:57:32.960]from one of our clients, and they’ve got soil samples

[00:57:38.020]and we have their multi elementary chemistry,

[00:57:40.670]and we were able to determine perspective areas

[00:57:45.080]through that particular data.

[00:57:47.720]<v Host>Thanks, Allan.</v>

[00:57:49.320]Next question is for Putra,

[00:57:51.680]is there a recommendation you have

[00:57:53.240]for the number of elements required for workflow like this,

[00:57:56.810]do we need 33 as in the example that you used today?

[00:58:02.000]<v Putra>So look in terms of elements,</v>

[00:58:03.870]it’s really not how much elements you have analyzed,

[00:58:07.990]even though that could be nice,

[00:58:09.610]but it’s also how they’re analyzed,

[00:58:12.790]and with the certain analytical methods that’s required

[00:58:16.520]to get the best out of your data.

[00:58:19.370]I guess the answer to that is pretty broad,

[00:58:22.155]but having a full list of 33 elements,

[00:58:29.500]having high quality, less of 20 something elements

[00:58:33.610]versus 60 element dataset,

[00:58:36.310]that’s analyze using the lowest cost analytical method.

[00:58:40.040]It might be better to go

[00:58:41.190]with ones that are more high quality.

[00:58:43.850]And I think it’s always good

[00:58:45.510]to emphasize that it’s preferable

[00:58:48.170]to know what you’re trying to look for,

[00:58:50.140]so what alteration assemblage,

[00:58:52.700]what lithology and what face of alteration

[00:58:57.400]or lithology that you’re expecting in your data

[00:59:00.830]and that sort of determines how many elements you need,

[00:59:03.800]what elements you need to look out for,

[00:59:06.020]and the quality that you want to seek

[00:59:09.450]in your data, because ultimately that will determine

[00:59:12.930]what sort of diagrams, classification diagrams,

[00:59:15.640]and what sort of advanced analytics tools

[00:59:18.600]that you can use in ioGAS.

[00:59:21.640]And of course, if you’re dealing

[00:59:22.940]with historical dataset

[00:59:24.220]as well, you have to bear in mind the sort

[00:59:28.648]of elements that will be advantages

[00:59:31.465]to compliment your historical dataset,

[00:59:34.460]if you’re not re-assaying them, for instance.

[00:59:38.070]So just that sort of thing will be need to keep in mind.

[00:59:43.134]So that’s a long winded answer

[00:59:44.680]to your question, but the short answer is 33 elements,

[00:59:48.800]as long as they’re of good quality,

[00:59:50.440]then you can go far with that, or even less,

[00:59:54.670]especially if they’re of high quality and they’re useful

[00:59:57.410]for the sort of thing you’re looking for.

[01:00:01.490]<v Host>Awesome, thanks Putra.</v>

[01:00:02.860]I have another question here.

[01:00:05.180]What do you find are the most effective alteration tools

[01:00:08.420]when working with meta sediments,

[01:00:10.710]so if you have things like detrital silicates?

[01:00:13.963]That question is for Putra.

[01:00:15.950]<v Putra>So when you’re looking at detrital silicates,</v>

[01:00:18.980]we don’t have a particular diagram that’ll be quite useful

[01:00:23.250]so that even though we have a bit of sort of like

[01:00:27.480]some more weathering trend sort of alteration diagrams.

[01:00:31.690]So if you’re familiar with the ACNK sort of diagram,

[01:00:37.575]they may be useful for what you’re trying to achieve,

[01:00:40.410]but ultimately I think

[01:00:42.313]if you’re working with meta sediments

[01:00:44.820]and looking at, which include detrital silicates,

[01:00:48.100]you probably will do well with just plotting them up

[01:00:51.700]in a few scatterplots, in ioGAS and using a combination

[01:00:55.630]of weathering trend alteration diagrams within ioGAS,

[01:00:59.110]so within that folder and just exploring what sort

[01:01:01.660]of works with your data and seeing if things are plotting

[01:01:04.120]where you expect them to be.

[01:01:06.280]<v Host>Awesome, thanks Putra.</v>

[01:01:07.133]So this is question based on your experience,

[01:01:10.070]for both Allan and Putra,

[01:01:12.110]that if you had different classifications

[01:01:14.140]based on lithology and geochemistry,

[01:01:16.590]like we saw a little bit in today’s workflow,

[01:01:19.490]what are your best options for evaluating

[01:01:21.700]which classification has more confidence?

[01:01:25.540]<v Putra>So I can start with this one, Allan.</v>

[01:01:27.780]So I guess based on next experience,

[01:01:29.220]just if you’re looking at just purely geochemical data,

[01:01:34.620]and if you’re trying to reconcile

[01:01:36.795]between what one diagram says and what your geology says,

[01:01:40.940]it’s always a good idea to step outside the circle

[01:01:43.860]and sort of think outside the box

[01:01:45.610]on what sort of other data that you can put in.

[01:01:49.810]And even though it doesn’t directly answer your question,

[01:01:51.630]but it’s always a good idea

[01:01:53.900]to add things like the spectral data

[01:01:57.120]from Osiris for example, and if you use that in conjunction

[01:02:00.360]with the workload that we presented here today,

[01:02:02.350]to be more certain of what you’re looking for,

[01:02:06.130]because ultimately, what we presented here today,

[01:02:09.010]so for example with the sodium

[01:02:11.489]over aluminum versus potassium over aluminum

[01:02:14.870]molar element ratio diagram,

[01:02:16.640]it’s not a silver bullet to sort of

[01:02:20.050]indicate the sort of alteration that you will see.

[01:02:22.440]So for example, and as status said, there’s no information

[01:02:25.560]in the file was of alteration is present.

[01:02:28.260]If I can just quickly share my screen.

[01:02:30.440]So in ioGAS we’re able

[01:02:31.980]to basically display a label right next to each data point.

[01:02:40.330]Basically, if I can show item labels.

[01:02:43.360]So each of the rock type here

[01:02:45.640]in each of the data point has been logged visually

[01:02:48.370]in the field, so what I’ve classified as prioritize rocks,

[01:02:55.510]and when you zoom into these data points a bit,

[01:02:57.990]and you see how they’re classified

[01:03:00.715]in the field, chloretic mafic flow.

[01:03:02.620]So that’s what they’re classified

[01:03:04.170]as chloride carbonate altered basalt

[01:03:07.740]and chloride carbonate shears, chloride porphyry.

[01:03:13.580]So it’s this sort of thing

[01:03:15.480]where you’re reconciling your log alteration

[01:03:19.580]with the alteration modeling

[01:03:21.480]that you’ve identified through this sort of diagrams.

[01:03:23.920]And if you move up a bit

[01:03:25.320]into the sericite alteration field,

[01:03:27.330]you see that it’s quite consistent

[01:03:29.550]with what we’ve identified

[01:03:30.990]using our geochemical classification.

[01:03:33.100]So you got your sericite chloride quartz porphyry

[01:03:37.880]and sericite altered quartz porphyry again,

[01:03:40.740]and sericite quartz,

[01:03:42.500]and a few other samples that’s classified

[01:03:45.180]as sericite altered.

[01:03:46.900]So if you want to be certain

[01:03:49.170]that you’re looking at the same thing,

[01:03:51.890]it’s always a good idea to first bring it back

[01:03:54.840]to your geology, so that’s number one.

[01:03:57.590]And number two is you want to use a combination

[01:04:01.630]of not just one molar element ratio.

[01:04:05.910]So say you can also plot things up

[01:04:07.980]in a chloride molar element ratio diagram.

[01:04:12.310]So this is a way to separate out

[01:04:15.380]between your muscovite from chloride alteration.

[01:04:18.180]And you can see that in a tie line

[01:04:19.710]between muscovite and chloride you have some samples

[01:04:22.300]to exhibit sort of that halfway point

[01:04:24.200]between chloride and muscovitic composition.

[01:04:27.740]And of course you can update this classification, right?

[01:04:31.210]So this is an interesting one.

[01:04:32.270]So you have a couple of samples here

[01:04:35.450]that’s classified as sericite chloride quartz porphyry,

[01:04:38.140]and chloride carbonate sericite,

[01:04:39.990]that’s sort of in a tie line between muscovite and chloride.

[01:04:43.410]And linking it back to log geology and using a combination

[01:04:46.800]of not just one molar element ratio diagram,

[01:04:50.160]but using multiple molar element ratio diagrams,

[01:04:52.910]you’re able to identify processes,

[01:04:55.970]and then you can refine your classification more

[01:04:58.250]and then seeing them being reflected

[01:05:00.100]in different geo and real-time

[01:05:01.650]and update your alteration modeling in real-time as well.

[01:05:05.320]So that’s how you can reconcile

[01:05:07.140]sort of your different classification that exist,

[01:05:10.210]but it’s always a good idea to not just use one diagram,

[01:05:13.080]just have to use multiple and use your best judgment.

[01:05:16.440]And ioGAS is easy to use

[01:05:19.510]in terms of applying colored groups and that sort of thing,

[01:05:23.060]which means what you interpret is reflected visually

[01:05:27.760]in real-time instantaneously, in fact.

[01:05:32.060]<v Allan>Just adding to what Putra is saying,</v>

[01:05:35.450]you need to have at least the four ingredients

[01:05:38.460]in order to make your best judgment.

[01:05:42.930]The four Gs are actually your GIS data

[01:05:46.280]where it lies in 3D space.

[01:05:50.740]I need to consider also the logging,

[01:05:54.830]the logging, the mapping, and also the geochem data as well.

[01:06:00.560]If you’ve got multi element and geochem data, sewer data

[01:06:04.860]as well, in terms of geophysics,

[01:06:09.067]you need to combine it with the magnetics, gravity, IP, EM.

[01:06:13.670]So aside from having robust geochemical data,

[01:06:18.810]you need to basically,

[01:06:21.030]in order to classify this more confidently,

[01:06:23.850]you need to combine this together

[01:06:25.130]with all the other ingredients such

[01:06:26.810]as geology, geophysics, and GIS data.

[01:06:30.410]<v Host>Thanks, Allen and Putra, those are great responses.</v>

[01:06:33.310]That’s all the questions we have time for today.

[01:06:36.320]<v Putra>And lastly, if you want</v>

[01:06:37.260]to get in touch with the Seequent support team,

[01:06:41.110]please get in touch with us through [email protected],

[01:06:45.230]or if you want to get in touch with the ioGAS team,

[01:06:48.620]if you want to learn more about ioGAS, you can get

[01:06:51.420]in touch with them through their global help desk,

[01:06:55.210]which is under reflex.com/product/ioGAS.

[01:07:00.690]Again, thank you so much for your time.

[01:07:02.830]I hope to see you all soon again

[01:07:04.610]to our future technical Tuesday webinars.

[01:07:07.540]Thank you so much.

[01:07:09.280]<v Allan>Thanks, everyone.</v>