Seismic Facies Identification Challenge
[Explainer] - EDA of Seismic data by geographic axis
Here’s my EDA notebook on how the seismic data varies by geographic axis, along with some ideas for training.
dipam_chakraborty
EDA on variation by geographic axis
Here’s my EDA notebook on how the seismic data varies by geographic axis, along with some ideas for training.
A peek some of the stuff in the notebook
How the patterns look per label:
How the facies vary by z-axis
Splitting the data for training based on the EDA results
Do share your feedback. 
AICrowd Seismic Facies Identification Challenge¶
https://www.aicrowd.com/challenges/seismic-facies-identification-challenge
The goal of the Seismic Facies Identification challenge is to create a machine-learning algorithm which, working from the raw 3D image, can reproduce an expert pixel-by-pixel facies identification.
Problem description: 3D image Semantic Segmentation¶
Setup Environment 📚¶
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Domain knowledge 💡¶
The following are the geologic descriptions of each labels:
1 : Basement/Other: Basement - Low S/N; Few internal Reflections; May contain volcanics in places
2 : Slope Mudstone A: Slope to Basin Floor Mudstones; High Amplitude Upper and Lower Boundaries; Low Amplitude Continuous/Semi-Continuous Internal Reflectors
3 : Mass Transport Deposit: Mix of Chaotic Facies and Low Amplitude Parallel Reflections
4 : Slope Mudstone B: Slope to Basin Floor Mudstones and Sandstones; High Amplitude Parallel Reflectors; Low Continuity Scour Surfaces
5 : Slope Valley: High Amplitude Incised Channels/Valleys; Relatively low relief
6 : Submarine Canyon System: Erosional Base is U shaped with high local relief. Internal fill is low amplitude mix of parallel inclined surfaces and chaotic disrupted reflectors. Mostly deformed slope mudstone filled with isolated sinuous sand-filled channels near the basal surface.
According to the literature, the facies are identified using the curves in the readings
📌 Remember¶
The curves seems to be low level to mid level features, so a majority of the prediction likely depends on that, but the broader context may also help
Load data 💾¶
# Downloading Data
!wget https://datasets.aicrowd.com/default/aicrowd-public-datasets/seamai-facies-challenge/v0.1/public/data_train.npz
# Download Labels
!wget https://datasets.aicrowd.com/default/aicrowd-public-datasets/seamai-facies-challenge/v0.1/public/labels_train.npz
# Download Labels
!wget https://datasets.aicrowd.com/default/aicrowd-public-datasets/seamai-facies-challenge/v0.1/public/data_test_1.npz
train_full = np.load('data_train.npz', allow_pickle=True, mmap_mode='r')["data"]
labels_full = np.load('labels_train.npz', allow_pickle=True)["labels"]
test = np.load('data_test_1.npz', allow_pickle=True)["data"]
A tiny peek at the data ❕¶
fig, ax = plt.subplots(1,3, sharey=True);
fig.set_size_inches(20, 8);
fig.suptitle("2D slice of the 3D seismic data volume", fontsize=20);
yc = 100
print(np.unique(labels_full[:, :, yc]))
ax[0].imshow(train_full[:, :, yc], cmap='terrain');
ax[0].set_xlabel('X Axis: West - East', fontsize=14);
ax[0].set_ylabel('Z Axis: Top - Bottom', fontsize=14);
ax[1].imshow(labels_full[:, :, yc]);
ax[1].set_xlabel('X Axis: West - East', fontsize=14);
ax[2].imshow(train_full[:, :, yc], cmap='terrain');
ax[2].imshow(labels_full[:, :, yc], alpha=0.4, cmap='twilight');
ax[2].set_xlabel('X Axis: West - East', fontsize=14);
EDA on images 📷¶
## Data histogram, min, max, mean, std
tr_ravel = train_full.ravel()
minval, maxval, mean, std = np.min(tr_ravel), np.max(tr_ravel), np.mean(tr_ravel), np.std(tr_ravel)
print('Min: %0.4f, Max: %0.4f, Mean: %0.4f, Std: %0.4f' %
(minval, maxval, mean, std))
hist = plt.hist(tr_ravel, bins=100);
plt.title("Histogram of data values");
📌 Remember¶
Data is nearly zero mean, range is much higher than std. Probably good to normalize inputs
## Let's increase the contrast on the above data view by clipping at mean ± 3*std
normclip = lambda img: np.clip(img, mean-3*std, mean+3*std)
fig, ax = plt.subplots(1,3, sharey=True);
fig.set_size_inches(20, 8);
fig.suptitle("2D slice of the 3D seismic data volume", fontsize=20);
ax[0].imshow(normclip(train_full[:, :, yc]), cmap='terrain');
ax[0].set_xlabel('X Axis: West - East', fontsize=14);
ax[0].set_ylabel('Z Axis: Top - Bottom', fontsize=14);
ax[1].imshow(labels_full[:, :, yc]);
ax[1].set_xlabel('X Axis: West - East', fontsize=14);
ax[2].imshow(normclip(train_full[:, :, yc]), cmap='terrain');
ax[2].imshow(labels_full[:, :, yc], alpha=0.4, cmap='twilight');
ax[2].set_xlabel('X Axis: West - East', fontsize=14);